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Economic Evaluation and Statistical Methods for Detecting Hot Spots of Social and Housing Difficulties in Urban Policies

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Computational Science and Its Applications – ICCSA 2009 (ICCSA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5592))

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Abstract

The paper shows a reasoning about how the new national policies on social housing can be implemented in connection with regional and local economic analyses. In this light the role of evaluation is examined, to show which kind of approach can be referred to each dimension, context and level. After a general introduction explaining the main aspects of the National Housing Plan, an example of integrate assessment of rent market and difficulty in housing access is shown, obtained by a scaling that profile some Italian metropolitan reality. All our examples are aiming to demonstrate that only an integrate, multilevel approach, supported by appropriate analysis can succeed in improving supply and quality of housing stock. After a general economic analysis the paper speaks about use statistical data to identify territorial zones (by the use of hot spots) characterized by the presence of urban poverty related to property ownership and the availability of residential services.

The contribution is the result of joint reflections by the authors, with the following contributions attributed to S. Montrone (chapter 1), to M. Bilancia (chapter 2), to P. Perchinunno (chapter 3), and to C. M. Torre (chapters 4 and 5).

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Author information

Authors and Affiliations

  1. Department of Statistical Science, University of Bari, Via C. Rosalba 53, 70100, Bari, Italy

    Silvestro Montrone, Massimo Bilancia & Paola Perchinunno

  2. Department of Architecture and Urban Planning, Polytechnic of Bari, via Orabona 4, 70125, Bari, Italy

    Carmelo Maria Torre

Authors
  1. Silvestro Montrone
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  2. Massimo Bilancia
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  3. Paola Perchinunno
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  4. Carmelo Maria Torre
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

  2. School of Business Systems, Monash University, 3800, Clayton, Victoria, Australia

    David Taniar

  3. L.I.S.U.T. - D.A.P.I.T., University of Basilicata, Viale dell’Ateneo Lucano 10, 85100, Potenza, Italy

    Beniamino Murgante

  4. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, 06123, Perugia, Italy

    Antonio Laganà

  5. School of Computing, Computer Communication Laboratory, SoongSil University, 1-1 Sang-do 5 Dong, Dong-jak Ku, 156-743, Seoul, Korea

    Youngsong Mun

  6. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

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Montrone, S., Bilancia, M., Perchinunno, P., Torre, C.M. (2009). Economic Evaluation and Statistical Methods for Detecting Hot Spots of Social and Housing Difficulties in Urban Policies. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2009. ICCSA 2009. Lecture Notes in Computer Science, vol 5592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02454-2_18

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  • DOI: https://doi.org/10.1007/978-3-642-02454-2_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02453-5

  • Online ISBN: 978-3-642-02454-2

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