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

Moretto, Valentino; Elia, Gianluca; Schirinzi, Sara; Vizzi, Roberto; Ghiani, Gianpaolo

doi:10.1108/md-01-2021-0104

Cited by 7 publications

(7 citation statements)

References 86 publications

(120 reference statements)

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“…The seven dimensions of analysis include 15 sub-categories and 30 indicators (Moretto et al , 2021a) that allow to describe completely a territorial area by considering tangible and intangible resources, cultural and natural assets, in the aim to address its marginality (Pateman, 2011; Zuindeau, 2007; European Commission, 1991; World Commission on Environment and Development, 1987). The 30 indicators have updated and certified values (LAU2 – ex-NUTS4) and are present in both national (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, as for the third task related to the implementation of the knowledge visualization tool, a python-based system has been implemented to show the results of the application of each clustering algorithm to a data set composed of 30 indicators describing the 97 municipalities in the province of Lecce (Moretto et al , 2021a). The choice of the province of Lecce has been done based on the following three considerations: a deep knowledge of the territory by the authors from the inner areas perspective, which is valuable to validate the output of the approach proposed; the availability of data for each of the 97 municipalities, considering that this issue may be really a time-consuming task; and the presence of a significant number of municipalities that can be substantially considered as marginal areas.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, Moretto et al (2021a) focused on discovering similarities existing among different municipalities by using a knowledge discovery algorithm based on association mining method. Besides, by focusing on the tourism industry, Moretto et al (2021b) elaborated a composite index to measure the tourist competitiveness of marginal areas, in the final aim to support local policy makers in the process of local territorial development.…”

Section: Theory Backgroundmentioning

confidence: 99%

“…naturalistic and cultural resources of an Italian alpine region. A further study carried out byMastronardi et al (2020) proposed a cooperative-based approach as a factor capable of contributing to the reduction of regional gaps and increasing resilience and rebirth of inner areas by offering collective services, such as mobility and elderly care, and creating job opportunities in traditional profitable sectors, such as agriculture and land maintenance.Recently,Moretto et al (2021a) focused on discovering similarities existing among different municipalities by using a knowledge discovery algorithm based on association mining method. Besides, by focusing on the tourism industry,Moretto et al (2021b) elaborated a composite index to measure the tourist competitiveness of marginal areas, in the final aim to support local policy makers in the process of local territorial development.…”

mentioning

confidence: 99%

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

Moretto

Elia

Ghiani

2022

JKM

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Theory Backgroundmentioning

confidence: 99%

mentioning

confidence: 99%

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

Moretto

Elia

Ghiani

2022

JKM

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“…Over the last years, knowledge visualization has become an emerging area of research and has received more and more attention in managerial decision making (Moretto et al, 2022;Schiuma et al, 2022). Graphic representations support the strategic planning process (Eppler and Platts, 2009), help to map strategic consensus (Tarakci et al, 2014), and support transparency and inclusion in open strategy contexts (Paroutis and Knight, 2019).…”

Section: Calibration Of Knowledge and The Promise Of Visualizationsmentioning

confidence: 99%

Improving decision making through visual knowledge calibration

Muntwiler

Eppler

2023

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PurposeThis article aims to explore the so-called illusion of explanatory depth (IOED) of managers regarding their understanding of digital technologies and examines the effect of knowledge visualization one’s current understanding and decision making. Its purpose is to show that managers think they know more than they do and that this affects decision making but can be reduced through knowledge visualization.Design/methodology/approachIn two experiments with experienced managers, the authors investigate the size and impact of the IOED bias in decision making and examine if sketched self-explanations are as effective as written self-explanations to reduce the bias.FindingsThe findings show that experienced managers suffer from a significant illusion concerning their explanatory understanding of digital technologies and that sketching one’s current level of explanatory understanding of these technologies supports the accurate calibration of one’s knowledge. The findings indicate that sketching knowledge is a helpful modality for the detection and subsequent recalibration of biased knowledge in domain-dependent decision making.Originality/valueThis article is the first to explore the effect of sketched knowledge externalization on the calibration of explanatory knowledge of managers. It extends the literature on both, the IOED and on knowledge visualization as an instrument of knowledge calibration.

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