In this study we use geographic information systems (GIS) and computational intelligence for geomarketing analysis. GIS technology offers a powerful set of tools for the input, management, and output of data, whereas computational intelligence is used for the analysis and the clustering of data by the use of unsupervised fuzzy clustering and the Gustafson–Kessel algorithm. The advantage of fuzzy geomarketing segmentation is that a customer is not assigned exclusively to one segment only, but rather with a membership value to each cluster. The proposed methodology is applied to the metropolitan area of Athens, Greece. A dataset describes 130 demographic, lifestyle, and economy variables, and the results are analysed and discussed.
ArapoglouV P, 2006, “Immigration, segregation and urban development in Athens: The relevance of the LA debate for southern European metropolises”The Greek Review of Social Research, Special Issue in English121 (C) 11–38
2.
ArapoglouV PSayasJ P, 2009, “New facets of urban segregation in southern Europe: Gender, migration and social class change in Athens”European Urban and Regional Studies16345–362
3.
ArbaciS, 2008, “(Re)viewing ethnic residential segregation in southern European cities: Housing and urban regimes as mechanisms of marginalisation”Housing Studies23589–613
4.
BabuskaR, 1998Fuzzy Modelling for Control (Kluwer, Boston, MA)
5.
BalaskoBAbonyiJFeilB, 2003Fuzzy Clustering and Data Analysis Toolbox: For Use with Matlab PhD thesis, Department of Process Engineering, University of Veszprem
6.
BateyPBrownP, 1995, “From human ecology to customer targeting: The evolution of geodemographics”, in GIS for Business and Service Planning Eds LongleyPClarkeG P, (GeoInformation, Cambridge) pp 77–103
7.
BensaidA MHallL OBezdekJ CClarkeL PSilbigerM LArringtonJ AMurtaghR F, 1996, “Validity-guided (re)clustering with applications to image segmentation”IEEE Transactions on Fuzzy Systems4112–123
8.
BeriatosEGospodiniA, 2004, “‘Glocalising’ urban landscapes: Athens and the 2004 Olympics”Cities21187–202
9.
Bezdek, 1981Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, New York)
10.
BlakeMOpenshawS, 1995, “Selecting variables for small area classifications of 1991 UK Census Data”, RP 95-2, School of Geography, University of Leeds, http://www.geog.leeds.ac.uk/papers/95-2/
11.
BoyerLBurgaudD, 2000Le marketing avance: Du one to one au e-business (Éditions d'Organisation, Paris)
12.
CliquetG, (Ed.), 2006Geomarketing: Methods and Strategies in Spatial Marketing translated by HughesE, (ISTE, London)
13.
DouardJ P, 2006, “Geomarketing and consumer behaviour”, in Geomarketing: Methods and Strategies in Spatial Marketing Ed. CliquetG, translated by HughesE, (ISTE, London) chapter 4
14.
EmmanuelD, 2004, “Socio-economic inequalities and housing in Athens: Impacts of the monetary revolution of the 1990s”The Greek Review of Social Research113 (A) 121–144
FengZFlowerdewR, 1998, “Fuzzy geodemographics: A contribution from fuzzy clustering method”, in Innovations in GIS 5 Ed. CarverS, (Taylor and Francis, London) pp 119–127
18.
FengZFlowerdewR, 1999, “The use of fuzzy classification to improve geodemographic targeting”, in Innovations in GIS 6 Ed. GittingsB, (Taylor and Francis, London) pp 133–144
19.
FoxallR G, 2010, “Invitation to consumer behavior analysis”Journal of Organizational Behavior Management3092–109
20.
FritzSSeeLCarverS, 2000, “A fuzzy modeling approach to wild land mapping in Scotland”, in GIS and Geocomputation: Innovations in GIS 7 Eds AtkinsonPMartinD, (Taylor and Francis, London) pp 219–230
21.
GaborM R, 2010, “Explanatory methods of marketing data analysis—theoretical and methodological considerations”Management and Marketing Journal2373–384
22.
GallopelK, 2006, “Advertising policy and geographic information”, in Geomarketing: Methods and Strategies in Spatial Marketing Ed. CliquetG, translated by HughesE, (ISTE, London) chapter 16
23.
GerlaG, 2006, “Effectiveness and multivalued logics”Journal of Symbolic Logic71137–162
24.
GrekousisGPhotisN Y, 2011, “A fuzzy index for detecting spatiotemporal outliers”Geoinformatica16597–619
25.
GustafsonD EKesselW C, 1978, “Fuzzy clustering with fuzzy covariance matrix”, in Proceedings of the IEEE CDC, San Diego17761–766
26.
HagenbuchnerMSperdutiATsoiAh Chung, 2003, “A self-organizing map for adaptive processing of structured data”IEEE Transactions on Neural Networks14491–492
27.
HallG BWangFSubaryono, 1992, “Comparison of Boolean and fuzzy classification methods in land suitability analysis by using geographical information systems”Environment and Planning A24497–516
28.
HarrisRLongleyP, 2004, “Targeting clusters of deprivation within cities”, in Applied GIS and Spatial Analysis Eds StillwellJClarkeG, (John Wiley, Chichester, Sussex) pp 89–110
29.
HarrisRSleightPWebberR, 2005Geodemographics, GIS and Neighborhood Targeting (John Wiley, Chichester, Sussex)
30.
HatzichristosT, 1999Delineation of Ecoregions Using GIS and Computational Intelligence PhD thesis, School of Rural and Surveying Engineering, National Technical University of Athens (in Greek)
31.
HatzichristosT, 2004, “Delineation of demographic regions with GIS and computational intelligence”Environment and Planning B: Planning and Design3139–49
KandylisG, 2006, “From assimilation to national hierarchy: Changing dominant representations in the formation of the Greek city”The Greek Review of Social Research121157–174
34.
KohonenT, 1982, “Self-organised formation of topologically correct feature maps”Biological Cybernetics4359–69
35.
LatourPLe FlochJ, 2001Geomarketing: Principles, Methods and Applications (Éditions d'Organisation, Paris)
36.
LazaridesGPsimenosI, 2000, “Migrant flows from Albania to Greece: Economic, social and spatial exclusion”, in Eldorado or Fortress? Migration in South Europe Eds KingRLazaridisGTsardanidisC, (Mamillan, London) pp 170–185
37.
LefebureGVenturiR, 2000La gestion de la relation client (Éditions d'Organisation, Paris)
38.
LeontidouL, 1990The Mediterranean City in Transition (Cambridge University Press, Cambridge)
39.
LongleyPGoodchildMMaguireDRhindD, 2001Geographical Information Systems and Science (John Wiley, Chichester, Sussex)
40.
McBratneyAde GruijterJ, 1992, “A continuum approach to soil classification by modified fuzzy k-means with extra grades”Journal of Soil Science43159–175
MaloutasT, 2004, “Segregation and residential mobility. Spatially entrapped social mobility and its impact on segregation in Athens”European Urban and Regional Studies11195–211
43.
MaloutasT, 2007, “Segregation, social polarization and immigration in Athens: Theoretical expectations and contextual difference”International Journal of Urban and Regional Research31733–758
44.
MaloutasTKaradimitriouN, 2001, “Vertical social differentiation in Athens: Alternative or complement to urban segregation?”International Journal of Urban and Regional Research25699–716
45.
MorphetC, 1993, “The mapping of small-area census data—a consideration of the effects of enumeration district boundaries”Environment and Planning A251267–1277
46.
Odeh, 1990, “Design of optimal sample spacings for maping soil using fuzzy k-means and regionalized variable theory”Geoderma4793–122
47.
OpenshawS, 1989, “Making geodemographics more sophisticated”Journal of the Market Research Society31111–131
48.
OpenshawS, 1994a“Neuroclassification of spatial data”, in Neural Nets: Applications in Geography Eds HewitsonBCraneR, (Kluwer Academic, Dordrecht) pp 53–70
49.
OpenshawS, 1994b, “Developing smart and intelligent target marketing systems: Part I”Journal of Targeting, Measurement and Analysis for Marketing2289–301
OpenshawSTurtonI, 1996, “A parallel Kohonen algorithm for the classification of large spatial data sets”Computers and Geosciences221019–1026
52.
OpenshawSWymerC, 1991, “A neural net classifier system for handling census data”, in Proceedings of the Neural Networks for Statistical and Economic Data Conference Dublin Ed. MurtaghF, (Munotec Systems, Dublin) pp 73–86
53.
OpenshawSBlakeMWymerC, 1995“Using neurocomputing methods to classify Britain's residential areas”, in Innovations in GIS 2 Ed. FisherP, (Taylor and Francis, London) pp 97–112
54.
ParthasarathyS, 2003Lecture Notes for CIS 694Z: Introduction to DataminingOhio State University, Colombus, OH, http://www.cse.ohio-state.edu/∼srini/
55.
PetersenJGibinMLongleyPMateosPAtkinsonPAshbyD, 2010, “Geodemographics as a tool for targeting neighbourhoods in public health campaigns”Journal of Geographical Systems13173–192
56.
PetrakosGEconomouD, 1999, “Internationalisation and structural changes in the European urban system”, in The Development of Greek Cities Eds EconomouDPetrakosG, (Gutenberg and University of Thessaly Publications, Athens) (in Greek) pp 13–44
57.
PunjGStewartD W, 1983, “Cluster analysis in marketing research: Review and suggestions for application”Journal of Marketing Research20134–148
SammonJ WJr, 1969, “A nonlinear mapping for data structure analysis”IEEE Transactions on Computers18401–409
60.
SayasJ P, 2006, “Urban sprawl in the periurban coastal zones of Athens”The Greek Review of Social Research121 (C) 71–104
61.
SchürmannJ, 1996Pattern Classification. A Unified View of Statistical and Neural Approaches (John Wiley, New York)
62.
SeeLOpenshawS, 2001, “Fuzzy geodemographic targeting”, in Regional Science in Business Eds ClarkeGMaddenM, (Springer, Berlin) pp 269–282
63.
ShevkyEBellW, 1955Social Area Analysis: Theory, Illustrative Application, and Computational Procedures (Stanford University Press, Stanford, CA)
64.
SingletonALongleyP, 2008, “Creating open source geodemographics—refining a national classification of census output areas for applications in higher education”Papers in Regional Science88643–666
65.
SingletonALongleyP, 2009, “Geodemographics, visualisation, and social networks in applied geography”Applied Geography29289–298
66.
SleightP, (Ed.), 2004Targeting Customers: How to Use Geodemographic and Lifestyle Data in Your Business (World Advertising Research Centre, Henley on Thames, Oxon)
67.
SpielmanS EThillJ C, 2008, “Social area analysis, data mining and GIS”Computers, Environment and Urban Systems32110–122
68.
Van GaansP F MBurroughP A, 1993, “The use of fuzzy logic and continuous classification in GIS applications”, in Proceedings of the 5th European Conference on Geographical Information Systems Paris Eds HartsJ JOttensH F LScholtenH J, (EGIS Foundation, Utrecht) pp 1025–1033
69.
VickersDReesP, 2007, “Creating the UK national statistics 2001 output area classification”Journal of the Royal Statistical Society Statistics in Society A170379–403
