International Journal of Environmental Research and Public Health
Department of Geography
Geisel School of Medicine
Limited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations between disease and environmental factors. Method: We propose to use a Restricted and Controlled Monte Carlo (RCMC) process to disaggregate polygon-level location data to achieve mapping aggregate data at an approximated individual level. RCMC assigns a random point location to a polygon-level location, in which the randomization is restricted by the polygon and controlled by the background (e.g., population at risk). RCMC allows analytical processes designed for individual data to be applied, and generates high-resolution raster maps. Results: We applied RCMC to the town-level birth defect data for New Hampshire and generated raster maps at the resolution of 100 m. Besides the map of significance of birth defect risk represented by p-value, the output also includes a map of spatial uncertainty and a map of hot spots.
Shi X, Miller S, Mwenda K, Onda A, Reese J, Onega T, Gui J, Karagas M, Demidenko E, Moeschler J. Mapping disease at an approximated individual level using aggregate data: a case study of mapping New Hampshire birth defects. Int J Environ Res Public Health. 2013 Sep 6;10(9):4161-74. doi: 10.3390/ijerph10094161. PMID: 24018838; PMCID: PMC3799515.
Dartmouth Digital Commons Citation
Shi, Xun; Miller, Stephanie; Mwenda, Kevin; and Onda, Akikazu, "Mapping Disease at an Approximated Individual Level Using Aggregate Data: A Case Study of Mapping New Hampshire Birth Defects" (2013). Dartmouth Scholarship. 1012.