Geographically corrected clustering applied to establish medical service areas

Abstract. In the current paper, we discover a case study of medical service areas zoning automation needed to ensure effective operation of phthisiatric service. The study was conducted for the administrative area of Saint Petersburg city (Russia). Originally, the process of phthisiatric service areas zoning bases upon outdated interpretation of medical maintenance of territories, and assumes splitting of living buildings list compiled for some administrative territory. This leads to appearing of different zoning features (service area geometry and topology, workload imbalance, etc.), which impact the quality and effectiveness of medical service. No unified and(or) open source tools for geographically corrected (in the meaning of accounting of the different spatial factors and variables) medical service areas zoning are available now. Our study is focussed onto closing of this lack basing on geospatial techniques and data management methods. To ensure the automated phthisiological medical service areas zoning we elaborated and tested a set of scripts available to be running in QGIS. Elaborated methodology was documented and considered as a possible for implementation into technological chain of Saint Petersburg phthisiatric service.