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

Crooks, Andrew; Castle, Christian J. E.; Batty, Michael

doi:10.1016/j.compenvurbsys.2008.09.004

Cited by 311 publications

(177 citation statements)

References 18 publications

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“…A challenge of this framework, but also of ABM in general, is the validation of the model (Crooks et al 2008;Messina et al 2008). Although sensitivity analyses, the visualisation of uncertainty, and multi-temporal surveys and census provide relevant datasets to verify the simulated processes (Bousquet and Le Page 2004;Crooks et al 2008), the availability of detailed data on the willingness and ability of the whole population is often lacking or restricted. Still, statistical methods to control further the bias, noise and collinearity in such probabilistic models can be also carried out (Santner et al 2003).…”

Section: Discussionmentioning

confidence: 99%

An agent-based approach to model land-use change at a regional scale

et al. 2009

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Land-use/cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. A common approach to analyse and simulate LUCC as the result of individual decisions is agent-based modelling (ABM). However, ABM is often applied to simulate processes at local scales, while its application in regional studies is limited. This paper describes first a conceptual framework for ABM to analyse and explore regional LUCC processes. Second, the conceptual framework is represented by combining different concepts including agent typologies, farm trajectories and probabilistic decision-making processes. Finally, the framework is illustrated through a case study in the Netherlands, where processes of farm cessation, farm expansion and farm diversification are shaping the structure of the landscape. The framework is a generic, straightforward approach to analyse and explore regional LUCC with an explicit link to empirical approaches for parameterization of ABM.

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Section: Discussionmentioning

confidence: 99%

An agent-based approach to model land-use change at a regional scale

et al. 2009

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“…The total-order sensitivity includes all the interaction effects with other parameters, 6) where E(·) is the expectation value, V (·) the variance (see also , we first compute the expectation value of the model output over all other parameters, keeping θ i fixed. We then compute the variance of the resulting expectation value over the possible values of θ i .…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

“…Features like tipping points and resilience can nevertheless be expected in complex ecological simulation models. Therefore there is a dire need for methodologies that can analyse these models ( [6,10,19]). …”

Section: Introductionmentioning

confidence: 99%

Detecting Tipping points in Ecological Models with Sensitivity Analysis

Broeke

Voorn

Kooi³

et al. 2016

Math. Model. Nat. Phenom.

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Abstract. Simulation models are commonly used to understand and predict the development of ecological systems, for instance to study the occurrence of tipping points and their possible ecological effects. Sensitivity analysis is a key tool in the study of model responses to changes in conditions. The applicability of available methodologies for sensitivity analysis can be problematic if tipping points are involved. In this paper we demonstrate that not considering these tipping points may result in misleading statistics on model behaviour. In turn, this limits the applicability of simulation models in ecological research. Tipping points are best revealed when asymptotic model behaviour is considered, i.e. by applying bifurcation analysis. Bifurcation analysis, however, is limited to deterministic dynamic models, whereas many ecological simulation models are nondeterministic and can only be analysed using sensitivity analysis methodologies. In this paper we explore the possibilities for applying methodologies of sensitivity analysis to analyse models with tipping points. The Bazykin-Berezovskaya model, a deterministic ecological model of which the structure regarding tipping points is known a priori, is used as case study. We conclude that important clues about the occurrence of tippings points can be revealed from different sensitivity analysis methodologies, if proper statistical and graphical measures are used. The results raise awareness about how tipping points affect temporal model responses in ecological simulation models, and may also be more generally applicable for nondeterministic models that cannot be analysed using bifurcation analysis.

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“…There have been many applications of the spatial ABMs in DRM. For instance, authors in [30,31] provided models of emergency evacuation for urban area. These models provide estimates for the evacuation time given GIS inputs on roads, buildings, and population density.…”

Section: Bottom-up Methodologies Based On Gis and Rsmentioning

confidence: 99%

Critical Data Source; Tool or Even Infrastructure? Challenges of Geographic Information Systems and Remote Sensing for Disaster Risk Governance

Fekete

Tzavella

Armaş

et al. 2015

IJGI

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Disaster risk information is spatial in nature and Geographic Information Systems (GIS) and Remote Sensing (RS) play an important key role by the services they provide to OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 1849 society. In this context, to risk management and governance, in general, and to civil protection, specifically (termed differently in many countries, and includes, for instance: civil contingencies in the UK, homeland security in the USA, disaster risk reduction at the UN level). The main impetus of this article is to summarize key contributions and challenges in utilizing and accepting GIS and RS methods and data for disaster risk governance, which includes public bodies, but also risk managers in industry and practitioners in search and rescue organizations. The article analyzes certain method developments, such as vulnerability indicators, crowdsourcing, and emerging concepts, such as Volunteered Geographic Information, but also investigates the potential of the topic Critical Infrastructure as it could be applied on spatial assets and GIS and RS itself. Intended to stimulate research on new and emerging fields, this article's main contribution is to move spatial research toward a more reflective stance where opportunities and challenges are equally and transparently addressed in order to gain more scientific quality. As a conclusion, GIS and RS can play a pivotal role not just in delivering data but also in connecting and analyzing data in a more integrative, holistic way.

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

Cited by 311 publications

References 18 publications

An agent-based approach to model land-use change at a regional scale

An agent-based approach to model land-use change at a regional scale

Detecting Tipping points in Ecological Models with Sensitivity Analysis

Critical Data Source; Tool or Even Infrastructure? Challenges of Geographic Information Systems and Remote Sensing for Disaster Risk Governance

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