The potential use of Python in network-distance-based spatial accessibility analysis

Abstract

This paper demonstrates from a case study that the Python language can practically be used for network-distance-based spatial accessibility analysis. In the case study, we analyze the spatial accessibility to convenience stores in Hirosaki. First, we show that road network data provided by OpenStreetMap can easily be obtained with the OSMnx library. Second, we show the Python code snippet that calculates a road-network-based distance matrix. Finally, we calculate two types of accessibility measures: nearest neighbor distance and cumulative opportunities. We show that the calculation can be done very effectively by list comprehension in Python.