How to efficiently manage a city or to plan a territory under the perspective of sustainable development? How to involve citizens in thinking about their future? What can be the assistance of information technologies in this context? What can be the main requirements for organizing smart cities? Possible answers lie in the concept of territorial intelligence.

Even if computers have been used in urban and environment planning (Baxter, 1976) from several decades especially under the form of Geographic Information Systems –GIS– (Laurini, 2001) for storing, analyzing and mapping, little has been made in the use of artificial intelligence in this domain. Moreover, the involvement of citizens is also a key-issue in this perspective.

In the context of companies, business intelligence is overall seen as the use of business rules which are a way to model the key-elements for reasoning or making decisions essentially based on mathematical logics (first-order, fuzzy, predicate-based, propositional, etc.). For territories, it is more complex because in addition to those logics, 2D space must be taken into account, i.e. by including topology and geometry in reasoning.

The role of this paper will be to analyze several definitions of territorial intelligence and to shape ITC tools which can help local decision-makers in their mission of planning and managing territories.

Remember that the ultimate goal is to implement such a system; but a lot of preliminary studies must be carried out and integrated into a research agenda before reaching this goal; and one of them is to model reality in order to be acceptable in artificial intelligence systems, and more precisely knowledge management systems. Three theoretical directions must be followed, (i) to clarify the semantics of geographic knowledge, (ii) to clarify what is need in territorial intelligence for efficient planning and governance, and (iii) to exhibit a knowledge model integrating all those issues.

In the literature, few studies have been made concerning knowledge-based territorial planning. However, let us mention Yeh and Qiaoô (2004) and Yigitcanlar (2016) targeting more on economic development than governance.

This paper will be organized as follows. First the role of business intelligence in companies will be examined. Then various works and perspectives dealing with territorial intelligence will be detailed. Finally, the structure of a geographic inference engine linked to a knowledge base will be presented as fundamental steps to answer the research problem.

There are several definitions of the expression “Business Intelligence”. For instance, Ranjan (2009) declares that: &&

Business Intelligence –BI– has two basic different meanings related to the use of the term intelligence. The primary, less frequently, is the human intelligence capacity applied in business affairs/activities. Intelligence of Business is a new field of the investigation of the application of human cognitive faculties and artificial intelligence technologies to the management and decision support in different business problems. The second related to the intelligence as information valued for its currency and relevance. (Ranjan, 2009, p. 60)

In companies, business intelligence is very commonly used because benefits are important, and is always linked to knowledge, and in particular, rules. According to von Halle:

business rules are the ultimate levers with which business management is able to guide and control the business. In fact, the business’s rules are the means by which an organization implements competitive strategy, promotes policy, and complies with legal obligations. (von Halle & Goldberg, 2006, p. 25)

Moreover, Joosten, Wedemeijer, & Michels (2010) declare that a rule must have a scope, stakeholders and are verifiable. For Ross (2011), rules must be considered as first-class citizens in information technologies and can be essentially modeled by IF-THEN fact or IF-THEN action.

As business intelligence appears as the basis for the management of companies, what about the management and planning in local authorities in which several stakeholders can have very different points of view with different background, experiences and knowledge? This will be the scope of this paper to examine how business intelligence can be transformed into territorial intelligence.

In 2004, Bertacchini defined territorial intelligence as:

an informational and anthropological, regular and continuous process initiated by local actors, physically present and/or remote, who appropriate some space resources by mobilizing and then transforming the energy of the territorial systems into the figuratively project's ability. As such, territorial intelligence can be assimilated to territoriality which results from the phenomenon of appropriation of the resources of a territory and then to transfers of responsibilities between local actors from different cultures. The objective of this approach is to ensure, literally as well as figuratively, to equip the territorial level to be developed what we have named the territorial formal capital. (Bertacchini, 2004, online consultation, plain text)

But, according to Girardot (2008), a first definition is as follows “Territorial intelligence is the science having for object the sustainable development of territories and having for subject the territorial community”. More precisely, he continues by claiming:

Territorial intelligence is a way for researchers, for the actors and the territorial community to acquire a better knowledge of the territory, but also to have a better control of its development. The appropriation of technologies of information and communication, and the information itself, is a necessary step to actors for entering into a learning process that will enable them to be relevant and efficient. Territorial intelligence is particularly useful to help the territorial actors to plan, define, animate and evaluate the policies and actions of sustainable territorial development. (Girardot, 2008, online consultation, plain text)

Then in 2010, Girardot declared “If there is a relationship between economic intelligence and territorial intelligence in the sense where they use both technologies of information, they are different however in what concerns their objectives for development and governance” (Girardot, 2010, online consultation, plain text).

Figure 1 Figure 1 Definition of territorial intelligence Source: Own elaboration.

All those definitions refer to governance and decision-making, but for our concern, we think that the cross-fertilization between human intelligence and computer intelligence must be included. So, let us propose a schema as illustrated in Figure 1 in which territorial intelligence supposes to deal with some territory where people are living, and with the assistance of artificial intelligence towards is a path to sustainable development. In other words, the combination of collective human intelligence with computer intelligence must lead to a better future.

One of the key aspects of urban planning is public participation. The ways of citizens are involved in urban planning can vary a lot from countries. Even if the number of existing experiments is very limited, we think of a paramount importance in a modern democracy to offer city-dwellers some tools for, at the maximum for designing the city, or at the minimum, to be fully aware about what their future urban environment will be. In order to reach those ideals, among the problems to solve, let us mention:

Participative plan design, that is to say the way of involving citizens in the design of local plans; one of the possibilities is the organization of debates concerning environmental and urban planning;

In this domain, we need to distinguish a very common character in the domain of public participation, named NIMBY (Not in my backyard!) representing people defending only their own property, often very aggressive in environmental dispute, and using general interest to protect their private interests. Sometimes, they “pollute” the debate. According to Craig (1998), organizing public participation in a city can have the following objectives:

Expand the public's role in defining questions and making decisions in which location or geography have a bearing on the issues addressed;

More practically, the public can be involved for the following collaborative planning processes such as public dispute resolution, facility siting/design review, futures and scenario planning.

The problem of the various degrees of involving people in land use planning is very old. In 1969, Arnstein (1969) proposed the first ladder for public participation with eight steps, manipulation, therapy, informing, consultation, placation, partnership, power delegation and citizen control. But this ladder was not seen adequate. Starting from a previous work made some years before, Kingston (1998) has proposed a six-step ladder (Figure 2) which appears more relevant for our purpose. Among the steps, one can successively find from bottom to top (the lower steps meaning no real public participation):

Public right to know; in this first level phase, the public has only the possibility to be aware that some planning issue could be of interest;

Figure 2 Figure 2 The public participation ladder according to Kingston (1998) with modifications Source: Own elaboration.

As a conclusion to this section, let us say that Territorial Intelligence by involving and empowering citizens at the higher level, is truly at the upper layer of this ladder. But the amalgamation of human collective and computer-based intelligence is truly a challenge for territorial planning and management. A key-element is that other disciplines than logics must be integrated to solve those issues.

The purpose of a declarative rule-based model is to authorize automatic reasoning. In contrast to expert systems of the past (Kim et al., 1989; Batty & Yeh, 1991) that were using simple logics, in our case, it will be very different. Here are some examples:

Define the location of a new airport, a new hospital, a new stadium, social housing, etc.

From these examples, it can be seen that geographic reasoning refers not only to logics (predicate, propositional or fuzzy) but also to other mathematical disciplines such as spatial reasoning by the integration of topology and computational geometry, graph theory, spatial analysis, simulation techniques, logic fuzzy and reasoning, multi-criteria decision theory and operations research.

Once the language and territory selected, a geographic knowledge base may include an ontology, a gazetteer, geographic objects and the relationships between these objects, rules and mathematical models, together with external knowledge. Remember that Laurini (2017):

Ontologies, as especially geographic ontologies or urban ontologies must not only include feature types, but also spatial relationships between them as explained in Kavouras, Kokla & Tomai (2005), Laurini (2012) and Laurini (2014).

As input, there will be a territorial project to study so as to determine, through the geographic inference engine, the consequences in output. These consequences will be grouped in a result of feasibility that may be in the form of maps, schemata, texts, etc. Figure 4 shows such a knowledge base linked to a geographic inference engine that allows reasoning.

Figure 4 Figure 4 Structuring a geographic knowledge base Source: Own elaboration.

The goal of this paper was to emphasize the importance of human and artificial intelligence for territorial governance. By means of a geographic knowledge base, local shareholders can, not only store their own knowledge about the territory they want to manage or to plan, but also make simulations to about the future.

We need to continue the analysis in order to include all aspects of geographic knowledge. By doing so, an efficient model will be proposed especially for geographic rules.

For the moment being, only 2D and 2.5D knowledge was dealt with, but we have the perspective to include as far as possible three-dimensional and temporal issues.

As explained in the introduction, this research can be seen as a preliminary step to design and implement a knowledge-based system for territorial intelligence. More generally, other preliminary steps should include:

Identification of knowledge chunks, including rules, and their characteristics,

Only after those steps are fully completed, such a system will be operational and applied to a real territory.