Skip to main content

Advertisement

SpringerLink
Log in

Exploring local well-being and vulnerability through OpenStreetMap: the case of Italy

  • Published:
Quality & Quantity Aims and scope Submit manuscript

Abstract

This article investigates the potential of OpenStreetMap (OSM) data in predicting local well-being and resilience in Italy. The linear Least Absolute Shrinkage and Selection Operator (LASSO) is used to handle multicollinearity problems and select the most influential OSM features. The data-driven approach provides evidence that OSM information is highly correlated with several socioeconomic metrics at a provincial scale (NUTS-3 level). Moreover, it claims that some specific points of interest—e.g., bookmakers—can be used for a rapid territorial appraisal of vulnerable territories, i.e., areas that are affected by economic backwardness, poor institutions, low human capital and that, for these adverse conditions, deserve special attention by policymakers concerned with a reduction of regional disparities. While OSM can become a powerful source for policy planning, monitoring and evaluation, future works in the field should explore the scalability of the approach, its use for forecasting purposes, and the adoption of various models and tools such as machine learning techniques to grasp even non-linear relationships between variables.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. Following Martin (2012), Martin et al. (2016) and Modica and Reggiani (2015), this work refers to two notions of resilience: engineering resilience and ecological resilience. The notion of engineering resilience refers to the capability of economic systems to rapidly bounce back to an equilibrium after a shock. Ecological resilience refers to the possibility that a system could evolve in a different direction if the magnitude of the shock is above a threshold. In other words, the system may move toward a new equilibrium thanks its capacity to adapt. Martin (2012) suggests to refers to adaptive resilience for regions and local systems. In the evolutionary theoretical framework, adaptive resilience refers to the capacity of a region to adapt its structure to the changing “environmental” conditions to keep a certain growth path of employment, output, or wealth over time.

  2. https://wiki.openstreetmap.org/wiki/Features.

  3. https://wiki.openstreetmap.org/wiki/Map_features.

  4. Computations for the provinces of Forlì-Cesena, Valle d'Aosta, Pordenone, and South Sardinia were not feasible due to flaws in the function counting procedure R.

  5. Due to limited data availability, the analysis at the municipality level is performed on the sample of Italian municipalities with at least one thousand inhabitants, as of 1 January 2022 (2860 observations). The sample of the provinces included in the analysis is instead the initial 103 observations of the baseline analysis. The values of the target variable were constructed as the ratios between taxable incomes and the number of taxpayers with taxable income for each municipality. Subsequently, the same 60 OSM features were counted at both spatial scales and normalized per one thousand inhabitants.

  6. The POI counts were normalized for 5,000 inhabitants using the data on residents, as in the computation of employment at the provincial level.

  7. We have investigated more in deep the possible reasons behind these largest rank differences (Real vs Predicted). In this manner, it was possible to uncover that, for these specific municipalities (i.e., Municipio Roma IV, Municipio Roma VII, Municipio Roma VIII, Municipio Roma XI), the OSM categories with greater variable importance are Bookmakers, Landuse industrial, Recycling. In particular, it is possible to suggest that a high concentration of bookmakers in Municipi VII and VIII evidenced by the OSM data contributes considerably to determining the overestimation of the unemployment rate for these two municipalities. On this control, further details for the interested reader are available upon request.

References

  • Andruszek, K., Wójcik, P.: Predicting well-being based on features visible from space–the case of Warsaw (no. 2020-37) (2020)

  • Armiento, M.: The sustainable welfare index: towards a threshold effect for Italy. Ecol. Econ. 152, 296–309 (2018)

    Article  Google Scholar 

  • Arsanjani, J., Zipf, A., Mooney, P., Helbich, M. (Eds.): OpenStreetMap in GIScience. Springer International Publishing, Cham (2015)

  • Bachtrögler-Unger, J., Dolls, M., Schüle, P., Taubenböck, H., Weigand, M.:Evaluating EU cohesion policy using satellite data, Bertelsmann Stiftung, doi:https://doi.org/10.11586/2021047. Available at https://www.bertelsmann-stiftung.de/en/publications/publication/did/evaluating-eu-cohesion-policy-using-satellite-data-en (2021)

  • Balducci, F.: Is OpenStreetMap a good source of information for cultural statistics? The case of Italian museums. Environ. Plann. B: Urban Anal. City Sci. 48(3), 503–520 (2021)

    Google Scholar 

  • Barron, C., Neis, P., Zipf, A.: A comprehensive framework for intrinsic OpenStreetMap quality analysis. Trans. GIS 18(6), 877–895 (2014)

    Article  Google Scholar 

  • Bickenbach, F., Bode, E., Lange, M., Nunnenkamp, P.: Night lights and regional GDP. Rev. World Econ. 152(2), 425–447 (2016)

    Article  Google Scholar 

  • Bright, J., De Sabbata, S., Lee, S., Ganesh, B., Humphreys, D.: OpenStreetMap data for alcohol research: reliability assessment and quality indicators. Health Place 50, 130–136 (2018)

    Article  Google Scholar 

  • Broccoli, M., Bruzzone, S.: Geographical localization of road accidents on WGS84 system and use of Open Street Map for road safety performance indicators on the Italian road network, Istat – Brussels, 24 January 2018, Available at https://www.istat.it/it/files//2021/05/IT-PP-Geographical-coordinates.pdf (2018)

  • Brovelli, M.A., Minghini, M., Molinari, M.E., Zamboni, G.: Positional accuracy assessment of the OpenStreetMap buildings layer through automatic homologous pairs detection: the method and a case study. ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 49, 615–620 (2016).

  • Bustamante, A., Sebastia, L., Onaindia, E.: On the representativeness of openstreetmap for the evaluation of country tourism competitiveness. ISPRS Int. J. Geo Inf. 10(5), 301 (2021)

    Article  Google Scholar 

  • Cobb, C., Halstead, T., Rowe, J.: The genuine progress indicator: summary of data and methodology. Redefining Progress, San Francisco (1995)

    Google Scholar 

  • Daly, H.E., Cobb, Jr., J.B.: For the Common Good: Redirecting the Economy toward Community, the Environment, and a Sustainable Future. Beacon Press, Boston (1989)

  • Fan, H., Zipf, A., Fu, Q., Neis, P.: Quality assessment for building footprints data on OpenStreetMap. Int. J. Geogr. Inf. Sci. 28, 700–719 (2014)

    Article  Google Scholar 

  • Feldmeyer, D., Meisch, C., Sauter, H., Birkmann, J.: Using OpenStreetMap data and machine learning to generate socio-economic indicators. ISPRS Int. J. Geo Inf. 9(9), 498 (2020)

    Article  Google Scholar 

  • Feldmeyer, D., Wilden, D., Kind, C., Kaiser, T., Goldschmidt, R., Diller, C., Birkmann, J.: Indicators for monitoring urban climate change resilience and adaptation. Sustainability 11, 2931 (2019)

    Article  Google Scholar 

  • Forghani, M., Delavar, M.R.: A quality study of the OpenStreetMap dataset for Tehran. ISPRS Int. J. Geo Inf. 3(2), 750–763 (2014)

    Article  Google Scholar 

  • Franzini, M., Annovazzi-Lodi, L., Casella, V.: Assessment of the Completeness of OpenStreetMap and Google Maps for the Province of Pavia (Italy). In: Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management, Prague, Czech Republic, 7–9 May 2020, pp. 270–277.

  • Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1–22 (2010)

    Article  Google Scholar 

  • Girres, J.F., Touya, G.: Quality assessment of the French OpenStreetMap dataset. Trans. GIS 14, 435–459 (2010)

    Article  Google Scholar 

  • Hardeman, S., Dijkstra, L.: The EU regional human development index. JRC science and policy reports, Luxembourg: Publications Office of the European Union (2014)

  • Herfort, B., Lautenbach, S., de Albuquerque J.P., et al.: A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap. Nat Commun. 14, 3985 (2023). https://doi.org/10.1038/s41467-023-39698-6

  • Hung, J.: How the application of satellite imagery offers new opportunities to explore China’s urbanisation and development. Qual. Quant. (2022)

  • Iorio, C., Pandolfo, G., D’Ambrosio, A., Siciliano, R.: Mining big data in tourism. Qual. Quant. 54, 1655–1669 (2020)

    Article  Google Scholar 

  • Kopczewska, K.: Regional development in Central and Eastern Europe and Asia. Reg. Sci. Policy Pract. 14(4), 697–698 (2022)

    Article  Google Scholar 

  • Ludwig, C., Fendrich, S., Zipf, A.: Regional variations of context-based association rules in OpenStreetMap. Trans. GIS 25, 602–621 (2021a). https://doi.org/10.1111/tgis.12694

    Article  Google Scholar 

  • Ludwig, C., Hecht, R., Lautenbach, S., Schorcht, M., Zipf, A.: Mapping Public Urban green spaces based on OpenStreetMap and sentinel-2 imagery using belief functions. ISPRS Int. J. Geo-Inf. 10, 251 (2021b)

    Article  Google Scholar 

  • Martin, R.: Regional economic resilience, hysteresis and recessionary shocks. J. Econ. Geogr. 12, 1–32 (2012)

    Article  Google Scholar 

  • Martin, R., Sunley, P., Gardiner, B., Tyler, P.: How regions react to recessions: resilience and the role of economic structure. Reg. Stud. 50(4), 561–585 (2016)

    Article  Google Scholar 

  • Mashhadi, A., Quattrone, G., Capra, L.: The impact of society on volunteered geographic information. In: Arsanjani, J., Zipf, A., Mooney, P., Helbich, M. (Eds.), OpenStreetMap in GIScience. Springer International Publishing, Cham, pp. 125–141 (2015)

  • Meinshausen, N., Bühlmann, P.: Stability selection. J. R. Stat. Soc. Ser. B Stat Methodol. 72(4), 417–473 (2010)

    Article  Google Scholar 

  • Modica, M., Reggiani, A.: Spatial economic resilience: overview and perspectives. Netw. Spat. Econ. 15(2), 211–233 (2015)

    Article  Google Scholar 

  • Neumayer, E.: The ISEW: not an index of sustainable economic welfare. Soc. Indicators Res., 77–101 (1999)

  • Nielsen, J., Loranger, H.: Web usability 2.0. L'usabilità che conta. Apogeo Editore (2006)

  • Nordhaus, W.D., Tobin, J.: Economic Research: Retrospect and Prospect, Volume 5, Economic Growth (No. nord72–1). National Bureau of Economic Research (1972)

  • OECD: How’s Life? Measuring well-being. OECD Publishing (2011)

  • OECD: The Future of Productivity, OECD Publishing, Paris, https://doi.org/10.1787/9789264248533-en (2015)

  • Olivari, B., Cipriano, P., Napolitano, M., Giovannini, L.: Are Italian cities already 15-minute? Presenting the Next Proximity Index: a novel and scalable way to measure it, based on open data. J. Urban Mobility 4, 100057 (2023)

    Article  Google Scholar 

  • OpenStreetMap Contributors. Open Database License. Available online: https://wiki.openstreetmap.org/wiki/Stats. Accessed on 19 Jan 2023

  • Pomianek, I., Chrzanowska, M.: A spatial comparison of semi-urban and rural gminas in Poland in terms of their level of socio-economic development using Hellwig’s method. Bull. Geogr. Socio-Econ. Ser. 33(33), 103–117 (2016)

    Google Scholar 

  • Rabiei-Dastjerdi, H., McArdle, G., & Ballatore, A. (2020). Urban consumption patterns: OpenStreetMap quality for social science research. In The 6th International Conference on Geographical Information Systems Theory, Applications and Management, Online Event, 7–9 May 2020. SciTePress.

  • Resce, G., Lagravinese, R., Benedetti, E., Molinaro, S.: Income-related inequality in gambling: evidence from Italy. Rev. Econ. Household 17(4), 1107–1131 (2019)

    Article  Google Scholar 

  • Roberts, S., Nowak, G.: Stabilizing the lasso against cross-validation variability. Comput. Stat. Data Anal. 70, 198–211 (2014)

    Article  Google Scholar 

  • Salvati, L., Guandalini, A., Carlucci, M., Chelli, F.M.: An empirical assessment of human development through remote sensing: Evidences from Italy. Ecol. Indic. 78, 167–172 (2017)

  • Salvucci, G., Salvati, L.: Official statistics, building censuses, and OpenStreetMap completeness in Italy. ISPRS Int. J. Geo Inf. 11(1), 29 (2021)

    Article  Google Scholar 

  • Sarretta, A., Napolitano, M., Minghini, M.: Openstreetmap as an input source for producing governmental datasets: the case of the italian military geographic institute. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 48, 193–200 (2023)

    Article  Google Scholar 

  • Stiglitz, J., Sen, A., Fitoussi, J.P.: The Measurement of Economic Performance and Social Progress Revisited: Reflections and Overview. Sciences Po Publications 2009–2033 (2009)

  • Ternès, N., Rotolo, F., Michiels, S.: Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models. Stat. Med. 35(15), 2561–2573 (2016)

    Article  Google Scholar 

  • Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc.: Ser. B (methodol.) 58(1), 267–288 (1996)

    Article  Google Scholar 

  • Ubaldi, E., Monechi, B., Chiappetta, C., Loreto, V.: Heterogeneity and segregation of mobility patterns. In: Reggiani, A., Schintler, L.A., Czamanski, D., Patuelli, R. (eds), Handbook on Entropy, Complexity and Spatial Dynamics. A Rebirth of Theory?; Cheltenham, Edward Elgar Publishing Ltd, pp. 486–509 (2021)

  • UNDP: Human Development Report 2010: The Real Wealth of Nations - Pathways to Human Development, New York (2010)

  • UNDP: UNDP National Human Development Report. Poland 2012. Local and Regional Development (2012)

  • United Nations: Global Indicator Framework for the Sustainable Development Goal sand Targets of the 2030 Agenda for Sustainable Development: Sustainable Development Goal Indicators should be Disaggregated, where Relevant, by Income, Sex, Age, Race, Ethnicity, Migratory Status, Disability and Geographic Location, or Other Characteristics, in Accordance with the Fundamental Principles of Official Statistics. Available online: https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%20refinement_Eng.pdf. Accessed on 18 Jan 2023 (2018)

  • Wójcik, P., Andruszek, K.: Predicting intra-urban well-being from space with nonlinear machine learning. Reg. Sci. Policy Pract. 14(4), 891–913 (2022)

    Article  Google Scholar 

Download references

Acknowledgements

The authors declare that funds and grants were received during the preparation of this manuscript. Eleonora Cutrini acknowledges partial research support from the European Union—NextGenerationEU under the Italian Ministry of University and Research (MUR) National Innovation Ecosystem grant ECS00000041—VITALITY—CUPD83C22000710005. Federico Ninivaggi acknowledges financial support from the European Union—European Social Fund, under the program "Innovative Doctorate" – PhD Scholarships for the innovation of the regional system- 2020 edition, Marche Region (Italy).

Funding

NextGenerationEU-Vitality, ECS00000041, Eleonora Cutrini, European Union—ESF, program "Innovative Doctorate"—PhD Scholarships for the innovation of the regional system- 2020 edition, Federico Ninivaggi.

Author information

Authors and Affiliations

  1. Department of Economics and Law, University of Macerata, Via Crescimbeni 14, 62100, Macerata, Italy

    Federico Ninivaggi & Eleonora Cutrini

Authors
  1. Federico Ninivaggi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eleonora Cutrini
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Both authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Federico Ninivaggi. The first draft of the manuscript was written by Federico Ninivaggi and both authors revised and commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Eleonora Cutrini.

Ethics declarations

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table

Table 10 List of target variables
Full size table

10,

Table 11 OSM categories and their composition
Full size table

11, and

Table 12 Results of the LASSO regressions: OSM categories with significant coefficients for different target variables
Full size table

12; Fig.

Fig. 7
figure 7

OSM variable importance in predicting the 2020 Pre-tax income

Full size image

7.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ninivaggi, F., Cutrini, E. Exploring local well-being and vulnerability through OpenStreetMap: the case of Italy. Qual Quant 58, 3435–3473 (2024). https://doi.org/10.1007/s11135-023-01805-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11135-023-01805-6

Keywords

Mathematics Subject Classification

Search

Navigation