Identifying and assessing the residency effect in Pocatello,Idaho, using combined census and parcel data |
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| Institution: | 1. Cargill Protein, Wichita, KS 67202;;2. Noble Research Institute, LLC, Ardmore, OK 73401;;3. Department of Agricultural Economics, Oklahoma State University, Stillwater 74078;;4. National Cattlemen’s Beef Association, Centennial, CO 80112;;5. Nichols Family Farms Co., Sentinel, OK 73664; and;6. Whitley Ag Services, Madill, OK 73446;1. Department of Geology, School of Natural Sciences, Trinity College Dublin, Dublin 2, Ireland;2. Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland;3. Hydrometric & Groundwater Section, Environmental Protection Agency, Richview, Dublin 14, Ireland;4. Groundwater Section, Geological Survey of Ireland, Haddington Road, Dublin 4, Ireland |
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| Abstract: | Human residency is the spatial effect source on ecosystem and thus it should be studied and assessed. Supporting residency effect research, this study developed and applied procedures and a model to combine census and parcel data for the assessment. The case study is in Pocatello, Idaho, where revealing land service associated with flood control and locating/evaluating resident effect are needed. Methods include (1) data mining, (2) land service valuation, (3) data screening, (4) integration of census and parcel data, (5) data screening, and (5) analysis and modeling with R programing language and ArcMap. Results are, for land service assessment, land value per area unit in residence areas (LAND) along the concrete channel (for flood control) was less than that along the Portneuf River. Spatial responses under LAND to a source effect (either the concrete channel or the river) are the same. The applied methods helped locate and assess a variety of residency effects spatio-temporally. Results informed the human preferences under LAND and the effect distribution to support decision-making. Technically, using the parcels as a baseline provided comprehensive results with a fine resolution for the effect study, particularly as combined with the census data. This study suggests using a data screening and validation procedure besides the mining approach to minimize outcome uncertainty. |
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| Keywords: | Census and parcel Data mining Residency Impact assessment Modeling R programing |
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