A Stepwise Procedure to Determinate a Suitable Scale for the Spatial Delimitation of Urban Slums

  • Juan C. Duque
  • Vicente Royuela
  • Miguel Noreña
Part of the Advances in Spatial Science book series (ADVSPATIAL)


The globalisation era in which we live has made the world an interconnected space with several global trends. We find developing countries with very high growth rates, what helps to find world economic convergence. As a complement to this trend, within those countries there is a dramatic growth pattern of cities into megacities, as economic activity concentrates in space to exploit agglomeration economies. According to UN-Habitat, in the next two decades the global population living in urban areas will move from 50 % to 70 %.


Analytical Region Spatial Cluster Housing Unit Spatial Contiguity Aggregation Bias 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Aldstadt J, Getis A (2006) Using AMOEBA to create a spatial weights matrix and identify spatial clusters. Geog Anal 38(4):327–343CrossRefGoogle Scholar
  2. Amrhein CG, Flowerdew R (1992) The effect of data aggregation on a Poisson regression model of Canadian migration. Environ Plann A 24:1381–1391CrossRefGoogle Scholar
  3. Anselin L (1994) Local indicators of spatial association—LISA. Geogr Anal 27:93–115CrossRefGoogle Scholar
  4. Anselin L, Lozano N, Koschinsky J (2006). Rate transformations and smoothing. GeoDaCenter Research ReportGoogle Scholar
  5. Arbia G (1989) Spatial data configuration in statistical analysis of regional economic and related problems, vol 14. Kluwer Academic, The NetherlandsCrossRefGoogle Scholar
  6. Berry BJL (1960) An inductive approach to the regionalization of economic development. In: Ginsburg N (ed) Essays on geography and economic development. University of Chicago Press, ChicagoGoogle Scholar
  7. Burra T, Jerrett M, Burnett RT, Anderson M (2002) Conceptual and practical issues in the detection of local disease clusters: a study of mortality in Hamilton, Ontario. Can Geogr/Le Géographe canadien 46(2):160–171CrossRefGoogle Scholar
  8. Calciu M (1996) Une méthode de classification sous contrainte de contiguïté en géo-marketing. Institut d’administration des entreprises, Université des sciences et technologies de LilleGoogle Scholar
  9. Caldas de Castro M, Singer BH (2006) Controlling the false discovery rate: a new application to account for multiple and dependent tests in local statistics of spatial association. Geogr Anal 38:180–208CrossRefGoogle Scholar
  10. Caro F, Shirabe T, Guignard M, Weintraub A (2004) School redistricting: eembedding GIS tools with integer programming. J Oper Res Soc 55:836–849CrossRefGoogle Scholar
  11. Diehr P (1984) Small areas statistics – large statistical problems. Am J Public Health 74(4):313–314CrossRefGoogle Scholar
  12. Duque JC, Artís M, Ramos R (2006) The ecological fallacy in a time series context: evidence from Spanish regional unemployment rates. J Geogr Syst 8:391–410CrossRefGoogle Scholar
  13. Duque JC, Ramos R, Surinach J (2007) Supervised regionalization methods: a survey. Int Reg Sci Rev 3:195–220CrossRefGoogle Scholar
  14. Duque JC, Aldstadt J, Velasquez E, Franco JL, Betancourt A (2010) A computationally efficient method for delineating irregularly shaped spatial clusters. J Geogr Syst. DOI: 10.1007/s10109-010-0137-1 13(4):355–372Google Scholar
  15. Duque JC, Dev B, Betancourt A, Franco JL (2011) ClusterPy: library of spatially constrained clustering algorithms, Version 0.9.9. RiSE-group (Research in Spatial Economics), EAFIT UniversityGoogle Scholar
  16. Duque JC, Anselin L, Rey SJ (2012) The max-p-regions problem. J Reg Sci 52(3):397– 419Google Scholar
  17. Eurostat (2006) Nomenclature of territorial units for statistics—NUTS: statistical regions of Europe. Accessed 19 June 2006
  18. Fischer M (1980) Regional taxonomy. A comparison of some hierarchic and non-hierarchic strategies. Reg Sci Urban Econ 10:503–537CrossRefGoogle Scholar
  19. Fotheringham AS, Wong DWS (1991) The modifiable areal unit problem in multivariate statistical-analysis. Environ Plann A 23:1025–1044CrossRefGoogle Scholar
  20. Getis A, Ord JK (1992) The analysis of spatial association by distance statistics. Geogr Anal 24:189–206CrossRefGoogle Scholar
  21. Goovaerts P (2009) Medical geography: a promising field of application for geostatistics. Math Geosci 41(3):243–264CrossRefGoogle Scholar
  22. Johnston RJ (1968) Choice in classification: the subjectivity of objective methods. Ann AAG 58:575–589Google Scholar
  23. Kulldorff M (1997) A spatial scan statistic. Commun Stat Theory Method 26:1487–1496Google Scholar
  24. Levin SA (1992) The problem of pattern and scale in ecology. Ecology 73(6):1943–1967CrossRefGoogle Scholar
  25. Openshaw S (1977a) A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modelling. Trans Inst Br Geogr 2:459–472CrossRefGoogle Scholar
  26. Openshaw S (1977b) Optimal zoning systems for spatial interaction models. Environ Plann A 9:169–184CrossRefGoogle Scholar
  27. Openshaw S (1984) The modifiable areal unit problem, vol 38, Concepts and techniques in modern geography. GeoBooks, NorwichGoogle Scholar
  28. Openshaw S (1995) Census users handbook. GeoInformation International, CambridgeGoogle Scholar
  29. Openshaw S, Taylor PJ (1981) The modifiable areal unit problem. In: Wrigley N, Bennett RJ (eds) Quantitative geography. Routledge, London, pp 60–70Google Scholar
  30. Paelinck JHP (2000) On aggregation in spatial econometric modeling. J Geogr Syst 2:157–165CrossRefGoogle Scholar
  31. Paelinck JHP, Klaassen H (1979) Spatial econometric. Saxon House, FarnboroughGoogle Scholar
  32. Robinson WS (1950) Ecological correlations and the behaviour of individuals. Am Sociol Rev 15:351–357CrossRefGoogle Scholar
  33. Royuela V, Suriñach J, Artís M (2008) La influencia de la calidad de vida en el crecimiento urbano. El caso de la provincia de Barcelona, Investigaciones Regionales 13:57–84Google Scholar
  34. Segal M, Weinberger DB (1977) Turfing. Oper Res 25:367–386CrossRefGoogle Scholar
  35. Spence NA (1968) A multifactor uniform regionalization of British counties on basis of employment data for 1961. Reg Stud 2:87–104CrossRefGoogle Scholar
  36. Vélez CE, Castaño E, Deutsch R (1998) An economic interpretation of colombia’s SISBEN: a composite welfare index derived from the optimal scaling algorithm. Mimeo, Poverty and Inequality Advisory Unit. Inter American Development Bank, WashingtonGoogle Scholar
  37. Weeks JR, Hill A, Stow D, Getis A, Fugate D (2006) The impact of neighborhood structure on health inequalities in Accra, Ghana. In: Population association of America 2006 annual meeting program, Los Angeles, USA, pp 1–34Google Scholar
  38. Yamada T (2009) A mini-max spanning forest approach to the political districting problem. Int J Syst Sci 40(5):471–477CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Juan C. Duque
    • 1
  • Vicente Royuela
    • 2
  • Miguel Noreña
    • 1
  1. 1.Research in Spatial Economics (RISE-group), School of Economics and FinancesEAFIT UniversityMedellínColombia
  2. 2.AQR Research Group-IREAUniversidad de BarcelonaBarcelonaSpain

Personalised recommendations