Advancing GeoMarketing analyses with improved spatiotemporal distribution of population at high resolution

Knowing the spatiotemporal distribution of population at the local level is fundamental for many applications, including risk management, health and environmental studies, territorial planning and management, and GeoMarketing. Census figures register where people reside and usually sleep, and are frequently the only data source available for such analyses. Currently, the analysis of service areas and population served is mostly made considering only census data as source of population distribution, while some businesses clearly serve mostly a daytime population. However, population density is not constant within census enumeration areas. Also, due to human activities, population counts and their distribution vary widely from nighttime to daytime, especially in metropolitan areas, and this variation is not captured by census data. Raster dasymetric mapping within geographic modeling allows transforming raw population counts to population density limited to specific areas where the variable is present, in more detailed temporal periods, by using ancillary data sets and zonal interpolation. In GeoMarketing, this information is especially useful for retail sales, banking, insurance, lodging, real estate, and franchising. These refined distributions can be used to improve such analyses as site selection, service area and population served, assessment of potential markets, routing activities, location-allocation, and gravity models. This study uses such a dasymetric mapping approach for detailed modeling and mapping of the spatiotemporal distribution of population in the daily cycle. These data sets are used to assess the location and the varying population contained in the service areas of existing and prospective commercial facilities in the daily cycle, for different types of businesses. Applications in GeoMarketing using spatial analysis are illustrated for three different scenarios involving private sector services where maximizing coverage of target population is paramount for success. The case studies show that when the spatiotemporal distribution of population is considered, the obtained set of solutions differs from the one produced by using census-based data alone. The results demonstrate that enhancing population distribution data through geographical modeling can greatly benefit spatial analysis in GeoMarketing, resulting in the production of better information that ultimately allows improved decision-making.