Resettling Refugees in Georgia in the “best” Locations!

We are so excited to be participating in Data Science for Social Good 2016! Our project is with New American Pathways, an organization that helps place refugees in the metro Atlanta area. From our first impressions it looks like our assignment will be both exciting and intellectually challenging. Pathways has tasked us with helping them streamline their process for finding apartments for their clients. As you can imagine, this can be an arduous process. Refugees often arrive without a vehicle and are reliant on public transportation. They also need to be in a safe area with access to good schools for their children. Oh, and on top of that it needs to be affordable! Any native Atlantan can tell you, this is basically the impossible trilemma of apartment hunting.

On Tuesday we met the New American Pathways team and had the opportunity to attend a cultural orientation given to newly arrived refugees. We gained insight into the process that Pathways takes to resettle refugees which is vitally important for us to deliver a product that not only performs the task but is constructed in such a way that it provides a pleasant user experience to the relocation team. Pathways has settled around 470 refugees in the last year and they expect this number to grow in the coming years.

Viable Zone Zoom


Our goal is to  design a tool that automatically provides a list of resettlement locations based on the specific criteria which Pathways seeks, namely affordability, access to public transit, community facilities, and good schools. We obtained a list of location which Pathways has used in the past (many of which are around Clarkston, GA), which we will use to further tailor our analysis. 

Apart from that, we showed them some visualizations of the data we had collected over the past few days. Our data includes optimal public transit zones in Dekalb and Fulton counties, schools locations, and a list of apartments with their details scraped from Zillow. 

Our next steps are to get data such as international supermarkets in the locations of interest, faith centers, apartment complexes and then to put it all together on a web app.