The Impact of Data Quality in the Context of Pedestrian Movement Analysis

Positioning data sets gathered from GPS recordings of moving people or vehicles and usage logs of telecommunications networks are being increasingly used as a proxy to capture the mobility of people in a variety of places. The purpose of use of these data sets is wide-ranging and requires the development of techniques for collaborative map construction, the analysis and modelling of human behaviour, and the provision of context- aware services and applications. However, the quality of these data sets is affected by several factors depending on the technology used to collect the position and on the particular scenario where it is collected. This paper aims at assessing the quality and suitability of GPS recordings used in analysing pedestrian movement in two different recreational applications. Therefore, we look at two positioning data sets collected by two distinct groups of pedestrians, and analyse their collective movement patterns in the applications of a mobile outdoor gaming and as well as a park recreational usage. Among other findings, we show that the different reading rates of the pedestrians’ position lead to different levels of inaccuracy in the variables derived from it (e.g. velocity and bearing). This was significant in the case of bearing values that were calculated from GPS readings which, in turn, has shown a strong impact on the size of clusters of movement patterns.

[1]  Eduardo Dias,et al.  Analysing and aggregating visitor tracks in a protected area , 2007 .

[2]  N. Andrienko,et al.  Basic Concepts of Movement Data , 2008, Mobility, Data Mining and Privacy.

[3]  M. Wachowicz,et al.  The spatial knowledge representation of players movement in mobile outdoor gaming , 2008 .

[4]  Mohamed Zaït,et al.  A comparative study of clustering methods , 1997, Future Gener. Comput. Syst..

[5]  Chiara Renso,et al.  Characterising the Next Generation of Mobile Applications Through a Privacy-Aware Geographic Knowledge Discovery Process , 2008, Mobility, Data Mining and Privacy.

[6]  Dirk Helbing,et al.  Self-Organizing Pedestrian Movement , 2001 .

[7]  Vania Bogorny,et al.  Dynamic Modeling of Trajectory Patterns using Data Mining and Reverse Engineering , 2007, ER.

[8]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[9]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[10]  Vipin Kumar,et al.  Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data , 2003, SDM.

[11]  Andreas Rudolph,et al.  Techniques of Cluster Algorithms in Data Mining , 2002, Data Mining and Knowledge Discovery.

[12]  Dirk Helbing,et al.  A mathematical model for the behavior of pedestrians , 1991, cond-mat/9805202.

[13]  D. Helbing,et al.  Self-organizing pedestrian movement; Environment and Planning B , 2001 .

[14]  Dino Pedreschi,et al.  Mobility, Data Mining and Privacy: A Vision of Convergence , 2008, Mobility, Data Mining and Privacy.

[15]  Gian Luca Foresti,et al.  On-line trajectory clustering for anomalous events detection , 2006, Pattern Recognit. Lett..