Marginality Assessment: Computational Applications on Italian Municipalities

Ghiani

2021

MBE

Purpose Differently from traditional approaches that rely on the analysis of single dimensions of the tourism phenomenon, this study aims to experiment a systemic approach based on structured and unstructured data sources to elaborate a composite index to measure the tourist competitiveness of marginal areas, with the final aim to design and plan proper socio-economic development strategies. Design/methodology/approach The methodology adopted to carry out the study follows a four-step process and relies on indicators that are both relevant and accessible. The first step concerns the analysis of the literature about the existing approaches to calculate a tourism index. The second step concerns the definition of the indicators and the collection of data by using both structured and unstructured sources. The third step focuses on the population of the data set. Finally, the fourth step aims at calculating the tourism index through a composite-based methodology and using it for a pilot application in a Southern Italy province. Findings The study calculates a synthetic tourism index for each of the 97 municipalities of the Province of Lecce (a city located in the southeast of Italy). The proposed index combines administrative, institutional and open data sources to derive a single indicator for each municipality, thus supporting decision-makers in understanding the complex reality and competitiveness level of territories in the tourism industry. Originality/value The main elements of originality of the study are the breadth and typology of data sources considered to calculate the composite indicator of tourism competitiveness (both structured and unstructured); and the use of weighting and aggregation procedures in the methodological issues.

Section: Discussionmentioning

confidence: 69%

A composite tourism index for the competitiveness of marginal areas: a pilot application in a Southern Italy province

Ghiani

2021

MBE

“…Thus, such approach can enhance previous studies (Bertolini and Pagliacci, 2017;Marucci et al, 2020) that focus only on structured datasets. The results obtained show that policy makers can adopt different perspectives to define and identify inner areas, with the ultimate objective to elaborate a more coherent strategy for local territorial development.…”

Section: Knowledge Visualization For Inner Areasmentioning

confidence: 71%

“…In the last decade, several attempts have been developed to categorize inner areas (Bertolini and Pagliacci, 2017; Battaglia, 2019; Ferri, 2017; Marucci et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

A knowledge visualization approach to identify and discovery inner areas: a pilot application in the province of Lecce

Schirinzi³

et al. 2021

PurposeThe paper aims to propose a knowledge visualization approach and algorithm to support public decision makers to define the inner areas, which represents a strategic topic in the European debate about territorial inequality and development.Design/methodology/approachThe study has been developed by following the design science research, which includes six steps: problem identification and motivation; identification of the objectives for a solution; design and development; demonstration; evaluation; and communication. As for the design and development step, the proposed approach and algorithm ground on association mining to discover hidden relationships existing among municipalities. They have been applied to analyse the 97 municipalities of the Lecce province, and each municipality has been described through 30 multi-domain indicators organized into seven categories, whose data have been collected from institutional datasets, local sources or web-scraping process.FindingsA set of complementary analyses has been generated through the construction of dynamic and interactive knowledge maps that show “similar” municipalities according to the indicators selected.Originality/valueThe approach and algorithm proposed allow discovering similarities existing among distinct municipalities, based on the analysis of a set of multi-domain indicators. The approach may complement or completely substitute the existing ones used to define inner areas, thus overcoming both the methodological limits of the “top-down” line imposed by the central legislator, and the “bottom-up” paradox consisting in the illusion that single (and often small) towns have the economic and cognitive resources necessary to implement effective territorial mapping and development strategies. In such a way, policy makers can be aware on similarities existing among distinct towns and can thus share cognitive and financial resources to define a common plan and a set of practices for territorial development.

“…Actually, knowledge discovery aims to extract interesting and potentially useful information from massive collections of data (Wu et al , 2009) and ultimately derive understandable patterns in data (Marucci et al , 2020; Feyyad, 1996; Vercellis, 2009) that the human ability is usually not able to recognize (Kasemsap, 2018; Manaligod et al , 2020; Tao et al , 2020). Knowledge discovery embraces multiple domains and communities including computational learning theory, machine learning and data mining (Grobelnik and Mladenić, 2005).…”

Section: Theory Backgroundmentioning

confidence: 99%

Leveraging knowledge discovery and knowledge visualization to define the “inner areas”: an application to an Italian province

Ghiani

2022

JKM

Purpose Starting from a critical analysis of the main criteria currently used to identify marginal areas, this paper aims to propose a new classification model of such territories by leveraging knowledge discovery approaches and knowledge visualization techniques, which represent a fundamental pillar in the knowledge-based urban development process. Design/methodology/approach The methodology adopted in this study relies on the design science research, which includes five steps: problem identification, objective definition, solution design and development, demonstration and evaluation. Findings Results demonstrate how to exploit knowledge discovery and visualization to obtain multiple mappings of inner areas, in the aim to identify good practices and optimize resources to set up more effective territorial development strategies and plans. The proposed approach overcomes the traditional way adopted to map inner areas that uses a single indicator (i.e. the distance between a municipality and the nearest pole where it is possible to access to education, health and transportation services) and leverages seven groups of indicators that represent the distinguishing features of territories (territorial capital, social costs, citizenship, geo-demography, economy, innovation and sustainable development). Research limitations/implications The proposed model could be enriched by new variables, whose value can be collected by official sources and stakeholders engaged to provide both structured and unstructured data. Also, another enhancement could be the development of a cross-algorithms comparison that may reveal useful to suggest which algorithm can better suit the needs of policy makers or practitioners. Practical implications This study sets the ground for proposing a decision support tool that policy makers can use to classify in a new way the inner areas, thus overcoming the current approach and leveraging the distinguishing features of territories. Originality/value This study shows how the availability of distributed knowledge sources, the modern knowledge management techniques and the emerging digital technologies can provide new opportunities for the governance of a city or territory, thus revitalizing the domain of knowledge-based urban development.