کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
83160 158692 2016 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Identifying and assessing the residency effect in Pocatello, Idaho, using combined census and parcel data
ترجمه فارسی عنوان
شناسایی و ارزیابی اثر اقامت در پوکتلو، آیداهو، با استفاده از داده های سرشماری ترکیبی و داده های بسته
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک جنگلداری
چکیده انگلیسی


• Introduce a process to mine census and parcel data for residency effect studies.
• Use parcel data to assess land service associated with flood control.
• Integrate parcel and census data to assess the residency effect.

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Geography - Volume 69, April 2016, Pages 10–24
نویسندگان
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