Authors: Francisco Artigas, Leonard Beilory, Richard Holowczak, Kumar Patel

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 2000Sunday 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. 1997 1998 Male 280 305 Female 261 329 Black 485 561 White 2 3 Filipino 0 1 Other 4 35 Unknown 49 34 Age 1997 Min /Max 0 / 82 Average 17 Median 8 Len.of Stay 1997 1998 Min 1 1 Max 24 87 Average 3 3.1 Median 2 2 Overview of Data

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

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

16. n/a n/a Cluster Homogeneity

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

21. 1998 Cases in terms of Public Assistance Less than half on PA Half on PA More than half on PA 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. 1998 cases in terms of population density Low pop. density Medium pop. density High pop. density Cluster Centers

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

26. Urban Landscape Texture Low density housing High density housing

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

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

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 sourcesAsthma 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

45. Image 11

46. Image 12

47. Image 13

48. Image 15

49. Image 16

50. Windrose