1. Asthma Distribution patterns and their relationship with the urban landscape and social conditions in Newark NJ Authors: Francisco Artigas, Leonard Beilory, Richard Holowczak, Kumar Patel Primary author affiliation: CIMIC - Rutgers University, NJ artigas@cimic.rutgers.edu IHGC 2000 Sunday March 19, 2000

2. Problem Statement Recent estimates suggest that roughly 50% of school children in the City of Newark suffer from some form of asthma. Similar urban areas across the country exhibit much lower rates. Hospital admissions: –110 per 100,000 in Newark –46 per 100,000 in surrounding Suburban/rural

3. Research Objectives Build a robust spatial data-set about asthma cases in Newark (focus area). Find spatial correlation between asthma case locations and urban landscape features

4. Data Sources Admission records from UMDNJ University Hospital 1997-1998 (n = 542 and n = 624) Landsat 5 thermal images (1997) High resolution aerial photographs (1995) Geo-coded street address vector coverage of Newark Census tracts from 1990

5. Overview of Data 19971998 Male280305 Female261329 Black485561 White23 Filipino01 Other435 Unknown4934 Age1997 Min/Max0 / 82 Average17 Median8 Len.of Stay19971998 Min11 Max2487 Average33.1 Median22

6. Analytical Tools ARC/INFO and ARCVIEW MapObjects IDRISI Image processing software SPSS statistics software Wizsoft data mining software

7. Research Approach Clean and organize asthma case data from UMDNJ University Hospital Generate X and Y coordinates from address lists for 1997 and 1998 Perform cluster analysis Intersect asthma cases with census data Spatial analysis of asthma cases with Landscape texture and features

8. Assumption Asthma cases are uniformly distributed across: –Streets –Landscape texture –Socio-economics indicators Asthma cases are uniformly distributed from: –Emission focal points

9. Cluster Analysis Method: Use K-means cluster analysis (K=5, K=10 and K=15) on X, Y coordinates Characteristics of clusters: –Size (membership) of clusters –Location of cluster centers –Cluster migration from year to year –Cluster homogeneity

10. 1997 data K=5 Clusters tend to align with Newark Ward boundaries Black - cluster center H UMDNJ Hospital H

11. 1997 data K=15 Clusters tend to align with neighborhood boundaries H UMDNJ Hospital H

12. 1998 data K=5 Clusters tend to align with Newark Ward boundaries Black - cluster center H UMDNJ Hospital H

13. 1998 data K=15 Clusters tend to align with neighborhood boundaries H UMDNJ Hospital H

14. Cluster Migration ‘97 to ‘98 Blue: 1997 Cluster centers Red: 1998 Cluster centers Central ward clusters tend to migrate less H UMDNJ Hospital H Rt. 280

15. Cluster Homogeneity Compare %Race in population with %Race of cases n/a

16. Cluster Homogeneity n/a

17. Cluster Homogeneity We expected the number of Asthma Cases to be proportional to the Racial makeup of the clusters However, our data suggests that asthma cases among Blacks are disproportionately higher compared to the racial makeup of the clusters

18. Spatial analysis Intersection of tract census data with asthma cases Observation of asthma cases and urban landscape texture Asthma cases at the street level Spatial relationship between diesel fume sources and asthma cases Spatial correlation between urban heat islands (UHI) and asthma cases.

19. Intersection of census tract information and asthma cases

20. Less than half on PAV Half on PA More than half on PA Cluster Centers 1997 Cases in terms of Public Assistance

21. Less than half on PA Half on PA More than half on PA 1998 Cases in terms of Public Assistance Cluster Centers

22. Landscape Texture “Expect to see more cases in high-density housing areas than in low-density housing areas”

23. 1997 cases in terms of population density Low pop. density Medium pop. density High pop. density Cluster Centers

24. Low pop. density Medium pop. density High pop. density Cluster Centers 1998 cases in terms of population density

25. Urban Landscape Texture High density housing Low density housing South Orange Ave.

26. Low density housingHigh density housing Urban Landscape Texture

27. Housing Density Effect Cluster centers which had the greatest recruitment of cases occurred in low density neighborhoods in central ward (many vacant lots)

28. “Sick” Streets “All streets should exhibit a proportional number of cases”

29. Sick Streets 1997 Yellow 1998 Green H UMDNJ Hospital S. Orange Ave. H Fairmount Cemetery S. 11 th St.

30. Sick Streets 1997 Yellow 1998 Green S. Orange Ave. Manufacturing Facility

31. Sick Streets An unusually high number of asthma cases congregate along specific streets We need to further investigate the impact of nearby manufacturing facilities and TRI sites

32. Spatial Relationship between Diesel Fumes and Asthma Extracted addresses from digital yellow pages of trucking facilities in Newark where trucks are likely to congregate X and Y coordinates were extracted for each facility Trucking facility locations were mapped together with asthma case locations

33. Diesel fume sources Asthma case

34. Newark Urban Heat Islands Landsat 5 Thermal Ground level ozone is a photo chemical reaction Greater ozone levels are expected in hotter areas of the city

35. Newark Urban Heat Islands 1997 asthma cases correlated against urban heat islands

36. Newark Urban Heat Islands 1998 asthma cases correlated against urban heat islands

37. Newark Urban Heat Islands

38. Conclusions Asthma cases tend to congregate in the central ward Great majority of cases are: –African American –Less than 10 years old –Under public assistance –From low density neighborhoods (Social dislocation effect, Wallace et al)

39. Conclusions (continued) Cluster centers tend to persist in the central ward and along heavy traffic corridors. Some streets in mixed industrial/residential neighborhoods have an unusually high number of asthma cases According to our data (limited number of years and only 1 hospital) we found no significant correlation between diesel fume sources or urban heat islands and asthma

40. Conclusions (continued) The evidence suggests that the external environmental conditions we studied are not strong indicators of asthma

41. Future Work Continue to build asthma database for different years and from different hospitals in Newark Incorporate daily and seasonal air quality measurements from monitoring stations to the data set Map TRI sites in Newark Employ more robust statistical tools Investigate temporal relationships (seasons vs. admissions)

42. End

43. High-D vs. Low-D housing

44. Socio-economics “Expect asthma cases uniformly distributed across all socio-economic indicators” –Race –Income: % Public assistance –Home ownership/Rentals –Population density People per census tract People per household

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50. Windrose