Finally, the paper concludes with some design recommendations for such a toolkit. This survey is not meant to be a comparative review of these toolkits but rather it was conducted to determine what useful design principles can be gathered from them that might inform a new " ideal " ABM-GIS toolkit. The toolkits were surveyed to see how well they fulfill some of the design principles. However, the same workflow can of course be applied to other types of output. We first outline several design principles for an ABM-GIS toolkit and then describe a survey of extant toolkits (RepastPy, NetLogo, and MobiDyc) that were selected based on the design principles. In this example we write ascii raster files using the GIS extension in NetLogo. This paper is a first step toward that goal. Thus providing a detailed GUI to access an integrated ABM-GIS toolkit would vastly expand the number of users for such a toolkit. One problem with an integrated toolkit is that most GIS users are not programmers, but most GIS users are familiar with the use of detailed graphical user interfaces (GUIs) in order to create complex visualizations of data. The integration of these two tool sets into a cohesive package would allow for elegant modeling of both process and pattern. The NetLogo environment possesses several of the basic characteristics of GIS software, in the sense that it keeps track of spatial data in a systematic way, and can be used to create visualizations of spatial phenomena. In order to do that, more powerful spatial modeling techniques, like those within geographical information systems (GIS), are necessary. However, less emphasis has been placed in ABM on developing its ability to replicate spatial patterns of phenomena. Many of the early successes of ABM were due to its ability to represent the processes of a phenomenon. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.Īgent-based modeling (ABM) has proved useful in a number of fields. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. In this article, we introduce ABM and its utility for studying geographical systems. Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems.
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