Of cells and cities: a comparative Econometric and Cellular Automata approach to Urban Growth Modeling

This paper presents a comparative assessment of two distinct urban growth modeling approaches. The first urban model uses a traditional Cellular Automata methodology, based on Markov transition chains to prospect probabilities of future urban change. Drawing forth from non-linear cell dynamics, a multi-criteria evaluation of known variables prospects the weights of variables related to urban planning (road net- works, slope and proximity to urban areas). The latter model, frames a novel approach to urban growth modeling using a linear Logit model (LLM) which can account for region specific variables and path depen- dency of urban growth. Hence, the drivers and constraints for both models are used similarly and the same study area is assessed. Both models are projected in the segment of Faro-Olh ̃ao for 2006 and a comparative assessment to ground truth is held. The calculation of Cohenââ¬â¢s Kappa for both projections in 2006 allows for an assessmentof both models. This instrumental approach illuminates the differ- ences between the traditional model and the new type of urban growth model which is used. Both models behave quite differently: While the Markov Cellular Automata model brings an over classification of ur- ban growth, the LLM responds in the underestimation of urban sprawl for the same period. Both excelled with a Kappa calculation of over 89%, and showed to have fairly good estimations for the study area. One may conclude that the Markov CA Model permits a riper un- derstanding of urban growth, but fails to analyze urban sprawl. The LLM model shares interesting results within the possibility of identi- fying urban sprawl patterns, and is therefore an interesting solution for some locations. Another advantage of the LLM is directly linked to the possibility of establishing probability for urban growth. Thus, while the traditional methodology shared better results, LLM can be also an interesting estimate for urban patterns from an econometric perspective. Hence further research is needed in exploring the utility of spatial econometric approaches to urban growth.

STREETS: an agent-based pedestrian model.

8-11 Septembe

The Repast Simulation/Modelling System for Geospatial Simulation

The use of simulation/modelling systems can simplify the implementation of agent-based models. Repast is one of the few simulation/modelling software systems that supports the integration of geospatial data especially that of vector-based geometries. This paper provides details about Repast specifically an overview, including its different development languages available to develop agent-based models. Before describing Repast’s core functionality and how models can be developed within it, specific emphasis will be placed on its ability to represent dynamics and incorporate geographical information. Once these elements of the system have been covered, a diverse list of Agent-Based Modelling (ABM) applications using Repast will be presented with particular emphasis on spatial applications utilizing Repast, in particular, those that utilize geospatial data

Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded

Exploring Cities Using Agent-Based Models and GIS

Cities are faced with many problems such as urban sprawl, congestion, and segregation. They are also constantly changing. Computer modelling is becoming an increasingly important tool when examining how cities operate. Agent based models (ABM) allow for the testing of different hypotheses and theories for urban change, thus leading to a greater understanding of how cities work. This paper presents how ABMs can be developed by their integration with Geographical Information System (GIS). To highlight this, a generic ABM is presented. This is then applied to two model applications: a segregation model and a location model. Both models highlight how different theories can be incorporated into the generic model and demonstrate the importance of space in the modelling process. Cities are faced with many problems such as urban sprawl, congestion, and segregation. They are also constantly changing. Computer modelling is becoming an increasingly important tool when examining how cities operate. Agent based models (ABM) allow for the testing of different hypotheses and theories for urban change, thus leading to a greater understanding of how cities work. This paper presents how ABMs can be developed by their integration with Geographical Information System (GIS). To highlight this, a generic ABM is presented. This is then applied to two model applications: a segregation model and a location model. Both models highlight how different theories can be incorporated into the generic model and demonstrate the importance of space in the modelling process

Key challenges in agent-based modelling for geo-spatial simulation

Agent-based modelling (ABM) is fast becoming the dominant paradigm in social simulation due primarily to a worldview that suggests that complex systems emerge from the bottom-up, are highly decentralised, and are composed of a multitude of heterogeneous objects called agents. These agents act with some purpose and their interaction, usually through time and space, generates emergent order, often at higher levels than those at which such agents operate. ABM however raises as many challenges as it seeks to resolve. It is the purpose of this paper to catalogue these challenges and to illustrate them using three somewhat different agent-based models applied to city systems. The seven challenges we pose involve: the purpose for which the model is built, the extent to which the model is rooted in independent theory, the extent to which the model can be replicated, the ways the model might be verified, calibrated and validated, the way model dynamics are represented in terms of agent interactions, the extent to which the model is operational, and the way the model can be communicated and shared with others. Once catalogued, we then illustrate these challenges with a pedestrian model for emergency evacuation in central London, a hypothetical model of residential segregation tuned to London data which elaborates the standard Schelling (1971) model, and an agent-based residential location built according to spatial interactions principles, calibrated to trip data for Greater London. The ambiguities posed by this new style of modelling are drawn out as conclusions

Astrobiological Complexity with Probabilistic Cellular Automata

Search for extraterrestrial life and intelligence constitutes one of the major endeavors in science, but has yet been quantitatively modeled only rarely and in a cursory and superficial fashion. We argue that probabilistic cellular automata (PCA) represent the best quantitative framework for modeling astrobiological history of the Milky Way and its Galactic Habitable Zone. The relevant astrobiological parameters are to be modeled as the elements of the input probability matrix for the PCA kernel. With the underlying simplicity of the cellular automata constructs, this approach enables a quick analysis of large and ambiguous input parameters' space. We perform a simple clustering analysis of typical astrobiological histories and discuss the relevant boundary conditions of practical importance for planning and guiding actual empirical astrobiological and SETI projects. In addition to showing how the present framework is adaptable to more complex situations and updated observational databases from current and near-future space missions, we demonstrate how numerical results could offer a cautious rationale for continuation of practical SETI searches.Comment: 37 pages, 11 figures, 2 tables; added journal reference belo