OpenComm: Open community platform for data integration and privacy preserving for 311 calls

Sustainable Cities and Society(2022)

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摘要
Local governments are increasingly leveraging administrative data to drive performance. Likewise, cities are interested in improving responsiveness to citizens’ demands and cost savings through data analytics. However, city managers face many challenges when utilizing secondary data, such as 311 call records and the US Census. The challenge of interest to the current study is boundary issues as a result of data being collected at divergent geographic levels over different time horizons. Accordingly, an inductive analytical methodology was developed to create units of analysis that were both pragmatically and analytically appropriate for city managers and local policymakers. We created an open data analytics framework called OpenComm to harmonize administrative and secondary data using administrative data derived from Kansas City, Missouri. This framework produced robust inferences regarding the spatial and temporal aspects for the communities. Privacy-preserving technology, in particular, has been applied to public data to protect community privacy. The findings illustrate the power of inductive data aggregation, leading to empirical insights into hidden patterns of city service disparity over a decade-long time horizon. An application for the Open Data Platform is available at http://kc311.herokuapp.com/.
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关键词
311 calls,Data curation,Data privacy,Data integration,Categorization,Data visualization,Machine learning
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