OpenStreetMap Data Quality based on the Contributions History

Working with community-built data as OpenStreetMap forces to take care of data quality. We have to be confident with the data we work with. Is this road geometry accurate enough? Is this street name missing?

Our first idea was to answer to this question: can we assess the quality of OpenStreetMap data? (and how?).

This project is dedicated to explore and analyze the OpenStreetMap data history in order to classify the contributors.

There are a serie of articles on the Oslandia's blog site which deal with this topic. Theses articles are also in the articles folder.

How to install

This projects runs with Python3, every dependencies are managed through poetry.

Installation from source

$ git clone [email protected]:Oslandia/osm-data-classification.git
$ cd osm-data-classification
$ virtualenv -p /usr/bin/python3 venv
$ source venv/bin/activate
(venv)$ pip install poetry
(venv)$ poetry install

How does it work?

There are several Python files to extract and analyze the OSM history data. Two machine learning models are used to classify the changesets and the OSM contributors.

• Dimension reduction with PCA
• Clustering with the KMeans

The purpose of the PCA is not to reduce the dimension (you have less than 100 features). It's to analyze the different features and understand the most important ones.

Running

Get some history data

You can get some history data for a specific world region on Geofabrik. You have to download a *.osh.pbf file. For instance, on the Greater London page, you can download the file greater-london.osh.pbf.

Warning: Since GDPR, Geofabrik has modified its API. You have to be logged in to the website with your OSM contributor account to download osh.pbf files, as OSM history files contain some private informations about OSM contributors.

Organize your output data directories

Create a data directory and some subdirs elsewhere. The data processing should be launched from the folder where you have your data folder (or alternatively, where a symbolic link points out to it).

• mkdir -p data/output-extracts
• mkdir data/raw

Then, copy your fresh downloaded *.osh.pbf file into the data/raw/ directory.

Note: if you want another name for your data directory, you'll be able to specify the name thanks to the --datarep luigi option.

The limits of the data pipeline

The data pipeline processing is handled by Luigi, which can build a direct acyclic dependency graph of your different processing tasks and launch them in parallel when it's possible.

These tasks yield output files (CSV, JSON, hdf5, png). Some files such as all-changesets-by-user.csv and all-editors-by-user.csv needed for some tasks was built outside of this pipeline. Actually, these files come from the big changesets-latest.osm XML file which is difficult to include in the pipeline because:

• the processing can be a quite long
• you should have a large amount of RAM

Thus, you can get these two CSV files in the user-data folder and copy them into your data/output-extracts directory (latest date of download: 2019-09).

See also the I want to parse the changesets.osm file section.

Run your first analyze

You should have the following files:

data
data/raw
data/raw/region.osh.pbf
data/output-extracts
data/output-extracts/all-changesets-by-user.csv
data/output-extracts/all-editors-by-user.csv

In the virtual environment, launch:

luigi --local-scheduler --module analysis_tasks AutoKMeans --dsname region

or

python3 -m luigi --local-scheduler --module analysis_tasks AutoKMeans --dsname region

dsname mean "dataset name". It must have the same name as your *.osh.pbf file.

Note: The default value of this parameter is bordeaux-metropole. If you do not set another value and if you do not have such .osh.pbf file onto your file system, the program will crash.

Most of the time (if you have an Python import error), you have to prepend the luigi command by the PYTHONPATH environment variable to the osm-data-quality/src directory. Such as:

PYTHONPATH=/path/to/osm-data-quality/src luigi --local-scheduler ...

The MasterTask chooses the number of PCA components and the number of KMeans clusters in an automatic way. If you want to set the number of clusters for instance, you can pass the following options to the luigi command:

--module analysis_tasks KMeansFromPCA --dsname region --n-components 6 --nb-clusters 5

In this case, the PCA will be carried out with 6 components. The clustering will use the PCA results to carry out the KMeans with 5 clusters.

See also the different luigi options in the official luigi documentation.

Results

You should have a data/output-extracts/<region> directory with several CSV, JSON and h5 files.

• Several intermediate CSV files;
• JSON KMeans report to see the "ideal" number of clusters (the key n_clusters);
• PCA hdf5 files with /features and /individuals keys;
• KMeans hdf5 files with /centroids and /individuals keys;
• A few PNG images.

Open the results analysis notebook to have an insight about how to exploit the results.

I want to parse the changesets.osm file

See http://planet.openstreetmap.org/planet/changesets-latest.osm.bz2 (up-to-date changeset data).

1. Download the latest changesets files changesets-latest.osm.bz2
2. bunzip2 changesets-latest.osm.bz2 to decompress the file. It can be a quite long.

This file is a XML file (>30Gb) which looks like

<?xml version="1.0" encoding="UTF-8"?>
<osm license="http://opendatacommons.org/licenses/odbl/1-0/" copyright="OpenStreetMap and contributors" version="0.6" generator="planet-dump-ng 1.1.6" attribution="http://www.openstreetmap.org/copyright" timestamp="2019-09-08T23:59:49Z">
 <bound box="-90,-180,90,180" origin="http://www.openstreetmap.org/api/0.6"/>
 <changeset id="1" created_at="2005-04-09T19:54:13Z" closed_at="2005-04-09T20:54:39Z" open="false" user="Steve" uid="1" min_lat="51.5288506" min_lon="-0.1465242" max_lat="51.5288620" max_lon="-0.1464925" num_changes="2" comments_count="11"/>
 <changeset id="2" created_at="2005-04-17T14:45:48Z" closed_at="2005-04-17T15:51:14Z" open="false" user="nickw" uid="94" min_lat="51.0025063" min_lon="-1.0052705" max_lat="51.0047760" max_lon="-0.9943439" num_changes="11" comments_count="2"/>
 <changeset id="3" created_at="2005-04-17T19:32:55Z" closed_at="2005-04-17T20:33:51Z" open="false" user="nickw" uid="94" min_lat="51.5326805" min_lon="-0.1566335" max_lat="51.5333176" max_lon="-0.1541054" num_changes="7" comments_count="0"/>
 <changeset id="4" created_at="2005-04-18T15:12:25Z" closed_at="2005-04-18T16:12:45Z" open="false" user="sxpert" uid="143" min_lat="51.5248871" min_lon="-0.1485492" max_lat="51.5289383" max_lon="-0.1413791" num_changes="5" comments_count="0"/>
 <changeset id="5" created_at="2005-04-19T22:06:51Z" closed_at="2005-04-19T23:10:02Z" open="false" user="nickw" uid="94" min_lat="51.5266800" min_lon="-0.1418076" max_lat="51.5291901" max_lon="-0.1411505" num_changes="3" comments_count="0"/>

...

 <changeset id="74238743" created_at="2019-09-08T23:59:21Z" closed_at="2019-09-08T23:59:23Z" open="false" user="felipeedwards" uid="337684" min_lat="-34.6160090" min_lon="-55.8347627" max_lat="-34.5975123" max_lon="-55.8167882" num_changes="10" comments_count="0">
  <tag k="import" v="yes"/>
  <tag k="source" v="Uruguay AGESIC 2018"/>
  <tag k="comment" v="Importación de datos de direcciones AGESIC 2019 #Kaart-TM-351 Ruta 11 José Batlle y Ordóñez"/>
  <tag k="hashtags" v="#Kaart-TM-351"/>
  <tag k="created_by" v="JOSM/1.5 (15238 es)"/>
 </changeset>
 <changeset id="74238744" created_at="2019-09-08T23:59:49Z" open="true" user="kz4" uid="8587542" min_lat="37.1581344" min_lon="29.6576262" max_lat="37.1690847" max_lon="29.6774139" num_changes="6" comments_count="0">
  <tag k="host" v="https://www.openstreetmap.org/edit"/>
  <tag k="locale" v="en-US"/>
  <tag k="comment" v="Added speed limit"/>
  <tag k="created_by" v="iD 2.15.5"/>
  <tag k="imagery_used" v="Maxar Premium Imagery (Beta)"/>
  <tag k="changesets_count" v="9150"/>
 </changeset>
</osm>

You must have the user id uid for each changeset. Most of the time, you'll have the created_by key with the name of the editor, e.g. iD, JOSM, etc. with its version.

3. Run the script extract-changesets.py to turn the XML data into a CSV file (>32Gb), e.g.

   > path/to/osmdq/extract-changesets.py changesets-latest.osm changesets-latest.csv
   

   There will be one line by key/value pair for each changeset.

   id	created	uid	min_lat	min_lon	max_lat	max_lon	num_changes	comments	key	value
   53344191	2017-10-29T15:03:03Z	2130431	60.6909827	16.2826338	60.8337425	16.3889430	600	0	"comment"	"Added roads and lakes from Bing"
   53344191	2017-10-29T15:03:03Z	2130431	60.6909827	16.2826338	60.8337425	16.3889430	600	0	"created_by"	"iD 2.4.3"
   55673783	2018-01-23T05:50:50Z	6401144	53.4504430	49.5804026	53.4504430	49.5804026	1	0	"comment"	"Edit Resort."
   55673783	2018-01-23T05:50:50Z	6401144	53.4504430	49.5804026	53.4504430	49.5804026	1	0	"created_by"	"OsmAnd+ 2.8.2"
   55673784	2018-01-23T05:51:10Z	6892267	9.9459612	8.8879152	9.9469054	8.8917745	1	0	"source"	"Bing"
   55673784	2018-01-23T05:51:10Z	6892267	9.9459612	8.8879152	9.9469054	8.8917745	1	0	"comment"	"changed classification from tertiary to residential"
   55673784	2018-01-23T05:51:10Z	6892267	9.9459612	8.8879152	9.9469054	8.8917745	1	0	"created_by"	"JOSM/1.5 (13053 en)"

   If you read this file with pandas, you should have at least +50Gb RAM. But you can use dask which allows you to process in parallel the data without loading all the data.

4. To group each user by editor and changeset thanks to dask, run the script process-changesets-user-history.py

   > python path/to/osmdq/process-changesets-user-history.py -i changesets-latest.csv -o all-editors-by-user.csv editor
   > python path/to/osmdq/process-changesets-user-history.py -i changesets-latest.csv -o all-changesets-by-user.csv changeset
   

   We don't add dask as a dependency of this project for this few Python scripts. If you want to run the 'process' script, you can install dask, cloudpickle, toolz and ffspec packages in a dedicated virtualenv. If the script does not fit your memory, check the blocksize and num_workers arguments of the dd.read_csv and dask.config.set functions respectively and adjust them.

5. The all-editors-by-user.csv file contains information about the user favorite editors (and their associated versions). You can transform these data to another CSV where you have one column by editor (without the version).

   > python path/to/osmdq/extract_user_editor.csv all-editors-by-user.csv editors-count-by-user.csv
   

Who uses this project?

• A thesis about Quality Assessment of Volunteered Geographic Information: An Investigation into the Ottawa-Gatineau OpenStreetMap Database by @ktjaco