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Existing data and conceptual frameworks for investigating spatial patterns of urban growth within and among Russia's largest cities obscure contemporary growth. We propose a new conceptual framework for urban spatial data development that utilizes remote sensing, GIS, and external data to infer socioeconomic, ecological, and infrastructure data. Capturing urban growth patterns requires using multispectral (MS) land-use classifications, dasymetric mapping, areal interpolation, and visual interpretation of MS and panchromatic images. Employing these same methods to analyze rural and urban districts (rayons) allows development of the first compatible datasets for all urban administrative units, improving investigative capabilities because all data are aggregated at the same level.
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Perepechko et al. (2005) studied this question.
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