Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alvares, L. O., Bogorny, V., Macedo, J. and Spaccapietra, S. (2007) Dynamic Modeling of Trajectory Patterns using Data Mining and Reverse Engineering, Proceedings of the 26 International Conference on Conceptual Modeling (ER'2007), Auckland, pp. 149-154.
Andrienko, N., Andrienko, G., Pelekis, N. and Spaccapietra, S. (2008) Basic Concepts on Movement Data, in Giannotti, F. and Pedreschi, D. (Eds.): Mobility, Data Mining and Privacy, Springer-Verlag, pp. 15-38, 2008.
Dias, E., Edwards, A. J. and Purves, R. S. (2008) Analysing and aggregating visitor tracks in a protected area, in A. Stein, J. Shi and W. Bijker (Eds): Quality Aspects in Spatial Data Mining, CRC Press, Taylor & Francis Group.
Ertoz, L., Steinbach, M. and Kumar, V. (2002) Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data, Proceedings of the Second SIAM International Conference on Data Mining, San Francisco.
Giannotti, F., Nanni, M., Pedreschi, D. and Pinelli, F. (2007) Trajectory Pattern Mining, Proceedings of the Knowledge Discovery in Databases (KDD'07) Conference, San Jose, pp. 330-339.
Giannotti, F. and Pedreschi, D. (2008) Mobility, Data Mining and Privacy: A Vision of Convergence, in Giannotti, F. and Pedreschi, D. (Eds.): Mobility, Data Mining and Privacy, Springer-Verlag, pp. 1-11.
Grabmeier, J. (2002) Techniques of Cluster Algorithms in Data Mining, Data Mining and Knowledge Discovery, 6(4), pp. 303-360.
Helbing, D. (1991) A Mathematical Model for the Behaviour of Pedestrians, Behavioral Science, 36, pp. 298-310.
Helbing, D., Molnár, P., Farkas, I. and Bolay, K. (2001) Self-Organising Pedestrian Movement, Environment and Planning B: Planning and Design, 28, pp. 361-383.
Lee, J.-G., Han, J. and Whang, K.-Y. (2007) Trajectory Clustering: A Partitionand-Group Framework, Proceedings of SIGMOD Conference (SIGMOD'07), Beijing, pp. 593-604.
Piciarelli, C. and Foresti, G. L. (2006) On-line trajectory clustering for anomalous events detection, Pattern Recognition Letters, 27, pp. 1835-1842.
Wachowicz, M., Ligtenberg, A., Renso, C. and Gürses, S. (2008a) Characterising the Next Generation of Mobile Applications Through a Privacy-Aware Geographic Knowledge Discovery Process, in Giannotti, F. and Pedreschi, D. (Eds.): Mobility, Data Mining and Privacy, Springer-Verlag, pp. 39-72.
Wachowicz, M., Orellana, D., Renso, C., Moraga, E. and Parada, J. (2008b) The spatial knowledge representation of players movement in mobile outdoor gaming, Proceedings of the 4th International Conference on Monitoring and Management of Visitors Flows in Recreational and Protected Areas, Italy, pp. 456-460.
Zaït, M. and Messatfa, H. (1997) A comparative study of clustering methods, Future Generation Computer Systems, 13(2), pp. 149-159.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Moreira, A., Santos, M.Y., Wachowicz, M., Orellana, D. (2010). The Impact of Data Quality in the Context of Pedestrian Movement Analysis. In: Painho, M., Santos, M., Pundt, H. (eds) Geospatial Thinking. Lecture Notes in Geoinformation and Cartography, vol 0. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12326-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-12326-9_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12325-2
Online ISBN: 978-3-642-12326-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)