Spatial and Distance Statistics of the Trucking and Warehousing Industries using GIS Tools

The changing pattern of urban logistics with the growth in supply chain management and the increased adoption of e-commerce has implications for the location of warehousing and trucking facilities. Intuitively, as the supply chain becomes more sophisticated then more localized depots and warehousing is to be anticipated. The nature of the resultant pattern is likely to vary according to specific function and the nature of the urban area under review. The aim of this paper is to look at the potential of Geographical Information Systems (GIS) in describing the nature of potential clustering effects. Statistical analyses of trucking terminals and warehousing located in Washington Consolidated Metropolitan Standard Area and Detroit Consolidated Metropolitan Standard Area is carried out using GIS and spatial statistical tools. The results include both global and local level statistics for these Metro areas. Global spatial statistics are deployed to describe the overall spatial distribution of the trucking and warehousing industry in the regions while the local spatial statistics provide distance analyses in terms of clustering and nearest neighbor analyses. The global spatial statistics (also known as first-order statistics) describe the spatial orientation of logistics related industrial location patterns while the local statistics (also called second order or distance analyses) describes results of nearest neighbor analyses associated with the metro areas'' road networks. Hot-spot analyses is used to describe the local clustering of logistics industries either along road networks or across a metro region.