Finding and fixing vacant properties through crowdsourced data collection

Shantanu Singh is the founder of Vacant Voices, an organization committed to solving the problem of abandoned homes in America’s neighborhoods. In this guest post on Nafundi‘s blog, Shantanu explains how Vacant Voices uses ODK to collect and deliver crowd-sourced neighborhood data to community organizations who can make a difference. The full blog post is available here.

An excerpt of Shantanu blog post:

Vacant properties are an important issue that needs an urgent solution. A 2015 report concluded that 1 out of 4 foreclosures in the United States are vacant. The actual number at the end of January 2015 was over 142,000. The number is alarming when you consider the contagion and deleterious impact caused by vacant homes. Research has established that these blight-causing properties reduce property values, increase crime rates, and worsen physical and emotional health.

At Vacant Voices, we wanted to develop a tool to help these communities. One that would allow residents to collect data (e.g., a GPS tag for the location, a standardized description, and an optional image) of problem properties. The collection could be spontaneous or scheduled. Community-sourced data that we could deliver to decision makers was our goal.

Through prior experience, we understood municipal categorization of vacant properties to be technical and not uniform across the country – in short, not user friendly. At the same time, we knew geographic information systems (GIS) and government data already existed (the statistics above were extrapolated from federal and local data). However, these systems can be hard for communities to find, can be complicated to use, and use old data that may not even be relevant for the specific need.

To collect and analyze data sourced from the community, we needed a better way. After a lot of research, we came across ODK. It seemed too good to be true. In front of us, there was a solution, one that not only provided a checklist interface, it also organized the data into a database (no data entry!), and you could visualize the spatial relationship of properties on a map, along with performing analytics useful to decision makers. Now we could standardize the definition of a vacant home, in a rubric that eliminated subjectivity.

The full blog post is available here.

A map showing the concentration of homes with peeling paint. From this map, users can immediately see clusters of vacant properties which are priorities.



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