An Approach for spatial and temporal data analysis: Application for mobility modeling of workers in Luxembourg and its bordering areas

In this paper, we propose two general analytic methods for building cartographical representations of large amounts of spatial data collected in the form of Origin-Destination (OD) matrices. The use of classical techniques, such as the Linear Directional Mean (LDM), for the mapping of OD data, may lead to cartographical representations which are difficult to interpret visually, especially when the size of the OD matrix is large. The two methods that we propose, which are extensions of Toblers LDM method, overcome this limitation and allow the discovery of interesting mobility patterns: a first extension, the Weighted Linear Directional Mean (WLDM), computes the mean direction of movement by weighting the volumes of mobility flows. The second extension, the Dempster-Shafer Weighted Linear Directional Mean (DS-WLDM), takes into account ignorance (which corresponds to a missing origin and/or destination for a particular movement), as well as incertitude, which may both occur in real OD data. The paper also presents an example in which the two proposed methods are applied to administrative data, in order to evaluate the spatial and temporal aspects of daily and residential mobility of workers between Luxembourg and its bordering areas. The methods we propose are generic and allow the use of multiple spatial scales (e.g. locality, district, municipality etc.), with potential fields of application including the mapping of social and demographic information (e.g. mobility of people, goods and information) as well as the cartographic representation of traffic flows.