Dr. rer. nat.
Now showing 1 - 4 of 4
PublicationVisit potential: A common vocabulary for the analysis of entity-location interactions in mobility applications( 2010)
; ; ;A growing number of companies and public institutions use mobility data in their day-to-day business. One type of usage is the analysis of spatio-temporal interactions between mobile entities and geographic locations. In practice the employed measures depend on application demands and use context-specific terminology. Thus, a patchwork of measures has evolved which is not suitable for methodological research and interdisciplinary ex-change of ideas. The measures lack a systematic formalization and a uni-form terminology. In this paper we therefore systematically define meas-ures for entity-location interactions which we name visit potential. We provide a common vocabulary that can be applied for an entire class of mobility applications. We present two real-world scenarios which apply entity-location interaction measures and demonstrate how the employed measures can be precisely defined in terms of visit potential.
PublicationSample bias due to missing data in mobility surveys( 2010)
; ; ;A growing number of companies use mobility information in their day-to-day business. One requirement thereby is that inference about population-wide mobility patterns can be made. Therefore, it is not only important to find mobility patterns in a given data sample but also to assert their validity for the total population. This aspect of analysis has been largely neglected in mobility data mining research, which limits the applicability of the whole algorithmic field. In this paper we will analyze one aspect of sample bias due to incomplete mobility data. We will provide a systematic approach to detect dependencies between mobility behavior, socio-demography and missing data. Further, we apply the approach to a large GPS mobility survey in Switzerland and show that our concerns are justified and require attention in future research. We hope that our paper will raise the awareness that representativity of mobile behavior cannot be taken for granted in mobility surveys du e to missing data and is a research direction of utmost importance.
PublicationModelling missing values for audience measurement in outdoor advertising using GPS data( 2009)
; ; ; ;Pasquier, M. ;Hofmann, UrsMende, F.GPS technology has made it possible to evaluate the performance of outdoor advertising campaigns in an objective manner. Given the GPS trajectories of a sample of test persons over several days, their passages with arbitrary poster campaigns can be calculated. However, inference is complicated by the early dropout of persons. Other than in most demonstrations of spatial data mining algorithms where the structure of the data sample is usually disregarded, poster performance measures such as reach and gross impressions evolve continuously over time and require non-intermittent observations. In this paper, we investigate the applicability of survival analysis to compensate for missing measurement days. We formalize the task of modeling the visit potential of geographic locations based on trajectory data as our variable of interest results from dispersed events in space-time. We perform experiments on the cities of Zurich and Bern simulating different dropout mechanisms and dropout rates and show the adequacy of the applied method. Our modeling technique is at present part of a business solution for the Swiss outdoor advertising branch and serves as pricing basis for the majority of Swiss poster locations.
PublicationPedestrian flow prediction in extensive road networks using biased observational data( 2008)
; ; ; ;In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniques are not able to appropriately deal with its challenges and constraints, in particular with sample selection bias. For this purpose, we introduce s-knn-apriori, an efficient nearest neighbor based spatial mining algorithm that allows prior knowledge and deductive models to be included in a straightforward and easy way.