This week was our first full week in data-collection mode. We have all started making calls and are beginning to better understand the challenges that we’ll be facing. We had a predictably low yield rate, with 4 interviews for the 20 calls conducted. We came into contact with some preliminary challenges, like phone numbers that are no longer in service and clients that felt uncomfortable speaking to us.
The data that we collected helped get our creative juices flowing with regards to our end product. We consulted with David in the GIS lab about possible options for visualization on our project and he advised us on a few options. The first is to visualize the data in terms of cities; we would aggregate the data by place and show movement by showing change in population of one city and growth in another. The second option is to visualize the data as individual data points. The visualization on this option would be to make a distributive flow map [e.g. http://www.gislounge.com/wp-content/uploads/2014/04/network-flow-map.png] which is similar to what our original vision for the project would be.
We also welcomed another member to our team this week; the CLS-EPA intern, Ashley. Ashley will be taking on some of our calls moving forward, and thankfully, will lighten Toni’s Spanish call load! We need to make sure to clarify with Jason & Ashley how involved she wants to be in our project. Deland suggested that we invite her to our final presentations, which we all think is a great idea.
What We Observed/Learned
Another task for this week was to begin engaging more with the existing community involved with mapping eviction on a broader scale. In thinking about the end products that we wish to create, we thought the natural first step would be to consult other projects such as the Anti-Eviction Mapping Projects (http://antievictionmap.squarespace.com/) and previous projects completed through this course. Our group was in awe at the depth of information and evidence of effort put in to projects such as the ones above. There is seemingly no way to make sure that our deliverables make an impact if our audiences don’t take the time to look at them and the more aesthetically pleasing the graphic, the more time we spent engrossed in it. This thus validates our need to make careful decisions in constructing our maps.
In the beginning stages of data collection, we are already starting to prioritize which data is going to be the most important and impactful to showcase in our interactive maps. The trend that we are finding to be most pertinent so far is with rent comparisons. The clients that we have surveyed have indicated that after their eviction, they were forced into living situations with the same rent or higher. The unsustainability of high rent prices is problematic because the client could potentially face another eviction. We have also found that the majority of people are moving to places without rent control, where they could be subjected to substantial increases in rent without warning.
One surprising trend that we are finding is that people are not necessarily moving to different cities or far away from their previous residence. This could just be unique to the sample of people that we have contacted thus far, however, if it is a clear trend throughout the rest of the data, the implications and intent of our project could change drastically. If the movement of people as a result of evictions is not of particular interest, this project’s central question may not be “where are these people moving after their eviction?” but rather “are these people experiencing multiple evictions as a result of staying the in the same areas?”
Our choice in visualization will ultimately come down to what the data looks like. If the data shows that people are moving into the Central Valley and out of the bay, it would be feasible to do it by city over time. However, if there is mostly just movement between the main cities, then it might conflate the data by showing no change. If that’s the case, the flow map would be a good option.
The experiences from this week are teaching us to try to leave our biases out of the data. At the beginning of this project, we were so sure that we would see certain trends or get back certain types of information, but as we get further along, we realize that our expectations were a little off the mark. This, however, does not deem the received data as less valuable, but forces us to keep asking questions about why we are seeing certain trends and reevaluate the purpose and ideas of this project.