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Examining the Government-Constituent Relationship. With data. And trees.

Image of a HurricaneIn 2012, Hurricane Sandy inflicted tens of billions of dollars in losses, destroying or damaging thousands of homes and vehicles and bringing the Northeast US to a grinding halt. A lesser cited impact was the storm’s toll on the city’s urban forestry. Sandy knocked down or seriously damaged over 20,000 street trees in New York City (NYC) alone. In the hours and days that followed, thousands of New Yorkers contacted local government via the City of New York’s 311 system asking for tree service to remove downed trees from cars, buildings and sidewalks.

Within this interaction — residents contacting local officials to request help — lies the opportunity to better understand and improve public
engagement around public services. A downed tree can yield one, or many, requests for assistance depending on the number of people who observe a tree fall. But demographics and an individuals’ propensity to request help can also influence the rate of demand for government services.

Devastating storm events are natural experiments – i.e., well-defined, independent, observable events. Immediately following a storm, it is highly likely that tree damage reported to 311 was generated from that event.

To explore this paradigm, in 2014, Jonathan Auerbach, from Columbia University’s Statistics Department, and Christopher Eshleman, recently graduated from Columbia’s School of International and Public Affairs, set out to analyze the underlying demand patterns generated by a series of storm events which recently hit New York City. They used a combination of data from the City of New York’s open data portal and the US Census Bureau, and worked closely with the City’s Parks Department and NYC311. With this data they produced a series of statistical analyses that helped to identify communities in the City that had a higher propensity to report storm damage.

Why are these findings important? They can help enable municipal officials and policymakers to develop strategies that better target underserved communities – those less likely to report storm damage – who may be underreporting issues not because they aren’t affected, but because they aren’t aware or have adequate access to information on the services the city provides.

Through the Commerce Data Usability Project and in collaboration with the Commerce Data Service, we have presented Auerbach and Eshleman’s work as a two-part R tutorial focused on extracting insights from spatial data utilizing recent developments in Bayesian statistics. Part one focuses on the fundamentals of processing geospatial data. Part two, soon to be released, focuses on hard statistical research that enables data-driven policy and operations.

This tutorial offers a roadmap and code for data scientists everywhere to more easily explore Auerbach and Eshleman’s work – an exploration that could benefit municipalities the world over looking to better engage with their citizens around a whole host of public services.

Jeff Chen (DOC Chief Data Scientist), Christopher Eshleman (Columbia University School of International and Public Affairs), and Jonathan Auerbach (Columbia University's Statistics Department)

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