Abstract
Spatial data mining, space-temporal modelling and visual exploratory data analysis are tools that are useful not only for the analysis of multi-characteristics spatial data, but can also be used for the development of Spatial Decision Support Systems. Such system enables the optimisation of decision-making based on a thorough Spatial Multicriteria Decision Analysis. The authors of the present study have developed a set of multicriteria analyses with use of spatial data mining (SDM) techniques for the analysis of the spatial distribution of the allocation and spending of EU funds in Poland. The ten-year period of Poland’s membership in the EU enables not only the analysis of spatial differentiation of EU subsidies in different regions of the country, but also the dynamics of changes in this differentiation in time.
The proposed analytical system based on information technologies combines the possibilities offered by GIS packages and advanced statistical software, thus enabling to conduct highly complex analyses. One of the methods to carry out such analysis is the application of so-called data mining and data enrichment to detect patterns, rules and structures “hidden” in the database.
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
Gotlib, D., Iwaniak, A., Olszewski, R.: GIS. Obszary zastosowań [GIS. Application fields]. Wydawnictwo Naukowe PWN, Warszawa (2007)
Olszewski, R.: Kartograficzne modelowanie rzeźby terenu metodami inteligencji obliczeniowej [Cartographic modelling of terrain relief using computational intelligence methods], Prace Naukowe - Geodezja, z. 46, Oficyna Wydawnicza Politechniki Warszawskiej (2009)
Fiedukowicz, A., Gąsiorowski, J., Kowalski, P., Olszewski, R., Pillich-Kolipińska, A.: The statistical geoportal and the cartographic “added value”– creation of the spatial knowledge infrastructure. Geodesy & Cartography 61(1), 47–70 (2012)
Miller, H.J., Han, J.: Geographic Data Mining and Knowledge Discovery. Taylor & Francis, London (2001)
Cabena, P., Hadjinian, P., Dtadler, R., Verhees, J., Zanasi, A.: Discovering Data Mining: From Concept to Implementation. Prentice Hall, Upper Saddle River; Journal of Laws of 1995, No. 88, item 43, New York (1998)
Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)
Gatnar, E.: Symboliczne metody klasyfikacji danych [Symbolic methods of data classification]. Wydawnictwo Naukowe PWN, Warsaw (1998)
Sokolowski, A.: Metody stosowane w data mining [Methods used in data mining]. In: Data mining – metody i przykłady [Data mining – methods and examples]. StatSoft, Cracow (2002)
Franzese, R.J., Hays, J.C.: Spatial econometric models of cross-sectional interdependence in political science panel and time-series-cross-section data. Political Analysis 15(2), 140–164 (2007)
Getis, A., Mur, J., Zoller, H. (eds.): Spatial Econometrics and Spatial Statistics. Palgrave Macmillan, New York (2004)
Kuijpers, B., Paredaens, J., Van den Bussche, J.: Lossless representation of topological spatial data. In: Egenhofer, M., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 1–13. Springer, Heidelberg (1995)
Bao, S., Anselin, L., Martin, D., Stralberg, D.: Seamless integration of spatial statistics and GIS: The S-PLUS for ArcView and the S+Grassland Links. Journal of Geographical Systems 2(3), 287–306 (2000)
Greenacre, M.: Correspondence Analysis in Practice, 2nd edn. Chapman & Hall/CRC, London (2007)
Everitt, B.S.: The Cambridge Dictionary of Statistics. Cambridge University Press, Cambridge (1998)
Shiklomanov, N.I., Nelson, F.E.: Active-Layer Mapping at Regional Scales: A 13-Year Spatial Time Series for the Kuparuk Region. North-Central Alaska. Permafrost Periglac. Process. 13, 219–230 (2002)
Sadahiro, Y., Kobayashi, T.: Exploratory analysis of time series data: Detection of partial similarities, clustering, and visualization. Computers, Environment and Urban Systems 45, 24–33 (2014)
Holmes, M.J., Otero, J., Panagiotidis, T.: Modelling the behavior of unemployment rates in the US over time and across space. Physica A: Statistical Mechanics and its Applications 392(22), 5711–5722 (2013)
Bronars, S.G., Jensen, D.W.: The geographic distribution of unemployment rates in the U.S.: A spatial-time series analysis. Journal of Econometrics 36(3), 251–279 (1987)
Kyriakidis, P.C., Miller, N.L., Kim, J.: A Spatial Time Series Framework for Modeling Daily Precipitation at Regional Scales. Journal of Hydrology 297(1–4), 236–255 (2004)
Kyriakidis, P.C., Journel, A.G.: Stochastic modeling of atmospheric pollution: A spatial time-series framework. part I: Methodology. Atmospheric Environment 35(13), 2331–2337 (2001)
Shumway, R.H.: Applied Statistical Time Series Analysis. Prentice Hall, Englewood Cliffs (1988)
Papritz, A., Stein, A.: Spatial prediction by linear kriging. In: Spatial Statistics for Remote Sensing. Remote Sensing and Digital Image Processing, vol. 1, pp. 83–113 (2002)
Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and Knowledge Discovery 7(4), 349–371 (2003)
Castels, M.: The Rise of the Network Society. The Information Age: Economy, Society and Culture. Wiley-Blackwell (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Olszewski, R., Gasiorowski, J., Hajkowska, M. (2015). Spatio-Temporal Modeling as a Tool of the Decision-Making System Supporting the Policy of Effective Usage of EU Funds in Poland. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9157. Springer, Cham. https://doi.org/10.1007/978-3-319-21470-2_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-21470-2_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21469-6
Online ISBN: 978-3-319-21470-2
eBook Packages: Computer ScienceComputer Science (R0)