NDLI: Mining Administrative Data to Spur Urban Revitalization

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Mining Administrative Data to Spur Urban Revitalization

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Mining Administrative Data to Spur Urban Revitalization

Content Provider	ACM Digital Library
Author	Conway, Matthew Green, Ben Caro, Alejandra Plagge, Tom Manduca, Robert Miller, Abby
Abstract	After decades of urban investment dominated by sprawl and outward growth, municipal governments in the United States are responsible for the upkeep of urban neighborhoods that have not received sufficient resources or maintenance in many years. One of city governments' biggest challenges is to revitalize decaying neighborhoods given only limited resources. In this paper, we apply data science techniques to administrative data to help the City of Memphis, Tennessee improve distressed neighborhoods. We develop new methods to efficiently identify homes in need of rehabilitation and to predict the impacts of potential investments on neighborhoods. Our analyses allow Memphis to design neighborhood-improvement strategies that generate greater impacts on communities. Since our work uses data that most US cities already collect, our models and methods are highly portable and inexpensive to implement. We also discuss the challenges we encountered while analyzing government data and deploying our tools, and highlight important steps to improve future data-driven efforts in urban policy.
Starting Page	1829
Ending Page	1838
Page Count	10
File Format	PDF MP4
ISBN	9781450336642
DOI	10.1145/2783258.2788568
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Neighborhood distress Urban revitalization Social good
Content Type	Audio Text
Resource Type	Article

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