Disproportionate Proximity to Environmental Health Hazards: Methods, Models, and Measurement

Jayajit Chakraborty, Juliana Maantay, Jean Brender

Environmental Justice deals with the disproportionate distribution of environmental ‘bads’ falling mainly on racial and ethnic minorities, lower income populations, and other vulnerable groups. GIS has been a valuable tool for mapping the proximity of vulnerable populations to environmental hazards because of its ability to integrate multiple data sets for spatial analysis. This study selected 80 quantitative case studies dealing with environmental justice/equity/racism and conducted a historical analysis of the methods, models, and data used to represent environmental justice issues.

Spatial coincidence is a technique commonly used for environmental justice research that assumes potential exposure to environmental hazards being confined to the boundaries of predefined geographic entities or census unites containing such hazards. Unit-hazard coincidence is most widely used for environmental justice research and is calculated by using the presence of a hazard within an analytical unit as a proxy for environmental exposure. The analytical unit can be zip codes, census tracts, counties, etc. The unit of analysis is then compared with a unit that does not contain the hazard to determine disproportionate exposure.

                                    

While this method has proven to be useful, the unit-hazard method can potentially be problematic for three reasons: 1) most applications do not distinguish between units that have more than one hazardous source; 2) many studies do not recognize that non-host units located close to the edge of a host unit may be equally exposed to the hazard source; 3) this method assumes that hazardous exposure is distributed equally and restricted to the unit boundaries.

Distance based analysis actually measures the distance from environmental hazard sources. The most widely used method is buffer analysis. The buffer generation technique in GIS creates new polygons around features on a map to identify areas and populations at risk inside the buffer zones. This technique addresses issues in spatial coincidence modeling because it provides an analysis that is not limited by boundaries. Some issues with buffer analysis are that the radius of the circle is often times chosen arbitrarily and is the same size for different hazards in the same study. To address this issue, continuous distances are often utilized which are calculated based on calculating the exact distance between each hazard and exposed population.

                                         



To better account for the effects of airborne toxic exposure, geographic plume analysis integrates air dispersion modeling with GIS to estimate which areas and populations are being exposed. These models utilize data on the quantity and properties of the chemical with data on the location’s characteristics, release parameters and atmospheric conditions to calculate the chemical’s plume footprint. For environmental justice research, the air dispersion model, Areal Locations of Hazardous Atmospheres (ALHOA), is most frequently used for short-duration chemical releases. This type of modeling requires large volumes of data and can be a very expensive and time consuming process. Therefore, there are few national or regional data sets using the plume analysis method.



                                              
               

Since most environmental justice research is focused on the disproportionate exposure in relation to race/ethnicity and socioeconomic status, sociodemographic characteristics must be analyzed in these models. Point interpolation is the easiest method when addresses of individuals and households can be determined. Individuals inside the exposed buffer zones can then be estimated and analyzed.

Areas that are represented by a distance-based or plume-based model are unlikely to match the shape of the buffer zone within the population data. There are three methods of containment used to address this issue. Polygon containment creates a buffer zone using an aggregation of census units that are intersected or entirely enclosed by the buffer. Centroid containment method includes only the census tracts that have geographic centers located within the buffer zone. Buffer containment is most commonly used and includes all census units located within the buffer as well as portions of units intersected by the buffer.

Polygon Containment

Centroid Containment

Buffer Containment

The use of GIS technology to represent and display environmental justice findings has greatly evolved and improved. We now have multiple ways to represent the data and analyze affected populations more accurately. While there are still small issues and problems that can be addressed and modified in the future, our current methods of analysis do indeed provide very valuable information.

-Lisa Morse




source: Chakraborty, J., J. Maantay, and J. Brender. 2011. Disproportionate Proximity to Environmental Health Hazards: Methods, Models, and Measurement. American Journal of Public Health 101(1)s27-s36.