Evaluating living conditions of residents in NYCHA developments

Rae Lei
Data Mining the City 2022
3 min readMar 14, 2022

Rae Lei | sl4976

  1. Description

Based on 311 service data in the past 1 year and location of MTA subway stations, this digital twin model evaluates the living qualities of residents in NYCHA developments within Manhattan borough by looking at the distribution of in-progress and unsolved 311 complaints and accessibility of each NYCHA neighborhood to mass transit.

The purpose of using two different kinds of data sets is to demonstrate the living conditions both inside and outside the NYCHA neighborhoods. While the amount of 311 complaints reflects issues like gas outage that happens inside the community, infrastructure accessibility shows us the level of connectivity between these building blocks and external urban fabric.

2. Data Visualization

fig 1. 3D visualization of 311 complaints in Manhattan borough

3. Analysis Breakdown

This analysis involves the use of basic data analysis tools in Pandas for Python, QGIS and Urbano for grasshopper.

Step 1: Filtering data:

The 311 service requests data on NYC Open Data includes 28 million rows and 41 columns, which can take a really long time to manually drop unnecessary information in Excel, while in Pandas, it only takes seconds:

script for filtering data

Step 2: converting csv into shapefile and merge each datasets based on location.

fig 2. 2D visualization of 311 complaints in Manhattan borough
fig 3. accessibility of subway stations within 5min walking radius

Step 3: extracting metadata and visualizing data

fig.4 batteries for fig 1
fig.5 batteries for 5min walking accessibility of metro stations

CONCLUSION:

Based on the research above, most of the NYCHA developments sits at the margin of the 5min walking radius of subway stations, which means mass transit accessibility is unequal to NYCHA residents comparing to people living in other communities in Manhattan borough. And 311 complaints rate of NYCHA neighborhoods is slightly higher than their surrounding neighborhoods.

4. Data Sources

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Rae Lei
Data Mining the City 2022

MS. Architecture and Urban design candidate at Columbia GSAPP