Sustainable Cities is a service-learning course offered through the Program on Urban Studies and Earth Systems Program. Students learn and work collaboratively with Bay Area government agencies and community organizations to support their sustainability goals. Now in its sixth year, the class attracts undergraduate and graduate students from a multitude of disciplines, ranging from urban studies to civil and environmental engineering to law and public policy majors, to support clients on meaningful fieldwork-based projects.
The Winter 2015 class worked with five community partners on the following projects: 1) assessing feasibility of an equitable and integrated Bay Area public transportation fare structure - Friends of Caltrain; 2) mapping residential displacement and demographic shifts in San Mateo County - Community Legal Services in East Palo Alto; 3) developing a public engagement strategy for household hazardous waste disposal in the City of San Jose - Department of Environmental Services; 4) creating a toolkit for Women Bike SF to increase bike ridership in San Francisco - San Francisco Bicycle Coalition; 5) providing technical and policy analysis for the City of Oakland soft story retrofit program - Resilient Oakland Initiative.
The final presentations took place on March 11, 2015 at Stanford University (Video).
Update on Project Activities
This week, we finished up our calls for good and began working on our deliverables! We dove into consolidating and analyzing our data, and have imagined fully the ways in which we will be able to create a deliverable to represent the story that we have uncovered through our calls. After our meeting with Jason and Ashley last week, it felt good for our team to be on the same page and ready to create something meaningful and impactful.
In class, we discussed data visualization and the importance of color and typography basics, along with an overview of AIM (Audience, Intent, Message) with an emphasis on maps. On Monday during our work session, as a group we discussed how we would split the work so that it would best suit our strengths. What we decided is that Toni would primarily work with analyzing the data and using Excel to reorganize and produce trends. Jana will be working on the written report, with an emphasis on the Literature Review. Lastly, Jazlyn will be focusing on the website and infographic. We want to maintain an open line of communication through this whole process, but decided that it was more productive to split up the tasks rather than trying to have everyone pitch in a little on everything. This also provides a good framework to divide up our presentation time on Monday.
What We Observed/Learned
As we all finished up our calls, it became very clear truly how deeply these evictions were impacting our populations in a way that we didn’t expect. So many of our clients were telling us that they had spent months homeless, couch surfing, or living with multiple relatives. Story after story emerged of people who, even despite the few months of preparation that CLSEPA’s settlements usually resulted in, still were unable to find stable housing after their evictions.
On a more personal note, this week, we all took a moment to appreciate how smoothly this whole process has gone for us as a group. We are all in multiple group projects this quarter, and we were so appreciative of the fact that we have all managed to be there for each other in a flexible way, stepping up and stepping back as needed to create a really strong team overall. We have been pretty much on the same page all the way through the project, which has made working together seamless and easy.
During class on Wednesday, we began to think about the AIM of our own project. Our project is complex because there are many stakeholders with incredibly oppositional viewpoints. Are we pitching to those who are suffering, to empower them to make change within their own communities? Are we trying to show lawmakers that this is something that cannot continue? Or are we trying to convince landlords that their eviction practices are unjust and inhumane? Our project obviously is going to be at the intersection of all three, but ultimately, the ones that can effect dramatic change on the legislative landscape and rights of evictees in these cases are lawmakers. Even though our deliverable has changed, our mandate is still the same: there should be renter protection ordinances everywhere in the Bay Area. Renter protection is not an option. As we craft our deliverables, we want to make sure that this message comes across overwhelmingly clear, so that CLSEPA can make a case to those who make the policy into reality.
Update on Project Activities
As illustrated by our blog post from last week, we came in to this week feeling uneasy about the quantity of data that we have been able to collect thus far. We felt as if the quality of the data is high, but didn’t know if the low number of responses would somehow discredit that quality. Another insecurity that we were experiencing was that the data that we were receiving told a different story than we had originally anticipated. We had a brief meeting with Deland to express and work through some of these insecurities. She assured us that the work that we were doing would retain its impact and importance despite low response rate and that data points we had were indeed telling a story with heavy social implications.
We also scheduled a meeting with Jason, our community partner for Thursday afternoon. Because of traffic accidents and similar addresses in neighboring towns, we were not able to meet up with Jason in person. Instead, we took this opportunity with the three us all being together and Jason being free to have a very productive conference call. We updated him on how data collection was going and expressed our opinions about changing what our end deliverables will look like. Our team has had similar thoughts about the fact that the data we are receiving isn’t best depicted by a map illustrating movement as we previously thought. Luckily he was open to the idea of changing the deliverable to best display our data. We had previously planned to be done with data collection last week, but we decided with Jason’s endorsement to carry out making calls in to the early parts of next week to get any last minute data points.
What We Observed/Learned
This week we learned just how much the formation of expectations and unconscious biases can shape the process and products of a project. Because we had set such clear expectations for the end products of this project, the fact that we are most likely not going to be able to meet those goals feels disappointing. However, we have decided that being flexible to our plan and tailoring the deliverables to best suit the needs of the data, emphasizes the authenticity of the information we are presenting.
Ironically, this shift in the direction of the project is almost refreshing because it validates the importance of on-the-ground data collection and the fact that the only way to understand what a community is actually facing, apart from broad generalizations or predictions, is to ask those directly involved. It is easy for us, as outsiders, to impose our own opinions on to the situation and find “evidence” to support our claim, but when you let the people in the community dictate where the project is headed, you’re headed toward making a more significant social impact.
At the start of this project, our group was set on letting the data speak for itself, apart from personal narratives because that is what we believed would be most convincing to policymakers and local governments. However, because we have come to realize that our project has different implications that we first expected it to, we are beginning to understand the value of coupling raw data with the stories of the people that provided it. How you choose to present data is just as important as collecting it because the way that people interpret data is far from objective. This realization could promote stronger community engagement by emphasizing the human aspect of this project in putting faces to this cause.
Update on Project Activities
This past week was our final week to administer surveys and collect data. As we mentioned last week, we have a low response rate for our surveys for a variety of reasons: 1) some phone numbers are no longer in service; 2) former clients are not picking up their phones; and 3) some clients refuse to provide feedback through the survey. We’ve emailed our community leader to meet with him this coming week so that we can look through the limited data we do have and come up with a final approach to our data visualization and deliverable.
What We Observed/Learned
Now that our data collection is coming to a close, we are starting to notice surprising trends in the data. For example, while most clients have experienced a marginal increase in rent payment, they have not experienced a significant change in commute time. Some clients were even able to lower their rent payment when they moved. However, there have been a few cases in which clients have been homeless for up to several weeks, but the challenge for us now is conveying that information in an appropriate manner. As a group, we realized that this data may be more difficult to visualize than we thought.
Certainly, there are pieces of information that we feel are more important to convey than others, but we also want to give an adequate representation of the data we’ve collected. It will be important for us to showcase that this is the information we’ve been given and there may be certain volunteer biases associated with it. Those clients who still have working phone numbers and are willing to divulge information may have improved their situation, but what about the 80% of those clients who have not provided feedback to our survey, a good portion of whose phones are no longer working?
With “big” data comes big responsibility, but, our group isn’t exactly amassing large quantities of data that we can computationally analyze to reveal patterns, trends, and associations. However, we recognize that it’s not always about what you see. The grand majority of our calls are going unanswered, despite repeated attempts at leaving voicemails with friendly messages and encouragement to contribute to the cause that we’re trying to support. Some phone numbers are out of service, which surfaces a larger question and issue: are evicted clients unable to afford mobile phones once they’ve moved? How many of them have changed their numbers and how many simply can’t afford to have phones after they’ve been evicted? What percentage of these inaccessible clients are homeless or have had to move far away from their original home and work? Big pieces of data are missing, which, to some extent, could compromise our project message.
There’s a general sentiment in engineering know as “fast-cheap-good”. This sentiment summarizes the traditional project management triangle that graphically represents an intersection relationship (i.e., project quality) between project cost, scope, and schedule. Usually, it is believed that you must pick at least one to, in some part, sacrifice because it is nearly impossible to succeed in all three. The point that we are getting at here is that we believe that the scope and schedule of Mapping the Effects of Silicon Valley are reasonable, but the theoretical “cost” is the deficit we are experiencing in data collection. Ideally, the clients would come to the clinics, have their settlement reached, and have their stories followed throughout the process of eviction and relocation. Albeit, a more arduous route to take, following through with the client on a frequent basis would provide a clearer story and humanize the effects of eviction. This approach, however, is close to impossible because it would likely take massive amounts of money and resources that CLSEPA and other involved parties simply do not have. As a result, the next challenge is to showcase the scope of what we have done for the project, the data we haven’t “collected,” and perhaps provide some recommendations to further this study and benefit CLSEPA and communities within the Bay Area.
Update on Project Activities
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.