Geospatial Modeling and Simulation Based Approach for Developing Commuting patterns of School Children
- ORNL
Numerous socio-environmental studies, including those in public health, utilize population data as one of the essential elements of modeling and analysis. Typically population data are reported by administrative or accounting units. For example, in the US the Census Bureau reports population counts by census blocks, block groups, and tracts. At any resolution, a uniform population distribution is assumed and the population figures and demographic characteristics are typically associated with block (polygon) centroids. In geographic analyses these points are considered representative of the population for census polygons. Traditional spatial modeling approaches commonly include intersection of census data with buffers of influence to quantify target population, using either inclusion-exclusion (of the centroids) or the area weighted population estimation methods. However, it is well understood that uniform population distribution is the weakest assumption and by considering census polygon centroids as representative of population all analytical approaches are very likely to overestimate or underestimate the analytical results. Given that population is spatially restricted by Census accounting units (such as blocks), there often is great uncertainty about spatial distribution of residents within those accounting units. This is particularly appropriate in suburban and rural areas, where the population is dispersed to a greater degree than urban areas. Because of this uncertainty, there is significant potential to misclassify people with respect to their location from pollution sources, and consequently it becomes challenging to determine if certain sub-populations are actually more likely than others to get differential environmental exposure. In this paper, we describe development and utilization of a high resolution demographic data driven approach for modeling and simulation at Oak Ridge National Laboratory.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- Work for Others (WFO)
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 961563
- Journal Information:
- CSI Communications, Vol. 32, Issue 9
- Country of Publication:
- United States
- Language:
- English
Similar Records
Analysis of School Commuting Data for Exposure Modeling Purposes
Transportation Anslysis Simulation System