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A description of data sources and methodology used in generating the maps and conclusions in my recent blog post, "Visualizing the topography of Citibike"
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methodology.md

Citibike topography methodology

This document outlines the methodology and data sources used in Visualizing the topography of Citibike

Data Sources

  • Used this Python script to download all citibike stations
  • Used NYC Neighborhood Tabluation Area geography files.
  • Used MapPLUTO data for household calculations.
  • Fetched 2015-2019 ACS data and census tract geographies from the National Historical Geographic Information System data portal.

Steps

  1. Areas within 0.5km of a Citibike station
    • Imported Citibike station CSV from Python script
    • Reproject to New York/Long Island CRS
    • Create a buffer of 0.5km around each station
    • Dissolve all buffers into a single polygon
    • Clip the polygon using the NTA polygons
  2. Households served (within the 0.5km range)
    • Imported MapPLUTO data
    • Clip using the 0.5km station buffers
    • Ran the Basic statistics operation on...
      • Unclipped MapPLUTO data to get the total number of households
      • Clipped MapPLUTO data to get the total number of households within 0.5km of a station
    • Calculated percentages based on these values
  3. Neighborhood station capacity
    • Imported Citibike station CSV from Python script
    • Ran Join attributes by location (summary) operation
      • Summed up capacity column of each station per neighborhood
    • Created a new column: capacity_count / ($area * 100) to generate capacity_per_100sqkm
    • Visualized the column onto the NTA map
  4. Neighborhood station capacity in NTAs below the poverty line
    • Fetched and imported census tract geographies sourced from NHGIS
    • Fetched and joined NHGIS 2015-2019 ACS median income per-household data
    • Used NTA geography files
    • Generate centroids of each polygon
    • Run Join attributes by location (summary) operation to merge ACS data into NTA polygons
      • Used the median of the median income field
    • Filtered out NTAs below the poverty line of $35k
    • Ran Join attributes by location (summary) to merge station data with NTA polygons
      • Summed up capacity column
    • Created a new column: capacity_count / $area * 100 to generate capacity_per_100sqkm
    • Visualized the column onto map along with station locations
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