Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Understanding Social and Ecological Processes

Gimblett, H. Randy

doi:10.1093/oso/9780195143362.001.0001

Cited by 60 publications

(23 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, perhaps with the demonstration of the Sugarscape model by Epstein and Axtell (1996), which showed how the environment can affect agents' wealth and survival, modelers started to realize that the artificial world that the agents inhabited could be stylized on geographical data. From earlier works such as those by Gimblett (2002) or Benenson and Torrens (2004) to current day work (e.g. Crooks et al 2019), researchers have utilized data not only to represent the physical aspects of the artificial world (e.g.…”

Section: Integrating Data and Decision-making Into Agent-based Modelsmentioning

confidence: 99%

Agent-Based Modeling and the City: A Gallery of Applications

et al. 2021

View full text Add to dashboard Cite

Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.

show abstract

Section: Integrating Data and Decision-making Into Agent-based Modelsmentioning

confidence: 99%

Agent-Based Modeling and the City: A Gallery of Applications

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In contrast to earlier Cellular Automata approaches, which model discrete dynamic systems-that is, land use change-ABMs do not reach equilibrium but elaborate on the question how a system reacts to changed situations (Macal & North, 2010). The integration of ABMs and GIS is described in (e.g., Batty et al, 2012;Brown et al, 2005;Gimblett, 2002;Hofmann et al, 2014;Johnston, 2013;Koch, 2008;Mandl, 2003;Minar, Burkhart, Langton, & Askenazi, 1996;Raubal, 2001;Turner & Penn, 2002;Van Berkel & Verburg, 2012). Bonabeau (2002) discuss the application of ABM to social, economical and ecological problems-referred to as human problems in this article.…”

Section: Relevant Literaturementioning

confidence: 99%

Simulating the forest fuel market as a socio‐ecological system with spatial agent‐based methods: A case study in Carinthia, Austria

Scholz

Breitwieser

Mandl³

2020

Natural Resource Modeling

View full text Add to dashboard Cite

The paper presents an agent-based modeling and simulation approach to model the forest fuel supply chain for heating purposes (i.e., heating plants). The paper focuses on the simulation of the processes of timber harvesting by forest enterprises and the competition of heating plants for the limited resource of wood chips. In particular, the work identifies different stakeholders having an adaptive behavior-with respect to the overall market conditions and timber prices. The agent-based model developed here-called SimFoMa-uses three types of agents-forest enterprises, heating plants, and traders. The agents are interacting in an environment that has rich information on the forests and road network. The SimFoMa model is applied to a test area, the province of Carinthia, Austria. We defined six different simulation scenarios that cover different market situations-from increasing timber prices, volatile market conditions, or decreasing market conditions-and evaluated the harvest patterns, transport distances and the forest itself. The paper utilizes the agent-based modeling methodology to model the agent's adaptive behavior of

show abstract

“…In the process of coupling human and environmental systems with urban modelling, understanding how humans make decisions is of paramount importance (Gimblett, 2002; An, 2012). Human decisions and subsequent actions change the structure and function of many environmental systems, which in turn influence human decisions and actions.…”

Section: Contemporary Issues In Urban Ca Modellingmentioning

confidence: 99%

Modelling urban change with cellular automata: Contemporary issues and future research directions

Liu

Batty

Wang

et al. 2019

Progress in Human Geography

View full text Add to dashboard Cite

The study of land use change in urban and regional systems has been dramatically transformed in the last four decades by the emergence and application of cellular automata (CA) models. CA models simulate urban land use changes which evolve from the bottom-up. Despite notable achievements in this field, there remain significant gaps between urban processes simulated in CA models and the actual dynamics of evolving urban systems. This article identifies contemporary issues faced in developing urban CA models and draws on this evidence to map out four interrelated thematic areas that require concerted attention by the wider CA urban modelling community. These are: (1) to build models that comprehensively capture the multi-dimensional processes of urban change, including urban regeneration, densification and gentrification, in-fill development, as well as urban shrinkage and vertical urban growth; (2) to establish models that incorporate individual human decision behaviours into the CA analytic framework; (3) to draw on emergent sources of ‘big data’ to calibrate and validate urban CA models and to capture the role of human actors and their impact on urban change dynamics; and (4) to strengthen theory-based CA models that comprehensively explain urban change mechanisms and dynamics. We conclude by advocating cellular automata that embed agent-based models and big data input as the most promising analytical framework through which we can enhance our understanding and planning of the contemporary urban change dynamics.

show abstract

Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Understanding Social and Ecological Processes

Cited by 60 publications

References 0 publications

Agent-Based Modeling and the City: A Gallery of Applications

Agent-Based Modeling and the City: A Gallery of Applications

Simulating the forest fuel market as a socio‐ecological system with spatial agent‐based methods: A case study in Carinthia, Austria

Modelling urban change with cellular automata: Contemporary issues and future research directions

Contact Info

Product

Resources

About