Analyses of school commuting data for exposure modeling purposes

Abstract

Human exposure models often make the simplifying assumption that school children attend school in the same census tract where they live. This paper analyzes that assumption and provides information on the temporal and spatial distributions associated with school commuting. The data were obtained using Oak Ridge National Laboratory's LandScan USA population distribution model applied to Philadelphia, PA. It is a high-resolution model used to allocate individual school-aged children to both a home and school location, and to devise a minimum-time home-to-school commuting path (called a trace) between the two locations. LandScan relies heavily on Geographic Information System (GIS) data. With respect to school children attending school in their home census tract, the vast majority does not in Philadelphia. Our analyses found that: (1) about 32% of the students walk across two or more census tracts going to school and 40% of them walk across four or more census blocks; and (2) 60% drive across four or more census tracts going to school and 50% drive across 10 or more census blocks. We also find that: (3) using a 5-min commuting time interval F as opposed to the modeled ''trace'' F results in misclassifying the ''actual'' path taken in 90% of the census blocks, 70% of the block groups, and 50% of the tracts; (4) a 1-min time interval is needed to reasonably resolve time spent in the various census unit designations; and (5) approximately 50% of both the homes and schools of Philadelphia school children are located within 160 m of highly traveled roads, and 64% of the schools are located within 200 m. These findings are very important when modeling school children's exposures, especially, when ascertaining the impacts of near-roadway concentrations on their total daily body burden. As many school children also travel along these streets and roadways to get to school, a majority of children in Philadelphia are in mobile source-dominated locations most of the day. We hypothesize that exposures of school children in Philadelphia to benzene and particulate matter will be much higher than if home and school locations and commuting paths at a 1-min time resolution are not explicitly modeled in an exposure assessment. Undertaking such an assessment will be the topic of a future paper.

Statistical properties of longitudinal time-activity data for use in human exposure modeling

“…Examples could be the day-to-day autocorrelation for time spent in residences or walking near roadways by children going to school. 19 Correct characterization of the population A for time spent in such activities is important for correctly reproducing episodic exposures in individuals. Both Pearson (raw) and Spearman (rank) lag 1 autocorrelations in time spent in activities and locations were calculated.…”

The Effects of Quality Control on Decreasing Error Propagation in the LandScan USA Population Distribution Model: A Case Study of Philadelphia County

“…The Philadelphia school district was the focus of an Environmental Protection Agency (EPA) study that examined children's exposure to hazards as they traveled to and from school (Xue et al 2009). The exact location of K‐12 schools was required in point format for this project and necessitated the creation of a highly accurate school dataset for Philadelphia County that contained the correct location and enrollment values for all schools.…”

Ensuring Confidentiality of Geocoded Health Data: Assessing Geographic Masking Strategies for Individual-Level Data

“…The widespread use of geocoding not only presents unprecedented opportunities for analysis, for example, [ 23 – 25 ]; it also presents challenges to preserving the confidentiality of public health datasets [ 2 , 6 , 26 ]. In short, the release of geographic information at the individual level can breach confidentiality.…”