This paper is uniquely focused on mapping business land in satellite imagery, with the aim to introduce a standardized approach to estimating how much land in an observed area is allocated to business. Business land and control categories of land are defined and operationalized in a straightforward setting of pixel-based classification. The resultant map as well as information from a sample-based quantification of the map's accuracy are used jointly to estimate business land's total area more precisely. In particular, areas where so-called errors of omission are possibly concentrated are accounted for by post-stratifying the map in an extension of recent advances in remote sensing. In specific, a post-stratum is designed to enclose areas where business activity is co-located. This then enhances the area estimation in a spatially explicit way that is informed by urban and regional economic thought and observation. In demonstrating the methodology, a map for the San Francisco Bay Area metropolitan area is obtained at a producer's accuracy of 0.89 (F1-score = 0.84) or 0.82 to 0.94 when sub-selecting reference sample pixels by confidence in class assignment. Overall, the methodological approach is able to infer the allocation of land to business (in km2 ± 95% C.I.) on a timely and accurate basis. This inter-disciplinary study may offer some fundamental ground for a potentially more refined assessment and understanding of the spatial distribution of production factors as well as the related structure and implications of land use.
Copyright: © 2023 Michiel N. Daams. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.