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Using 311 data to develop an algorithm to identify urban blight for public health improvement.

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Publicly available 311 call data can identify urban blight using natural language processing. This method offers precise monitoring of community health conditions, correlating with building vacancy rates.

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Area of Science:

  • Urban Health
  • Public Health Surveillance
  • Geospatial Analysis

Background:

  • Urban blight, characterized by physical disorder and decay, negatively impacts community health.
  • Traditional blight measurement relies on costly street audits or imprecise proxy measures.
  • Publicly available administrative data offers novel opportunities for real-time health monitoring.

Purpose of the Study:

  • To evaluate the use of New York City's 311-call data with natural language processing (NLP) for urban blight measurement.
  • To assess the geographic and temporal precision of NLP-derived blight indicators.
  • To correlate NLP-based blight measures with established proxy indicators like building vacancy rates.

Main Methods:

  • Developed an urban blight algorithm using keyword (token) counts from NYC 311 calls.
  • Applied NLP techniques to categorize blight-related calls.
  • Analyzed correlations between census tract-level blight indicators and commercial/residential building vacancy rates.

Main Results:

  • The urban blight algorithm achieved ~90% sensitivity and 55-76% specificity.
  • The percentage of blight-related 311 calls correlated with building vacancy rates.
  • The strongest association was observed with long-term (>1 year) commercial vacancies (r=0.16, p<0.001).

Conclusions:

  • NYC 311 data, processed with NLP, can serve as a valuable tool for monitoring urban blight.
  • Further validation and algorithm refinement are needed to capture the multifaceted nature of blight.
  • Improved blight measurement can facilitate targeted remediation and enhance community health outcomes.