Regional Knowledge: Sources, Representation and Management

The contribution analyses the forms of regional knowledge, its sources, the characteristics of information that can be collected, filtered, represented and managed to extract knowledge to benefit territorial decision makers. Regional knowledge emerges and develops over long periods of time, shaping types of regional activities, vocations and industry structures. Thus, main aspects of the definition of regional knowledge are the geographic and temporal dimensions. Starting from a categorization of regional knowledge into conscious knowledge, that is explicit knowledge held by individuals; objectified knowledge, that is explicit knowledge held by organizations and experts; preconscious knowledge, that is automatic knowledge held by individuals expressed unintentionally; and collective knowledge, implicit knowledge expressed in the practice of organizations, three frameworks for representing and managing information of different nature are indicated as suitable to extract different forms of knowledge, namely: non classical logics, and specifically fuzzy logic, to represent and reason on imprecise conscious and collective knowledge; machine learning to extract preconscious knowledge implicit in geo big data and created by both IoT and within social networks; Petri Nets to formalize objectified knowledge in the form of laws and regulations. For each form of knowledge case studies are illustrated and discussed highlighting the benefits of the adopted formal frameworks. Conclusions summarize the main message and open issues.