ABSTRACT
Chapter 2 discusses two common tasks in spatial analysis: measuring distance or time and modeling distance decay behaviors. The distance decay rule is also referred to as the “first law of geography.” Calibrating Euclidean and geodesic distances is straightforward in GIS. Computing network travel time via driving or public transit requires the corresponding network data and takes more effort to implement. Most GIS data of transit systems (e.g., in GTFS format) are publicly accessible and can be converted and integrated into ArcGIS with reasonable effort. Calibrating a drive or transit time matrix can also be achieved by using online routing platforms, such as the Google Maps API. While the use of public transit is limited in geographic coverage and ridership, especially in the USA, it is an important transportation mode to be considered in many studies (e.g., disparity in spatial accessibility of health care, mobility, risk in exposure to infectious diseases, etc.). Two approaches, namely, the spatial interaction model and the complementary cumulative distribution curve, are used to derive the best-fitting distance decay function. A case study demonstrates how various distance and travel time measures and the distance decay function fittings are implemented.
