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.

中文翻译：

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利用知识发现和知识可视化来定义“内部区域”：意大利某省的应用

目的

本文从对目前用于识别边缘地区的主要标准进行批判性分析开始，旨在通过利用知识发现方法和知识可视化技术提出一种新的边缘地区分类模型，这是知识型城市发展的基本支柱过程。

设计/方法/方法

本研究采用的方法论依赖于设计科学研究，包括五个步骤：问题识别、目标定义、解决方案设计与开发、论证与评估。

发现

结果展示了如何利用知识发现和可视化来获得内部区域的多个映射，旨在识别良好实践并优化资源以制定更有效的区域发展战略和计划。所提议的方法克服了绘制内部区域的传统方法，该方法使用单一指标（即市镇与可以访问教育、健康和交通服务的最近极点之间的距离），并利用七组指标代表领土的显着特征（领土资本、社会成本、公民身份、地理人口、经济、创新和可持续发展）。

研究限制/影响

提议的模型可以通过新变量来丰富，其价值可以由官方来源和参与提供结构化和非结构化数据的利益相关者收集。此外，另一个改进可能是跨算法比较的发展，这可能有助于建议哪种算法可以更好地满足政策制定者或从业者的需求。

实际影响

本研究为提出决策支持工具奠定了基础，决策者可以使用该工具以新的方式对内部区域进行分类，从而克服当前的方法并利用区域的显着特征。

原创性/价值

本研究展示了分布式知识资源的可用性、现代知识管理技术和新兴的数字技术如何为城市或地区的治理提供新的机会，从而振兴以知识为基础的城市发展领域。