Abstract
For millennia, spatial planning has been based on human knowledge about the context and its environment together with some objectives of development. Now, with artificial intelligence and especially knowledge engineering, practices of spatial planning can be renovated. Presently, novel practices can be designed. In addition to human collective knowledge, some new chunks of knowledge can be introduced, coming from physical laws, administrative regulations, standards, data mining, and best practices. By big data analytics, some regularities and patterns can be discovered, which again will lead to new actions towards cities: in other words, there is a virtuous circle linking smart territories and big data that can be the basis for novel spatial planning. The role of this chapter will be to analyze those new chunks of knowledge and to explain how human knowledge, possibly coming from different stakeholders, can be harmonized with machine-processable knowledge as to be the basis for territorial intelligence.
TopIntroduction
The use of computing in urban planning began with the Baxter’s book (BAX, 1976) which was including several statistical and mathematical modeling aspects. Then gradually cartography and databases were integrated to give the well-known Geographic Information Systems in the 80s. Little by little some spatial analysis tools were included. Now the context is different for several reasons.
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The existence of many sensors, especially for environment and traffic controls is now mainstream; the consequence is that the novel GIS must capture those data in real time.
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The new development of visualization, giving the so-called geo-visualization has greatly renovated cartography by integrating animation, research of salient features and so on.
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The development of big data has led local authorities to envisage new methods to use those data by enriching their knowledge about city’s evolution.
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The trend of volunteered geographic information and the will of people to participate in decision making, under the banner of crowdsourcing have implied the necessity of dealing efficiently with those characteristics.
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Empowerment of people has increased the importance of several stakeholders who can have different logics; those logics can interfere with local authorities and can be in contradiction.
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The advances in artificial intelligence can also be integrated, transforming GIS into Geographic Knowledge systems.
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The multiplicity of experiences in various cities throughout the world has led to new organizations not only for technology watching, but overall by sociological watching, i.e. examining novel experiences which can be imported.
The objective of this chapter will be to study how urban knowledge can help smart urban planning. After presenting the background of this study, we will examine how to combine various types ok knowledge.
TopBackground
In this section, the concept of knowledge and especially geographic knowledge will be examined. Then a structure of geographic knowledge base will be studied.
Key Terms in this Chapter
Spurious Correlations: A spurious correlation is a mathematical relationship in which two or more events or variables are not causally related to each other, yet it may be wrongly inferred that they are, due to either coincidence or the presence of a certain third, hidden factor.
Knowledge: In information technology, an information able to be used to solve a problem.
Geographic Knowledge Base: A repository for geographic knowledge.
Gazetteer: A database for place names or toponyms.
Urban Knowledge: Information potentially useful to explain, manage, monitor, understand the past, plan a city or a metropolis, and innovate.
Geographic Knowledge: To information potentially useful to explain, manage, monitor, understand the past, plan a territory, and innovate.
Geographic Ontology: A semantic network describing the relationships between types of geographic objects.
Geospatial Rule/Geographic Rule: In information technology, a rule in which geographic places are involved.
Geographic Object: Computer description of a geographic feature as stored in a geographic data or knowledge base.