Local Indicators of Network-Constrained Clusters in Spatial Patterns Represented by a Link Attribute

Clustering in a spatially distributed phenomenon is an important focus of spatial analysis because it not only suggests characteristics of the pattern itself but also of its background processes. Traditional methods of spatial cluster detection mostly rely on the planar space assumption, yet a variety of spatial phenomena do not support its logic. This article expounds on an exploratory spatial data analysis methodology named local indicators of network-constrained clusters (LINCS) introduced elsewhere for detecting local-scale clustering in a spatial phenomenon that is constrained by a network space. In particular, this article focuses on two types of LINCS methods that are network extensions of traditional methods for analyzing spatial associations in zone-based planar-space data, namely, the local Moran I statistic and the local Getis and Ord G statistic. They are designed for phenomena that are represented by attribute values of individual network links. Examples of such phenomena include traffic volume, traffic speed, and the number of vehicle crashes aggregated at the link level. When the phenomenon of interest can be seen as a subset of a more generic spatial phenomenon, for example, vehicle crashes in relation to the entire traffic observed in a study region, the LINCS methods are capable of taking into account the distribution of such a base phenomenon so that one can avoid the detection of spurious clusters merely reflecting the base distribution. The article illustrates the application of the two LINCS methods using data on highway vehicle crashes in the Buffalo, New York, area in 1997.