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Spring of 2015, I drove down to Philly and found myself signing up for a hackathon bridging civic tech and media. What’s not to like? We tackled property taxes and leveraged the open-source data that the City of Philadelphia supplied. The goal was to link expenditures by GIS location to property taxes paid by GIS location. Aggregating tax lots was one hurdle. Breaking down expenditures by location was almost impossible.

Working on the concept

The thing is, that this is a great problem to solve for municipalities to at least understand how they are allocating resources. I’ve sat through so many public works meetings over the years that routinely talk about miles of roads paved, but no breakdown over where the frequency of potholes, or repairs are located. It doesn’t take a rocket scientist to recognize that heavier vehicles, combine with poor water drainage leads to more potholes or surface damage. Instead of filling in potholes over and over again, an assessment of the property tax revenue versus capital and operational costs would help a city weigh the difficult decisions of how to allocate funding.

The Winning Team

Should a street featuring bars and restaurants have taxes or fees targeting the wear on curbing and sidewalks? Does it make sense for a city to tax warehouses with the lowest mill rate? Is it a shocker that urban dense neighborhoods generate more revenue per square mile but it’s the sprawl of exurbs that cost more to maintain?

Following this hackathon, there was a Mayor change in Philly, and poof went the open data. I brought the code and the concept to Stamford, CT, and was met with the dismissive wave of the hand by the mayor there. It seems that open-data scares elected officials because they no longer control the narrative of what the data ultimately means. Lesson learned.

Meanwhile our team won first place at the hackathon.