This project investigates the correlation between air quality, median housing cost, and racial demographics in Harris County, Texas. The study aims to understand how environmental factors interact with economic and racial variables, addressing key research questions:

• How does air pollution vary across neighborhoods in Harris County?

• What is the relationship between air quality and median housing costs?

• How do air quality levels align with racial demographic patterns?

By integrating environmental and socioeconomic data, this analysis seeks to highlight disparities that could inform public policy and urban planning efforts in the region.

In this research report, we observe values surrounding toxicity and pollution in the Harris county region. Much of this is derived from the Risk Screening Environmental Indicators hazard index, abbreviated to the RSEI hazard index. This score, developed by the Environmental Protection Agency (EPA), sums values like toxicity data, population data, and exposure assumptions to come up with a value that explains areas of interest for their risk of being exposed to toxic substances, no matter what the source material is. These RSEI values are linked to many oil refineries and factories that are scattered throughout Houston, which is what we will focus on today.

A plethora of methods were used to source our information, primarily coming from US EPA Data and U.S. Census Data. We applied this data using different methods of analysis and visualization techniques with both ArcGIS and RScript as well as supporting graphics from Microsoft Excel.

Data Sources:

• Air Quality Data: Sourced from the Texas Commission on Environmental Quality (TCEQ) and AirNow, focusing on real-time and historical air quality indices (AQI). Specific facility data, such as Risk-Screening Environmental Indicators (RSEI) Hazard values, were also analyzed.

• Socioeconomic Data: U.S. Census Bureau data provided insights into housing costs, racial demographics, and median household income at the zip code level.

• Geospatial Data: Shapefiles for Harris County zip codes were used to map spatial distributions.

Analysis Approach:

• Spatial Analysis: Conducted using GIS to overlay air quality data with socioeconomic metrics and identify geographical trends.

• Statistical Analysis: Correlation coefficients were calculated to evaluate the strength of relationships between air quality, housing costs, and racial demographics.

• Data Visualization: Charts and maps were created to illustrate these relationships and to make the data more accessible for stakeholders.

Tools and Methods in ArcGIS Pro

ArcGIS Pro was the primary tool for mapping and analyzing spatial data. Key features used included:

• Spatial Joins: Emission site point data were joined with zip code boundaries to associate environmental factors with socioeconomic metrics.

• Gradient Mapping: Median housing costs and income levels were displayed using color gradients to show economic disparities across zip codes.

• Point Mapping: Facility locations, such as TRI (Toxic Release Inventory) sites, were mapped to correlate with nearby demographic data.

• Heatmaps: Kernel density analysis was applied to visualize hotspots of air pollution based on RSEI Hazard scores.

• Layering: Overlays combined air quality, housing costs, and racial demographics to highlight spatial intersections.

Additionally, RScript was used to clean the data for easier interpretation as well as creating unique visuals that demonstrated similar information as the ArcGIS data while still remaining easy to understand. This process included the following:

• Grid Maps: By creating a grid overlay for the spatial boundary of Harris county, Texas and additionally overlaying point data to this spatial boundary, we were able to do a count of how many RSEI hazard pollutant types appeared in each entry. This helped to visualize where the main cluster of pollutant sites are in Houston.

• Waffle Charts: A waffle chart of race in the Harris county region based on zip code was developed in order to visualize the dispersion of diversity in the area from a non-mapped standpoint.

• Spatial Mapping: Boundaries were imported and joined to zip code data of both race and income in RScript to create a visual representation of dispersion levels.

• Overlaying Point Data: After obtaining the spatial mapping of the race and income data, we overlaid point data that expanded based on the strength of the RSEI hazard index onto the maps to determine any overlaps.

The analysis revealed significant correlations between air quality, housing costs, and racial demographics in Harris County:

1. Air Quality and Housing Costs: Areas with poorer air quality tended to have lower median housing costs, reflecting potential economic impacts of environmental degradation.

2. Air Quality and Racial Demographics: Neighborhoods with higher concentrations of racial minorities often experienced greater exposure to pollutants, emphasizing environmental justice concerns.

3. Geospatial Patterns: Hotspots of pollution were identified near industrial facilities, which were often adjacent to economically disadvantaged and racially diverse neighborhoods.

It appears much of our analysis highlighted a diagonal line drawn from the center of Houston Southwest out to the coastal region that functioned as the primary hotspot for high racial diversity, low median household income, and a large amount of pollution locations as well as high emission sites. Much of our data recognized that many of these polluted areas surrounded regions with more socioeconomic disparities, like a marginally high Hispanic or black population combined with lower economic standings relative to Harris county. Additionally, the heatmaps and point size graphics assisted in helping us recognize that the areas with highest RSEI hazard levels and toxicity always seem to surround marginalized and low-income communities.

This study highlights the interconnected nature of environmental and socioeconomic issues in Harris county, Texas. Addressing these disparities requires targeted policy interventions aimed at improving air quality and promoting equitable urban development. Future work could expand on these findings by incorporating additional variables such as health outcomes and long-term economic trends. By developing this analysis, researchers can go on to focus on the areas with high hazard rates that overlap with low income and high percentage of a not white population. We can then ensure these populations get the attention they deserve and create preventative measures to reduce long term damage from the toxic exposures. If this study were to be conducted again, we would focus intensively on the neighborhood level rather than zip codes to curate a better understanding of race and income dispersion throughout Houston, Texas.