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

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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.

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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.

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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.

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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.

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Correspondence to Eleonora Cutrini.

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Appendix

Appendix

See Table

Table 10 List of target variables

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Table 11 OSM categories and their composition

11, and

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

12; Fig.

Fig. 7
figure 7

OSM variable importance in predicting the 2020 Pre-tax income

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Ninivaggi, F., Cutrini, E. Exploring local well-being and vulnerability through OpenStreetMap: the case of Italy. Qual Quant (2023). https://doi.org/10.1007/s11135-023-01805-6

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