A Linguistic Approach to Model Urban Growth

Abstract

This paper presents a linguistic approach for modeling urban growth. The authors developed a methodological framework which utilizes Fuzzy Set theory to capture and describe the effect of urban features on urban growth and applies Cellular Automata techniques to simulate urban growth. Although several approaches exist that combine Fuzzy Logic and Cellular Automata for urban growth modeling, the authors focused on the ability to use partial knowledge and combine theory-driven and data driven knowledge. To achieve this, a parallel connection between the input variables is introduced which further allows the model to disengage from severe data limitations. In this approach, a number of parallel fuzzy systems are used, each one of which focuses on different types of urban growth factors, different drivers or restrictions of development. The effects of all factors under consideration are merged into a single internal thematic layer that maps the suitability for urbanization for each area, providing thus an information flow familiar to the human conceptualization of the phenomenon. Following, cellular automata techniques are used to simulate urban growth. The proposed methodology is applied in the Mesogeia area in the Attica basin (Athens) for the period 1990-2004 and provides realistic estimations for urban growth.

Thinking platforms for smarter urban water systems: fusing technical and socio-economic models and tools

“…Therefore, a more sophisticated tool was developed and implemented that outputs a multiple-state view of urban growth. The tool is a fuzzy constrained cellular automata model (Liu 2009), based on the work of Mantelas et al (2012). The key characteristics of the tool are the combined use of fuzzy logic with CA techniques, as well as the multiple-state (i.e.…”

“…(1) An Urban Growth Algorithm, similar to the one presented and successfully tested by Mantelas et al (2012) and Rozos et al (2011), decides which non-urban cells are to be urbanized in each time step. Two rules of urban expansion and one rule of (distance-based) spontaneous growth (in areas without neighbouring urban cores) are applied (Mantelas et al 2012).…”

“…Two rules of urban expansion and one rule of (distance-based) spontaneous growth (in areas without neighbouring urban cores) are applied (Mantelas et al 2012). This rule is based on an (intermediate) binary urban image of each time step (including distraction between urban and non-urban cell types only), so in step 1, different cell states are ignored.…”

Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: application to Maragheh, Iran

“…In this regard, LUC models (Verburg et al 2004;Estoque and Murayama 2014) are valuable tools to detect ecological areas that are threatened due to urban growth (Shafizadeh Moghadam and Helbich 2015;Jokar Arsanjani, Helbich, and Mousivand 2014). Even though several LUC models have been proposed in the literature, including, for example, statistical approaches (Shafizadeh Moghadam and Helbich 2015) and geometrical approaches (Tayyebi, Pijanowski, and Pekin 2011b;Jokar Arsanjani, Helbich, and Mousivand 2014), among these models, cellular automata (CA; Santé et al 2010) and linguistic approaches (Mantelas et al 2012) such as fuzzy logic-based approaches (Grekousis, Manetos, and Photis 2013) and ANNs (Tayyebi, Tayyebi, and Khanna 2014;Basse et al 2014) have received considerable attention due to their dynamic nature, being explicitly spatial with a highly flexible structure.…”

Socio-hydrology from the bottom up: A template for agent-based modeling in irrigation systems

“…Interesting areas of research emerge in case collected data allow for more elaboration on the 25 architecture of the decision-making rules; for instance, agents could base their probabilistic reasoning based on a linguistic approach that utilizes fuzzy logic, with the use of Fuzzy Implementation Systems (Bouziotas et al, 2014;Rozos et al, 2011) . This brings the agent decision-making process closer to the "fuzzier", real human reasoning (Li et al, 2004;Mantelas et al, 2012).…”