1. Clear title: What, Where, When. Clear, readable, neat labels. Good progression of colors. “Balanced” map. Legend labels. Legend includes units. No abbreviations. Good alignment. Legend boxes equal size and shape. Population density numbers are correct. City key alphabetical. Consistent format. No spelling mistakes.

2. GIS II: DATA AGGREGATION FOR SPATIAL ANALYSIS. The lab exercise this week is based on a semester project completed several years ago by a geography student (Deanna Sanchez) in my GIS class. Her topic was an Environmental Justice study. What’s Environmental Justice? Studies that focus on the question, “Do certain segments of society carry an unfair share of environmental burdens (e.g. exposure to pollution)? The geographic question Deanna asked was, “Are hazardous waste sites disproportionately located in low income/high minority neighborhoods in Dallas County?”. GIS work consisted of creating buffers around the waste sites and using them to extract and aggregate socio-economic data from underlying census tracts. This data was then input into a statistical analysis procedure in Excel. The study was presented and published in Papers of the Applied Geography Conference.

3. ENVIRONMENTAL JUSTICE IN DALLAS COUNTY, TEXAS: A SPATIAL ANALYSIS OF HAZARDOUS WASTE SITE LOCATIONS By Deanna Sanchez & Harry Williams Department of Geography

4. INTRODUCTION Urban growth is commonly accompanied by an increase in the number of local facilities that create, process, and dispose of hazardous wastes. There is mounting evidence to show that socio-economically disadvantaged groups are more likely to reside near hazardous facilities than predominantly white majority groups. The term “environmental justice” has been coined to encompass studies that focus on the unjust distribution of environmental burdens.

5. OBJECTIVES To create a GIS of Dallas County containing census tracts and the location of hazardous waste facilities. To use a buffering technique to assess socio-economic conditions in the vicinity of hazardous waste facilities, in terms of minority population and income levels. To compare minority population and income levels from the buffered regions to those of the whole county, to determine if statistically significant differences exist.

6. METHODOLOGY CENSUS DATA Decennial 1990 census data were used in the study (assumed to represent 1985-1995). Data downloaded from the Census Bureau included race breakdowns and per capita income. Minority population was defined as all categories except “White, Not of Hispanic origin.” These data were linked to a map of census tracts in Dallas County derived from the Census Bureau’s 1990 TIGER files.

7. HAZARDOUS FACILITIES Hazardous waste sites include landfills, waste incinerators, facilities producing toxic air emissions and Superfund sites. Data sources included North Central Texas Council of Governments (NCTCOG), the Texas Natural Resources Conservation Commission and the Environmental Protection Agency. Some site data included spatial coordinates, other sites were geocoded to provide points for the GIS. Three hundred and twenty five sites were mapped (Figure 1).

8. Figure 1. Hazardous Waste Sites In Dallas County, 1985- 1995.

9. SPATIAL ANALYSIS Buffers were used to represent the area impacted by each waste site and to represent the “local neighborhood”. The size and shape of the buffers was problematic because different hazards have different shapes and extend over different distances. It was decided to use circular buffers (which are consistent with distance decay) and to base buffer size on consistency of spatial scales. The purpose of the buffers is to extract demographic information from the census tracts (data aggregation); consequently, buffer size was made equal to the average size of census tracts – 2.197 square miles or a radius of 0.836 miles.

10. The GIS was used to aggregate proportionally per capita income and minority percentage values from the census tract(s) to the overlying buffer (i.e. if a buffer covered parts of several adjoining census tracts, the demographic data were weighted according to the proportion of the buffer within each census tract). This is where the data aggregation comes in – in this example, data from three census tracts will be proportioned and combined into the overlying buffer.

11. 50% 30% 20% $20,000 $40,000 $30,000 Proportional weighted average income = (50% of 20,000)+(30% of 30,000)+(20% of 40,000) = 10,000+9,000+8,000 = $27,000. Aggregating data is combining data from several regions into one region. Average = (20,000+30,000+40,000)/3 = $30,000.

12. STATISTICAL ANALYSIS Statistical analysis was performed to compare per capita income and minority percentage values extracted from the buffers (the neighborhoods of the waste sites) to the same values for every census tract in the county (the whole county). A z test for comparison of two sample means was used to test for statistically significant differences* between these two sample groups (Table 1). * The difference between two groups is statistically significant if it can not be explained by chance alone. The z test gives you the probability that the difference is just random chance. If this probability is low (e.g. 1 in a 1000), then you reject random chance and say there really is a difference (the odds are 999 in a 1000 in this example).

13. The null hypotheses tested were: Buffer minority percentage = county minority percentage, and Buffer per capita income = county per capita income (i.e. there is no difference). TABLE 1. Z TEST RESULTS Minority Percentage BuffersCounty Mean56.6641.09 z score7.1495* Critical z value 3.0902 Per Capita Income CountyBuffers Mean$17,718.88$11,627.93 z score8.2304* Critical z value3.0902 * a z score larger than the critical z score indicates a statistically significant result at the 99.9% confidence level.

14. A non-statistical approach is purely visual: the coincidence of waste site locations and higher minority populations is illustrated in Figure 2, which shows waste sites and census tracts having minority population percentages greater than the highest one third of census tracts in regard to minority percentage. Figure 3 shows waste site locations and census tracts having per capita incomes below the lowest one third of census tracts in regard to per capita income.

15. FIGURE 2. RELATIONSHIP BETWEEN WASTE SITES AND CENSUS TRACTS WITH HIGH MINORITY POPULATIONS

16. FIGURE 3. RELATIONSHIP BETWEEN WASTE SITES AND CENSUS TRACTS WITH LOW PER CAPITA INCOME

17. CONCLUSIONS The study shows that mean minority population was significantly larger and mean per capita income was significantly lower in the vicinity of hazardous waste sites, compared to Dallas County as a whole. This study focuses purely on the spatial association of waste sites and socio-economically disadvantaged groups. The study findings suggest further research is warranted into related questions:

18. Which came first – the waste sites or the socio-economically disadvantaged neighborhoods? Were hazardous waste sites preferentially sited in low-income, minority neighborhoods? Were low-income minority groups attracted to neighborhoods around waste sites because of lower property values? Were public policies in place that fostered the concentration of waste sites in poorer minority neighborhoods? Is there a greater incidence of “waste-related illnesses” in these neighborhoods? What can be done in the future to address the apparent injustice?

19. Note: the data used in this week’s exercise is not the same as used in the original study, so you should NOT expect to get the same results.