Methodology of Decision Support through GIS and Artificial Intelligence: Implementation for Demographic Characterization of Andalusia based on Dwelling
DOI:
https://doi.org/10.18537/est.v006.n011.a03Abstract
Geographic Information Systems (GIS) have been widely used for the storage and management of territorial information, being especially useful for the analysis and verification of previously formulated hypotheses and coexisting with relevant spatial components. There are heuristic methodologies that, in contexts such as the present one, of data over-abundance, allow showing their coherence, not necessarily requiring hypotheses or previous formulations to generate knowledge. The combined use of (i) Artificial Intelligence techniques such as the Artificial Neural Network (ANN), namely the Self-Organized Maps (SOM), is proposed. They are very effective and robust by classifying and characterizing profiles in the data. They interact with (ii) machine learning techniques such as decision trees, which are singularly functional in the creation of predictive and interpretable models, with the intention of formulating explanatory hypotheses of the previous profiles, working with other different variables. The research proposes the combination of GIS, SOM and decision trees for the construction of explanatory models of the demographic and social profiles of Andalusia, based on low cost data on the residential dimension. The feasibility of such predictive models and their great value for understanding and as decision support on such territories are evaluated satisfactorily.
Keywords: GIS, decision tree, DSS, self-organizing map, SOM.Downloads
References
Abarca-Alvarez, F., & Fernandez-Avidad, A. (2010). Generation of downtown planning-ordinances using self organizing maps. En 10th International Conference on Design and Decision Support Systems, DDSS 2010.
Abarca-Alvarez, F., & Osuna Pérez, F. (2013). Cartografías semánticas mediante redes neuronales: los mapas auto-organizados (SOM) como representación de patrones y campos. EGA. Revista de expresión gráfica arquitectónica, 18(22). http://doi.org/10.4995/ega.2013.1692
Astudillo, C. A., & John Oommen, B. (2011). Imposing tree-based topologies onto self organizing maps. Information Sciences, 181(18), 3798-3815. http://doi.org/10.1016/j.ins.2011.04.038
Astudillo, C. A., & Oommen, B. J. (2013). On achieving semi-supervised pattern recognition by utilizing tree-based SOMs. Pattern Recognition, 46(1), 293-304. http://doi.org/10.1016/j.patcog.2012.07.006
Ayedi, B. (1998). The design of spatial decision support systems in urban and regional planning. En Timmermans, H. Decesion Support Systems in Urban Planning. Routledge.
Bação, F., Lobo, V., & Painho, M. (1995). The Self-Organizing Map and it’s variants as tools for geodemographical data analysis: the case of Lisbon’s Metropolitan Area. Computers & Geosciences, 31(Goss), 155-163. http://doi.org/10.1016/j.cageo.2004.06.013
Bação, F., Lobo, V., & Painho, M. (2005). Self-organizing maps as substitutes for k-means clustering. Computational Science–ICCS 2005, 3516, 476-483. http://doi.org/10.1007/11428862_65
Basara, H. G., & Yuan, M. (2008). Community health assessment using self-organizing maps and geographic information systems. International journal of health geographics, 7, 67. http://doi.org/10.1186/1476-072X-7-67
Behnisch, M., & Ultsch, A. (2009). Urban data-mining: spatiotemporal exploration of multidimensional data. Building Research & Information, 37(5-6), 520-532. http://doi.org/10.1080/09613210903189343
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Chapman & Hall.
Buzai, G. D. (2007). Sistemas de Información Geográfica: Aspectos conceptuales desde la teoría de la Geografía. XI Conferencia Iberoamericana de Sistemas de Información Geográfica (XI CONFIBSIG). En Sociedad Iberoamericana de Sistemas de Información Geográfica, Luján, Argentina.
Buzai, G.D. (2015). Geografía global y Neogeografía. La dimensión espacial en la ciencia y la sociedad. Polígonos. Revista de Geografía, 27, 49-60.
Cao,L. S.; Y. Philip, C. Zhang, & H. Zhang (eds.) (2009). Data mining for business applications. New York.
Coe, R., & Merino, C. (2003). Magnitud del efecto: Una guía para investigadores y usuarios. Revista de Psicología, 21(1), 147-177.
Cohen, J. (1998). Statistical Power Analysis for the Behavioral Sciences (Vol. 2nd Editio). Lawrence Erlbaum Associates, Publishers. http://doi.org/10.1234/12345678
Delmelle, E. C., Thill, J. C., Furuseth, O., & Ludden, T. (2012). Trajectories of Multidimensional Neighbourhood Quality of Life Change. Urban Studies, 50(5), 923-941. http://doi.org/10.1177/0042098012458003
Demartines, P., & Blayo, F. (1992). Kohonen Self-Organizing Maps : Is the Normalization Necessary? Complex Systems, 6(2), 105-123.
Diappi, L., Bolchim, P., & Buscema, M. (2004). Improved Understanding of Urban Sprawl Using Neural Networks. En J. P. Van-Leeuwen & H. J. P. Timmermans (Eds.), Recent Advances in Design and Decision Support Systems in Architecture and Urban Planning (pp. 33-49). Politecn Milan, Dept Architecture and Planning, I-20133 Milan, Italy.: Springer.
Faggiano, L., de Zwart, D., García-Berthou, E., Lek, S., & Gevrey, M. (2010). Patterning ecological risk of pesticide contamination at the river basin scale. Science of the Total Environment, 408(11), 2319-2326. http://doi.org/10.1016/j.scitotenv.2010.02.002
Feng, S., & Xu, L. D. (1999). Decision support for fuzzy comprehensive evaluation of urban development. Fuzzy Sets and Systems, 105(1), 1-12. http://doi.org/10.1016/S0165-0114(97)00229-7
Goodchild, M. F. (2010). Twenty years of progress: GISscience in 2010. Journal of Spatial Information Science, 1, 3-20. http://doi.org/10.5311/JOSIS.2010.1.2
Gomes, H., Ribeiro, A. B., & Lobo, V. (2007). Location model for CCA-treated wood waste remediation units using GIS and clustering methods. Environmental Modelling and Software, 22(12), 1788-1795. http://doi.org/10.1016/j.envsoft.2007.03.004
Gómez-Carracedo, M. P., Andrade, J. M., Carrera, G. V. S. M., Aires-de-Sousa, J., Carlosena, A., & Prada, D. (2010). Combining Kohonen neural networks and variable selection by classification trees to cluster road soil samples. Chemometrics and Intelligent Laboratory Systems, 102(1), 20-34. http://doi.org/10.1016/j.chemolab.2010.03.002
Guo, D., Chen, J., MacEachren, A. M., & Liao, K. (2006). A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP). IEEE Transactions on Visualization and Computer Graphics, 12(6), 1461-1474. http://doi.org/10.1109/TVCG.2006.84
Hamaina, R., Leduc, T., & Moreau, G. (2012). Towards Urban Fabrics Characterization based on Buildings Footprints. En J. Gensel (Ed.), Bridging the Geographic Information Sciences (pp. 231-248). http://doi.org/10.1007/978-3-642-29063-3_13
Hatzichristos, T. (2004). Delineation of demographic regions with GIS and computational intelligence. Environment and Planning B: Planning and Design, 31(1), 39-49. http://doi.org/10.1068/b1296
Hernández Orallo, J., Ramírez Quintana, M. J., & Ferri Ramírez, C. (2004). Introducción a la minería de datos. Pearson Prentice Hall.
Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15(July), 651-674. http://doi.org/10.1198/106186006X133933
Jarupathirun, S., & Zahedi, F. (2005). GIS as Spatial Decision Support Systems. En J. B. Pick (Ed.), Geographic information systems in business. Idea Group Pub.
Juanes Notario, P. (2014). La Geografía y la Estadística. Dos necesidades para entender Big Data. http://hdl.handle.net/10366/125197
Kaski, S., & Kohonen, T. (1996). Exploratory Data Analysis By The Self-Organizing Map: Structures Of Welfare And Poverty In The World (1996). Neural Networks in Financial Engineering. Proceedings of the Third International Conference on Neural Networks in the Capital Markets, 498-507. http://doi.org/10.1.1.53.3954
Kass, G. V. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, 29(2), 119-127. http://doi.org/10.2307/2986296
Kauko, T. (2005). Using the self-organising map to identify regularities across country-specific housing-market contexts. Environment and Planning B: Planning and Design, 32(1), 89-110. http://doi.org/10.1068/b3186
Keen, P. G. W. (1987). Decision support systems: The next decade. Decision Support Systems, 3(3), 253-265. http://doi.org/10.1016/0167-9236(87)90180-1
Kinaci, A. C., & Yucebas, S. C. (2015). Cost Reduction in Thyroid Diagnosis: A Hybrid Model with SOM and C4.5 Decision Trees. En International Conference on Neural Information Processing (pp. 440-448). http://doi.org/10.1007/11893257
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59-69. http://doi.org/10.1007/BF00337288
Kohonen, T. (1990). The Self-Organizing Map. En Proceeding of the IEEE (Vol. 78, pp. 1464-1480). http://doi.org/10.1109/5.58325
Kohonen, T. (1995). Self-Organizing Maps. Springer. http://doi.org/10.1007/978-3-642-88163-3
Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1-3), 1-6. http://doi.org/10.1016/S0925-2312(98)00030-7
Lin, W. (2008). Earthquake‐induced landslide hazard monitoring and assessment using SOM and PROMETHEE techniques: A case study at the Chiufenershan area in Central Taiwan. International Journal of Geographical Information Science, 22(9), 995-1012. http://doi.org/10.1080/13658810801914458
Luque Martínez, T. (2000). Técnicas de análisis de datos en investigación de mercados. (T. Luque Martínez, Ed.). Madrid: Pirámide.
Power, D. J., Sharda, R., & Burstein, F. (2015). Decision Support Systems. En C. L. Cooper (Ed.), Wiley Encyclopedia of Management (pp. 1-4). Chichester, UK: John Wiley & Sons, Ltd.
Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, 1(1), 81-106. http://doi.org/10.1023/A:1022643204877
Ritter, H., & Kohonen, T. (1989). Self-organizing semantic maps. Biological Cybernetics, 61(4), 241-254. http://doi.org/10.1007/BF00203171
Salah, M., Trinder, J., & Shaker, A. (2009). Evaluation of the self‐organizing map classifier for building detection from lidar data and multispectral aerial images. Journal of Spatial Science, 54(2), 15-34. http://doi.org/10.1080/14498596.2009.9635176
Shanmuganathan, S., & Li, Y. (2016). An AI based approach to multiple census data analysis for feature selection. Journal of Intelligent & Fuzzy Systems, 31(2), 859-872. http://doi.org/10.3233/JIFS-169017
Silver, M. S. (2008). On the Design Features of Decision Support Systems : The Role of System Restrictiveness and Decisional Guidance. En F. Burstein & C. W. Holsapple (Eds.), Handbook on Decision Support Systems 2: Variations (pp. 261-291). Springer-Verlag Berlin Heidelberg.
Simmuteit, S., Schleif, F. M., Villmann, T., & Kostrzewa, M. (2009). Hierarchical PCA using tree-som for the identification of bacteria. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5629 LNCS, 272-280. http://doi.org/10.1007/978-3-642-02397-2_31
Skupin, A., & Agarwal, P. (2008). Introduction: What is a Self-Organizing Map? En P. Agarwal & A. Skupin (Eds.), Self-organising maps : applications in geographic information science (pp. 1-20). Wiley.
Skupin, A., & Esperbé, A. (2011). An alternative map of the United States based on an n-dimensional model of geographic space. Journal of Visual Languages and Computing, 22(4), 290-304. http://doi.org/10.1016/j.jvlc.2011.03.004
Skupin, A., & Hagelman, R. (2003). Attribute space visualization of demographic change. Proceedings of the eleventh ACM international symposium on Advances in geographic information systems - GIS 2003, 56-62. http://doi.org/10.1145/956676.956684
Skupin, A., & Hagelman, R. (2005). Visualizing Demographic Trajectories with Self Organizing Maps. GeoInformatica, 9(2), 159-179.
Spielman, S. E., & Thill, J.-C. (2008). Social area analysis, data mining, and GIS. Computers, Environment and Urban Systems, 32(2),110-122. http://doi.org/10.1016/j.compenvurbsys.2007.11.004
Strasser, H., & Weber, C. (1999). On the Asymptotic Theory of Permutation Statistics. Mathematical Methods of Statistics, 8, 220-250. http://doi.org/10.1007/s10551-011-0925-7
Streich, B. (2005). Stadtplanung in der Wissensgesellschaft Ein Handbuch. VS Verlag für Sozialwissenschaften.
Strobl, C., Malley, J., & Tutz, G. (2010). An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests. Psychol Methods, 14(4), 323-348. http://doi.org/10.1037/a0016973.An
Takatsuka, M. (2001). An application of the Self-Organizing Map and interactive 3-D visualization to geospatial data. Proceedings of the 6th International Conference on GeoComputation, 24-26.
Tayebi, M. H., Hashemi Tangestani, M., & Vincent, R. K. (2014). Alteration mineral mapping with ASTER data by integration of coded spectral ratio imaging and SOM neural network model. Turkish Journal of Earth Sciences, 23(6), 627-644. http://doi.org/10.3906/yer-1401-9
Tsai, C.-F., Lin, Y.-C., & Wang, Y.-T. (2009). Discovering Stock Trading Preferences By Self-Organizing Maps and Decision Trees. International Journal on Artificial Intelligence Tools, 18(4), 603-611. http://doi.org/10.1142/S0218213009000299
Villmann, T., Merényi, E., & Hammer, B. (2003). Neural maps in remote sensing image analysis. Neural Networks, 16(3-4), 389-403. http://doi.org/10.1016/S0893-6080(03)00021-2
Voumvoulakis, E. M., Gavoyiannis, A. E., & Hatziargyriou, N. D. (2006). Dynamic Security Assessment and Load Shedding Schemes Using Self Organized Maps and Decision Trees. En Hellenic Conference on Artificial Intelligence (pp. 1-7).
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: context, process, and purpose. The American Statistician, 1305(April), 00-00. http://doi.org/10.1080/00031305.2016.1154108
Weiss, S. M., & Indurkhya, N. (1998). Predictive Data Mining: A Practical Guide.
Witten, I. H., Frank, E., & Hall, M. a. (2011). Data Mining Practical Machine Learning Tools and Techniques. Data Mining (Third Edit, Vol. 277). Elsevier. http://doi.org/10.1002/1521-3773(20010316)40:6<9823::AID-ANIE9823>3.3.CO;2-C
Wu, P. K., & Hsiao, T. C. (2015). Factor Knowledge Mining Using the Techniques of AI Neural Networks and Self-Organizing Map. International Journal of Distributed Sensor Networks, 2015. http://doi.org/10.1155/2015/412418
Yan, J., & Thill, J.-C. (2009). Visual data mining in spatial interaction analysis with self-organizing maps. Environment and Planning B: Planning and Design, 36(3), 466-486. http://doi.org/10.1068/b34019
Yang, C., Guo, R., Wu, Z., Zhou, K., & Yue, Q. (2014). Spatial extraction model for soil environmental quality of anomalous areas in a geographic scale. Environmental Science and Pollution Research, 21(4), 2697-2705. http://doi.org/10.1007/s11356-013-2200-1
Yang, H., Hu, Y., qi Deng, F., Tian, X., & Li, B. (2004). Fuzzy SOFM-GIS space cluster model and its application analysis. 2004-8th International Conference on Control, Automation, Robotics and Vision-Icarcv 1, (December), 6-9.
Yang, Z. R., & Chou, K.-C. (2003). Mining biological data using self-organizing map. J. Chem. Inf. Comput. Sci., 43(6), 1748-1753.
Yao, Z., Holmbom, A. H., Eklund, T., & Back, B. (2010). Combining unsupervised and supervised data mining techniques for conducting customer portfolio analysis. En Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6171 LNAI, pp. 292-307). http://doi.org/10.1007/978-3-642-14400-4_23
Yeh, A. G.-O. (2005). Urban planning and GIS. En: Longley, P.A.; Goodchild, M.F.; Maguire, D.J & Rhind, D.W. Geographical Information Systems: Principles, Techniques, Management and Applications. En 877-888. Recuperado a partir de http://www.geos.ed.ac.uk/~gisteac/gis_book_abridged/files/ch62.pdf
Zhang, J., Shi, H., & Zhang, Y. (2009). Self-organizing map methodology and google maps services for geographical epidemiology mapping. DICTA 2009 - Digital Image Computing: Techniques and Applications, 229-235. http://doi.org/10.1109/DICTA.2009.46
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Estoa. Journal of the Architecture and Urbanism Faculty of the Cuenca University
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The Journal declines any responsibility for possible conflicts derived from the authorship of the works that are published in it.
The University of Cuenca in Ecuador conserves the patrimonial rights (copyright) of the published works and will favor the reuse of the same ones, these can be: copy, use, diffuse, transmit and expose publicly.
Unless otherwise indicated, all contents of the electronic edition are distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.