GIS enabled service site selection: Environmental analysis and beyond
- 408 Downloads
Given its importance, the problem of selecting the right site for a service entity has attracted great attention in the literature. However, due to its complexity, the quantification of the interrelationships between the service site and its nearby business types is still a challenging task. To this end, in this paper, we propose a novel joint learning scheme for service site selection. This scheme employs both the Probabilistic Latent Semantic Analysis (PLSA) on the Geographical Information System (GIS) data and the partitional clustering on the service performance data. A case study for bank branch selection is provided to demonstrate the usefulness of our method. Finally, based on the joint learning scheme, we present a conceptual framework for the complete procedure of service site selection with a particular emphasis on the GIS enabled network analysis.
KeywordsSite selection Geographical Information System (GIS) Probabilistic Latent Semantic Analysis (PLSA) Joint learning
This research was partially supported by the National Natural Science Foundation of China (NSFC) (nos. 70901002, 71031001, 70890082). Also, we are grateful to the Information Systems Frontiers anonymous referees for their constructive comments on the paper.
- Basu, S., Bilenko, M., & Mooney, R. J. (2004). A probabilistic framework for semisupervised clustering. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining.Google Scholar
- Bruns, A. (2007). Where the customers are—Can ‘financialization’ be productive? In Site selection magazine.Google Scholar
- Cheng, E. W. L., Li, H., & Yu, L. (2007). A GIS approach to shopping mall location selection building and environment. Computers and Operations Research, 42, 884–892.Google Scholar
- Cover, T., & Thomas, J. (2006) Elements of information theory (2nd ed.). New York: Wiley-Interscience.Google Scholar
- Davidson, I., & Ravi, S. (2005). Clustering with constraints feasibility issues and the k-means algorithm. In Proceedings of the 2005 SIAM international conference on data mining (pp. 138–149).Google Scholar
- Davis, D. (2003). GIS for everyone (3rd ed.). Esri Press.Google Scholar
- Dempster, N. L., Laird, N. M., & Rubin, D. B. (1977). Maximum-likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society: Series B, 39, 1–38.Google Scholar
- Drezner, Z. (1995). Facility location: A survey of applications and methods. New York: Springer.Google Scholar
- Elkan, C. (2006). Clustering documents with an exponential-family approximation of the dirichlet compound multinomial distribution. In Proceedings of the 23rd international conference on machine learning.Google Scholar
- Ester, M., & Kriegel, H.-P. (1998). Clustering for mining in large spatial databases. KI-Journal, 1, 332–338.Google Scholar
- Estivill-Castro, V., & Lee, I. (2000). Autoclust+: Automatic clustering of point-data sets in the presence of obstacles. In Proceedings of international workshop on temporal, spatial and spatio-temporal data mining (pp. 133–146).Google Scholar
- Jain, A., & Dubes, R. (1988). Algorithms for clustering data. Englewood Cliffs: Prentice Hall.Google Scholar
- Johnson, R. A., & Wichern, D. W. (1998). Applied multivariate statistical analysis (4th ed.). Englewood Cliffs: Prentice Hall.Google Scholar
- Kaufman, L., & Rousseeuw, P. (1990). Finding groups in data: An introduction to cluster analysis, ser. Wiley series in probability and statistics. New York: Wiley.Google Scholar
- Korte, G. P. E., & Koret, G. P. (1997). The GIS book: Understanding the value and implementation of geographic information systems. Albany: Delmar.Google Scholar
- MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. L. Cam, & J. Neyman (Eds.), Proceedings of the 5th Berkeley symposium on mathematical statistics and probability. Volume I: Statistics. Berkeley: University of California Press.Google Scholar
- Mapping-Analytics (2007). Evaluating branch locations: A network optimization approach. Available online at: http://www.mappinganalytics.com/site-selection/bank-marketing.html.
- Melo, T., Nickel, S., & da Gama, F. S. (2007). Facility location and supply chain management—A comprehensive review. Berichte des Fraunhofer ITWM (no. 130).Google Scholar
- Nickel, S., & Puerto, J. (2005). Location theory: A unified approach. Berlin: Springer.Google Scholar
- Saaty, T. L. (2001). Decision making for leaders: The analytic hierarchy process for decisions in a complex world. RWS Publications.Google Scholar
- Sivic, J., & Fergus, R. (2005). Two bag-of-words classifiers. Available online at: http://people.csail.mit.edu/fergus/iccv2005/bagwords.html.
- Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Reading: Addison-Wesley.Google Scholar
- Tung, A., Hou, J., & Han, J. (2001). Spatial clustering in the presence of obstacles. In Proceedings of the 17th international conference on data engineering.Google Scholar
- Wang, X., Rostoker, C., & Hamilton, H. J. (2004). Density-based spatial clustering in the presence of obstacles and facilitators. In Proceedings of the 8th European conference on principles and practice of knowledge discovery in databases (pp. 446–458).Google Scholar
- Wu, J., Xiong, H., & Chen, J. (2008). Sail: Summation-based incremental learning for information-theoretic clustering. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining.Google Scholar
- Wu, J., Xiong, H., Chen, J., & Zhou, W. (2007). A generalization of proximity functions for k-means. In Proceedings of the 2007 IEEE international conference on data mining (pp. 361–370).Google Scholar
- Zaïane, O., & Lee, C. (2002). Clustering spatial data when facing physical constraints. In Proceedings of the IEEE international conference on data mining (pp. 737–740).Google Scholar