Guiding question #1 🕵️‍♀️
Do the political and social/government movements in the 20th-21st century– specifically motorization and the rise of eco capitalism– have an effect on the development of walkability in various states, and on whose voices are now seen as underrepresented in walkability?
As college students on UCLA’s campus, and what is the rightful land of the Tongvaar people, we had a great interest in how historical socio-political movements contribute to the development (or lack thereof) of land, and in who gets a say in how that land is or is not developed. Also informed by our background as Californians is the heavy reliance on personal automobiles. These parts of our identity drove us to investigate their intersections with walkability. How do automobiles reflect walkability (or vice versa)? Risova’s 2020 paper posits that automobiles have completely changed the way cities are built and have had detrimental effects on socioeconomic movements as pollution concerns continue to rise. On the other hand, green cities are being seen as more and more lucrative as nature is being commodified and gentrification of green spaces runs rampant (Anguelovski et al., 2018) in a phenomenon known as eco-capitalism, as detailed in our timeline. Those who lobbied for green spaces to be refurbished and more accessible, oftentimes the low-income or the Indigenous communities in the area (like the Tongvaar) end up getting weeded out in favor of posh apartment complexes for the white and wealthy. Below is an in-depth exploration of the intricacies that surround eco-capitalism, motorization, and walkability.
USA as a whole 🎆
How has motorization affected car ownership, and is there a relationship between rates of car ownership and walkability?
This is a bar graph that shows the walkability score of all United States provinces and land on the x-axis and measures their walkability on a scale of 1 (poor) to 20 (excellent) on the y-axis. Thus, a higher bar means higher walkability. Each bar is assigned a color based on what percentage of that population does not own cars– darker blue means a higher percentage, with the darkest blue equating to 0.3231, or 32% of the population. The colors are arranged from 0.00-0.3231 on an equal, four-step scale.
In terms of implication for our data, we are particularly interested in historical influences and how that could have impacted walkability– it is always pertinent to keep in mind that motorization is a relatively recent phenomenon that has hugely impacted urban planning, and therefore, walkability (Bieri, 2017). Our team was expecting to see more dramatic differences in states that did or did not own a car, but it is still interesting to see that New York and D.C. by far have the lowest car ownership rates. The former is likely skewed by the dense, subway-ed area that is Manhattan, and the latter by the fact that D.C. is a smaller region by area. It is also surprising to see that the outlying islands like Guam, the Mariana Islands, and American Samoa have very low rates of walkability as well as high rates of car ownership. It is interesting to see how colonization and capitalism may have impacted life and motorization on these lands. This is especially true because, in our preliminary research, several articles have connected the gentrification of green and walkable spaces and a dense urban environment with the gentrification of poor spaces or colonization of indigenous land (Anguelovski et al., 2018). In colonizing these Indigenous lands and taking power out of their hands, the United States makes these peoples reliant on the same sources on which we rely: motors and a capital economy. In other aspects of our data, these same lands are often not included in the census, meaning the development (or lack thereof) of these lands is hard to track, and it is easier for them to fall into economic or political hardship. This contributes further to the cycle of reliance on the greater United States and on the very resources that began this cyclical degeneration of indigenous land.
California zoom-in🔬
How does wealth correlate with walkability in California? What implications does this have?
The nature of this question aims to compare a continuous measure (walkability, ranging from 1-20) across a few categories of data (income level, 5 levels), thus a bar chart is employed due to its advantage of easy comparison of levels of data. Each bar’s length is proportional to the value it represents, and as we compare lengths of aligned objects, this graph could easily be interpreted for a comparison of each level. Not only is the comparison between them easy to read, but viewers could also utilize the negative space of the varying heights of bars to compare the incremental differences. For this research question, this visualization shows 5 bars of varying levels in the percentage of low-wage workers in California. The height of the bar represents how walkable these CBGs are on average. With the addition of color and labeled numbers, the graph highlights the degree of low income each of the graphs represents, and the viewers can easily see the difference in walkability between each of the 5 income levels.
The bar graph shows that while the walkability score for California regions with high, high-medium, and medium income levels) were relatively similar, a drastically lower walkability score happens for areas with medium-low and low-income levels. This suggests that in California, areas with less income tend to be significantly less walkable, whereas areas with medium or higher income tend to be more walkable to a similar degree. Previously, we have externally theorized gentrification of walkable and green-space heavy spaces in the United States as a whole, a process in which socially and racially marginalized groups advocated for living environments with more green spaces yet were driven out due to higher prices after developments (Anguelovski et al., 2018). With this bar graph, we were able to find specific evidence in support of the theories with an emphasis on the state of California: wealth is heavily associated with walkability especially. The fact that walkability is viewed as a commodity and correlated with wealth speaks volumes about the rise of eco-capitalism and the present-day profitability of walkability. Although directionality is hard to parse (does higher walkability cause more affluent people to move to an area, or does wealth motivate urban planners to make certain areas more walkable?) the fact that there is a correlation to begin with is undeniable.
In California, is car ownership correlated to walkability? What does this say about motorization?
The research question is looking for a relationship between 2 numerical values, which a scatter plot is perfect for as it plots the variables simultaneously along the axis. Scatterplot is often seen in scientific fields regarding data that researchers are trying to understand and is less often used as a means to communicate data. For our case study, we can visualize the data entries from 22,971 California census block groups (CBGs) onto the graph: Percentage of Zero-Car Households on the axis (0-100%), and national walkability score on the y-axis (0-20). Each plot will indicate both these measures as viewers align it with the axes.
First, the graph suggests that the vast majority of California households tend to have at least 1 vehicle, according to the overwhelming counts of dots plotted at the 0-10% range and the medium percentage of zero-car households being 6.1%. Secondly, although there is no distinct linear relationship that could be created through this graph, it is apparent that a) in areas where vehicles are relatively commonly owned in households, walkability could vary drastically due to the high counts of such phenomenon (possibly due to the nature of high car density in California); b) as the of zero-car household goes higher, it’s more likely that the dots are located at the upper portion of the graph meaning they usually have higher walkability scores. Putting the observation into the context of this project, it is hard to conclude whether walkability is exactly affected by the number of vehicles per household; however, a slight correlation could still be detected between the two variables. This is a testament to how intricate the factors that play into walkability are. It is simultaneously true that more affluent communities may own more cars for comfort, leisure, or collection; but it is also true that lower-income areas have more of a necessity to own cars as a means of transportation to their job(s). Similarly, an affluent area could be walkable because they could afford better infrastructure, implementation of public transit, or better sidewalks and bike lanes. On the other hand, less affluent areas may rely on walking because cars or other forms of transit are more expensive. It is extremely hard to parse these variables from each other, and it is especially hard to determine directionality. This is a limitation of our data that we discuss in our conclusion.
Even among our fellow peers, many people joke about how inaccessible California (or even just Los Angeles) is without a car. Our reliance on motor vehicles as a means of transport to work, grocery stores, classes, and even friends’ houses stand as proof of the industrial revolution and its long-lasting impacts.
How have motorization and capitalism affected the interaction between walkability and socioeconomic health (as measured by job accessibility) in California?
This map visually represents the relationship of the different California regions in terms of how many total jobs it contains that had a 45-minute travel time in auto vehicles (D5ar). The darker blue it was, the more 45 commute time for work the region contained, and vice versa for lighter blue. The walkability index is located under the region’s name providing the viewer context and highlighting how job travel time may affect walkability. Additionally, this relates to our data set and topic of walkability by layering total 45-minute commute jobs using auto vehicles with walkability index scores, revealing how an indicator of socioeconomic factors and job accessibility may impact people’s ability to walk. By indicating the number of possible jobs people are able to reasonably travel to in 45 minute auto vehicles, it also highlights the number of jobs not possible to travel to in the lack of numbers. This silent contrast made clear when analyzing the data is a reminder that despite the large existing amount of jobs people can travel to in 45 minutes using auto vehicles in city centers, the commute time for people without auto vehicles is much longer still. Thus, this access to different kinds of transportation in order to make a living is an important factor related to our study on walkability in California.
This map contains the visual representation of relationships between different California regions (based on CBSA) and a number of jobs with a commute time of 45 minutes using auto vehicles and walkability. Geographically, these regions can be seen by the viewer to have a variety of different land area sizes which contributes to the density of jobs and other factors. In addition, socioeconomically the viewer can determine by how light or dark blue the region is, the fewer or more jobs of a 45-minute auto vehicle commute it is. Along with the walkability score located underneath the region’s names, the viewer can combine these three factors and understand their relationship. This significant relationship is how walkability does not seem to be determined by the size of the region but perhaps by how urban it is, as Los Angeles San Bernardino-Anaheim and San Francisco-Oakland-Berkeley have very high walkability scores but different land areas. In addition, San Jose-Sunnyvale-Santa Clara and San Diego-Chula Vista-Carlsbad have high walkability scores with similar land areas as San Francisco-Oakland-Berkeley. Moreover, the number of jobs with a commute time of 45 minutes seems to follow a similar pattern of association. Thus, this same commute using a different mode of transportation would take longer for people of lower socioeconomic status who cannot afford auto vehicles. On the other hand, in regions with a low number of jobs with a commute time of 45 minutes, this may mean it takes even longer by auto vehicles, and by extension walkability. Originally, one might think if people do not drive to work they may walk, but this does not seem to be the case in many regions of California. Meaning, that instead of using auto vehicles or walking, people would need other modes of transit to get to their workplace, which could be a reason for the lower walkability in other California regions. For example, “The presumption that living in a rural area inevitably means being dependent on a personal vehicle and driving long distances to access essential services negates the identities, experiences, and needs of the people in these complex and diverse communities” (Smart Growth America 2023). Interestingly, by taking out the Los Angeles-San Bernardino-Anaheim region, the viewer can better compare the other regions of California due to excluding that skewed data. Furthermore, “In the United States, the number of US workers who traveled to work by bicycle increased from about 488,000 in 2000 (approximately 0.4% of total commuting to work) to about 786,000 (0.6%) in 2008–2012, a larger percentage increase than that of any other commuting mode. However, the proportion of people walking to work over the same period dropped from 3.9% to 2.8%, indicating that fewer people than ever were engaged in an active mode of travel to work (5.0% took public transport)” (Bieri 2017, 6).
TLDR 🤔
Across the United States and California, though, an obvious relationship between car ownership and walkability was harder to determine. D.C. and New York boast the lowest rates of car ownership as well as the highest rates of walkability, which can be attributed to their density (New York’s being skewed by Manhattan), as well as their access to public transit. Within California, a very weak directionality between less car ownership and higher walkability. As mentioned and as explored, car ownership is tied to multiple different variables such as wealth, access to public transit, and more, and not just to walkability. This was explored more in-depth by a map that examined the density of jobs within (and outside of) a 45-minute commute. The multidimensionality of car ownership makes it an interesting but also difficult variable to draw conclusions from, but generally, car ownership is tied most closely with urban density excluding a small number of outliers. This speaks to the fact that urban-dense cities are usually (with a few exceptions) designed around cars instead of people. Changing this pattern in the US’s infrastructure may help move the US towards higher rates of walkability.
On the other hand, walkability in California is correlated strongly with wealth, such that low and medium-low census block groups suffer from much lower levels of walkability when compared to medium, medium-high, and highly wealthy areas. This speaks to the fact that many impoverished groups tend to be people of color or Indigenous) suffer from a lack of access to necessary resources, despite also being deserving of them. This is a systemic pattern in the US that needs to be changed as we continue to plan urban growth.