1. Locating Pollutant Zones In Michigan: Analyzing Health And Environmental Justice Problems Presented To Dr. Ronald Briggs Course: GISC 6387 By Rumana Reaz Arifin Summer 2007

2. Project Objective Locating the pollutant zones in Michigan based on site density. Locating the pollutant zones in Michigan based on site density. Relating low weight birth density to dense pollutant zones by county. Relating low weight birth density to dense pollutant zones by county. Analyzing environmental justice in dense pollutant zones by zipcode. Analyzing environmental justice in dense pollutant zones by zipcode.

3. Literature Review Huang, Y. and S. Batterman. 2000. Residence location as a measure of environmental exposure: a review of air pollution epidemiology studies. Journal of Exposure Analysis and Environmental Epidemiology 10 (1): 66-85. Huang, Y. and S. Batterman. 2000. Residence location as a measure of environmental exposure: a review of air pollution epidemiology studies. Journal of Exposure Analysis and Environmental Epidemiology 10 (1): 66-85. Charles, E.M.H and M.Morris. 1998. Progress towards Environmental Justice: A Five-year Perspective of Toxicity, Race and Poverty in Michigan, 1990-1995. Journal of Environmental Planning and Management 41(2): 157-176. Charles, E.M.H and M.Morris. 1998. Progress towards Environmental Justice: A Five-year Perspective of Toxicity, Race and Poverty in Michigan, 1990-1995. Journal of Environmental Planning and Management 41(2): 157-176.

4. Huang, Y. and S. Batterman (1) Proximity indicators are used to locate pollutant zones by determining the distance from the pollutant source with the assumption that exposure to pollution increase as the distance decreases. Proximity indicators are used to locate pollutant zones by determining the distance from the pollutant source with the assumption that exposure to pollution increase as the distance decreases. Zoning is another type of exposure indicator which classifies the locations using ‘zones’ that have explicit boundaries. Individuals within each zone are assumed to have the same exposure. Zoning is another type of exposure indicator which classifies the locations using ‘zones’ that have explicit boundaries. Individuals within each zone are assumed to have the same exposure. Zoning can be done based on any definite geographic location like city, county, census tract or certain criteria like population density, poverty level etc. Zoning can be done based on any definite geographic location like city, county, census tract or certain criteria like population density, poverty level etc. Source: http://www.nature.com/jea/journal/v10/n1/abs/7500074a.html Source: http://www.nature.com/jea/journal/v10/n1/abs/7500074a.html http://www.nature.com/jea/journal/v10/n1/abs/7500074a.html

5. Huang, Y. and S. Batterman (2) Proximity and zone approaches may be combined. High pollution areas may be identified by the location of pollution sources, and subgroups within these areas demarcated by distance to the source. Proximity and zone approaches may be combined. High pollution areas may be identified by the location of pollution sources, and subgroups within these areas demarcated by distance to the source. Based on this review following steps are taken: Based on this review following steps are taken: 1. Location-based density of the pollutant sites is taken to locate the pollution zones. It is assumed that where the density of pollutant sites is higher, that supposed to be a high exposure zone. 1. Location-based density of the pollutant sites is taken to locate the pollution zones. It is assumed that where the density of pollutant sites is higher, that supposed to be a high exposure zone. 2. In addition to that, to determine risk zone for population, proximity of the pollution source to educational institute and hospitals is considered along with the population and housing density around the pollution sources. 2. In addition to that, to determine risk zone for population, proximity of the pollution source to educational institute and hospitals is considered along with the population and housing density around the pollution sources. Source: http://www.nature.com/jea/journal/v10/n1/abs/7500074a.html Source: http://www.nature.com/jea/journal/v10/n1/abs/7500074a.htmlhttp://www.nature.com/jea/journal/v10/n1/abs/7500074a.html

6. Charles, E.M.H and M.Morris (1) Much research has found a connection between poverty, minority racial status and exposure to environmental toxicity. Much research has found a connection between poverty, minority racial status and exposure to environmental toxicity. There exists, the argument goes a positive correlation between the location of hazardous waste sites and minority population, the higher the percentage of non-white population, the more sites there are in the community. There exists, the argument goes a positive correlation between the location of hazardous waste sites and minority population, the higher the percentage of non-white population, the more sites there are in the community. A study based on 1990 Census data by UCC (United Church of Christ) found that People of color are 47% more likely than the whites to live near a commercial hazardous waste facility. A study based on 1990 Census data by UCC (United Church of Christ) found that People of color are 47% more likely than the whites to live near a commercial hazardous waste facility. Source: http://www.informaworld.com/smpp/content~content=a713676457~db=all Source: http://www.informaworld.com/smpp/content~content=a713676457~db=all http://www.informaworld.com/smpp/content~content=a713676457~db=all

7. Charles, E.M.H and M.Morris (2) The issue of unit of analysis is an important one. The issue of unit of analysis is an important one. At the census tract level, neighboring tracts are often of similar racial and income composition. The result is a decrease in variability and so in correlation. At the census tract level, neighboring tracts are often of similar racial and income composition. The result is a decrease in variability and so in correlation. At the opposite end, when large geographic areas are used such as SMAs, too much variability is introduced and the result is inflated. At the opposite end, when large geographic areas are used such as SMAs, too much variability is introduced and the result is inflated. Zipcode - smaller than SMA, larger than census tracts, offers a compromised result. Zipcode - smaller than SMA, larger than census tracts, offers a compromised result. Hence, this project provides a comparative study among different pollutant zones based on race, income and education group at zipcode level. Hence, this project provides a comparative study among different pollutant zones based on race, income and education group at zipcode level. Source: http://www.informaworld.com/smpp/content~content=a713676457~db=all Source: http://www.informaworld.com/smpp/content~content=a713676457~db=allhttp://www.informaworld.com/smpp/content~content=a713676457~db=all

8. Charles, E.M.H and M.Morris (3) In Michigan, the location of pollutant sites has effect on health problems also. In Michigan, the location of pollutant sites has effect on health problems also. Mother living near hazardous waste sites, are more likely to give birth to children with birth defects, low weight birth is one of them. Mother living near hazardous waste sites, are more likely to give birth to children with birth defects, low weight birth is one of them. Hence, this project provides a study on the relation of low weight birth density and pollutant zones at county level. Hence, this project provides a study on the relation of low weight birth density and pollutant zones at county level. Source: http://www.informaworld.com/smpp/content~content=a713676457~db=all Source: http://www.informaworld.com/smpp/content~content=a713676457~db=all http://www.informaworld.com/smpp/content~content=a713676457~db=all

9. Data Sources Spatial Data (2001): MSU Map Library http://www.lib.msu.edu/coll/main/maps/datadown.ht ml Spatial Data (2001): MSU Map Library http://www.lib.msu.edu/coll/main/maps/datadown.ht ml http://www.lib.msu.edu/coll/main/maps/datadown.ht ml http://www.lib.msu.edu/coll/main/maps/datadown.ht ml Attribute Data (Census 2000): U.S Census Bureau, American Fact Finder http://factfinder.census.gov/home/saff/main.html?_la ng=en Attribute Data (Census 2000): U.S Census Bureau, American Fact Finder http://factfinder.census.gov/home/saff/main.html?_la ng=en http://factfinder.census.gov/home/saff/main.html?_la ng=en http://factfinder.census.gov/home/saff/main.html?_la ng=en Low weight birth Data (1999,2000,2001): Michigan Department of Community Health http://www.mdch.state.mi.us/pha/osr/Index.asp?Id=2 &MainFile=MainNat.htm&BookMark= Low weight birth Data (1999,2000,2001): Michigan Department of Community Health http://www.mdch.state.mi.us/pha/osr/Index.asp?Id=2 &MainFile=MainNat.htm&BookMark= http://www.mdch.state.mi.us/pha/osr/Index.asp?Id=2 &MainFile=MainNat.htm&BookMark http://www.mdch.state.mi.us/pha/osr/Index.asp?Id=2 &MainFile=MainNat.htm&BookMark

10. Projected Coordinate System All data in maps are projected in NAD 1983 Hotine Oblique Mercator Azimuth Natural Origin

11. Analysis Four types of Pollutant Sites Four types of Pollutant Sites  Air Pollution Sites  Water Pollution Sites  Toxic Sites  Hazardous Sites Source: http://www.lib.msu.edu/coll/main/ maps/datadown.html

12. Point Density Analysis

13. Overlay and Reclassification

14. Added low weight birth data by county to county layer. Added low weight birth data by county to county layer. Map shows: Map shows: Higher number of low weight birth density is related to dense pollutant zones in positive correlation. Higher number of low weight birth density is related to dense pollutant zones in positive correlation.Source: http://www.lib.msu.edu/coll/main/maps/data down.html http://www.lib.msu.edu/coll/main/maps/data down.html http://www.mdch.state.mi.us/pha/osr/Natalit y/LowWeightBirths.asp http://www.mdch.state.mi.us/pha/osr/Natalit y/LowWeightBirths.asp Low Weight Birth Density Analysis

15. Result of Numerical Analysis Result of Numerical Analysis Source:http://www.mdch.state.mi.us/pha/osr/Natality/LowWeightBirths.asp Source: http://www.mdch.state.mi.us/pha/osr/Natality/LowWeightBirths.asphttp://www.mdch.state.mi.us/pha/osr/Natality/LowWeightBirths.asp

16. Result of Analysis For each year, the top 5 counties with highest low weight birth density are as follows: For each year, the top 5 counties with highest low weight birth density are as follows: Wayne Wayne Oakland Oakland Macomb Macomb Kent Kent Genesee Genesee

17. Locating Risk Zone 3 Risk zones for population are located from the density zones for analyzing environmental justice in those zones. 3 Risk zones for population are located from the density zones for analyzing environmental justice in those zones. Criteria for locating risk zones: Criteria for locating risk zones:  Population density greater or equal to 1500 per sq.mile.  Housing density greater or equal to 500 per sq.mile.  Proximity to Educational institute and Hospital.

18. High Density Zone Source: http://www.lib.msu.edu/coll/main/maps/datadown.html http://www.lib.msu.edu/coll/main/maps/datadown.html

19. Medium Density Zone Source:http://www.lib.msu.edu/coll/main/maps/datadown.html http://www.lib.msu.edu/coll/main/maps/datadown.html

20. Low Density Zone http://www.lib.msu.edu/coll/main/maps/datadown.html

21. Risk Zones Source: http://www.lib.msu.edu/coll/main/maps/ http://www.lib.msu.edu/coll/main/maps/ datadown.html

22. Racial Distribution Source: http://library.csun.edu/Find_Resources/Go vernment_Publications/zipcen.html http://library.csun.edu/Find_Resources/Go vernment_Publications/zipcen.html http://library.csun.edu/Find_Resources/Go vernment_Publications/zipcen.html

23. Income Distribution Source: http://library.csun.edu/Find_Resources/Go vernment_Publications/zipcen.html http://library.csun.edu/Find_Resources/Go vernment_Publications/zipcen.html http://library.csun.edu/Find_Resources/Go vernment_Publications/zipcen.html

24. Education Distribution Education Distribution Source: http://library.csun.edu/Find_Resources/Go vernment_Publications/zipcen.html http://library.csun.edu/Find_Resources/Go vernment_Publications/zipcen.html http://library.csun.edu/Find_Resources/Go vernment_Publications/zipcen.html

25. Result of Numerical Analysis (1) Source:http://library.csun.edu/Find_Resources/Government_Publications/zipcen.html Source: http://library.csun.edu/Find_Resources/Government_Publications/zipcen.htmlhttp://library.csun.edu/Find_Resources/Government_Publications/zipcen.html

26. Result of Numerical Analysis (2) Source:http://library.csun.edu/Find_Resources/Government_Publications/zipcen.html http://library.csun.edu/Find_Resources/Government_Publications/zipcen.html

27. Result of Numerical Analysis (3) Source:http://library.csun.edu/Find_Resources/Government_Publications/zipcen.html http://library.csun.edu/Find_Resources/Government_Publications/zipcen.html

28. Summary Low weight birth density has positive correlation with the density of pollutant zones. Counties with higher low weight birth density are located in the proximity for high density pollutant zone. Low weight birth density has positive correlation with the density of pollutant zones. Counties with higher low weight birth density are located in the proximity for high density pollutant zone. Racial discrimination has positive correlation with density of pollutant zones. Percentage of AfAm population increases from the low density risk zone to high density risk zone, hence, this group is more exposed. So, there is violation of environmental justice in race issue. Racial discrimination has positive correlation with density of pollutant zones. Percentage of AfAm population increases from the low density risk zone to high density risk zone, hence, this group is more exposed. So, there is violation of environmental justice in race issue. There is no clear correlation of density of pollutant zones with the population of different income group and education level. Hence, for these two parameters, there is no evidence for environmental justice violation. There is no clear correlation of density of pollutant zones with the population of different income group and education level. Hence, for these two parameters, there is no evidence for environmental justice violation.

29. Problems Faced Problems Problems  To get appropriate data.  Needed to fix some projection problems.  To convert data in DBF format from tabular format: Entered data in editiing mode  To enter data by Zipcode: Around 150 zipcodes in 3 risk zones, so this has been huge task. Limitations Limitations  Could not get pollution score data, so could not determine exposure index.  This project is entirely based on location-based density.

30. Thank You