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Analyzing Commuting Behavior Changes in Downtown LA Through TOD and New Residences

This study investigates the commuting behavior changes in Downtown Los Angeles, home to extensive transit services. Over the past decade, many tracts have experienced declines in walking and public transit usage, coupled with rising commute times. The research highlights the correlation between new apartment developments and income changes, utilizing geocoding, geoprocessing, and hotspot analysis. This complex analysis examines the impact of increased residential density on commuting patterns, offering valuable insights into transit-oriented development (TOD) and urban living.

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Analyzing Commuting Behavior Changes in Downtown LA Through TOD and New Residences

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Presentation Transcript

  1. Downtown LA as Large-Scale TOD?

  2. Study Area Study Area

  3. New Residences

  4. Downtown Has Extensive Transit Service Available Throughout Area

  5. Downtown Has Extensive Transit Service Available Throughout Area

  6. IN THE PAST 10 YEARS, MOST TRACTS HAVE SEEN DECREASES IN WALKING AND PUBLIC TRANSIT USE, AND INCREASES IN COMMUTE TIME Changes in Commuting Behavior

  7. Index Of Commute Changes Index = Public Transit + Walking + Commute Time

  8. Tracts with Most New Apartments Generally Have Seen Increases in Median Income Changes in Median Income by Census Tract

  9. Models Model One Used to Project Tracts Model Two Used to Create Rasters

  10. Metadata

  11. Skills Used 1) Geocoding – Addresses of apartments and condominiums built between 2000 and 2009 2) Geoprocessing – Clipping . . . lots of clipping 3) Buffering – Buffer of one mile radius around employment centers 4) Indexing – Index of changes in commute patterns 5) Spatial Analyst – Creation and reclassification of rasters 6) Hot Spot Analysis – Identification of locations with most changes in commuting patterns

  12. Skills Used 7) Aggregating Attribute Fields – Creation of fields for changes in commute types and times. 8) Boundary Sub-Set Selection – Selection of Los Angeles tracts from larger shapefile. 9) Graduated Colors – Identification of differing levels income, transit use and walking among tracts. 10) Attribute Sub-Set Selection – Selecting major roads from larger shapefile.

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