- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"11538\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"11538\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
+ "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"
\\n\"+\n \"- \\n\"+\n \"
- re-rerun `output_notebook()` to attempt to load from CDN again, or \\n\"+\n \"
- use INLINE resources instead, as so: \\n\"+\n \"
\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1002\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
"application/vnd.bokehjs_load.v0+json": ""
},
"metadata": {},
@@ -445,7 +445,7 @@
"data": {
"text/html": [
"\n",
- " \n"
+ " \n"
]
},
"metadata": {},
@@ -453,12 +453,12 @@
},
{
"data": {
- "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"d38e01fd-51e1-47ce-ab8e-55cc95eb35fc\":{\"defs\":[],\"roots\":{\"references\":[{\"attributes\":{\"sizing_mode\":\"scale_both\",\"tabs\":[{\"id\":\"12132\"}]},\"id\":\"12133\",\"type\":\"Tabs\"},{\"attributes\":{},\"id\":\"12048\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"11544\",\"type\":\"LinearScale\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"11633\"},\"glyph\":{\"id\":\"11634\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"11636\"},\"nonselection_glyph\":{\"id\":\"11635\"},\"view\":{\"id\":\"11638\"}},\"id\":\"11637\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"11741\"},\"glyph\":{\"id\":\"11742\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"11744\"},\"nonselection_glyph\":{\"id\":\"11743\"},\"view\":{\"id\":\"11746\"}},\"id\":\"11745\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_alpha\":0.2,\"line_color\":\"#9C755F\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"11870\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"12047\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"label\":{\"value\":\"Local Events\"},\"renderers\":[{\"id\":\"11745\"}]},\"id\":\"11780\",\"type\":\"LegendItem\"},{\"attributes\":{\"data\":{\"x\":{\"__ndarray__\":\"AAAAAWuoeEIAAMBmvah4QgAAgMwPqXhCAABAMmKpeEIAAACYtKl4QgAAwP0GqnhCAACAY1mqeEIAAEDJq6p4QgAAAC/+qnhC\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[9]},\"y\":{\"__ndarray__\":\"Mx9Ce9lX5T+/HGZnzsDlPza6xhjmf+Y/x16ryNs55j/Fd5xBOYrkP2wA+ZrMIuM/rOSVVEbI4T83iP+o/DnhP9mOJm9Jy+A/\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[9]}},\"selected\":{\"id\":\"11898\"},\"selection_policy\":{\"id\":\"11897\"}},\"id\":\"11867\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"11633\"}},\"id\":\"11638\",\"type\":\"CDSView\"},{\"attributes\":{\"source\":{\"id\":\"11741\"}},\"id\":\"11746\",\"type\":\"CDSView\"},{\"attributes\":{\"line_color\":\"#E15759\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"11634\",\"type\":\"Line\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#9C755F\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"11869\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"11542\",\"type\":\"DataRange1d\"},{\"attributes\":{\"line_alpha\":0.2,\"line_color\":\"#EDC948\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"11744\",\"type\":\"Line\"},{\"attributes\":{\"label\":{\"value\":\"Restaurants\"},\"renderers\":[{\"id\":\"11917\"}]},\"id\":\"11960\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"11766\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"11897\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"11652\",\"type\":\"Selection\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"11588\"},{\"id\":\"11589\"},{\"id\":\"11590\"},{\"id\":\"11591\"},{\"id\":\"11592\"},{\"id\":\"11593\"},{\"id\":\"11594\"},{\"id\":\"11595\"},{\"id\":\"11596\"},{\"id\":\"11597\"},{\"id\":\"11598\"},{\"id\":\"11599\"}]},\"id\":\"11549\",\"type\":\"DatetimeTicker\"},{\"attributes\":{\"line_alpha\":0.2,\"line_color\":\"#E15759\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"11636\",\"type\":\"Line\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#EDC948\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"11743\",\"type\":\"Line\"},{\"attributes\":{\"click_policy\":\"hide\",\"coordinates\":null,\"group\":null,\"items\":[{\"id\":\"11600\"},{\"id\":\"11632\"},{\"id\":\"11666\"},{\"id\":\"11702\"},{\"id\":\"11740\"},{\"id\":\"11780\"},{\"id\":\"11822\"},{\"id\":\"11866\"},{\"id\":\"11912\"},{\"id\":\"11960\"},{\"id\":\"12010\"},{\"id\":\"12062\"}]},\"id\":\"11570\",\"type\":\"Legend\"},{\"attributes\":{\"data\":{\"x\":{\"__ndarray__\":\"AAAAAWuoeEIAAMBmvah4QgAAgMwPqXhCAABAMmKpeEIAAACYtKl4QgAAwP0GqnhCAACAY1mqeEIAAEDJq6p4QgAAAC/+qnhC\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[9]},\"y\":{\"__ndarray__\":\"4s7uAWwG3j9PkfvWczreP/SDd2NbUt4/gY/za2ci3j+wEA0XIPDcP/fuS/uMydw/5QpLrbLB3T+N7gD+NRveP56d9mVXsN4/\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[9]}},\"selected\":{\"id\":\"11652\"},\"selection_policy\":{\"id\":\"11651\"}},\"id\":\"11633\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"11765\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"11808\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"12642\",\"type\":\"AllLabels\"},{\"attributes\":{},\"id\":\"12639\",\"type\":\"AllLabels\"},{\"attributes\":{},\"id\":\"12068\",\"type\":\"DataRange1d\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#E15759\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"11635\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"12641\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"label\":{\"value\":\"Home Services\"},\"renderers\":[{\"id\":\"11671\"}]},\"id\":\"11702\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"12072\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"11651\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"coordinates\":null,\"group\":null,\"text\":\"National average post earthquake compared to 90 day prior 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baseline\"},\"id\":\"1527\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1603\",\"type\":\"AllLabels\"},{\"attributes\":{\"line_color\":\"#B07AA1\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1246\",\"type\":\"Line\"},{\"attributes\":{\"coordinates\":null,\"formatter\":{\"id\":\"1602\"},\"group\":null,\"major_label_policy\":{\"id\":\"1603\"},\"ticker\":{\"id\":\"1545\"}},\"id\":\"1544\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1565\",\"type\":\"DataRange1d\"},{\"attributes\":{\"source\":{\"id\":\"1331\"}},\"id\":\"1336\",\"type\":\"CDSView\"},{\"attributes\":{\"line_color\":\"#E15759\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1098\",\"type\":\"Line\"},{\"attributes\":{\"data\":{\"x\":{\"__ndarray__\":\"AAAAAWuoeEIAAMBmvah4QgAAgMwPqXhCAABAMmKpeEIAAACYtKl4QgAAwP0GqnhCAACAY1mqeEIAAEDJq6p4QgAAAC/+qnhCAADAlFCreEIAAID6oqt4QgAAQGD1q3hCAAAAxkeseEIAAMArmqx4QgAAgJHsrHhCAABA9z6teEIAAABdka14QgAAwMLjrXhCAACAKDaueEIAAECOiK54QgAAAPTarnhCAADAWS2veEIAAIC/f694QgAAQCXSr3hCAAAAiySweEIAAMDwdrB4QgAAgFbJsHhCAABAvBuxeEIAAAAibrF4QgAAwIfAsXhCAACA7RKyeEIAAEBTZbJ4QgAAALm3snhCAADAHgqzeEIAAICEXLN4QgAAQOqus3hCAAAAUAG0eEIAAMC1U7R4Qg==\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[38]},\"y\":{\"__ndarray__\":\"5NoNUhHC2j8U+F22fkfZP3iNAY2sI9g/oB76Pi+p1j+hV1D30Q/WP8V6rD03sdU/vwZG3xim1j/xbcpxJMXXP2EB5E4Xntk/xpzSNjML2z99L56GLkfcPzJ/5CEOQd0/oVq9xOX43T87YEsdTwnfP03KoxLk7d8/XItvbeZ64D81fcv5Ui7gP1YBoxAeoN8//5+9mPD93j9IkZMa28veP7r94EKvPd4/vJIcjp003j+AgTC5LS3dP+yCswnf9t0/FsmRP/E73z8pwAqp6n7fP3oKZb22w98/gUAEs2sJ4D+Xi3oxkd7fP3ksYqkSLuA/W/nhHK0o4D8sDBB1MrLfP5QWb7lMCOA/IVdxt4bu3z83l1P+6crfP2+5rvrmF+A/tgRH8HGE3z9x1pEcmADfPw==\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[38]}},\"selected\":{\"id\":\"1460\"},\"selection_policy\":{\"id\":\"1459\"}},\"id\":\"1425\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1553\",\"type\":\"HelpTool\"},{\"attributes\":{\"below\":[{\"id\":\"1540\"}],\"border_fill_alpha\":0,\"center\":[{\"id\":\"1543\"},{\"id\":\"1547\"}],\"height\":40,\"left\":[{\"id\":\"1544\"}],\"outline_line_width\":0,\"title\":{\"id\":\"1530\"},\"toolbar\":{\"id\":\"1555\"},\"toolbar_location\":null,\"width\":800,\"x_range\":{\"id\":\"1532\"},\"x_scale\":{\"id\":\"1536\"},\"y_range\":{\"id\":\"1534\"},\"y_scale\":{\"id\":\"1538\"}},\"id\":\"1529\",\"subtype\":\"Figure\",\"type\":\"Plot\"}],\"root_ids\":[\"1597\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n const render_items = [{\"docid\":\"a78154dc-2e81-455b-b1a1-e7d52dd2f845\",\"root_ids\":[\"1597\"],\"roots\":{\"1597\":\"c8e31e8a-28c3-4bb1-a820-2fb52e49fa0e\"}}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);",
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
- "id": "12133"
+ "id": "1597"
}
},
"output_type": "display_data"
@@ -492,7 +492,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 23,
"metadata": {
"tags": [
"hide-input"
@@ -504,7 +504,7 @@
"text/html": [
"\n"
]
},
@@ -513,7 +513,7 @@
},
{
"data": {
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\\n\"+\n \"- \\n\"+\n \"
- re-rerun `output_notebook()` to attempt to load from CDN again, or \\n\"+\n \"
- use INLINE resources instead, as so: \\n\"+\n \"
\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"27502\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"27502\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
+ "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"
\\n\"+\n \"- \\n\"+\n \"
- re-rerun `output_notebook()` to attempt to load from CDN again, or \\n\"+\n \"
- use INLINE resources instead, as so: \\n\"+\n \"
\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1958\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1958\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
"application/vnd.bokehjs_load.v0+json": ""
},
"metadata": {},
@@ -523,7 +523,7 @@
"data": {
"text/html": [
"\n",
- " \n"
+ " \n"
]
},
"metadata": {},
@@ -531,12 +531,12 @@
},
{
"data": {
- "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = 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- "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"
\\n\"+\n \"- \\n\"+\n \"
- re-rerun `output_notebook()` to attempt to load from CDN again, or \\n\"+\n \"
- use INLINE resources instead, as so: \\n\"+\n \"
\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"45236\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"45236\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
+ "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"
\\n\"+\n \"- \\n\"+\n \"
- re-rerun `output_notebook()` to attempt to load from CDN again, or \\n\"+\n \"
- use INLINE resources instead, as so: \\n\"+\n \"
\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"15966\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"15966\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
"application/vnd.bokehjs_load.v0+json": ""
},
"metadata": {},
@@ -608,7 +608,7 @@
"data": {
"text/html": [
"\n",
- " \n"
+ " \n"
]
},
"metadata": {},
@@ -616,15 +616,23 @@
},
{
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diff --git a/_sources/notebooks/earthquake-intensity/earthquake_intensity.ipynb b/_sources/notebooks/earthquake-intensity/earthquake_intensity.ipynb
index 12531da..edf59da 100644
--- a/_sources/notebooks/earthquake-intensity/earthquake_intensity.ipynb
+++ b/_sources/notebooks/earthquake-intensity/earthquake_intensity.ipynb
@@ -27,11 +27,12 @@
"\n",
"## Data\n",
"\n",
- "Earthquake Intensity Data was obtained from [USGS](https://earthquake.usgs.gov/earthquakes/eventpage/us7000kufc/shakemap/intensity) and was overlayed with shapefiles of Morocco admin regions taken from [UNOCHA](https://data.humdata.org/dataset/cod-ab-mar?) and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized. \n",
+ "Earthquake Intensity Data was obtained from [USGS](https://earthquake.usgs.gov/earthquakes/eventpage/us7000kufc/shakemap/intensity) and was overlayed with shapefiles of Morocco admin regions taken from [UNOCHA](https://data.humdata.org/dataset/cod-ab-mar?) and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized. The MMI is a range that allows us to estimate the amount of damage done to buildings. \n",
+ "\n",
"\n",
"## Methodology\n",
"\n",
- "The impact of the earthquake in this situation is measured using the Modified Mercelli Intensity Scale (mmi). \n",
+ "The impact of the earthquake in this situation is measured using the Modified Mercalli Intensity Scale (mmi). \n",
"\n",
"The effect of an earthquake on the Earth's surface is called the intensity. The intensity scale consists of a series of certain key responses such as people awakening, movement of furniture, damage to chimneys, and finally - total destruction. Although numerousintensity scales have been developed over the last several hundred years to evaluate the effects of earthquakes, the one currently used in the United States is the Modified Mercalli (MM) Intensity Scale. It was developed in 1931 by the American seismologists Harry Wood and Frank Neumann. This scale, composed of increasing levels of intensity that range from imperceptible shaking to catastrophic destruction, is designated by Roman numerals. It does not have a mathematical basis; instead it is an arbitrary ranking based on observed effects.\n",
"\n",
@@ -39,11 +40,23 @@
"\n",
"The lower numbers of the intensity scale generally deal with the manner in which the earthquake is felt by people. The higher numbers of the scale are based on observed structural damage. Structural engineers usually contribute information for assigning intensity values of VIII or above.\n",
"\n",
- "Source: [USGS Modified Mercelli Intensity Scale](https://www.usgs.gov/programs/earthquake-hazards/modified-mercalli-intensity-scale#:~:text=The%20effect%20of%20an%20earthquake,%2C%20and%20finally%20%2D%20total%20destruction.)\n",
+ "Source: [USGS Modified Mercalli Intensity Scale](https://www.usgs.gov/programs/earthquake-hazards/modified-mercalli-intensity-scale#:~:text=The%20effect%20of%20an%20earthquake,%2C%20and%20finally%20%2D%20total%20destruction.)\n",
"\n",
"Alternatively, another variable that can be used is also the distance from the epicenter. "
]
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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -73,6 +86,108 @@
]
},
"outputs": [],
+ "source": [
+ "def get_mmi_class(mmi):\n",
+ " if mmi>7 and mmi<=8:\n",
+ " return 'High Damage'\n",
+ " elif mmi>8 and mmi<=9:\n",
+ " return 'Very High Damage'\n",
+ " elif mmi>9:\n",
+ " return 'Extreme Damage'\n",
+ " else:\n",
+ " return 'Low Damage'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def get_mmi_color(mmi):\n",
+ " if mmi>7 and mmi<=8:\n",
+ " return 'orange'\n",
+ " elif mmi>8 and mmi<=9:\n",
+ " return 'red'\n",
+ " elif mmi>9:\n",
+ " return 'purple'\n",
+ " else:\n",
+ " return 'grey'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Unexpected exception formatting exception. Falling back to standard exception\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Traceback (most recent call last):\n",
+ " File \"fiona\\ogrext.pyx\", line 136, in fiona.ogrext.gdal_open_vector\n",
+ " File \"fiona\\_err.pyx\", line 291, in fiona._err.exc_wrap_pointer\n",
+ "fiona._err.CPLE_OpenFailedError: ../../data/shapefiles/mar_adm2.shp: No such file or directory\n",
+ "\n",
+ "During handling of the above exception, another exception occurred:\n",
+ "\n",
+ "Traceback (most recent call last):\n",
+ " File \"c:\\Users\\sahit\\anaconda3\\envs\\turkey-rdna\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3460, in run_code\n",
+ " exec(code_obj, self.user_global_ns, self.user_ns)\n",
+ " File \"C:\\Users\\sahit\\AppData\\Local\\Temp\\ipykernel_43104\\3991273565.py\", line 1, in \\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"
\\n\"+\n \"- \\n\"+\n \"
- re-rerun `output_notebook()` to attempt to load from CDN again, or \\n\"+\n \"
- use INLINE resources instead, as so: \\n\"+\n \"
\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"References
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/drought-howto-characteristics.html b/docs/drought-howto-characteristics.html
index 6e3d041..7c4eb4b 100644
--- a/docs/drought-howto-characteristics.html
+++ b/docs/drought-howto-characteristics.html
@@ -70,7 +70,7 @@
-
+
@@ -1186,7 +1186,7 @@ Map Visualization
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/drought-howto-spianalysis.html b/docs/drought-howto-spianalysis.html
index 3832bfb..6b216dd 100644
--- a/docs/drought-howto-spianalysis.html
+++ b/docs/drought-howto-spianalysis.html
@@ -70,7 +70,7 @@
-
+
@@ -878,7 +878,7 @@ 5. Convert the result to GeoTIFF
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/drought-index.html b/docs/drought-index.html
index 9f4a3f9..67dceee 100644
--- a/docs/drought-index.html
+++ b/docs/drought-index.html
@@ -70,7 +70,7 @@
-
+
@@ -501,7 +501,7 @@ Drought
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/drought-summary.html b/docs/drought-summary.html
index aba2a83..e97f269 100644
--- a/docs/drought-summary.html
+++ b/docs/drought-summary.html
@@ -70,7 +70,7 @@
-
+
@@ -568,7 +568,7 @@ Summary
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/foundational_datasets_and_data_products.html b/docs/foundational_datasets_and_data_products.html
index 2df7019..a91eef8 100644
--- a/docs/foundational_datasets_and_data_products.html
+++ b/docs/foundational_datasets_and_data_products.html
@@ -70,7 +70,7 @@
-
+
@@ -693,7 +693,7 @@ Data Products Summary
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/introduction_to_data_goods.html b/docs/introduction_to_data_goods.html
index e5804ca..cb581c6 100644
--- a/docs/introduction_to_data_goods.html
+++ b/docs/introduction_to_data_goods.html
@@ -70,7 +70,7 @@
-
+
@@ -539,7 +539,7 @@ How Data Goods are Managed
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/morocco-earthquake-strategic-brief.html b/docs/morocco-earthquake-strategic-brief.html
index e01deb7..75b5332 100644
--- a/docs/morocco-earthquake-strategic-brief.html
+++ b/docs/morocco-earthquake-strategic-brief.html
@@ -70,7 +70,7 @@
-
+
@@ -1197,7 +1197,7 @@ #
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/team.html b/docs/team.html
index cb577d7..a1b6c63 100644
--- a/docs/team.html
+++ b/docs/team.html
@@ -69,7 +69,7 @@
-
+
@@ -517,7 +517,7 @@ Data Goods Team and Acknowledgements
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/genindex.html b/genindex.html
index 5b7f6dd..b3c8220 100644
--- a/genindex.html
+++ b/genindex.html
@@ -67,7 +67,7 @@
-
+
@@ -390,7 +390,7 @@ Index
diff --git a/notebooks/business-activity-trends/01-business-activity-trends.html b/notebooks/business-activity-trends/01-business-activity-trends.html
index cc05235..493adda 100644
--- a/notebooks/business-activity-trends/01-business-activity-trends.html
+++ b/notebooks/business-activity-trends/01-business-activity-trends.html
@@ -70,7 +70,7 @@
-
+
@@ -483,7 +483,7 @@ Earthquake triggered business activity dataset
-
+
@@ -514,7 +514,7 @@ Earthquake triggered business activity dataset
-
+
-
+
-
+
+})(window);The Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.
+
+
+
@@ -1604,7 +1607,7 @@ Change in Business Activity by business verticals in Marrakech - Tensfit - A
diff --git a/notebooks/business-activity-trends/README.html b/notebooks/business-activity-trends/README.html
index 1cfcb2b..5e0f94d 100644
--- a/notebooks/business-activity-trends/README.html
+++ b/notebooks/business-activity-trends/README.html
@@ -70,7 +70,7 @@
-
+
@@ -581,7 +581,7 @@ Citations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/conflict-acled.html b/notebooks/conflict-acled.html
index 25e26e2..b4f0425 100644
--- a/notebooks/conflict-acled.html
+++ b/notebooks/conflict-acled.html
@@ -70,7 +70,7 @@
-
+
@@ -586,7 +586,7 @@ A closer look at Marakkesh, Casablanca and Rabat
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/earthquake-intensity/earthquake_intensity.html b/notebooks/earthquake-intensity/earthquake_intensity.html
index dd84e75..ed1304f 100644
--- a/notebooks/earthquake-intensity/earthquake_intensity.html
+++ b/notebooks/earthquake-intensity/earthquake_intensity.html
@@ -70,7 +70,7 @@
-
+
@@ -430,16 +430,17 @@ Earthquake Intensity Map
Data#
-Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized.
+Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized. The MMI is a range that allows us to estimate the amount of damage done to buildings.
Methodology#
-The impact of the earthquake in this situation is measured using the Modified Mercelli Intensity Scale (mmi).
+The impact of the earthquake in this situation is measured using the Modified Mercalli Intensity Scale (mmi).
The effect of an earthquake on the Earth’s surface is called the intensity. The intensity scale consists of a series of certain key responses such as people awakening, movement of furniture, damage to chimneys, and finally - total destruction. Although numerousintensity scales have been developed over the last several hundred years to evaluate the effects of earthquakes, the one currently used in the United States is the Modified Mercalli (MM) Intensity Scale. It was developed in 1931 by the American seismologists Harry Wood and Frank Neumann. This scale, composed of increasing levels of intensity that range from imperceptible shaking to catastrophic destruction, is designated by Roman numerals. It does not have a mathematical basis; instead it is an arbitrary ranking based on observed effects.
The Modified Mercalli Intensity value assigned to a specific site after an earthquake has a more meaningful measure of severity to the nonscientist than the magnitude because intensity refers to the effects actually experienced at that place.
The lower numbers of the intensity scale generally deal with the manner in which the earthquake is felt by people. The higher numbers of the scale are based on observed structural damage. Structural engineers usually contribute information for assigning intensity values of VIII or above.
-Source: USGS Modified Mercelli Intensity Scale
+Source: USGS Modified Mercalli Intensity Scale
Alternatively, another variable that can be used is also the distance from the epicenter.
+
Results#
@@ -458,7 +459,11 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+
+
+morocco_adm4.boundary.plot(ax=ax, color='#f2f2f2', alpha=0.8, linewidth=0.5)
+affected_regions.plot(column='PARAMVALUE', cmap ='Reds', ax=ax, vmax=vmax, legend = True)
+
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -471,7 +476,7 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
fontsize=10, color='black')
@@ -479,11 +484,11 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='white', weight='bold')
+ fontsize=10, color='black', weight='bold')
# sm = plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=vmin, vmax=vmax))
@@ -503,13 +508,24 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# # Create a custom legend
+# legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+# legend_colors = ['grey', 'orange', 'red']
+
+# # Create custom legend handles
+# from matplotlib.lines import Line2D
+# legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# # Display the custom legend
+# plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm4.png')
-
+
@@ -527,7 +543,7 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@ Resultstext_to_add = gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='black')
+ fontsize=10, color='white')
text_to_add = gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label='Marrakech Medina')
+ax.scatter(x, y, c='black', marker='o', s=50, label='Marrakech Medina')
# Use the annotate function to add the text to the map
ax.annotate('Marrakech Medina', xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
@@ -572,13 +588,25 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# Create a custom legend
+legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+legend_colors = ['grey', 'orange', 'red']
+
+# Create custom legend handles
+from matplotlib.lines import Line2D
+legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# Display the custom legend
+plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
-
+
@@ -675,7 +703,7 @@ Results
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/google_trends/Morocco_GoogleTrends.html b/notebooks/google_trends/Morocco_GoogleTrends.html
index 5b80f39..1edaeee 100644
--- a/notebooks/google_trends/Morocco_GoogleTrends.html
+++ b/notebooks/google_trends/Morocco_GoogleTrends.html
@@ -70,7 +70,7 @@
-
+
@@ -817,7 +817,7 @@ Prediction
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/network-connectivity/meta-network-connectivity.html b/notebooks/network-connectivity/meta-network-connectivity.html
index 74031fa..f925a44 100644
--- a/notebooks/network-connectivity/meta-network-connectivity.html
+++ b/notebooks/network-connectivity/meta-network-connectivity.html
@@ -70,7 +70,7 @@
-
+
@@ -938,7 +938,7 @@ Limitations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/ntl-analysis/README.html b/notebooks/ntl-analysis/README.html
index 1c72081..b7d7c6d 100644
--- a/notebooks/ntl-analysis/README.html
+++ b/notebooks/ntl-analysis/README.html
@@ -70,7 +70,7 @@
-
+
@@ -668,7 +668,7 @@ Maps#
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/affected-population.html b/notebooks/population/affected-population.html
index de724af..a98c650 100644
--- a/notebooks/population/affected-population.html
+++ b/notebooks/population/affected-population.html
@@ -70,7 +70,7 @@
-
+
@@ -783,7 +783,7 @@ Population demographic in worst affected Admin 4 regions
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/settlement.html b/notebooks/population/settlement.html
index ff4262f..1b26120 100644
--- a/notebooks/population/settlement.html
+++ b/notebooks/population/settlement.html
@@ -70,7 +70,7 @@
-
+
@@ -412,8 +412,10 @@ Contents
@@ -436,9 +438,6 @@ Methodology
Limitations#
The building footprints are obtained from satellite imagery which is obstructed by cloud cover. Given Morocco is a dessert area, the results are somewhere near 80% accurate. Data from OpenStreetMaps is crowdsourced.
-
-
-Impact to financial services and tourist locations#
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+
+
+
+
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+Show code cell source
+
+
+
+percentage_tourist_damages
+
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+
+
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
5. Convert the result to GeoTIFF
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/drought-index.html b/docs/drought-index.html
index 9f4a3f9..67dceee 100644
--- a/docs/drought-index.html
+++ b/docs/drought-index.html
@@ -70,7 +70,7 @@
-
+
@@ -501,7 +501,7 @@ Drought
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/drought-summary.html b/docs/drought-summary.html
index aba2a83..e97f269 100644
--- a/docs/drought-summary.html
+++ b/docs/drought-summary.html
@@ -70,7 +70,7 @@
-
+
@@ -568,7 +568,7 @@ Summary
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/foundational_datasets_and_data_products.html b/docs/foundational_datasets_and_data_products.html
index 2df7019..a91eef8 100644
--- a/docs/foundational_datasets_and_data_products.html
+++ b/docs/foundational_datasets_and_data_products.html
@@ -70,7 +70,7 @@
-
+
@@ -693,7 +693,7 @@ Data Products Summary
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/introduction_to_data_goods.html b/docs/introduction_to_data_goods.html
index e5804ca..cb581c6 100644
--- a/docs/introduction_to_data_goods.html
+++ b/docs/introduction_to_data_goods.html
@@ -70,7 +70,7 @@
-
+
@@ -539,7 +539,7 @@ How Data Goods are Managed
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/morocco-earthquake-strategic-brief.html b/docs/morocco-earthquake-strategic-brief.html
index e01deb7..75b5332 100644
--- a/docs/morocco-earthquake-strategic-brief.html
+++ b/docs/morocco-earthquake-strategic-brief.html
@@ -70,7 +70,7 @@
-
+
@@ -1197,7 +1197,7 @@ #
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/team.html b/docs/team.html
index cb577d7..a1b6c63 100644
--- a/docs/team.html
+++ b/docs/team.html
@@ -69,7 +69,7 @@
-
+
@@ -517,7 +517,7 @@ Data Goods Team and Acknowledgements
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/genindex.html b/genindex.html
index 5b7f6dd..b3c8220 100644
--- a/genindex.html
+++ b/genindex.html
@@ -67,7 +67,7 @@
-
+
@@ -390,7 +390,7 @@ Index
diff --git a/notebooks/business-activity-trends/01-business-activity-trends.html b/notebooks/business-activity-trends/01-business-activity-trends.html
index cc05235..493adda 100644
--- a/notebooks/business-activity-trends/01-business-activity-trends.html
+++ b/notebooks/business-activity-trends/01-business-activity-trends.html
@@ -70,7 +70,7 @@
-
+
@@ -483,7 +483,7 @@ Earthquake triggered business activity dataset
-
+
@@ -514,7 +514,7 @@ Earthquake triggered business activity dataset
-
+
-
+
-
+
+})(window);The Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.
+
+
+
@@ -1604,7 +1607,7 @@ Change in Business Activity by business verticals in Marrakech - Tensfit - A
diff --git a/notebooks/business-activity-trends/README.html b/notebooks/business-activity-trends/README.html
index 1cfcb2b..5e0f94d 100644
--- a/notebooks/business-activity-trends/README.html
+++ b/notebooks/business-activity-trends/README.html
@@ -70,7 +70,7 @@
-
+
@@ -581,7 +581,7 @@ Citations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/conflict-acled.html b/notebooks/conflict-acled.html
index 25e26e2..b4f0425 100644
--- a/notebooks/conflict-acled.html
+++ b/notebooks/conflict-acled.html
@@ -70,7 +70,7 @@
-
+
@@ -586,7 +586,7 @@ A closer look at Marakkesh, Casablanca and Rabat
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/earthquake-intensity/earthquake_intensity.html b/notebooks/earthquake-intensity/earthquake_intensity.html
index dd84e75..ed1304f 100644
--- a/notebooks/earthquake-intensity/earthquake_intensity.html
+++ b/notebooks/earthquake-intensity/earthquake_intensity.html
@@ -70,7 +70,7 @@
-
+
@@ -430,16 +430,17 @@ Earthquake Intensity Map
Data#
-Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized.
+Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized. The MMI is a range that allows us to estimate the amount of damage done to buildings.
Methodology#
-The impact of the earthquake in this situation is measured using the Modified Mercelli Intensity Scale (mmi).
+The impact of the earthquake in this situation is measured using the Modified Mercalli Intensity Scale (mmi).
The effect of an earthquake on the Earth’s surface is called the intensity. The intensity scale consists of a series of certain key responses such as people awakening, movement of furniture, damage to chimneys, and finally - total destruction. Although numerousintensity scales have been developed over the last several hundred years to evaluate the effects of earthquakes, the one currently used in the United States is the Modified Mercalli (MM) Intensity Scale. It was developed in 1931 by the American seismologists Harry Wood and Frank Neumann. This scale, composed of increasing levels of intensity that range from imperceptible shaking to catastrophic destruction, is designated by Roman numerals. It does not have a mathematical basis; instead it is an arbitrary ranking based on observed effects.
The Modified Mercalli Intensity value assigned to a specific site after an earthquake has a more meaningful measure of severity to the nonscientist than the magnitude because intensity refers to the effects actually experienced at that place.
The lower numbers of the intensity scale generally deal with the manner in which the earthquake is felt by people. The higher numbers of the scale are based on observed structural damage. Structural engineers usually contribute information for assigning intensity values of VIII or above.
-Source: USGS Modified Mercelli Intensity Scale
+Source: USGS Modified Mercalli Intensity Scale
Alternatively, another variable that can be used is also the distance from the epicenter.
+
Results#
@@ -458,7 +459,11 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+
+
+morocco_adm4.boundary.plot(ax=ax, color='#f2f2f2', alpha=0.8, linewidth=0.5)
+affected_regions.plot(column='PARAMVALUE', cmap ='Reds', ax=ax, vmax=vmax, legend = True)
+
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -471,7 +476,7 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
fontsize=10, color='black')
@@ -479,11 +484,11 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='white', weight='bold')
+ fontsize=10, color='black', weight='bold')
# sm = plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=vmin, vmax=vmax))
@@ -503,13 +508,24 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# # Create a custom legend
+# legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+# legend_colors = ['grey', 'orange', 'red']
+
+# # Create custom legend handles
+# from matplotlib.lines import Line2D
+# legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# # Display the custom legend
+# plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm4.png')
-
+
@@ -527,7 +543,7 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@ Resultstext_to_add = gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='black')
+ fontsize=10, color='white')
text_to_add = gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label='Marrakech Medina')
+ax.scatter(x, y, c='black', marker='o', s=50, label='Marrakech Medina')
# Use the annotate function to add the text to the map
ax.annotate('Marrakech Medina', xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
@@ -572,13 +588,25 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# Create a custom legend
+legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+legend_colors = ['grey', 'orange', 'red']
+
+# Create custom legend handles
+from matplotlib.lines import Line2D
+legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# Display the custom legend
+plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
-
+
@@ -675,7 +703,7 @@ Results
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/google_trends/Morocco_GoogleTrends.html b/notebooks/google_trends/Morocco_GoogleTrends.html
index 5b80f39..1edaeee 100644
--- a/notebooks/google_trends/Morocco_GoogleTrends.html
+++ b/notebooks/google_trends/Morocco_GoogleTrends.html
@@ -70,7 +70,7 @@
-
+
@@ -817,7 +817,7 @@ Prediction
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/network-connectivity/meta-network-connectivity.html b/notebooks/network-connectivity/meta-network-connectivity.html
index 74031fa..f925a44 100644
--- a/notebooks/network-connectivity/meta-network-connectivity.html
+++ b/notebooks/network-connectivity/meta-network-connectivity.html
@@ -70,7 +70,7 @@
-
+
@@ -938,7 +938,7 @@ Limitations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/ntl-analysis/README.html b/notebooks/ntl-analysis/README.html
index 1c72081..b7d7c6d 100644
--- a/notebooks/ntl-analysis/README.html
+++ b/notebooks/ntl-analysis/README.html
@@ -70,7 +70,7 @@
-
+
@@ -668,7 +668,7 @@ Maps#
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/affected-population.html b/notebooks/population/affected-population.html
index de724af..a98c650 100644
--- a/notebooks/population/affected-population.html
+++ b/notebooks/population/affected-population.html
@@ -70,7 +70,7 @@
-
+
@@ -783,7 +783,7 @@ Population demographic in worst affected Admin 4 regions
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/settlement.html b/notebooks/population/settlement.html
index ff4262f..1b26120 100644
--- a/notebooks/population/settlement.html
+++ b/notebooks/population/settlement.html
@@ -70,7 +70,7 @@
-
+
@@ -412,8 +412,10 @@ Contents
@@ -436,9 +438,6 @@ Methodology
Limitations#
The building footprints are obtained from satellite imagery which is obstructed by cloud cover. Given Morocco is a dessert area, the results are somewhere near 80% accurate. Data from OpenStreetMaps is crowdsourced.
-
-
-Impact to financial services and tourist locations#
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+
+
+
+
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+Show code cell source
+
+
+
+percentage_tourist_damages
+
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+
+
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
Summary
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/foundational_datasets_and_data_products.html b/docs/foundational_datasets_and_data_products.html
index 2df7019..a91eef8 100644
--- a/docs/foundational_datasets_and_data_products.html
+++ b/docs/foundational_datasets_and_data_products.html
@@ -70,7 +70,7 @@
-
+
@@ -693,7 +693,7 @@ Data Products Summary
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/introduction_to_data_goods.html b/docs/introduction_to_data_goods.html
index e5804ca..cb581c6 100644
--- a/docs/introduction_to_data_goods.html
+++ b/docs/introduction_to_data_goods.html
@@ -70,7 +70,7 @@
-
+
@@ -539,7 +539,7 @@ How Data Goods are Managed
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/morocco-earthquake-strategic-brief.html b/docs/morocco-earthquake-strategic-brief.html
index e01deb7..75b5332 100644
--- a/docs/morocco-earthquake-strategic-brief.html
+++ b/docs/morocco-earthquake-strategic-brief.html
@@ -70,7 +70,7 @@
-
+
@@ -1197,7 +1197,7 @@ #
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/team.html b/docs/team.html
index cb577d7..a1b6c63 100644
--- a/docs/team.html
+++ b/docs/team.html
@@ -69,7 +69,7 @@
-
+
@@ -517,7 +517,7 @@ Data Goods Team and Acknowledgements
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/genindex.html b/genindex.html
index 5b7f6dd..b3c8220 100644
--- a/genindex.html
+++ b/genindex.html
@@ -67,7 +67,7 @@
-
+
@@ -390,7 +390,7 @@ Index
diff --git a/notebooks/business-activity-trends/01-business-activity-trends.html b/notebooks/business-activity-trends/01-business-activity-trends.html
index cc05235..493adda 100644
--- a/notebooks/business-activity-trends/01-business-activity-trends.html
+++ b/notebooks/business-activity-trends/01-business-activity-trends.html
@@ -70,7 +70,7 @@
-
+
@@ -483,7 +483,7 @@ Earthquake triggered business activity dataset
-
+
@@ -514,7 +514,7 @@ Earthquake triggered business activity dataset
-
+
-
+
-
+
+})(window);The Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.
+
+
+
@@ -1604,7 +1607,7 @@ Change in Business Activity by business verticals in Marrakech - Tensfit - A
diff --git a/notebooks/business-activity-trends/README.html b/notebooks/business-activity-trends/README.html
index 1cfcb2b..5e0f94d 100644
--- a/notebooks/business-activity-trends/README.html
+++ b/notebooks/business-activity-trends/README.html
@@ -70,7 +70,7 @@
-
+
@@ -581,7 +581,7 @@ Citations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/conflict-acled.html b/notebooks/conflict-acled.html
index 25e26e2..b4f0425 100644
--- a/notebooks/conflict-acled.html
+++ b/notebooks/conflict-acled.html
@@ -70,7 +70,7 @@
-
+
@@ -586,7 +586,7 @@ A closer look at Marakkesh, Casablanca and Rabat
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/earthquake-intensity/earthquake_intensity.html b/notebooks/earthquake-intensity/earthquake_intensity.html
index dd84e75..ed1304f 100644
--- a/notebooks/earthquake-intensity/earthquake_intensity.html
+++ b/notebooks/earthquake-intensity/earthquake_intensity.html
@@ -70,7 +70,7 @@
-
+
@@ -430,16 +430,17 @@ Earthquake Intensity Map
Data#
-Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized.
+Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized. The MMI is a range that allows us to estimate the amount of damage done to buildings.
Methodology#
-The impact of the earthquake in this situation is measured using the Modified Mercelli Intensity Scale (mmi).
+The impact of the earthquake in this situation is measured using the Modified Mercalli Intensity Scale (mmi).
The effect of an earthquake on the Earth’s surface is called the intensity. The intensity scale consists of a series of certain key responses such as people awakening, movement of furniture, damage to chimneys, and finally - total destruction. Although numerousintensity scales have been developed over the last several hundred years to evaluate the effects of earthquakes, the one currently used in the United States is the Modified Mercalli (MM) Intensity Scale. It was developed in 1931 by the American seismologists Harry Wood and Frank Neumann. This scale, composed of increasing levels of intensity that range from imperceptible shaking to catastrophic destruction, is designated by Roman numerals. It does not have a mathematical basis; instead it is an arbitrary ranking based on observed effects.
The Modified Mercalli Intensity value assigned to a specific site after an earthquake has a more meaningful measure of severity to the nonscientist than the magnitude because intensity refers to the effects actually experienced at that place.
The lower numbers of the intensity scale generally deal with the manner in which the earthquake is felt by people. The higher numbers of the scale are based on observed structural damage. Structural engineers usually contribute information for assigning intensity values of VIII or above.
-Source: USGS Modified Mercelli Intensity Scale
+Source: USGS Modified Mercalli Intensity Scale
Alternatively, another variable that can be used is also the distance from the epicenter.
+
Results#
@@ -458,7 +459,11 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+
+
+morocco_adm4.boundary.plot(ax=ax, color='#f2f2f2', alpha=0.8, linewidth=0.5)
+affected_regions.plot(column='PARAMVALUE', cmap ='Reds', ax=ax, vmax=vmax, legend = True)
+
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -471,7 +476,7 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
fontsize=10, color='black')
@@ -479,11 +484,11 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='white', weight='bold')
+ fontsize=10, color='black', weight='bold')
# sm = plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=vmin, vmax=vmax))
@@ -503,13 +508,24 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# # Create a custom legend
+# legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+# legend_colors = ['grey', 'orange', 'red']
+
+# # Create custom legend handles
+# from matplotlib.lines import Line2D
+# legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# # Display the custom legend
+# plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm4.png')
-
+
@@ -527,7 +543,7 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@ Resultstext_to_add = gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='black')
+ fontsize=10, color='white')
text_to_add = gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label='Marrakech Medina')
+ax.scatter(x, y, c='black', marker='o', s=50, label='Marrakech Medina')
# Use the annotate function to add the text to the map
ax.annotate('Marrakech Medina', xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
@@ -572,13 +588,25 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# Create a custom legend
+legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+legend_colors = ['grey', 'orange', 'red']
+
+# Create custom legend handles
+from matplotlib.lines import Line2D
+legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# Display the custom legend
+plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
-
+
@@ -675,7 +703,7 @@ Results
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/google_trends/Morocco_GoogleTrends.html b/notebooks/google_trends/Morocco_GoogleTrends.html
index 5b80f39..1edaeee 100644
--- a/notebooks/google_trends/Morocco_GoogleTrends.html
+++ b/notebooks/google_trends/Morocco_GoogleTrends.html
@@ -70,7 +70,7 @@
-
+
@@ -817,7 +817,7 @@ Prediction
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/network-connectivity/meta-network-connectivity.html b/notebooks/network-connectivity/meta-network-connectivity.html
index 74031fa..f925a44 100644
--- a/notebooks/network-connectivity/meta-network-connectivity.html
+++ b/notebooks/network-connectivity/meta-network-connectivity.html
@@ -70,7 +70,7 @@
-
+
@@ -938,7 +938,7 @@ Limitations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/ntl-analysis/README.html b/notebooks/ntl-analysis/README.html
index 1c72081..b7d7c6d 100644
--- a/notebooks/ntl-analysis/README.html
+++ b/notebooks/ntl-analysis/README.html
@@ -70,7 +70,7 @@
-
+
@@ -668,7 +668,7 @@ Maps#
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/affected-population.html b/notebooks/population/affected-population.html
index de724af..a98c650 100644
--- a/notebooks/population/affected-population.html
+++ b/notebooks/population/affected-population.html
@@ -70,7 +70,7 @@
-
+
@@ -783,7 +783,7 @@ Population demographic in worst affected Admin 4 regions
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/settlement.html b/notebooks/population/settlement.html
index ff4262f..1b26120 100644
--- a/notebooks/population/settlement.html
+++ b/notebooks/population/settlement.html
@@ -70,7 +70,7 @@
-
+
@@ -412,8 +412,10 @@ Contents
@@ -436,9 +438,6 @@ Methodology
Limitations#
The building footprints are obtained from satellite imagery which is obstructed by cloud cover. Given Morocco is a dessert area, the results are somewhere near 80% accurate. Data from OpenStreetMaps is crowdsourced.
-
-
-Impact to financial services and tourist locations#
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+
+
+
+
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+Show code cell source
+
+
+
+percentage_tourist_damages
+
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+
+
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
How Data Goods are Managed
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/morocco-earthquake-strategic-brief.html b/docs/morocco-earthquake-strategic-brief.html
index e01deb7..75b5332 100644
--- a/docs/morocco-earthquake-strategic-brief.html
+++ b/docs/morocco-earthquake-strategic-brief.html
@@ -70,7 +70,7 @@
-
+
@@ -1197,7 +1197,7 @@ #
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/docs/team.html b/docs/team.html
index cb577d7..a1b6c63 100644
--- a/docs/team.html
+++ b/docs/team.html
@@ -69,7 +69,7 @@
-
+
@@ -517,7 +517,7 @@ Data Goods Team and Acknowledgements
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/genindex.html b/genindex.html
index 5b7f6dd..b3c8220 100644
--- a/genindex.html
+++ b/genindex.html
@@ -67,7 +67,7 @@
-
+
@@ -390,7 +390,7 @@ Index
diff --git a/notebooks/business-activity-trends/01-business-activity-trends.html b/notebooks/business-activity-trends/01-business-activity-trends.html
index cc05235..493adda 100644
--- a/notebooks/business-activity-trends/01-business-activity-trends.html
+++ b/notebooks/business-activity-trends/01-business-activity-trends.html
@@ -70,7 +70,7 @@
-
+
@@ -483,7 +483,7 @@ Earthquake triggered business activity dataset
-
+
@@ -514,7 +514,7 @@ Earthquake triggered business activity dataset
-
+
-
+
-
+
+})(window);The Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.
+
+
+
@@ -1604,7 +1607,7 @@ Change in Business Activity by business verticals in Marrakech - Tensfit - A
diff --git a/notebooks/business-activity-trends/README.html b/notebooks/business-activity-trends/README.html
index 1cfcb2b..5e0f94d 100644
--- a/notebooks/business-activity-trends/README.html
+++ b/notebooks/business-activity-trends/README.html
@@ -70,7 +70,7 @@
-
+
@@ -581,7 +581,7 @@ Citations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/conflict-acled.html b/notebooks/conflict-acled.html
index 25e26e2..b4f0425 100644
--- a/notebooks/conflict-acled.html
+++ b/notebooks/conflict-acled.html
@@ -70,7 +70,7 @@
-
+
@@ -586,7 +586,7 @@ A closer look at Marakkesh, Casablanca and Rabat
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/earthquake-intensity/earthquake_intensity.html b/notebooks/earthquake-intensity/earthquake_intensity.html
index dd84e75..ed1304f 100644
--- a/notebooks/earthquake-intensity/earthquake_intensity.html
+++ b/notebooks/earthquake-intensity/earthquake_intensity.html
@@ -70,7 +70,7 @@
-
+
@@ -430,16 +430,17 @@ Earthquake Intensity Map
Data#
-Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized.
+Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized. The MMI is a range that allows us to estimate the amount of damage done to buildings.
Methodology#
-The impact of the earthquake in this situation is measured using the Modified Mercelli Intensity Scale (mmi).
+The impact of the earthquake in this situation is measured using the Modified Mercalli Intensity Scale (mmi).
The effect of an earthquake on the Earth’s surface is called the intensity. The intensity scale consists of a series of certain key responses such as people awakening, movement of furniture, damage to chimneys, and finally - total destruction. Although numerousintensity scales have been developed over the last several hundred years to evaluate the effects of earthquakes, the one currently used in the United States is the Modified Mercalli (MM) Intensity Scale. It was developed in 1931 by the American seismologists Harry Wood and Frank Neumann. This scale, composed of increasing levels of intensity that range from imperceptible shaking to catastrophic destruction, is designated by Roman numerals. It does not have a mathematical basis; instead it is an arbitrary ranking based on observed effects.
The Modified Mercalli Intensity value assigned to a specific site after an earthquake has a more meaningful measure of severity to the nonscientist than the magnitude because intensity refers to the effects actually experienced at that place.
The lower numbers of the intensity scale generally deal with the manner in which the earthquake is felt by people. The higher numbers of the scale are based on observed structural damage. Structural engineers usually contribute information for assigning intensity values of VIII or above.
-Source: USGS Modified Mercelli Intensity Scale
+Source: USGS Modified Mercalli Intensity Scale
Alternatively, another variable that can be used is also the distance from the epicenter.
+
Results#
@@ -458,7 +459,11 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+
+
+morocco_adm4.boundary.plot(ax=ax, color='#f2f2f2', alpha=0.8, linewidth=0.5)
+affected_regions.plot(column='PARAMVALUE', cmap ='Reds', ax=ax, vmax=vmax, legend = True)
+
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -471,7 +476,7 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
fontsize=10, color='black')
@@ -479,11 +484,11 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='white', weight='bold')
+ fontsize=10, color='black', weight='bold')
# sm = plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=vmin, vmax=vmax))
@@ -503,13 +508,24 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# # Create a custom legend
+# legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+# legend_colors = ['grey', 'orange', 'red']
+
+# # Create custom legend handles
+# from matplotlib.lines import Line2D
+# legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# # Display the custom legend
+# plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm4.png')
-
+
@@ -527,7 +543,7 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@ Resultstext_to_add = gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='black')
+ fontsize=10, color='white')
text_to_add = gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label='Marrakech Medina')
+ax.scatter(x, y, c='black', marker='o', s=50, label='Marrakech Medina')
# Use the annotate function to add the text to the map
ax.annotate('Marrakech Medina', xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
@@ -572,13 +588,25 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# Create a custom legend
+legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+legend_colors = ['grey', 'orange', 'red']
+
+# Create custom legend handles
+from matplotlib.lines import Line2D
+legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# Display the custom legend
+plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
-
+
@@ -675,7 +703,7 @@ Results
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/google_trends/Morocco_GoogleTrends.html b/notebooks/google_trends/Morocco_GoogleTrends.html
index 5b80f39..1edaeee 100644
--- a/notebooks/google_trends/Morocco_GoogleTrends.html
+++ b/notebooks/google_trends/Morocco_GoogleTrends.html
@@ -70,7 +70,7 @@
-
+
@@ -817,7 +817,7 @@ Prediction
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/network-connectivity/meta-network-connectivity.html b/notebooks/network-connectivity/meta-network-connectivity.html
index 74031fa..f925a44 100644
--- a/notebooks/network-connectivity/meta-network-connectivity.html
+++ b/notebooks/network-connectivity/meta-network-connectivity.html
@@ -70,7 +70,7 @@
-
+
@@ -938,7 +938,7 @@ Limitations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/ntl-analysis/README.html b/notebooks/ntl-analysis/README.html
index 1c72081..b7d7c6d 100644
--- a/notebooks/ntl-analysis/README.html
+++ b/notebooks/ntl-analysis/README.html
@@ -70,7 +70,7 @@
-
+
@@ -668,7 +668,7 @@ Maps#
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/affected-population.html b/notebooks/population/affected-population.html
index de724af..a98c650 100644
--- a/notebooks/population/affected-population.html
+++ b/notebooks/population/affected-population.html
@@ -70,7 +70,7 @@
-
+
@@ -783,7 +783,7 @@ Population demographic in worst affected Admin 4 regions
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/settlement.html b/notebooks/population/settlement.html
index ff4262f..1b26120 100644
--- a/notebooks/population/settlement.html
+++ b/notebooks/population/settlement.html
@@ -70,7 +70,7 @@
-
+
@@ -412,8 +412,10 @@ Contents
@@ -436,9 +438,6 @@ Methodology
Limitations#
The building footprints are obtained from satellite imagery which is obstructed by cloud cover. Given Morocco is a dessert area, the results are somewhere near 80% accurate. Data from OpenStreetMaps is crowdsourced.
-
-
-Impact to financial services and tourist locations#
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+
+
+
+
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+Show code cell source
+
+
+
+percentage_tourist_damages
+
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+
+
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
Index
diff --git a/notebooks/business-activity-trends/01-business-activity-trends.html b/notebooks/business-activity-trends/01-business-activity-trends.html index cc05235..493adda 100644 --- a/notebooks/business-activity-trends/01-business-activity-trends.html +++ b/notebooks/business-activity-trends/01-business-activity-trends.html @@ -70,7 +70,7 @@ - + @@ -483,7 +483,7 @@Earthquake triggered business activity dataset
-
+
@@ -514,7 +514,7 @@ Earthquake triggered business activity dataset
-
+
-
+
-
+
+})(window);The Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.
+
+
+
@@ -1604,7 +1607,7 @@ Change in Business Activity by business verticals in Marrakech - Tensfit - A
diff --git a/notebooks/business-activity-trends/README.html b/notebooks/business-activity-trends/README.html
index 1cfcb2b..5e0f94d 100644
--- a/notebooks/business-activity-trends/README.html
+++ b/notebooks/business-activity-trends/README.html
@@ -70,7 +70,7 @@
-
+
@@ -581,7 +581,7 @@ Citations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/conflict-acled.html b/notebooks/conflict-acled.html
index 25e26e2..b4f0425 100644
--- a/notebooks/conflict-acled.html
+++ b/notebooks/conflict-acled.html
@@ -70,7 +70,7 @@
-
+
@@ -586,7 +586,7 @@ A closer look at Marakkesh, Casablanca and Rabat
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/earthquake-intensity/earthquake_intensity.html b/notebooks/earthquake-intensity/earthquake_intensity.html
index dd84e75..ed1304f 100644
--- a/notebooks/earthquake-intensity/earthquake_intensity.html
+++ b/notebooks/earthquake-intensity/earthquake_intensity.html
@@ -70,7 +70,7 @@
-
+
@@ -430,16 +430,17 @@ Earthquake Intensity Map
Data#
-Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized.
+Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized. The MMI is a range that allows us to estimate the amount of damage done to buildings.
Methodology#
-The impact of the earthquake in this situation is measured using the Modified Mercelli Intensity Scale (mmi).
+The impact of the earthquake in this situation is measured using the Modified Mercalli Intensity Scale (mmi).
The effect of an earthquake on the Earth’s surface is called the intensity. The intensity scale consists of a series of certain key responses such as people awakening, movement of furniture, damage to chimneys, and finally - total destruction. Although numerousintensity scales have been developed over the last several hundred years to evaluate the effects of earthquakes, the one currently used in the United States is the Modified Mercalli (MM) Intensity Scale. It was developed in 1931 by the American seismologists Harry Wood and Frank Neumann. This scale, composed of increasing levels of intensity that range from imperceptible shaking to catastrophic destruction, is designated by Roman numerals. It does not have a mathematical basis; instead it is an arbitrary ranking based on observed effects.
The Modified Mercalli Intensity value assigned to a specific site after an earthquake has a more meaningful measure of severity to the nonscientist than the magnitude because intensity refers to the effects actually experienced at that place.
The lower numbers of the intensity scale generally deal with the manner in which the earthquake is felt by people. The higher numbers of the scale are based on observed structural damage. Structural engineers usually contribute information for assigning intensity values of VIII or above.
-Source: USGS Modified Mercelli Intensity Scale
+Source: USGS Modified Mercalli Intensity Scale
Alternatively, another variable that can be used is also the distance from the epicenter.
+
Results#
@@ -458,7 +459,11 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+
+
+morocco_adm4.boundary.plot(ax=ax, color='#f2f2f2', alpha=0.8, linewidth=0.5)
+affected_regions.plot(column='PARAMVALUE', cmap ='Reds', ax=ax, vmax=vmax, legend = True)
+
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -471,7 +476,7 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
fontsize=10, color='black')
@@ -479,11 +484,11 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='white', weight='bold')
+ fontsize=10, color='black', weight='bold')
# sm = plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=vmin, vmax=vmax))
@@ -503,13 +508,24 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# # Create a custom legend
+# legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+# legend_colors = ['grey', 'orange', 'red']
+
+# # Create custom legend handles
+# from matplotlib.lines import Line2D
+# legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# # Display the custom legend
+# plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm4.png')
-
+
@@ -527,7 +543,7 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@ Resultstext_to_add = gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='black')
+ fontsize=10, color='white')
text_to_add = gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label='Marrakech Medina')
+ax.scatter(x, y, c='black', marker='o', s=50, label='Marrakech Medina')
# Use the annotate function to add the text to the map
ax.annotate('Marrakech Medina', xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
@@ -572,13 +588,25 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# Create a custom legend
+legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+legend_colors = ['grey', 'orange', 'red']
+
+# Create custom legend handles
+from matplotlib.lines import Line2D
+legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# Display the custom legend
+plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
-
+
@@ -675,7 +703,7 @@ Results
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/google_trends/Morocco_GoogleTrends.html b/notebooks/google_trends/Morocco_GoogleTrends.html
index 5b80f39..1edaeee 100644
--- a/notebooks/google_trends/Morocco_GoogleTrends.html
+++ b/notebooks/google_trends/Morocco_GoogleTrends.html
@@ -70,7 +70,7 @@
-
+
@@ -817,7 +817,7 @@ Prediction
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/network-connectivity/meta-network-connectivity.html b/notebooks/network-connectivity/meta-network-connectivity.html
index 74031fa..f925a44 100644
--- a/notebooks/network-connectivity/meta-network-connectivity.html
+++ b/notebooks/network-connectivity/meta-network-connectivity.html
@@ -70,7 +70,7 @@
-
+
@@ -938,7 +938,7 @@ Limitations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/ntl-analysis/README.html b/notebooks/ntl-analysis/README.html
index 1c72081..b7d7c6d 100644
--- a/notebooks/ntl-analysis/README.html
+++ b/notebooks/ntl-analysis/README.html
@@ -70,7 +70,7 @@
-
+
@@ -668,7 +668,7 @@ Maps#
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/affected-population.html b/notebooks/population/affected-population.html
index de724af..a98c650 100644
--- a/notebooks/population/affected-population.html
+++ b/notebooks/population/affected-population.html
@@ -70,7 +70,7 @@
-
+
@@ -783,7 +783,7 @@ Population demographic in worst affected Admin 4 regions
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/settlement.html b/notebooks/population/settlement.html
index ff4262f..1b26120 100644
--- a/notebooks/population/settlement.html
+++ b/notebooks/population/settlement.html
@@ -70,7 +70,7 @@
-
+
@@ -412,8 +412,10 @@ Contents
@@ -436,9 +438,6 @@ Methodology
Limitations#
The building footprints are obtained from satellite imagery which is obstructed by cloud cover. Given Morocco is a dessert area, the results are somewhere near 80% accurate. Data from OpenStreetMaps is crowdsourced.
-
-
-Impact to financial services and tourist locations#
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+
+
+
+
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+Show code cell source
+
+
+
+percentage_tourist_damages
+
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+
+
Earthquake triggered business activity dataset
-
+
-
+
-
+
The Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.
+Citations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/conflict-acled.html b/notebooks/conflict-acled.html
index 25e26e2..b4f0425 100644
--- a/notebooks/conflict-acled.html
+++ b/notebooks/conflict-acled.html
@@ -70,7 +70,7 @@
-
+
@@ -586,7 +586,7 @@ A closer look at Marakkesh, Casablanca and Rabat
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/earthquake-intensity/earthquake_intensity.html b/notebooks/earthquake-intensity/earthquake_intensity.html
index dd84e75..ed1304f 100644
--- a/notebooks/earthquake-intensity/earthquake_intensity.html
+++ b/notebooks/earthquake-intensity/earthquake_intensity.html
@@ -70,7 +70,7 @@
-
+
@@ -430,16 +430,17 @@ Earthquake Intensity Map
Data#
-Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized.
+Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized. The MMI is a range that allows us to estimate the amount of damage done to buildings.
Methodology#
-The impact of the earthquake in this situation is measured using the Modified Mercelli Intensity Scale (mmi).
+The impact of the earthquake in this situation is measured using the Modified Mercalli Intensity Scale (mmi).
The effect of an earthquake on the Earth’s surface is called the intensity. The intensity scale consists of a series of certain key responses such as people awakening, movement of furniture, damage to chimneys, and finally - total destruction. Although numerousintensity scales have been developed over the last several hundred years to evaluate the effects of earthquakes, the one currently used in the United States is the Modified Mercalli (MM) Intensity Scale. It was developed in 1931 by the American seismologists Harry Wood and Frank Neumann. This scale, composed of increasing levels of intensity that range from imperceptible shaking to catastrophic destruction, is designated by Roman numerals. It does not have a mathematical basis; instead it is an arbitrary ranking based on observed effects.
The Modified Mercalli Intensity value assigned to a specific site after an earthquake has a more meaningful measure of severity to the nonscientist than the magnitude because intensity refers to the effects actually experienced at that place.
The lower numbers of the intensity scale generally deal with the manner in which the earthquake is felt by people. The higher numbers of the scale are based on observed structural damage. Structural engineers usually contribute information for assigning intensity values of VIII or above.
-Source: USGS Modified Mercelli Intensity Scale
+Source: USGS Modified Mercalli Intensity Scale
Alternatively, another variable that can be used is also the distance from the epicenter.
+
Results#
@@ -458,7 +459,11 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+
+
+morocco_adm4.boundary.plot(ax=ax, color='#f2f2f2', alpha=0.8, linewidth=0.5)
+affected_regions.plot(column='PARAMVALUE', cmap ='Reds', ax=ax, vmax=vmax, legend = True)
+
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -471,7 +476,7 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
fontsize=10, color='black')
@@ -479,11 +484,11 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='white', weight='bold')
+ fontsize=10, color='black', weight='bold')
# sm = plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=vmin, vmax=vmax))
@@ -503,13 +508,24 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# # Create a custom legend
+# legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+# legend_colors = ['grey', 'orange', 'red']
+
+# # Create custom legend handles
+# from matplotlib.lines import Line2D
+# legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# # Display the custom legend
+# plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm4.png')
-
+
@@ -527,7 +543,7 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@ Resultstext_to_add = gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='black')
+ fontsize=10, color='white')
text_to_add = gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label='Marrakech Medina')
+ax.scatter(x, y, c='black', marker='o', s=50, label='Marrakech Medina')
# Use the annotate function to add the text to the map
ax.annotate('Marrakech Medina', xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
@@ -572,13 +588,25 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# Create a custom legend
+legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+legend_colors = ['grey', 'orange', 'red']
+
+# Create custom legend handles
+from matplotlib.lines import Line2D
+legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# Display the custom legend
+plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
-
+
@@ -675,7 +703,7 @@ Results
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/google_trends/Morocco_GoogleTrends.html b/notebooks/google_trends/Morocco_GoogleTrends.html
index 5b80f39..1edaeee 100644
--- a/notebooks/google_trends/Morocco_GoogleTrends.html
+++ b/notebooks/google_trends/Morocco_GoogleTrends.html
@@ -70,7 +70,7 @@
-
+
@@ -817,7 +817,7 @@ Prediction
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/network-connectivity/meta-network-connectivity.html b/notebooks/network-connectivity/meta-network-connectivity.html
index 74031fa..f925a44 100644
--- a/notebooks/network-connectivity/meta-network-connectivity.html
+++ b/notebooks/network-connectivity/meta-network-connectivity.html
@@ -70,7 +70,7 @@
-
+
@@ -938,7 +938,7 @@ Limitations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/ntl-analysis/README.html b/notebooks/ntl-analysis/README.html
index 1c72081..b7d7c6d 100644
--- a/notebooks/ntl-analysis/README.html
+++ b/notebooks/ntl-analysis/README.html
@@ -70,7 +70,7 @@
-
+
@@ -668,7 +668,7 @@ Maps#
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/affected-population.html b/notebooks/population/affected-population.html
index de724af..a98c650 100644
--- a/notebooks/population/affected-population.html
+++ b/notebooks/population/affected-population.html
@@ -70,7 +70,7 @@
-
+
@@ -783,7 +783,7 @@ Population demographic in worst affected Admin 4 regions
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/settlement.html b/notebooks/population/settlement.html
index ff4262f..1b26120 100644
--- a/notebooks/population/settlement.html
+++ b/notebooks/population/settlement.html
@@ -70,7 +70,7 @@
-
+
@@ -412,8 +412,10 @@ Contents
@@ -436,9 +438,6 @@ Methodology
Limitations#
The building footprints are obtained from satellite imagery which is obstructed by cloud cover. Given Morocco is a dessert area, the results are somewhere near 80% accurate. Data from OpenStreetMaps is crowdsourced.
-
-
-Impact to financial services and tourist locations#
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+
+
+
+
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+Show code cell source
+
+
+
+percentage_tourist_damages
+
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+
+
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
Earthquake Intensity Map
Data#
-Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized.
+Earthquake Intensity Data was obtained from USGS and was overlayed with shapefiles of Morocco admin regions taken from UNOCHA and from GADM[https://gadm.org/download_country.html]. This can be used as a baseline map upon which other indicators can be visualized. The MMI is a range that allows us to estimate the amount of damage done to buildings.
Methodology#
-The impact of the earthquake in this situation is measured using the Modified Mercelli Intensity Scale (mmi).
+The impact of the earthquake in this situation is measured using the Modified Mercalli Intensity Scale (mmi).
The effect of an earthquake on the Earth’s surface is called the intensity. The intensity scale consists of a series of certain key responses such as people awakening, movement of furniture, damage to chimneys, and finally - total destruction. Although numerousintensity scales have been developed over the last several hundred years to evaluate the effects of earthquakes, the one currently used in the United States is the Modified Mercalli (MM) Intensity Scale. It was developed in 1931 by the American seismologists Harry Wood and Frank Neumann. This scale, composed of increasing levels of intensity that range from imperceptible shaking to catastrophic destruction, is designated by Roman numerals. It does not have a mathematical basis; instead it is an arbitrary ranking based on observed effects.
The Modified Mercalli Intensity value assigned to a specific site after an earthquake has a more meaningful measure of severity to the nonscientist than the magnitude because intensity refers to the effects actually experienced at that place.
The lower numbers of the intensity scale generally deal with the manner in which the earthquake is felt by people. The higher numbers of the scale are based on observed structural damage. Structural engineers usually contribute information for assigning intensity values of VIII or above.
-Source: USGS Modified Mercelli Intensity Scale
+Source: USGS Modified Mercalli Intensity Scale
Alternatively, another variable that can be used is also the distance from the epicenter.
+
Results#
@@ -458,7 +459,11 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+
+
+morocco_adm4.boundary.plot(ax=ax, color='#f2f2f2', alpha=0.8, linewidth=0.5)
+affected_regions.plot(column='PARAMVALUE', cmap ='Reds', ax=ax, vmax=vmax, legend = True)
+
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -471,7 +476,7 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
fontsize=10, color='black')
@@ -479,11 +484,11 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='white', weight='bold')
+ fontsize=10, color='black', weight='bold')
# sm = plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=vmin, vmax=vmax))
@@ -503,13 +508,24 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# # Create a custom legend
+# legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+# legend_colors = ['grey', 'orange', 'red']
+
+# # Create custom legend handles
+# from matplotlib.lines import Line2D
+# legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# # Display the custom legend
+# plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm4.png')
-
+
@@ -527,7 +543,7 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@ Resultstext_to_add = gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='black')
+ fontsize=10, color='white')
text_to_add = gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label='Marrakech Medina')
+ax.scatter(x, y, c='black', marker='o', s=50, label='Marrakech Medina')
# Use the annotate function to add the text to the map
ax.annotate('Marrakech Medina', xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
@@ -572,13 +588,25 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# Create a custom legend
+legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+legend_colors = ['grey', 'orange', 'red']
+
+# Create custom legend handles
+from matplotlib.lines import Line2D
+legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# Display the custom legend
+plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
-
+
@@ -675,7 +703,7 @@ Results
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/google_trends/Morocco_GoogleTrends.html b/notebooks/google_trends/Morocco_GoogleTrends.html
index 5b80f39..1edaeee 100644
--- a/notebooks/google_trends/Morocco_GoogleTrends.html
+++ b/notebooks/google_trends/Morocco_GoogleTrends.html
@@ -70,7 +70,7 @@
-
+
@@ -817,7 +817,7 @@ Prediction
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/network-connectivity/meta-network-connectivity.html b/notebooks/network-connectivity/meta-network-connectivity.html
index 74031fa..f925a44 100644
--- a/notebooks/network-connectivity/meta-network-connectivity.html
+++ b/notebooks/network-connectivity/meta-network-connectivity.html
@@ -70,7 +70,7 @@
-
+
@@ -938,7 +938,7 @@ Limitations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/ntl-analysis/README.html b/notebooks/ntl-analysis/README.html
index 1c72081..b7d7c6d 100644
--- a/notebooks/ntl-analysis/README.html
+++ b/notebooks/ntl-analysis/README.html
@@ -70,7 +70,7 @@
-
+
@@ -668,7 +668,7 @@ Maps#
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/affected-population.html b/notebooks/population/affected-population.html
index de724af..a98c650 100644
--- a/notebooks/population/affected-population.html
+++ b/notebooks/population/affected-population.html
@@ -70,7 +70,7 @@
-
+
@@ -783,7 +783,7 @@ Population demographic in worst affected Admin 4 regions
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/settlement.html b/notebooks/population/settlement.html
index ff4262f..1b26120 100644
--- a/notebooks/population/settlement.html
+++ b/notebooks/population/settlement.html
@@ -70,7 +70,7 @@
-
+
@@ -412,8 +412,10 @@ Contents
@@ -436,9 +438,6 @@ Methodology
Limitations#
The building footprints are obtained from satellite imagery which is obstructed by cloud cover. Given Morocco is a dessert area, the results are somewhere near 80% accurate. Data from OpenStreetMaps is crowdsourced.
-
-
-Impact to financial services and tourist locations#
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+
+
+
+
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+Show code cell source
+
+
+
+percentage_tourist_damages
+
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+
+
Methodology#
-The impact of the earthquake in this situation is measured using the Modified Mercelli Intensity Scale (mmi).
+The impact of the earthquake in this situation is measured using the Modified Mercalli Intensity Scale (mmi).
The effect of an earthquake on the Earth’s surface is called the intensity. The intensity scale consists of a series of certain key responses such as people awakening, movement of furniture, damage to chimneys, and finally - total destruction. Although numerousintensity scales have been developed over the last several hundred years to evaluate the effects of earthquakes, the one currently used in the United States is the Modified Mercalli (MM) Intensity Scale. It was developed in 1931 by the American seismologists Harry Wood and Frank Neumann. This scale, composed of increasing levels of intensity that range from imperceptible shaking to catastrophic destruction, is designated by Roman numerals. It does not have a mathematical basis; instead it is an arbitrary ranking based on observed effects.
The Modified Mercalli Intensity value assigned to a specific site after an earthquake has a more meaningful measure of severity to the nonscientist than the magnitude because intensity refers to the effects actually experienced at that place.
The lower numbers of the intensity scale generally deal with the manner in which the earthquake is felt by people. The higher numbers of the scale are based on observed structural damage. Structural engineers usually contribute information for assigning intensity values of VIII or above.
-Source: USGS Modified Mercelli Intensity Scale
+Source: USGS Modified Mercalli Intensity Scale
Alternatively, another variable that can be used is also the distance from the epicenter.
+
Results#
@@ -458,7 +459,11 @@Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+
+
+morocco_adm4.boundary.plot(ax=ax, color='#f2f2f2', alpha=0.8, linewidth=0.5)
+affected_regions.plot(column='PARAMVALUE', cmap ='Reds', ax=ax, vmax=vmax, legend = True)
+
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -471,7 +476,7 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['PARAMVALUE']== gdf_adm4['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
fontsize=10, color='black')
@@ -479,11 +484,11 @@ Resultstext_to_add = gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='white', weight='bold')
+ fontsize=10, color='black', weight='bold')
# sm = plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=vmin, vmax=vmax))
@@ -503,13 +508,24 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# # Create a custom legend
+# legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+# legend_colors = ['grey', 'orange', 'red']
+
+# # Create custom legend handles
+# from matplotlib.lines import Line2D
+# legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# # Display the custom legend
+# plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm4.png')
-
+
@@ -527,7 +543,7 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@ Resultstext_to_add = gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='black')
+ fontsize=10, color='white')
text_to_add = gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label='Marrakech Medina')
+ax.scatter(x, y, c='black', marker='o', s=50, label='Marrakech Medina')
# Use the annotate function to add the text to the map
ax.annotate('Marrakech Medina', xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
@@ -572,13 +588,25 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# Create a custom legend
+legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+legend_colors = ['grey', 'orange', 'red']
+
+# Create custom legend handles
+from matplotlib.lines import Line2D
+legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# Display the custom legend
+plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
-
+
Resultstext_to_add = gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['NAME_4'].iloc[0]
x, y =gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.x, gdf_adm4[gdf_adm4['NAME_4']== 'Marrakech Medina']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='white', weight='bold')
+ fontsize=10, color='black', weight='bold')
# sm = plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=vmin, vmax=vmax))
@@ -503,13 +508,24 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# # Create a custom legend
+# legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+# legend_colors = ['grey', 'orange', 'red']
+
+# # Create custom legend handles
+# from matplotlib.lines import Line2D
+# legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# # Display the custom legend
+# plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm4.png')
-
+
@@ -527,7 +543,7 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@ Resultstext_to_add = gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='black')
+ fontsize=10, color='white')
text_to_add = gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label='Marrakech Medina')
+ax.scatter(x, y, c='black', marker='o', s=50, label='Marrakech Medina')
# Use the annotate function to add the text to the map
ax.annotate('Marrakech Medina', xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
@@ -572,13 +588,25 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# Create a custom legend
+legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+legend_colors = ['grey', 'orange', 'red']
+
+# Create custom legend handles
+from matplotlib.lines import Line2D
+legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# Display the custom legend
+plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
-
+
-
+
+
@@ -527,7 +543,7 @@ Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@
Resultsvmin = gdf['PARAMVALUE'].min()
vmax = gdf['PARAMVALUE'].max()
-gdf.plot(column='PARAMVALUE', cmap = 'viridis', ax=ax, vmax=vmax, legend = True)
+gdf.plot(column='mmi_class', color = gdf['mmi_color'], ax=ax, vmax=vmax, legend = True)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
@@ -543,15 +559,15 @@ Resultstext_to_add = gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['PARAMVALUE']== gdf_adm3['PARAMVALUE'].max()]['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label=text_to_add)
+ax.scatter(x, y, c='black', marker='o', s=50, label=text_to_add)
# Use the annotate function to add the text to the map
ax.annotate(text_to_add, xy=(x-0.5, y-0.5), xytext=(10, 10), textcoords='offset points',
- fontsize=10, color='black')
+ fontsize=10, color='white')
text_to_add = gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['NAME_3'].iloc[0]
x, y =gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.x, gdf_adm3[gdf_adm3['NAME_3']== 'NA (Marrakech Medina)']['geometry'].iloc[0].centroid.y
-ax.scatter(x, y, c='red', marker='o', s=50, label='Marrakech Medina')
+ax.scatter(x, y, c='black', marker='o', s=50, label='Marrakech Medina')
# Use the annotate function to add the text to the map
ax.annotate('Marrakech Medina', xy=(x-0.05, y-0.3), xytext=(10, 10), textcoords='offset points',
@@ -572,13 +588,25 @@ Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='gray', weight='normal')
+# Create a custom legend
+legend_labels = ['Low Damage', 'High Damage', 'Very High Damage']
+legend_colors = ['grey', 'orange', 'red']
+
+# Create custom legend handles
+from matplotlib.lines import Line2D
+legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))]
+
+# Display the custom legend
+plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+
plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
Resultsax.text(0, 0, subtitle, ha='left', va='center', transform=ax.transAxes, fontsize=10, color='gray', weight='normal') +# Create a custom legend +legend_labels = ['Low Damage', 'High Damage', 'Very High Damage'] +legend_colors = ['grey', 'orange', 'red'] + +# Create custom legend handles +from matplotlib.lines import Line2D +legend_handles = [Line2D([0], [0], marker='o', color='w', label=legend_labels[i], markersize=10, markerfacecolor=legend_colors[i]) for i in range(len(legend_labels))] + +# Display the custom legend +plt.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.5), frameon=False) + + plt.savefig('../../docs/images/01_earthquake_intensity_adm3.png')
-
+
+
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
Prediction
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/network-connectivity/meta-network-connectivity.html b/notebooks/network-connectivity/meta-network-connectivity.html
index 74031fa..f925a44 100644
--- a/notebooks/network-connectivity/meta-network-connectivity.html
+++ b/notebooks/network-connectivity/meta-network-connectivity.html
@@ -70,7 +70,7 @@
-
+
@@ -938,7 +938,7 @@ Limitations
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/ntl-analysis/README.html b/notebooks/ntl-analysis/README.html
index 1c72081..b7d7c6d 100644
--- a/notebooks/ntl-analysis/README.html
+++ b/notebooks/ntl-analysis/README.html
@@ -70,7 +70,7 @@
-
+
@@ -668,7 +668,7 @@ Maps#
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/affected-population.html b/notebooks/population/affected-population.html
index de724af..a98c650 100644
--- a/notebooks/population/affected-population.html
+++ b/notebooks/population/affected-population.html
@@ -70,7 +70,7 @@
-
+
@@ -783,7 +783,7 @@ Population demographic in worst affected Admin 4 regions
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/settlement.html b/notebooks/population/settlement.html
index ff4262f..1b26120 100644
--- a/notebooks/population/settlement.html
+++ b/notebooks/population/settlement.html
@@ -70,7 +70,7 @@
-
+
@@ -412,8 +412,10 @@ Contents
@@ -436,9 +438,6 @@ Methodology
Limitations#
The building footprints are obtained from satellite imagery which is obstructed by cloud cover. Given Morocco is a dessert area, the results are somewhere near 80% accurate. Data from OpenStreetMaps is crowdsourced.
-
-
-Impact to financial services and tourist locations#
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+
+
+
+
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+Show code cell source
+
+
+
+percentage_tourist_damages
+
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+
+
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
Maps#
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/affected-population.html b/notebooks/population/affected-population.html
index de724af..a98c650 100644
--- a/notebooks/population/affected-population.html
+++ b/notebooks/population/affected-population.html
@@ -70,7 +70,7 @@
-
+
@@ -783,7 +783,7 @@ Population demographic in worst affected Admin 4 regions
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
diff --git a/notebooks/population/settlement.html b/notebooks/population/settlement.html
index ff4262f..1b26120 100644
--- a/notebooks/population/settlement.html
+++ b/notebooks/population/settlement.html
@@ -70,7 +70,7 @@
-
+
@@ -412,8 +412,10 @@ Contents
@@ -436,9 +438,6 @@ Methodology
Limitations#
The building footprints are obtained from satellite imagery which is obstructed by cloud cover. Given Morocco is a dessert area, the results are somewhere near 80% accurate. Data from OpenStreetMaps is crowdsourced.
-
-
-Impact to financial services and tourist locations#
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+
+
+
+
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+Show code cell source
+
+
+
+percentage_tourist_damages
+
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+
+
- Last updated on Oct 20, 2023.
+ Last updated on Oct 24, 2023.
Contents
@@ -436,9 +438,6 @@Methodology
Limitations#
The building footprints are obtained from satellite imagery which is obstructed by cloud cover. Given Morocco is a dessert area, the results are somewhere near 80% accurate. Data from OpenStreetMaps is crowdsourced.
-
-
-Impact to financial services and tourist locations#
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+
+
+
+
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+Show code cell source
+
+
+
+percentage_tourist_damages
+
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+
+
+
+
+Show code cell source
+
+
+
+fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+
+
Impact to financial services and tourist locations#
+
+
+
+
+
+
+Text(-0.35, -0.05, 'Source: USGS')
+
+Percentage of tourist locations damaged in each admnin 4 which ad >7 mmi scale earthquake impact#
+
+
+
+
++Show code cell source + +
+
+
+
+
+percentage_tourist_damages
+
+
+
+
+{'Al Haouz': {'No': 71.30801687763713, 'Yes': 28.69198312236287},
+ 'Chichaoua': {'No': 80.0, 'Yes': 20.0},
+ 'Taroudannt': {'No': 96.42857142857143, 'Yes': 3.5714285714285716},
+ 'Marrakech': {'No': 99.88331388564761, 'Yes': 0.11668611435239207}}
+
+
+
+
++Show code cell source + +
+
+
+
+
+percentage_population=round(100*financial_services['population_without_children'].sum()/total_population_affected_region['population_wihtou_children'].sum())
+print(f'{percentage_population}% of people in the affected regions lost access to financial services after the earthquake. These include {financial_service_types}')
+
+
+
+
+40% of people in the affected regions lost access to financial services after the earthquake. These include ['bank' 'post_office' 'bureau_de_change' 'atm' 'money_transfer']
+
+
+
+Show code cell source + +
+
+
fig, ax = plt.subplots()
+affected_regions.plot(cmap='Reds', column='PARAMVALUE', ax=ax)
+financial_services.to_crs(epsg=4326).plot(column='population_without_children', cmap='Blues', ax=ax, legend=True)
-eq_adm4.plot(column='PARAMVALUE', ax=ax, cmap = 'Reds', alpha = 0.5)
-affected_settlement.plot(ax=ax)
-financial_services.plot(color='purple', ax=ax, label = 'financial services')
-tourist_attractions.plot(color = 'green', ax=ax)
+plt.title('Total population within 5km radius of financial services', fontsize=12, ha='center')
-legend_labels = ['financial services', 'tourist attractions']
-handles = [Line2D([0], [0], marker='o', color='w', label=label, markerfacecolor=color_dict[label]) for label in legend_labels]
-ax.legend(handles=handles, loc='upper left', frameon=False)
+ax.spines['top'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)
-plt.axis('off')
+ax.set_xticks([])
+ax.set_yticks([])
-plt.title('Settlements in worst affected Admin 1 regions overlayed with Points of Inetrest', fontsize=20, ha='center')
-subtitle_text = "Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps"
-ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=15, ha='left')
+subtitle_text = "Source: Population estimates from Meta's HRSL and financial points from OSM"
+ax.text(-0.35, -0.05, subtitle_text, transform=ax.transAxes, fontsize=10, ha='left')
-
+
Text(-0.35, -0.05, 'Source: Settlement layer from Atlas AI, earthquake intensity from USGS and Points of Interest from OpenStreetMaps')
+Text(-0.35, -0.05, "Source: Population estimates from Meta's HRSL and financial points from OSM")
-
+