The Italian National Strategy for Inner Areas (SNAI): A Critical Analysis of the Indicator Grid

The National Strategy for Inner Areas (SNAI) is a public policy designed to tackle depopulation in inner areas, defined according to the distance from centers offering essential services. Such a policy’s success is crucial to address the new challenges for planning brought to light by the COVID-19 pandemic. In this sense, there is a need to adequately support its implementation by providing handy decision support tools, understanding the power balances among municipalities, and defining proper interventions. The Indicator Grid, already used by the SNAI for project areas selection, can answer this need. However, the Grid’s application to support public policy at the municipality level requires reviewing some of its features, such as the indicators’ large number and the impossibility of defining some of them at the municipal scale. Based on these premises, this paper aims at supporting inner areas policies by carrying out a critical analysis of the current SNAI Grid, aimed at improving its effectiveness. It relies on a hybrid methodology that merges qualitative data interpretations and statistical analyses. Thanks to this method, defining a parsimonious Grid by leaving its complexity and information level untouched is possible. The so-defined set of indicators can represent a valuable reference tool in pinpointing priorities for actions or selecting further territorial scopes from the SNAI perspective, even if it still brings some criticalities to be faced.

[1]  Jordan P. Smith,et al.  Planning urban community gardens strategically through multicriteria decision analysis , 2020 .

[2]  Davide Giacomini,et al.  The introduction of mandatory inter-municipal cooperation in small municipalities: preliminary lessons from Italy , 2018 .

[3]  A. Müller,et al.  The Role of Spatial Data and Spatial Information in Strategic Spatial Planning , 2012 .

[4]  An Gie Yong,et al.  A Beginner's Guide to Factor Analysis: Focusing on Exploratory Factor Analysis , 2013 .

[5]  Yi Shi,et al.  Revealing the Correlation between Population Density and the Spatial Distribution of Urban Public Service Facilities with Mobile Phone Data , 2020, ISPRS Int. J. Geo Inf..

[6]  A. G. Asuero,et al.  The Correlation Coefficient: An Overview , 2006 .

[7]  Zhen Huang,et al.  Minimizing data redundancy for high reliable cloud storage systems , 2015, Comput. Networks.

[8]  S. Lucatelli La strategia nazionale, il riconoscimento delle aree interne , 2015 .

[9]  J. D. P. Valenciano,et al.  Mapping green infrastructure and socioeconomic indicators as a public management tool: the case of the municipalities of Andalusia (Spain). , 2020 .

[10]  H. Rittel,et al.  Dilemmas in a general theory of planning , 1973 .

[11]  E. Asprogerakas,et al.  The EU territorial cohesion discourse and the spatial planning system in Greece , 2020, European Planning Studies.

[12]  F. Barca Alternative Approaches to Development Policy , 2011 .

[13]  Marilena Vecco,et al.  A definition of cultural heritage: From the tangible to the intangible , 2010 .

[14]  Arthur Zimek,et al.  Redundancies in Data and their Effect on the Evaluation of Recommendation Systems: A Case Study on the Amazon Reviews Datasets , 2017, SDM.

[15]  Thomas J. Archdeacon Correlation and regression analysis : a historian's guide , 1994 .

[16]  G. Cotella,et al.  The Italian National Strategy for Inner Areas , 2020 .

[17]  G. Dematteis Montagna e aree interne nelle politiche di coesione territoriale italiane ed europee , 2013 .

[18]  Grazia Brunetta,et al.  Mapping Urban Resilience for Spatial Planning—A First Attempt to Measure the Vulnerability of the System , 2019, Sustainability.

[19]  Marcin Stępniak,et al.  The decade of the big push to roads in Poland: Impact on improvement in accessibility and territorial cohesion from a policy perspective , 2015 .

[20]  Ayyoob Sharifi,et al.  The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management , 2020, Science of The Total Environment.

[21]  Francesca Abastante,et al.  Choice architecture for architecture choices: Evaluating social housing initiatives putting together a parsimonious AHP methodology and the Choquet integral , 2018, Land Use Policy.

[22]  D. Russell In Search of Underlying Dimensions: The Use (and Abuse) of Factor Analysis in Personality and Social Psychology Bulletin , 2002 .

[23]  A. Oppio,et al.  Cultural Heritage Preservation and Territorial Attractiveness: A Spatial Multidimensional Evaluation Approach , 2020 .

[24]  Fergal O’Leary,et al.  Towards greater collective impact: Building collaborative capacity in Cork city’s LCDC , 2020 .

[25]  A. Cavallo,et al.  Rural Identity, Authenticity, and Sustainability in Italian Inner Areas , 2020, Sustainability.

[26]  J. Schmude,et al.  The Right to Not Catch Up—Transitioning European Territorial Cohesion towards Spatial Justice for Sustainability , 2020, Sustainability.

[27]  Vikash V. Gayah,et al.  The potential of parsimonious models for understanding large scale transportation systems and answering big picture questions , 2012, EURO J. Transp. Logist..

[28]  E. Hanushek,et al.  Land-use Controls, Fiscal Zoning, and the Local Provision of Education , 2015 .

[29]  Eduardo Medeiros European Union Cohesion Policy and Spain: a territorial impact assessment , 2017 .

[30]  E. Hanushek,et al.  Private Schools and Residential Choices: Accessibility, Mobility, and Welfare , 2011 .

[31]  Carlos Aguirre-Nuñez,et al.  Towards a Walkable City: Principal Component Analysis for Defining Sub-Centralities in the Santiago Metropolitan Area , 2020, Land.

[32]  G. Carrosio A place-based perspective for welfare recalibration in the Italian inner peripheries: the case of the Italian strategy for inner areas , 2016 .

[33]  Maria Rosaria Guarini,et al.  A Methodology for the Selection of Multi-Criteria Decision Analysis Methods in Real Estate and Land Management Processes , 2018 .

[34]  Ashoke Kumar Sarkar,et al.  The 7 th International Conference on Ambient Systems , Networks and Technologies ( ANT 2016 ) Application of Principal Component Analysis for Outlier Detection in Heterogeneous Traffic Data , 2016 .