Abstract
Public services are essential to satisfy the needs of healthcare, education, justice, etc. in citizens’ daily life. Thus, individuals need these services in a certain proximity to their homes. Nonetheless, in big cities, some public services are not close enough. To tackle this problem, we propose a methodology to compute a Government Public Services Presence Index for measuring how well different zones are in a city are served. We apply our methodology to the city of Lima, showing the utility of the index while being simple to understand. We profile fifty different districts in four groups, allowing policymakers and urban planners to observe the lack of public services.
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Notes
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Administration Level Definitions: https://sedac.ciesin.columbia.edu/povmap/ds_defs_admin.jsp.
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Python recipe: https://code.activestate.com/recipes/119466/.
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GostNets: https://github.com/worldbank/GOSTnets.
References
Clustering - scikit-learn 0.24.2 documentation. https://scikit-learn.org/stable/modules/clustering.html#k-means
Barboza, M.H., Carneiro, M.S., Falavigna, C., Luz, G., Orrico, R.: Balancing time: using a new accessibility measure in Rio de Janeiro. J. Transp. Geogr. 90, 102924 (2021)
Berrouet, L., Villegas-Palacio, C., Botero, V.: A social vulnerability index to changes in ecosystem services provision at local scale: a methodological approach. Environ. Sci. Policy 93, 158–171, 102924 (2019)
Boeing, G.: OSMnx: a Python package to work with graph-theoretic openstreetmap street networks. J. Open Source Softw. 2(12) (2017)
Cutter, S.L., Boruff, B.J., Shirley, W.L.: Social vulnerability to environmental hazards. Soc. Sci. Q. 84(2), 242–261 (2003)
Daras, K., Alexiou, A., Rose, T.C., Buchan, I., Taylor-Robinson, D., Barr, B.: How does vulnerability to COVID-19 vary between communities in England? Developing a small area vulnerability index (SAVI). J. Epidemiol. Community Health 75, 729–734 (2021)
Delafontaine, M., Neutens, T.: Accessibility and the temporal organisation of public service facilities. In: 37th Colloquium Vervoersplanologisch Speurwerk (CVS-2010). Dipas Druk & Print (2010)
Gatto, A., Busato, F.: Energy vulnerability around the world: the global energy vulnerability index (GEVI). J. Clean. Prod. 253, 118691 (2020)
Hagberg, A., Conway, D.: NetworkX: Network analysis with Python
Haklay, M., Weber, P.: OpenStreetMap: user-generated street maps. IEEE Pervasive Comput. 7(4), 12–18 (2008)
Hillier, A., Cannuscio, C.C., Karpyn, A., McLaughlin, J., Chilton, M., Glanz, K.: How far do low-income parents travel to shop for food? Empirical evidence from two urban neighborhoods. Urban Geogr. 32(5), 712–729 (2011)
Jin, X., Han, J.: K-means clustering. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 563–564. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8_425
Jordahl, K.: GeoPandas: Python tools for geographic data (2014). https://github.com/geopandas/geopandas
Kelobonye, K., Zhou, H., McCarney, G., Xia, J.C.: Measuring the accessibility and spatial equity of urban services under competition using the cumulative opportunities measure. J. Transp. Geogr. 85, 102706 (2020)
Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recogn. 36(2), 451–461 (2003)
Meenar, M.R.: Using participatory and mixed-methods approaches in GIS to develop a place-based food insecurity and vulnerability index. Environ. Plan A 49(5), 1181–1205 (2017)
Moore, M., Gelfeld, B., Adeyemi Okunogbe, C.P.: Identifying future disease hot spots: infectious disease vulnerability index. Rand Health Q. 6(3) (2017)
Moro, E., Calacci, D., Dong, X., Pentland, A.: Mobility patterns are associated with experienced income segregation in large us cities. Nat. Commun. 12(1), 1–10 (2021)
Neutens, T., Delafontaine, M., Scott, D.M., De Maeyer, P.: A GIS-based method to identify spatiotemporal gaps in public service delivery. Appl. Geogr. 32(2), 253–264 (2012)
Neutens, T., Schwanen, T., Witlox, F., De Maeyer, P.: Evaluating the temporal organization of public service provision using space-time accessibility analysis. Urban Geogr. 31(8), 1039–1064 (2010)
Yeturu, K.: Machine learning algorithms, applications, and practices in data science. In: Handbook of Statistics, vol. 43, pp. 81–206 (2020)
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Nunez-del-Prado, M., Rojas-Bustamante, L. (2022). Government Public Services Presence Index Based on Open Data. In: Lossio-Ventura, J.A., et al. Information Management and Big Data. SIMBig 2021. Communications in Computer and Information Science, vol 1577. Springer, Cham. https://doi.org/10.1007/978-3-031-04447-2_4
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