While experimenting with different GIS techniques, I aimed to draw relationships between two economic data sets: unemployment, and number of families. I created a TIN of unemployment along the rail line, and a TIN of families in relation to local soup kitchens. By overlaying these TINS and splitting line graphs in Rhinoceros, I was able to create three dimensional graphs which represent the data sets across space. When these spatial graphs are overlayed, it starts to suggest which conditions are prevalent in different areas and also where the two data sets begin to coincide.

Setting aside the inherent issues with combining these specific data sets, what are some other directions or possibilities a graph like this could begin to reveal? Is this really any more effective at revealing latent relationships than the TIN or a basic line graph?

The aim of this map was to visualize the relationship between public gardens and vacant properties. Vacant properties intersecting both the railway and I81 were compared in this analysis. The TIN produced shows the vacant properties relative to public garden space around both major transportation routes. The rail data splits taken from the TINs helped produce a lofted surface that begins to compare the data between the two different TINs. How can this surface begin to inform us about the data and aid in comparing the two TIN models?

Amenities typically used by the homeless population in Syracuse are mapped as a starting point for urban analysis. The aim is to visualize the spatial community of an often uncounted population, and to think about what systems might be included in a network foreign to the map-maker. Through the creation of a TIN, we find that many soup kitchens, homeless shelters, and Syracuse city parks fall within a band of vacant properties.