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
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 (asthma related):
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.
Find spatial correlation between asthma case locations and urban landscape features

4. Data Sources
Admission records from UMDNJ University Hospital (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 1997 1998 Male 280 305 Female 261 329 Black 485 561
Male
Female
Black
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
Min 1 1
Max
Average 3 3.1
Median 2 2

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: Clusters
Streets
Landscape texture
Socio-economics
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. Cluster Homogeneity Compare %Race in population with %Race of cases
n/a

16. Cluster Homogeneity
n/a
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 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 PA
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. Sick Streets H UMDNJ Hospital H S. 11th St. 1997 Yellow 1998 Green
S. Orange Ave.
Fairmount
Cemetery H

30. Sick Streets Manufacturing Facility 1997 Yellow 1998 Green
S. Orange Ave.
Power
sub-station

31. South 11th Street, Newark NJ
Power Sub-station at end of S. 11th street
Intersection of S. Orange Ave.
and South 11th street (facing East)

32. South 11th street, Newark, NJ
Abandoned housing and empty lots next to inhabited structures

33. South 11th street, Newark, NJ
Abandoned lot with light
manufacturing facility
Adjoining multi-family dwellings
(cases from ‘97 and from ‘98)

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

35. 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

36. Diesel fume sources Asthma case

37. 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

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

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

40. Newark Urban Heat Islands

41. 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.)

42. 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 from 1 hospital) we found no significant correlation between diesel fume sources or urban heat islands and asthma

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

44. 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)

45. End