Delineating Perceived Chicago Neighborhood Boundaries Using Crowdsourced Geospatial Data
Session Number
Project ID: BHVSO 08
Advisor(s)
Dr. Crystal Bae, University of Chicago, Center for Spatial Data Science
Discipline
Behavioral and Social Sciences
Start Date
17-4-2024 8:35 AM
End Date
17-4-2024 8:50 AM
Abstract
Acknowledging the limitations of the formal designations of the 77 Community Areas identified over a century ago in Chicago, this research aims to close the gap between these outdated definitions and the dynamic perceptions of the city's residents. This project investigates the complex urban landscape of Chicago, a city renowned for its diverse and multifaceted neighborhoods, through a novel crowdsourced approach. Utilizing a web-based interactive tool, data was collected on residents' perceived neighborhood boundaries all throughout Chicago in the form of polygons, a method previously explored but not thoroughly analyzed for its research and policy implications. Crowdsourced polygons were analyzed utilizing R for advanced data analysis and visualization, while the application of QGIS facilitated detailed geospatial analysis and mapping, enabling the transformation of subjective perceptions into quantifiable, visual maps. Different mapping methods were employed as well, the most prominent being a raster-based method for evaluating polygon agreement. Through this novel approach, the research aims to offer a more accurate and resident-informed delineation of neighborhood boundaries that provides more of a contemporary urban landscape.
Delineating Perceived Chicago Neighborhood Boundaries Using Crowdsourced Geospatial Data
Acknowledging the limitations of the formal designations of the 77 Community Areas identified over a century ago in Chicago, this research aims to close the gap between these outdated definitions and the dynamic perceptions of the city's residents. This project investigates the complex urban landscape of Chicago, a city renowned for its diverse and multifaceted neighborhoods, through a novel crowdsourced approach. Utilizing a web-based interactive tool, data was collected on residents' perceived neighborhood boundaries all throughout Chicago in the form of polygons, a method previously explored but not thoroughly analyzed for its research and policy implications. Crowdsourced polygons were analyzed utilizing R for advanced data analysis and visualization, while the application of QGIS facilitated detailed geospatial analysis and mapping, enabling the transformation of subjective perceptions into quantifiable, visual maps. Different mapping methods were employed as well, the most prominent being a raster-based method for evaluating polygon agreement. Through this novel approach, the research aims to offer a more accurate and resident-informed delineation of neighborhood boundaries that provides more of a contemporary urban landscape.