Walking Syracuse with TIN shades

Video study to find the latent field conditions conceived in the TINs we made from GIS features targeting walkers across Hwy I-690.

This project aims to facilitate the interaction and awareness of the user groups of walkers and drivers in Syracuse, with respect to their relative speeds, in proximity to former or active pollutants within the city. By using I-690, a site of former industrialization, as an artery for future development, interventions (signage, sensors, frames, etc..) will counteract and increase awareness of these environmental factors.

Myway or the Highway!

We aim to facilitate the interaction between different scales of movement (walkers / drivers) in proximity to causes of environmental destruction within the city. By using I 690 as a focus, interventions will counteract and increase awareness of these environmental factors.

GIS has become a way for us to identify both location and potential program for interventions along I 690. These interventions could take many forms relating to car users, walkers, or both simultaneously. By taking a series of TINs from multiple features (streets crossing I 690, vacancies, parks, and recreation within 500 feet of pollution sources), we are employing previous techniques of surface and solid analysis to apply these abstract TINs back to the city.

Surface analysis extracts high points of car users and walkers as well as mid ranges where there are more even distributions; these will be future sites of intervention which either cater to walkers, drivers, or establish points of interaction between the two scales of movement.

Solid analysis extablishes areas of difference or similarity between the two user groups by extruding a solid between TINs of different heights. This will also identify areas of either autonomy or interaction between the walkers and drivers. By also splitting this solid according to parcels and overlaying with vacancy data, we can narrow our focus back to the original source features.

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?

Mapping Preference - Team Blue

This image is how Team Blue looked to combine the many different kinds of data collected, including Tweets, FourSquare checkins, Yelp reviews, GPS locations, streetscapes, and zoning information. At first when looking at all of the data it seemed overwhelmingly different; however, when laid out in comparison to each other, we were able to see some interesting trends.

The thing that stood out to me the most was that one side of our zone was much denser with social media activity than the other. Restaurants receive more attention than shops or other businesses - but the main factor missing from our data is time. Because we collected all of our data between Friday and Monday, we did not see if the trends differed from weekend to weekdays.

Another thing which stood out to me from the Antoine Picon lecture was the idea of mapping as a new form of personal expression. By focusing on these forms of data, our mapping becomes a new lens into the apparent inequalities of internet presence in downtown Syracuse. The map is now much more subjective because it is based on feelings towards particular places. Can this subjective knowledge affect how we objectively view the city?