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Independent Project: The Ecosystem Services of Greenspaces in Alabama, USA

As I’ve discussed previously, I am working on a project involving greenspaces in the Southeastern US. Our research aim is to use physical and socioeconomic factors to find suitable locations for urban greenspaces. We selected three cities in Alabama to test our model: Jefferson County (Birmingham), Montgomery County, and Lee County (Auburn/Opelika). We used the suitability modeler tool in ArcGIS Pro with population density, per capita income, and land cover type as inputs. These socioeconomic variables used are just a few that are correlated with the urban heat island effect (UHI) and ensure that we are capturing people who may be most susceptible to enhanced UHI. 

The suitability model proved successful and locating areas at the census tract level to install greenspaces. In Jefferson County, the south central and southeastern communities are highly suitable regions right around the Birmingham city center. Similarly, Montgomery County showed promising sites near the city center in the northwestern portion of the county. Lee County showed potential sites also near the Auburn city center just west of Auburn University. 

By and large, this model is useful for identifying prospective areas for greenspaces. As we continue through this project, we hope to better constrain our variables by entering these factors into a regression model that can determine how well socioeconomic factors can predict thermal comfort. This will also allow us to properly weight these variables in the suitability model. We hope to then expand this study into other regions along the I-85 corridor in the Southeastern US.

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Case Study 3: The Case for Incorporating Environmental Justice in Flood Hazard Mapping

Hazard mapping is a useful tool for evaluating risk and susceptibility of a region to natural disasters. Flood mapping in particular is important for islands and coastal cities around the world. However, flood mapping designations, as provided by the U.S. Federal Emergency Management Agency (FEMA), only consider physiographic parameters in the map products they produce. My team and I were curious about how well these FEMA flood zone designations were actually capturing vulnerable populations in their modeling. In this case study, we explored the FEMA designations in Puerto Rico to determine how certain socioeconomic factors are related to the flood zones. 

Puerto Rico is an island territory of the U.S. surrounded by the Atlantic Ocean to the North and the Gulf of Mexico to the South. Flooding is one of the primary natural disasters that affects Puerto Rico from regular storm precipitation and hurricane activity in the Atlantic. The flooding potential in Puerto Rico along the coasts is also important because the majority of the population lives there. 

My role on this project was to perform the spatial and statistical analyses on the relationship between housing density, median income, and percent elderly population and FEMA flood zone designations. Housing density was chosen as a proxy to population that can include property damage in the variable. Lower median income populations are assumed to need more aid in the event of a natural disaster. Lastly, elderly populations are expected to be more vulnerable to natural disaster due to their likely inability to be able to escape to safety. 

An ordinary least squares (OLS) regression model was used to determine whether these three factors could account for a large portion of the variance in FEMA flood zone designation. Although the model as a whole was significant, only housing density and median income were the only significant explanatory variables with the former having a much larger weight. Additionally, this model only accounted for 1.4% of the variance in FEMA flood zone designation, which means that there are a lot of variables for which FEMA is not accounting. 

For example, some areas that are in high risk zones are actually much better off socioeconomically than areas that are in lower risk zones. This implies that the people that may need aid the most are overlooked by inflated risk or vulnerability in these more well-off areas. Thus, the results of this study highlight the need for hazard mapping to include socioeconomic variables to more accurately designate areas for hazard preparedness, mitigation, and aid. 

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Case Study 2: Modeling Ash-fall Susceptibility in The U.S. from a Yellowstone Eruption

Volcanic ash can have detrimental impacts to both the environment and human health. It can pollute air and water, and particles can cause a host a respiratory illness, not to mention the effects to the human system. Ash can ground airplanes and travel for extended periods of time, block sunlight crops need to grow, and damage other infrastructure. Modeling the extent of volcanic ash deposition can help emergency personnel prepare for this natural disaster. 

The Yellowstone hotspot can produce felsic, or silica-rich, fine-grained material associated with explosive eruptions and large ash ejection. It is located mostly in Wyoming, but the plateau includes areas of Montana and Idaho as well. A Yellowstone eruption is one of the biggest volcanic threats to the U.S., so my team and I wanted to determine the extent of ash-fall deposition from an eruption. My role in this project was to gather the literature related to the geology of the Yellowstone plateau and the impacts to human health. 

Seasonality can affect wind speed and direction aloft, so we modeled the ash extent for each season and compiled them into a final map. The inputs for our models were ash deposition, population density by county, slope, and aspect to determine the susceptibility of certain regions in the U.S.

We found that the combined extent of the ash from a Yellowstone eruption for any given time throughout the year covered most of the U.S. Wind direction primarily would deposit ash from West to East. The most impacted regions are those immediately surrounding and immediately to the East of the Yellowstone volcano. We also see that larger or more populated counties appear to have higher susceptibility ratings than more rural counties.

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Case Study 1: Disparities in COVID-19 Case-Fatality Rates Among Racial Groups in the U.S.

One of the projects I worked on with a team of graduate researchers was on a timely topic of COVID-19. The novel coronavirus has wreaked havoc on many around the world. Historically, disparities in health outcomes have more negatively impacted people of color, especially in the United States. We wanted to explore if there were significant differences in how many people get COVID-19 and die from it among racial groups in the US. Racial demographic case and fatality data were obtained from The COVID Tracking Project for the United States and Washington D.C. 

My role on this project was to perform the statistical analysis on the data. I used the Kruskal-Wallis test to determine if the data were from different populations, meaning there were significant differences between the case-fatality rates among racial groups. To determine which specific groups were statistically from each other, I used the Wilcoxon rank-sum statistical test. 

The results of this analysis showed that the White population had the highest case-fatality rate among all racial group, while the lowest rates were Latine, Multiracial, and Other groups. These results were the opposite of what we expected, and could demonstrate that there are other factors, like belief in COVID-19 and its precautions, that could play a larger role in case fatality than simply racial demographics alone.

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Independent Project

Around the end of the Fall semester 2020, I was awarded a grant by the Southeast Climate Adaptation Science Center (SECASC) to explore ways to solve ecosystem service challenges in the region. I am working with a team of researchers including an undergraduate student I am mentoring, a post-doc, and two professors in my geosciences department. Together we have decided on a project evaluating the ecosystem services of greenspaces in rapidly urbanizing cities in the Southeast US. 

Ecosystem services are all of the benefits that natural systems give humans. When they are well managed, they can result in clean air, water, energy, and food. However, when policy allows for the degradation and unbridled consumption of Earth’s resources, then we end up with challenges on many scales, including the grand challenge of climate change.

Climate has been changing in the American southeast for a number of reasons. In the last ~100 years, there has been a downward trend in average daytime temperatures likely due to the transition from row-cropping agriculture to a more forestry-based ecosystem services model. However, in the last ~40 years, we have seen an upward trend in daytime temperatures, and this climate change is likely due to rapid urbanization occurring in the region.

Urbanization amplifies local warming by a phenomenon called the urban heat island effect (UHI). UHI is the extra warming experienced by urbanscapes than surrounding rural areas. Building materials, more automobiles, more people, and power generation are all reasons for increased warming seen in cities. The impacts to human health are also exacerbated by UHI. 

Extreme heat can cause or worsen human health conditions like respiratory, renal, and cardiovascular diseases. Hyperthermia, heat stroke, and dehydration are also factors to consider. This is where greenspaces can shine as a way to beat the heat! Urban greenspaces are areas within cities that have been converted by installing grasses, shrubbery, or trees. Greenspaces replace urbanscapes in an effort to beat the heat.

Check back for more updates on this project as we go along!

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Meet Me!

I am Elijah Johnson, an enthusiastic Earth System Science, PhD student and Adjunct Instructor teaching Introduction to Earth Science in the Southeastern US. I am passionate about teaching others how the world works as much as I love learning about the Earth myself. In my primary research field, Geoscience Education Research (GER), I study how students learn about the world around us and the challenges they face in and beyond the classroom that act as impediments to that learning. Secondarily, I love working with Geographic Information Systems (GIS). GIS is a way for us to visualize phenomena by digitally mapping them. I created this blog as a way to showcase the progress on some of the things I’ve been working on, and I am excited to have you join in on the journey!