Abstract
Census tracts are often used to investigate area-based correlates of a variety of health outcomes. This approach has been shown to be valuable in understanding the ways that health is shaped by place and to design appropriate interventions that account for community-level processes. Following this line of inquiry, it is common in the study of pedestrian injuries to aggregate the point level locations of these injuries to the census tracts in which they occur. Such aggregation enables investigation of the relationships between a range of socioeconomic variables and areas of notably high or low incidence. This study reports on the spatial distribution of child pedestrian injuries in a mid-sized U.S. city over a three-year period. Utilizing a combination of geospatial approaches, Near Analysis, Kernel Density Estimation, and Local Moran’s I, enables identification, visualization, and quantification of close proximity between incidents and tract boundaries. Specifically, results reveal that nearly half of the 100 incidents occur within roads that are also census tract boundaries. Results also uncover incidents that occur on tract boundaries, not merely near them. This geographic pattern raises the question of the utility of associating area-based census data from any one tract to the injuries occurring in these border zones. Furthermore, using a standard spatial join technique in a Geographic Information System (GIS), these points located on the border are counted as falling into census tracts on both sides of the boundary, which introduces uncertainty in any subsequent analysis. Therefore, two additional approaches of aggregating points to polygons were tested in this study. Results differ with each approach, but without any alert of such differences to the GIS user. This finding raises a fundamental concern about techniques through which points are aggregated to polygons in any study using point level incidents and their surrounding census tract socioeconomic data to understand health and place. This study concludes with a suggested protocol to test for this source of uncertainty in analysis and an approach that may remove it.
Highlights
Many studies report a relationship between area-based socio-economic characteristics and injury [1,2,3,4,5,6,7,8,9] and the census tract is often the geographic unit in which these relationships are operationalized [10]
Understanding of child pedestrian injuries has been advanced through such linkages, [11,12,13,14,15], which are more recently being facilitated through use of Geographic Information Systems (GIS) [16,17,18,19,20,21,22]
Using ArcGIS terminology [23], these options are: 1) Points to Polygons: Each polygon is appended with a summary of the numeric attribute of the points that fall inside it, and a count field of the points that fall inside it. 2) Points to Polygons: Each polygon is appended with the attributes of the point that is closest to its boundary, and a distance field showing how close the point is. 3) Polygons to Points: Each point is appended with the attributes of the polygon that it falls inside
Summary
Many studies report a relationship between area-based socio-economic characteristics and injury [1,2,3,4,5,6,7,8,9] and the census tract is often the geographic unit in which these relationships are operationalized [10]. Understanding of child pedestrian injuries has been advanced through such linkages, [11,12,13,14,15], which are more recently being facilitated through use of Geographic Information Systems (GIS) [16,17,18,19,20,21,22] In such studies, it is standard to overlay a) points of incidents with b) census tracts. Once the data are aggregated in this way, they can be normalized by an appropriate denominator and analyzed with statistical approaches to show relationships between outcomes and their socioeconomic context This technique of aggregating points to the polygons in which they occur, spatial join, is generally performed using one of four options depending on the data being utilized and the objectives of data manipulation. For numerous studies of health and place where outcomes or events are represented as points and the place is represented by tracts, Option 1 is the standard approach
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