Chapters 4–6 Blog and Thoughts
Chapter 4 of Urban Informatics provides an interesting dive into a project located in Puerto Baquerizo Moreno of the Galapagos Islands. The project's aim is to utilize computer vision and 360-degree video collection to accurately map and understand the city, and then establish a plan for "long-term sustainable development" in the Galapagos Islands. Coming from 2026, the implementation of this project isn't anything groundbreaking, but it is part of a trend showing that gathering quality urban data is getting easier. I'll definitely be noting some of the tools utilized in this project, e.g., mapping out the whole city with a user interface via Mapillary.
One final takeaway from this chapter/project is that local knowledge is paramount when working in urban science. I have been brainstorming ways to work within urban informatics as a personal project, but in my brainstorming, I'm mostly behind my computer, working with data from all sorts of distant places. I think this has convinced me to try working within the confines of my own city, one that I know intimately. I currently live in Fort Lauderdale, but I also want to pursue my hometown of St. Petersburg. It's only four hours away and is an incredibly walkable city that is rapidly developing its urban infrastructure.
I had been looking forward to Chapter 5, as it tackles the side of urban informatics that I firmly believe is the most important: humanity. The problem with cities can't be solved with a computer or an electric bus; it has to involve the people and how they choose to get to work each day.
Chapter 6 is a nice history lesson on urban human dynamics. All the way back in 1969, there was a paper titled Urban Dynamics, which utilized a 5-step model that considered economic, political, psychological, and sociological variables when modeling an urban environment. The problem with this model was, firstly, limited computing power back in '69, but also the fact that until the late 1900s, cities were considered systems that functioned like machines. The author of Urban Dynamics, Jay Forrester, utilized these various considerations as simple inputs without realizing how urban environments actually evolve. They are much more akin to living, breathing organisms with complex layers of feedback and hundreds of individual agents forming them from the bottom up.
Cellular automata are explored following the discussion on Forrester's work. I really like the idea of cellular automata for modeling a city. They undoubtedly do a great job of modeling how a city itself changes, utilizing GIS data and zoning to get a clear picture of future development. However, I think I'm most interested in attempting to model human behavior to predict those changes. Regardless of my specific focus, I will definitely be checking out DUEM, a very refined CA model for urban dynamics.
Personally I find the agent-based models are definitely the most fascinating models discussed so far in this textbook. As an innately bottom-up approach, they utilize independent agents to model a city. I want to focus my research on how to improve cities (obviously), and I think this is a great way to understand exactly where these improvements will actually be felt the most by citizens.
With agent-based modeling, having a deep understanding of how "agents" act in reality is a major factor in determining the validity of a model. When agents represent people or households, you need to firmly understand how these individuals think. That's where human dynamics as a standalone field can shine; there are many ways to study the decision patterns of people in urban environments. One such way discussed in Chapter 5 is time geography, developed by Torsten Hägerstrand. Basically, it provides a way to define relationships and activities over space-time, which in turn can be used as inputs for models and other ways to visualize or understand patterns. Hägerstrand published this work in 1970, which makes it feel quite outdated. However, Shaw and Yu have continued the study of time geography and have worked on incorporating the digital world into these relationships. This is another project I'm going to be looking into.
Furthering the concept of incorporating humans into urban research, Chapter 6 discusses geosmartness; one such example being Green SBB. This Swiss research project gave participants access to a suite of travel options and had them record each trip and why they chose the options they did. By varying the options provided, researchers gained an understanding of ways to encourage individuals to choose certain methods of transport. I find these kinds of research projects fascinating, and I think they are an absolutely fantastic approach to the problem of urban transit.
Another example, one that I think anyone could replicate, is GeoEco! This app tracked users' activity and determined how they were getting around. After an initial data collection step, the app would suggest certain changes and give insights to users. This app worked to help reduce users' emissions substantially, and it utilized leaderboards and badges to further "gamify" the process of changing habits. Human psychology is a powerful way to change how cities work for people and how people work within cities.
Conclusion
As for my own thoughts on this textbook thus far, it's long, but I find that the topics covered in each chapter are digestible and relevant. That being said, I have certain goals I want to achieve while reading this textbook, so I am going to begin skipping chapters that aren't relevant to what I have in mind. I am also going to take breaks from the textbook itself to dive into some of the sources that I find interesting; for example, from these three chapters, I have decided to read the papers on DUEM and Shaw and Yu's extension of Hägerstrand's time geography concept.