The Contribution of Neighborhood Tree and Greenspace to Asthma Emergency Room Visits: An Application of Advanced Spatial Data in Los Angeles County

Abstract

This paper aims to investigate the role of neighborhood tree and greenspace on asthma morbidity, especially asthma emergency room visits. We employed advanced spatial data which allow for precisely capturing both the quantity and the features of tree and greenspace within a neighborhood environment. The results from the spatial regression models in Los Angeles County revealed that the features of trees and greenspace, such as the configuration of the tree canopy, the level of tree clustering, and private neighborhood greenspaces contribute to decreasing asthma morbidity, in addition to the quantity of tree and greenspace acreages. Notably, however, large scale greenspace, such as golf courses, school playgrounds, and parks fails to reduce the number of asthma emergency room visits at the statistically significant level. These findings imply that the creation of dense or clustered tree patches and small-scale neighborhood greenspaces might play a substantial role in mitigating air quality and consequently reducing asthma emergency room visits.

Keywords: Light Detection and Ranging (LiDAR); asthma; neighborhood tree; open/greenspace; public health; spatial regression.

Figure 1

Asthma emergency room visits per 10,000 people across the Los Angeles County census tracts (2011–2013).

Figure 2

Example of tree and open/greenspace data. Note: (a) an aerial image that illustrates an actual urban area with diverse land uses with 0.3 m by 0.3 m resolution (source: ESRI), (b) conventional land cover data from Landsat satellite imagery that captures greenspaces (white) with 30 m by 30 m resolution (source: National Land Cover Database), (c) LARIAC land cover data that capture greenspaces (white), and (d) LARIAC tree data that capture trees (white) with 0.2286 m by 0.2286 m resolution (source: LARIAC).

Figure 2

Example of tree and open/greenspace data. Note: (a) an aerial image that illustrates an actual urban area with diverse land uses with 0.3 m by 0.3 m resolution (source: ESRI), (b) conventional land cover data from Landsat satellite imagery that captures greenspaces (white) with 30 m by 30 m resolution (source: National Land Cover Database), (c) LARIAC land cover data that capture greenspaces (white), and (d) LARIAC tree data that capture trees (white) with 0.2286 m by 0.2286 m resolution (source: LARIAC).