Data Visualization / Data Analysis / ArcGIS

Urban Dynamics: Walkability and Population Patterns in Los Angeles

This project explores the relationship between walkability and population distribution in Los Angeles County through a geographic lens. Using public datasets and GIS tools, I created layered visualizations that uncover patterns in urban accessibility and transit infrastructure.

Research Goals


  • Examine how walkability aligns with transit access and population density

  • Visualize walkability levels across census blocks and cities in Los Angeles County

  • Investigate the correlation between public green spaces (parks) and walkability

  • Experiment with visual techniques to show urban planning challenges and insights

Datasets Used


Methodology

  1. Filtered National Walkability Data

  • Narrowed my scope to California using state boundaries

  • Reclassified walkability into low, moderate, and high groups

  1. Overlaid Parks and Transit Lines

  • Performed spatial joins and used buffers for visual proximity

  • Found that walkability was typically lower near parks

  • Overlaying bus lines showed higher walkability along transit corridors

  1. City-Level Aggregation

  • Dissolved block-level data to city boundaries for macro-level analysis

  • Used white outlines to layer cities over census blocks for dual-scale insights

  1. Supplemental Charts

  • Created scatterplots and bar charts to visualize population-walkability trends

  • These visualizations filled in for hotspot analysis which crashed due to processing limits

Tools Used within ArcGIS:

geoprocessing tools and techniques


  • Spatial Join

  • Dissolve

  • Symbology

  • KML Overlay

Final Map Visualization

Displays walkability index and population trends overlaid with transit infrastructure and city outlines.

Insights & Final Thoughts


  • Areas with high walkability strongly align with denser transit infrastructure, particularly near downtown LA.

  • National parks and natural areas do not correlate with high walkability, as these regions lack surrounding infrastructure.

  • City level aggregation provides broader policy insights compared to individual census blocks.

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