Abstract
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.
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Notes
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Irish postal address and geographic coordinates database.
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A map of the European environmental landscape which includes 44 land cover classes.
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Acknowledgments
This research was supported by a Marie Curie International Outgoing Fellowship (GeoSInPo) within the 7th European Community Framework Program.
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Shahumyan, H., Liu, C., Williams, B., Knaap, G., Engelberg, D. (2017). Identifying and Using Key Indicators to Determine Neighborhood Types in Different Regions. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10406. Springer, Cham. https://doi.org/10.1007/978-3-319-62398-6_13
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DOI: https://doi.org/10.1007/978-3-319-62398-6_13
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