Mobility-in-US-Cities

Project exploring the mobility patterns in Chicago, Los Angeles, New York City, and San Francisco. The work leverages GPS point data scraped from cellphone/device applications. The works focuses on pattern detection and comparison across cities, network topology of mobility networks through clustering, and node-edge volume/mode prediction using machine learning.

Contributors and Authors:

• Colin Bradley
• Yuxuan Cui
• Abdullah Kurkcu
• Jianwei Li
• Minqui Lu
• Kaan Ozbay
• Karan Saini

Visualizations (D3, hosted):

• Interactive tools to visualize mobility patterns.
• Data is cleaned and filtered data and not the entire raw data.
• Data aggregated at different levels for various plots.
• Please refer to github link for details.

1) City + Time wise Individual CT level data:

• https://karansaini282.github.io/mpi_d3/

2) City wise Origin-Dest Puma level data:

• https://karansaini282.github.io/mpi_d3_2/

Visualizations (Bokeh, local):

• Interactive tools for EDA to check data quality.
• Data is sponsor provided raw data.
• Data aggregated at CT level for all plots.
• Please refer to python notebooks with same name as plots for details.

1) Mode Time Map

2) Bokeh Heat Map

3) Bokeh Arc Map

Visualizations (NetworkX, local):

• View of the Origin-Dest data in the form of a network.
• Nodes represent centroids of PUMA level geometry.
• Edge weights represent aggregated GPS pings.