A comparative analysis of manhattan, euclidean and network distances. Why are network distances more useful to urban professionals?

Accessibility to urban facilities and public services is nowadays one of the key factors that impacts the quality of urban live. That being said, accessibility is a complex concept, which although in common use, has a range of meanings, and measuring it raises numerous methodological problems. For instance, accessibility is closely related to the concept of distance, but as a society evolves, other factors such as travel time, economic cost, different transport options, interest or attraction of the destination come into play, as well as the personal preference of each individual. Several methods for measuring accessibility are recognized among researchers and urban planning professionals. They can be classified into several categories, such as spatial separation measures, cumulative-opportunity measures, gravity measures, utility measures and time-space measures. In this study, the physical distance between urban residents and urban facilities and public services is measured. This can be done by measuring distance in three ways: Euclidean, Manhattan, and Network. The distance from the cadastral built plots (origin) to different urban facilities and public services (destination) is calculated. Destinations include educational centres, sports facilities, transportation system stops, libraries, health centres, hospitals, chemist´s shop and green areas. The three results are compared to identify and quantify correlations. Depending on the method used to calculate the distance between a give origin and destination, there may be a population underestimation or overestimation, which needs to be assessed. The data were obtained from a range of sources: National Institute of Statistics to obtain the number of residents; Cadastral online registry to access data on the location of buildings; and the National Geographic Institute and the Valencian Cartographic Institute to locate urban facilities and public services. The main results show different correlations between the three types of distances. Euclidean distances overestimate the population compared to Network and Manhattan distances. Network and Manhattan distances offer similar results but the Network distance is much accurate and represents much better the real distance. © SGEM 2018