Comparative analysis of different methods and obtained results for delineation of functional urban areas
European Spatial Planning Observation Network (ESPON) recognizes Potential Urban Strategic Horizons (PUSH) and Potential Polycentric Integration Areas (PIA) as territory of one or more neighboring Functional Urban Areas (FUA). Delineation of FUA territory can be done by using general ESPON methodology, based on a 45-minute car travel time from the center of respective FUAs. This approach is based on network proximity by using shortest path in road network between two nodes. Later, results are approximated on administrative or statistical territorial units, so that PUSH areas are determined. However, other methods for delineation of FUA territory can be used. This paper deals with other methods that can be used for delineation of FUA territory. Some of those methods are based on machine learning, a branch of artificial intelligence which develops algorithms that take as input empirical data, such as that from sensors or databases. Created algorithms identify complex relationships thought to be features of the underlying mechanism that generated the data, and engage these identified patterns to make predictions based on new data. Clustering and artificial neural networks are some of approaches that can be undoubtedly used for delineation of FUAs territory, based on unsupervised learning and statistical data analysis. This is statistical approach, which clusters administrative or statistical territorial units based on statistical data, and not by network proximity. Such methods involve usage of Self Organizing Maps (SOM) which implies usage of neighborhood function to preserve the topological properties, or using k-means clustering, which partition observations into clusters by dividing space into Voronoi cells. Results obtained from both approaches will be analyzed in order to define the most appropriate method for FUAs territory delineation in Serbia.
Amrhein, C. G. (1995) Searching for the elusive aggregation effect: evidence from statistical simulation, Environment and Planning A 27, pp. 105-120
Bação F., Lobo, V., Painho, M. (2004) Geo-Self- Organizing Map (Geo-SOM) for Building and Exploring Homogeneous Regions, in Egenhofer, M.; Miller, H.; Freksa, C.(Eds.); GIScience 2004 Chandrasekaran, V., M. Palaniswami. (1995): Spatio-temporal Feature Maps using Gated Neuronal Architecture, IEEE Transactions on Neural Networks 6(5), pp. 1119-1131.
ESPON (2005), ESPON 1.1.1 Potentials for polycentric development in Europe - Project report, Luxembourg, The ESPON Monitoring Committee: Luxembourg.
Esri ArcGIS Resources (2013) http://resources.arcgis.com
Fort, J.C. (2006), SOM’s mathematics, Neural Networks, 19, pp. 812–816.
Fotheringham, A. S., Wong, D. W. S. (1991) The modifiable areal unit problem in multivariate statistical analysis, Environment and Planning A 23, pp. 1025-1044.
Haggett, P., Cliff, A. D., Frey, A. E. (1977) Locational Analysis in Human Geography, Second Edition, London, Arnold.
Harnad, S. (2008) The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence, in Epstein, Robert; Peters, Grace (eds.), The Turing Test Sourcebook: Philosophical and Methodological Issues in the Quest for the Thinking Computer, Kluwer.
Kangas, J. (1992) Temporal Knowledge in Locations of Activations in a Self-Organizing Map, Proceedings of the International Conference on Artificial Neural Networks, Brighton, England, pp. 117-120
Korcelli, P. (2008) Review of Typologies of European Rural-Urban Regions, PLURAL
Kohonen, T. (1982) Clustering, Taxonomy, and Topological Maps of Patterns, Proceedings of the 6th International Conference on Pattern Recognition, Munich, pp. 114-128
Kohonen, T. (1991) The Hypermap Architecture, in: T. Kohonen, K. Mäkisara, O. Simula, J. Kangas (eds.) Artificial Neural Networks, 1: Helsinki, Elsevier pp. 1357-1360
Kohonen, T. (2001), Self-Organizing Maps, Springer-Verlag New York
Kohonen, T., Honkela, T.(2011), Kohonen network, Scholarpedia
Liu, B. (2007) Data Mining and Text Mining, lecture on Department for Computer Sciences at University of Illinois, (UIC), USA.
Lobo, V., Bação, F., Painho, M. (2004) Regionalization and homogeneous region building using the spatial kangas map, in F. Toppen and P. Prastacos (eds.) 7th AGILE Conference on Geographic Information Science, Heraklion, Greece, pp. 301-313.
MacQueen, J. B. (1967) Some Methods for classification and Analysis of Multivariate Observations, Proceedings vol 5., Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1, pp. 281-297.
Matteucci, M. (2012) A Tutorial on Clustering Algorithms, http://chrome.ws.dei.polimi.it/index.php/Matt%27s_Home_Page
Moore, A. (2004) K-means and Hierarchical Clustering Tutorial, School of Computer Science, Carnegie Mellon University, http://www.autonlab.org/tutorials/kmeans.html
Openshaw, S. (1984) The Modifiable Areal Unit problem, CATMOG, Geo-abstracts, Norwich, UK.
Samuel, A. (1959) Some Studies in Machine Learning Using the Game of Checkers, IBM Journal 3 (3), pp. 210–229
Tobler W. (1970) A computer movie simulating urban growth in the Detroit region, Economic Geography, 46(2), pp. 234-240
Tošić, D. (2012) Principi regionalizacije, Geografski fakultet Univerziteta u Beogradu: Beograd.
Turing, A.M. (1950) Computing machinery and intelligence, Mind, 59, pp. 433-460
Vesanto, J., Alhoniemi, E. (2000) Clustering of the Self-Organizing Map, in IEEE Transactions on Neural Networks, Volume 11, Number 3, pp. 586-600. © 2000 IEEE
Wikipedia (2013), article: Dijsktra’s algorithm, http://en.wikipedia.org/wiki/Dijkstra%27s_algorithm
Wikipedia (2013), article: Machine learning, http://en.wikipedia.org/wiki/Machine_learning
Wikipedia (2013), article: Polycentrism, http://en.wikipedia.org/wiki/Polycentrism