UT 230 Reflection Journal
Assignment 1-
Research Journal Entry 1
Reading Reflection — 99% Invisible: Inconspicuous Ubiquitous
The opening line of 99% Invisible: Inconspicuous Ubiquitous is essentially about how, once you notice something in the built environment, you start seeing it everywhere and wonder how you ever missed it before. That idea stuck with me deeply because I have experienced it firsthand. During the Cities Intensive in UT 102 and 103, once we were asked to observe accessibility features across different cities, I could not unsee them. As someone who is able bodied, I normally move through cities without thinking about curb ramps, tactile paving, or crossing signals. But once accessibility became the lens, I realized how many everyday spaces are actually inaccessible. It completely shifted how I move through urban environments.
Another moment that stood out was learning about the DigAlert program, which was created after a tragedy that resulted in more than ten deaths. It is unsettling that major infrastructure protections often come only after loss. It makes me question how urban developments are approved at the government level and whether preventative thinking is prioritized or if change only happens reactively.
The discussion about private property markers engraved into sidewalks also made me think critically about ownership. The idea that someone must visibly mark territory to prevent claims of forfeiture feels performative. Why must ownership be constantly displayed to the public? This shows how infrastructure is not neutral but tied to legal and political systems.
The YouTube video reinforced that data is not neutral or easily transferable. Data is entangled with people and place. I strongly agree that distant readings of data must be combined with close readings. Large datasets can reveal patterns, but without engaging directly with communities, the analysis risks missing lived experience.
The most powerful idea for me was that data can be racist and sexist. The example of searching “black” versus “white” at the Smithsonian shows how whiteness is treated as default while other identities must be specified. I have experienced this in education as well. History classes often center white narratives, while African American history is treated as a specialization. Why is one considered standard and the other considered specific?
An example of urban technology that relates to these ideas is Building Detroit.
Research Journal Entry 2
Reading Reflection — Race, Technology, and Urban Infrastructure
This week’s readings pushed me to rethink how I understand infrastructure and technology in cities. Before, I tended to see things like transit systems, crosswalk signals, and digital platforms as mostly technical solutions to urban problems. But the readings made it clear that technology is never neutral. It reflects values, priorities, and power structures.
One idea that stood out to me was the concept that systems can appear objective while quietly reinforcing inequality. I kept thinking about how many urban technologies are framed as efficient or data driven, especially in cities like Detroit. Whether it is housing platforms, mapping tools, or predictive systems, they are often presented as improvements. But the readings made me question who defines improvement and who benefits from it.
I was especially struck by the idea that data is shaped by history. If a dataset is built from patterns that were already unequal, then any system trained on that data may continue those same patterns. It made me reflect on how cities are often mapped through crime statistics, vacancy rates, or economic risk indicators. Those numbers do not exist in isolation. They are tied to redlining, disinvestment, and policy decisions.
Another important takeaway for me was the idea that absence and silence in data matter. What is not counted can be just as powerful as what is counted. In urban systems, this might look like neighborhoods that lack detailed data, or communities whose lived experiences are not reflected in dashboards or reports. Missing data is not just a gap. It can signal neglect.
As someone studying Urban Technology and focusing on mobility infrastructure like crosswalks and bus stops, this reading made me more aware of how even small design decisions reflect broader systems. A crossing signal, a curb ramp, or the placement of a bus stop may seem technical, but they shape who can move safely through the city. Technology organizes space and movement, and therefore it organizes opportunity.
Overall, this reflection helped me move beyond thinking about bias as just a flaw in code. Instead, I am starting to see how urban technology can normalize inequality if it is not intentionally designed to challenge it.
Research Journal Entry 3
Lunch UP+T Lecture — Mariama Ndiaye
I went to the Lunch UP+T lecture with Mariama Ndiaye, and I honestly really liked how real and practical it felt. She talked about civic design and community centered design, but not in a buzzword way. It was more about how you actually move projects forward in a city.
One thing that stuck with me was making power and interest maps before proposing anything. Instead of assuming everyone cares about the same goal, you map out who has power, who actually cares, and who needs to be brought in differently. That made me realize urban tech isn’t just about building something smart or efficient. It’s about navigating relationships.
She also said “relationships over requests,” which I loved. Build connections before you ask for something. A lot of times communities only get approached when someone needs approval or data. She emphasized “their why over your why,” meaning different groups care about different things. You might need totally different slide decks depending on who you’re talking to. That made so much sense. Some people care about equity, some care about cost, some care about safety. Same project, different framing.
Another big point was closing loops. Don’t ghost people. Even if you don’t have an answer yet, send an update. Follow through on dates. That idea of follow through felt important. If you say you’re going to do something, do it, or at least communicate. She also mentioned that too much trust can actually lead to groupthink, which I hadn’t really considered. Collaboration is good, but you still need critical thinking.
One thing that challenged me was when she said you cannot design for every single person. That hit me because I feel like in urban tech we always talk about universal design. But she reframed it as being clear about who you’re centering and why. You can’t do everything at once.
She also said to meet people where they are. Don’t give someone a book if they don’t read. Take them to a coffee shop and talk. Tailor everything. That made design feel way more human.
It made me think about my crosswalk and bus stop project. If I were actually proposing changes, who has power? Transit officials? City planners? Residents? And how would I talk about accessibility differently depending on who I’m speaking to? This lecture reminded me that innovation in cities is just as much about relationships as it is about technology.
Research Journal Entry 4
Fieldwork Reflection — Collecting Data on Bus Stops + Crosswalks
This week was mostly about physically collecting the data, and I honestly found it more interesting than I expected. Walking around and documenting bus stops and nearby crosswalks made the project feel way more real. It wasn’t just a spreadsheet anymore. It was actual infrastructure people depend on every day.
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One thing that surprised me a lot was how many bus stops didn’t have crosswalks nearby once I moved away from central campus. Near campus, it feels like pedestrian infrastructure is everywhere. There are marked crossings, signals, curb ramps, and everything feels intentionally designed for foot traffic. But once I started surveying areas further out, especially places that felt more car oriented, I noticed bus stops placed along roads with no marked crosswalk within a reasonable distance.
That really made me think about who transit riders are assumed to be. If someone gets off a bus and there is no safe crossing nearby, what are they supposed to do? Walk out of their way to find one? Cross mid block? It feels like transit and pedestrian infrastructure are sometimes treated as separate systems, even though they are completely connected. You cannot use the bus without walking.
It also made me realize how uneven investment can be within the same city. Campus areas feel highly prioritized, which makes sense because of density and student presence. But other areas feel like an afterthought. This observation is starting to push my research question in a more systems focused direction. It’s not just about whether a crosswalk exists. It’s about how mobility is valued differently depending on location.
Collecting the data made me more aware of absence as information. A missing crosswalk says something. It reflects priorities, planning decisions, and maybe assumptions about who is moving through that space.
Research Journal Entry 5
Data Visualization Reflection — Struggling with Felt
This week I’ve been working on turning my data into maps on Felt, and it’s been more difficult than I expected. I had a really clear vision in my head for how I wanted everything to look. I imagined different crosswalk types clearly symbolized, proximity to bus stops visually obvious, and accessibility features layered in a way that felt intuitive. But translating that into the actual platform has been frustrating.
Some of the design limitations make it hard to communicate what I’m trying to show. For example, adjusting marker size only by latitude or longitude doesn’t really align with how I want to represent typology or proximity. I’ve had to rethink how to encode information visually, and sometimes it feels like the tool is shaping my narrative instead of the other way around.
It’s also challenging to balance clarity and complexity. I have multiple variables: control type, ADA ramps, tactile paving, and distance from bus stops. When I try to show everything at once, the map feels cluttered. But when I simplify it, I feel like I’m losing important context.
At the same time, this struggle is teaching me something important about urban technology. Visualization is not neutral. The way data is displayed influences what patterns people see and what conclusions they draw. If something isn’t visually prominent, it can feel less important, even if it isn’t.
Research Journal Entry 6
Round 1 of Briarwood Mall Observations
This week marked the start of our observation phase at Briarwood Mall, and it honestly felt a little disorganized at first. As a group, we had trouble fully aligning on what we wanted to focus on and how to narrow our observations into something clear. I spent time helping think through what kinds of behaviors we should actually be paying attention to and how we could structure our second round better. There were also some issues with our website, which made it harder to organize our work early on. We even talked about doing a third observation later, but nothing was fully decided yet.
Research Journal Entry 7
After reviewing the first round of observations from the mall, I noticed an interesting pattern in how technology is used. Inside individual stores, there was relatively little visible use of technology by visitors. Most interactions seemed focused on shopping itself.
However, in the hallways and transitional spaces between stores, there was significantly more use of technology. People were frequently on their phones while walking, navigating, or pausing between destinations.
This contrast suggests that different areas within the mall support different types of behavior. Stores encourage focused, task-oriented activity, while hallways function more as social and transitional spaces where people engage more with their devices.
Research Journal Entry 8
On another day when we met as a group, we focused more on narrowing our research question, and this was honestly one of the hardest parts of the entire project. We had collected observations, but we didn’t yet have a clear, focused direction for what we were trying to prove or explore. I contributed by trying to push us toward something more specific instead of keeping everything too broad. At times it felt like we were holding onto too many ideas at once, which made it harder to actually move forward. We kept going back and forth on what actually mattered most in our observations, whether it was movement, technology use, social behavior, or something else. Even though it was frustrating, I think these conversations were important because they forced us to think more critically about our data instead of just describing what we saw. It also made me realize how important it is to define a clear research question early on.
Research Journal Entry 9
Later in the week, we started questioning how much data was actually “enough,” which became another challenge for the group. At one point, we seriously considered going back for a third observation because we felt unsure about whether our patterns were strong enough or if we needed more evidence to support our claims. I shared my thoughts during this discussion and helped weigh the pros and cons of going back versus moving forward with what we already had. On one hand, more data could have made our findings stronger, but on the other hand, we were dealing with time constraints, other assignments, and scheduling conflicts. In the end, we decided to work with what we had. That decision felt a little rushed, but also realistic. It showed me that in real projects, you don’t always get perfect data, and sometimes you have to make decisions based on limited time and resources.
Research Journal Entry 10
During my second observation round at the mall, I paid close attention to how people interacted in transitional spaces like hallways and seating areas. I contributed detailed notes on how people moved, where they stopped, and how long they stayed in certain areas. I also noticed how groups formed and how people used space differently depending on whether they were shopping or just hanging out. This was probably my most direct contribution to the data collection. I tallied by archetype.
Research Journal Entry 11
After finishing the second round on another day, we had another group discussion about whether we needed a third observation. I gave my input that while more data could help, we probably already had enough to identify patterns, especially with time constraints. I tried to keep the group focused on moving forward instead of getting stuck. In the end, we decided not to go back, which felt like a practical decision even if it wasn’t perfect.
Research Journal Entry 12
Around this time, we were also working on the website, and that came with its own challenges. I contributed by helping organize some of the content and thinking about how to present our observations clearly. The website itself had some issues, which made it harder to keep everything structured the way we wanted. It felt like we were spending a lot of time just trying to make things work technically.
Research Journal Entry 13
As we moved into presentation prep on another day, I helped connect our observations to bigger ideas about urban systems. Even though I wasn’t fully motivated by the topic, I still contributed to making sure our points made sense and were supported by what we observed. I also worked closely with Armana, and we had a lot of conversations about how to explain our findings clearly and make our presentation flow better.
Research Journal Entry 14
On the day of our presentation, one of the judges didn’t like that we used stock photos, which was frustrating for me. I knew that we had actually tried to take our own photos in the mall, but people were not comfortable with being photographed. Some people seemed annoyed or didn’t want to be included, which made it difficult to document the space in the way we originally planned. I tried to explain this context during the presentation so they understood why we made that decision. That experience made me realize how difficult it can be to document public spaces, even when you’re doing it for academic purposes. Even though the feedback wasn’t what we wanted, I still felt like we explained our work well and showed that we understood our observations. I also think the way we presented it made the website look like a presentation, but I also think thats because everyone before us made theirs into games, which made ours seem boring.
Research Journal Entry 15
Looking back on the project as a whole, I realized that my mindset definitely affected my level of effort. I didn’t feel very connected to studying a mall, so I wasn’t as motivated as I could have been. At the same time, I still contributed through observations, group discussions, and helping shape our analysis. Working with Armana was a strong part of the experience, and our conversations made the project more interesting. Overall, even though I wish we chose a different location, I still learned how to observe everyday spaces and turn those observations into something meaningful.
