, Volume 4, Issue 4, pp 217-233,
Open Access This content is freely available online to anyone, anywhere at any time.

Spatial association techniques for analysing trip distribution in an urban area

Abstract

Purpose

Urban processes and transportation issues are intrinsically spatial and space dependent. For analysing the spatial pattern of urban and transportation features, the spatial statistics techniques can be applied. This paper presents a spatial association statistics for mobility data, and particularly the daily trips made by people from home to work and study places (commuter trips).

Methods

In the last few years, urban analysis has been supported by the adoption of Geographic Information Systems (GIS). 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) was elaborated.

Results

The application of spatial association statistics led to find clusters and to identify eventual hot spots of the mobility data set. The results showed that the spatial distribution of trips among the census parcels displays spatial dependence in the data set.

Conclusions

This work provided interesting results about the spatial distribution of commuter trips because it showed spatial auto-correlation of the daily trips variable.