Identifying and Using Key Indicators to Determine Neighborhood Types in Different Regions
- 1.2k Downloads
Identification of a key indicators capturing essential patterns in a region can be a cost-effective solution for neighborhood classification and targeted policy making. Yet, such a “core” set of indicators can vary from region to region. Here, we define set of indicators measuring education, housing, accessibility, and employment which can be used to classify neighborhoods. We test these indicators in two study regions: the Baltimore Metropolitan Area and the Greater Dublin Region. We apply factor analysis to distill indicators to smaller sets that capture differences in neighborhood types in terms of social, economic, and environmental dimensions. We use factors loadings in cluster analyzes to identify unique neighborhood types spatially. Comparison of the core set of indicators and clustering patterns for case study regions sheds new lights on the important factors for both regions. The proposed approach will help compare variations in neighborhood types between and within different regions internationally.
KeywordsIndicators Factor analysis Cluster analysis Baltimore Dublin
This research was supported by a Marie Curie International Outgoing Fellowship (GeoSInPo) within the 7th European Community Framework Program.
- 5.Liu, C., Knaap, E., Knaap, G.: Reclassification of sustainable neighborhoods: an opportunity indicator analysis in baltimore metropolitan area. In: AESOP-ACSP Joint Congress, Dublin, Ireland (2013)Google Scholar
- 6.Reece, J., et al.: The geography of opportunity: mapping to promote equitable community development and fair housing in King county, WA. Kirwan Institute for the Study of Race and Ethnicity, Ohio State University, US (2010)Google Scholar
- 7.Reece, J., et al.: People, Place and Opportunity: Mapping Communities of Opportunity in Connecticut. Kirwan Institute for the Study of Race And Ethnicity, Ohio State University, US (2009)Google Scholar
- 8.Couch, C., Leontidou, L., Petschel-Held, G.: Urban sprawl in Europe: landscapes, land-use change & policy. Real Estate Issues, xix, 273 p. Blackwell, Oxford, Malden (2007)Google Scholar
- 9.NSS: National spatial strategy for Ireland 2002–2020: People, Places and Potential, D.o.t.E.a.L. Government, Editor, 160 p. Stationery Office, Dublin (2002)Google Scholar
- 10.CSO Regional Population Projections 2016–2031. CSO statistical release (2013)Google Scholar
- 11.Field, A.: Discovering Statistics Using IBM SPSS Statistics, 4th edn. Sage Publications Ltd., UK (2013)Google Scholar
- 12.Tabachnick, B.G., Fidell, L.S.: Using Multivariate Statistics. Pearson Education Inc., Boston (2007)Google Scholar
- 13.Hair, J., et al.: Multivariate Data Analysis, 4th edn. Prentice-Hall Inc., Upper Saddle River (1995)Google Scholar
- 20.Costello, A.B., Osborne, J.W.: Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract. Assess. Res. Eval. 10(7), 1–9 (2005)Google Scholar
- 21.Fiedler, J.A., Mcdonald, J.J.: Market figmentation clustering on factor scores versus individual variables. In: Second Annual Advanced Research Techniques Forum, pp. 118–129 (1992)Google Scholar