Spatial association techniques for analysing trip distribution in an urban area

Urban processes and transportation issues are intrinsically spatial and space dependent. For analyzing the spatial pattern of urban and transportation features, spatial statistics techniques can be applied. In the last few years, the adoption of Geographic Information Systems (GIS) has supported urban analysis. This paper presents spatial association statistic for mobility data, and particularly the daily trips made by people from home to work and study places (commuter trips). Using GIS, statistics of global autocorrelation (Getis-Ord General G and Global Moran’s Index I) and statistics of local autocorrelation (Gi* and Local Moran’s I) were elaborated to find clusters and to identify eventual hot spots of the data set.