Abstract
Housing is often considered a crucial element in determining the level of income and social well-being, in recognition of the ways in which housing shortages, or the use of poor-quality housing, are statistically linked to income levels and can negatively affect on people’s well-being (Rolfe et alii, 2020). So, planning social housing isn’t just a question of houses, but it claims for a deep understanding of people living conditions, social and economic dynamics. That’s why is necessary to integrate different statistical data to develop a model of social and economic living conditions of people to address better and context-based housing policies. The paper analyses methods and tools to integrate appropriate statistical data to guide housing policies in the case of the city of Taranto, selecting those most useful for determining supply and demand to guide urban planning in subsequent participatory and implementation paths. Planning social housing not just to improve physical spaces, but to interpret the needs of living.
The contribution is the result of joint reflections by the authors, with the following contributions attributed to F. Rotondo (paragraphs 1 and 4) and to P. Perchinunno (paragraphs 2 and 3).
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Notes
- 1.
ISTAT, 2011, Censimento Popolazioni e abitazioni (www.istat.it). ISTAT is the Italian Institute of Statistics.
- 2.
Index 1 - Incidence of the elderly population: population aged over 70 compared to the total population.
Index 2 - Incidence of the population by educational qualification: population with a qualification below middle school compared to the total population.
Index 3 - Incidence of the illiterate population: illiterate population compared to the total population.
Index 4 - Incidence of empty houses: number of empty houses compared to the total number of houses.
Index 5 - Incidence of period buildings: number of buildings built before 1960 compared to the total number of buildings.
Index 6 - Incidence of residential buildings with poor or poor state of conservation: number of buildings with poor or mediocre state of conservation compared to the total number of buildings.
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Perchinunno, P., Rotondo, F. (2021). Integrated Statistical Data for Planning Social Housing in the City of Taranto. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12954. Springer, Cham. https://doi.org/10.1007/978-3-030-86979-3_11
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