Guiding question #2 🕵️♀️
How does walkability impact individuals who live within different tiers of walkability, such as accessible occupation/commute time, access to education, access to healthcare, individual physical health, and more?
It is known that a multitude of factors can impact one’s mental and physical health, and many factors also impact the accessibility of jobs, public transit, or green/third spaces within a community. For example, Wood et al. 2017 study revealed that access to greater numbers of recreational or nature spaces within a neighborhood is associated with positive mental health Our team was interested in investigating how much of these dynamics are impacted by walkability, and if individuals’ quality of life could be made better or worse depending on the urban planning of their communities. In literature like Risova (2020) and Wang et al. (2022), walkability is cited as a key factor in mental health. Walkability promotes socialization with others (Risova 2020) as well as reduced obesity rates, lower BMI, and lowered diabetes risk (Wang et al., 2022). This led us to further investigate these relationships via our walkability data.
USA as a whole 🎆
This graph is a scatterplot of the United States, with the walkability index score plotted on the x-axis and the density of healthcare employees (D8Health) plotted on the y-axis. We propose that higher healthcare worker density is an indication of more access to healthcare, as more hospitals require more nurses, doctors, physical therapists, and more. In this graph, we were hoping to find some sort of relationship between walkability and healthcare accessibility- and we did!
This graph reveals how higher walkability is correlated with a higher density of healthcare workers, and therefore, higher access to healthcare. Other literature reviews we have performed have also revealed that more walkable cities tend to boast health patterns such that their residents have lower levels of diabetes and obesity (Marshall et. al, 2009). It is highly likely that this pattern isn’t attributable to walkability (and the physical activity associated with it) alone, it could also be that those in more walkable cities also have more access to treating illnesses and drawbacks than those who live in less walkable cities. Though it seems the slope of the line of directionality in this graph is not too strong, we can see a large amount of data points in the upper right corner of the graph. This led us to further investigate the relationship between healthcare access (and various other variables) and walkability in California specifically.
California zoom-in 🔬
What is the relationship of education and healthcare access to walkability in California?
Similarly to the dataset above, we used a scatterplot to analyze the relationships between walkability (x-axis) and healthcare access, education access, and percent of low-wage workers (y-axes) in California in particular. According to our literature reviews, we hypothesized that lower access to healthcare and education and a higher percentage of low-wage workers would correlate to lower levels of walkability. We’ve argued that in areas characterized by limited walkability, residents often find themselves struggling to have the same education and healthcare accessibility as their higher walkability counterparts. The figures (direction) show the relationship between employment density and walkability for healthcare and education jobs in all of California’s counties. It’s important to note that we chose to analyze density in specific as opposed to raw employment numbers so that we accommodate smaller counties which naturally have fewer people and fewer jobs. As the walkability scores increase we see the trend line indicating increasing employment densities for both employment and healthcare. Counties in California that are less walkable have a tendency to have fewer healthcare and education employees in their area making it more difficult for these residents to have access to such important resources. Counties that have higher walkability have the infrastructure to employ more healthcare workers, in turn, providing residents with more access to treatments and other health necessities. The same conclusion can be drawn for access to education. The relationship between the density of employees in their respective fields and the walkability for a given area remains consistent outside of California. This trend is applicable to the entirety of the United States. Counties across the country are met with the same grim reality, they are not all created equal.
The third graph reveals an even more concerning reality about counties with limited walkability. The relationship between the percentage of low-income workers and walkability is negative, such that the more low-wage workers there are in a county, the lower the walkability of that county. As we’ve mentioned in other parts of our project, we cannot ascertain whether low-wage workers cause lower walkability, or if lower walkability causes more low wage workers, but the implications of these three graphs when put together are grim. How will we advocate for these communities to gain more access to healthcare and education (and walkability) if they are falling into a negative feedback cycle? Higher walkability is seen as an investment in individuals and their health and vitality (Risova 2020) because walkable neighborhoods are seen as more conscious of microclimate, noise, and health- all of these points are seemingly upheld by our graphs. But, what then happens to the individuals living in the cracks, for whom it is no doubt more difficult to achieve wealth and health?
For the individuals that live in less walkable communities, the risk is undeniable. Less social interaction, less wealth, less education, less healthcare. On an individual level, this could mean higher obesity rates, more reliance on unhealthy foods, more risk to mental health, less access to daily social interaction, and more. On a broader scope, this graph could also fit into our other investigative question on the systemic barriers and history that have contributed to walkability. Undoubtedly, what we are seeing today will be analyzed as systemic barriers and history in the not-far future.
How do income levels vary across different Combined Statistical Areas (CSAs) in California, and what does this reveal about regional economic disparities?
Low Wage: # of workers earning $1250/month or less (home location)(except PR) (thousands)
Medium Wage: # of workers earning more than $1250/month but less than $3333/month (home location) (except PR) (thousands)
High Wage: # of workers earning $3333/month or more (home location) (except PR) (thousands)
Combined Statistical Areas (CSAs): Represent multiple metropolitan or micropolitan areas
The stacked bar chart was selected to effectively illustrate the distribution of average worker wages across different Combined Statistical Areas(CSAs) in California. This visualization shows the average number of workers earning low, medium, and high wages((R_LowWageWk, R_MedWageWk, and R_HiWageWk) within each Csa, categorized by predominantly metropolitan and mixed urban-rural areas. Each bar represents a Csa, with segments indicating the average number of workers within each wage bracket. This format provides a clear visual comparison, highlighting regional economic disparities and revealing which CSAs have higher concentrations of workers earning low, medium, or high wages. The “null” category accounts for areas not assigned to a specific CSA, ensuring comprehensive data representation.
This visualization contextualized our research questions on income and demographics, specifically exploring how income distribution varies across different metropolitan and mixed urban-rural CSAs in California. By comparing the average number of workers earning low, medium, and high wages in these areas, the chart highlights economic disparities and regional differences in income levels. Notably, regions such as Los Angeles-Long Beach and San Jose-San Francisco_Oakland have significantly higher numbers of high-wage workers compared to regions like Redding-Red Bluff and Fresno-Madera-Handford, which show higher proportions of low-wage and medium-wage workers. This disparity emphasizes the economic divide between more affluent metropolitan areas and less affluent urban-rural areas. Understanding these differences is crucial for assessing how present-day cities are being built with considerations for urbanization and walkability that address the economic and demographic diversity within the state. Furthermore, these disparities in income levels can have significant impacts on individual health outcomes. For instance, in more affluent metropolitan areas, higher income levels can contribute to better access to health care, healthier lifestyles, and improved mental well-being. Individuals in higher wage brackets are more likely to afford preventative healthcare services, gym memberships, and healthier food options, leading to lower rates of chronic diseases such as obesity, diabetes, and cardiovascular conditions. Additionally, higher income can reduce stress and anxiety by providing financial security and the means to afford leisure activities and vacations, which contribute to better mental health.
In comparison, regions with higher proportions of low-wage workers may face greater challenges related to health and wellness, including limited access to healthcare and higher rates of stress anxiety. Limited financial resources can restrict access to quality healthcare and nutritious food, increasing the risk of poor health outcomes. Lower-income workers are also more likely to live in areas with higher pollution levels and fewer green spaces, which can exacerbate conditions like asthma and other respiratory issues. Financial strain can lead to chronic stress, which is linked to a host of mental health issues, including depression and anxiety. Research indicates that “…lower-income areas tend to have higher NO concentrations and walkability and lower O3 concentrations. Higher-income areas tend to have lower pollution (NO and O3). ‘Sweet spot’ neighborhoods (low pollution, high walkability) are generally located near but not at the city center and are almost exclusively higher income” (Marshall, Brauer, and Frank 2009, pp. 1752-59). This exemplifies that wealthier areas not only benefit economically but also enjoy better environmental conditions.
The correlation between income levels and health outcomes accentuates the importance of equitable urban development that promotes both economic opportunities and healthy living environments. As stated by the author, the average GPD per capita in highly walkable metro areas is significantly higher than in less walkable areas, demonstrating the economic benefits of walkability: “…in metro areas ranked as ‘highly walkable,’ the average GDP per capita is $60,400 compares to $43,900 in those that do not”(Davies, 2014). The connection between walkability, income, and health outcomes underlines the critical need for policies that foster equitable access to resources, enhance public transportation, and create walkable communities, ultimately supporting healthier and more economically vibrant urban environments.
Does the population of a CBSA impact walkability? What are the costs and benefits associated with higher walkability?
This Data Visualization represents the state of California’s population, broken down into 5 areas, with respect to their individual walkability score. Each individual column displays an area/area within California, and how walkable their neighborhoods are respectively. The rows represent the total population of these given areas to showcase whether or not population plays a role in this National Walkability Index Score. It appears that regardless of the population, most counties regress to a standard deviation with a mean walkability score of 13-14. Since the score is set on a scale of 1-20, with 20 being the best walking conditions, these scores reflect that California overall has decent pedestrian infrastructure, with some amenities within walking distance. We also felt that it was critical to look into population density and how it affects walkability, as how rural or urban an area plays a large role in (1) who decides to reside in that area, (2) how social an area is, and (3) how much wealth is allocated to a certain area, especially via taxpayer dollars or infrastructure design.
What we found surprising is that high walkability scores also correlate with higher exposure to pollution from vehicle exhaust and higher traffic density (Marshall et. al, 2009). Specifically, California holds 9 out of the 15 most polluted cities in the United States, and this can lead to asthma, reduced lung functioning, cardiac arrhythmia, and preterm births. We can see from the graph that LA-Long Beach counties have the highest population density, making them lead contributors to pollution in California. Though walkability tends to be viewed as a tool that can create healthier and cleaner environments, it is still important that we recognize there are both positive and negative health outcomes based on walkability index scores for individual communities. This can go on to influence the choices of those considering moving to a city-based purely on its walkability or urban access.
That being said, much research has also been done to reveal the benefits of walkability for both mental and physical health. Residents that have walking distance access to public green spaces are positively impacted. This can be seen in findings conducted by the RESIDE Project, where residents who perceived their neighborhood as highly green had higher odds of improved mental health (Wood et. al, 2017). The greater Los Angeles area is not reflective of this research, as it has disproportionately less green coverage and lower average life expectancies. If we wish to create more sustainable and healthy environments in the future, specifically in California, it is crucial that we recognize there are drawbacks and benefits to creating highly walkable neighborhoods.
TLDR 🤔
There are many different benefits and costs associated with walkability, all of which are important to fully consider when discussing future goals for urban planning and city building. Higher rates of walkability have their own risks; as they tend to be urban centers, they are also often associated with higher levels of pollution and vehicle exhaust (which is also a testament to US states’ reliance on the automobile). However, the consequences associated with lower levels of walkability contribute to detrimental impacts on an individual and historical level. Our research has revealed that lower levels of walkability are presently associated with less access to healthcare, less access to education, and less wealth. This can impact an individual’s mental and physical health, but if allowed to fester, it can turn much worse and become a systemic barrier similar to those that we analyzed via our other research question.
Moving forward, urban planning needs to be more conscious of the ongoing cycles that are being perpetrated and haste to rectify these cycles before they become engrained patterns.