Urban blight—manifestations of adverse social processes in the urban environment, including physical disorder, decay, and loss of anchor institutions—comprises many conditions considered to negatively affect the health of communities.
However, measurement strategies for urban blight have been complicated by a lack of uniform data, often requiring expensive street audits or the use of proxy measures that cannot represent the multifaceted nature of blight.
More refined measures of urban blight would allow for better targeted remediation efforts and improved community health. This paper, published in PLOS-ONE by NYHealth staff, evaluates how publicly available data from New York City’s 311-call system can be used in a natural language processing approach to represent urban blight across the City with greater geographic and temporal precision. A clearer view on how blight is distributed geographically can help urban planners, public health practitioners, and local government officials better understand, identify, and propose solutions for areas in need of health interventions.