Modeling the emergence of riots: A geosimulation approach

Pires, Bianica; Crooks, Andrew

doi:10.1016/j.compenvurbsys.2016.09.003

Cited by 33 publications

(17 citation statements)

References 43 publications

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“…Like the proposed GNA modelling approach, CA and ABM under the GAS theoretical framework are coupled with geographic information systems (GIS) and geospatial data sets and have been used to model a variety of real geographic phenomena with early studies in urban [54,55], social [56], and ecological systems [57]. The addition of the proposed GNA modelling approach helps to better theoretically situate other modelling approaches that are in some ways "hybrids", including for example graph-cellular automata [58] and network-based ABMs [59][60][61].…”

Section: Discussionmentioning

confidence: 99%

Representing Complex Evolving Spatial Networks: Geographic Network Automata

Anderson

Dragićević

2020

IJGI

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Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.

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Section: Discussionmentioning

confidence: 99%

Representing Complex Evolving Spatial Networks: Geographic Network Automata

Anderson

Dragićević

2020

IJGI

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“…These last years, more and more works propose to carry out spatial simulation at large scale with a one-to-one representation of people. For example, Pires and Crooks [54] simulates the Carrying simulations, with a large number of agents and/or very complex dynamics, requires many computations. A common way to deal with this issue is to use parallel computation.…”

Section: High Performance Computingmentioning

confidence: 99%

Building, composing and experimenting complex spatial models with the GAMA platform

Taillandier¹,

et al. 2018

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: Taillandier AbstractThe agent-based modeling approach is now used in many domains such as geography, ecology, or economy, and more generally to study (spatially explicit) socio-environmental systems where the heterogeneity of the actors and the numerous feedback loops between them requires a modular and incremental approach to modeling. One major reason of this success, besides this conceptual facility, can be found in the support provided by the development of increasingly powerful software platforms, which now allow modelers without a strong background in computer science to easily and quickly develop their own models. Another trend observed in the latest years is the development of much more descriptive and detailed models able not only to better represent complex systems, but also answer more intricate questions. In that respect, if all agent-based modeling platforms support the design of small to mid-size models, i.e. models with little heterogeneity between agents, simple representation of the environment, simple agent decisionmaking processes, etc., very few are adapted to the design of large-scale models. GAMA is one of the latter. It has been designed with the aim of supporting the writing (and composing) of fairly complex models, with a strong support of the spatial dimension, while guaranteeing non-computer scientists an easy access to high-level, otherwise complex, operations. This paper presents GAMA 1.8, the latest revision to date of the platform, with a focus on its modeling language and its capabilities to manage the spatial dimension of models. The capabilities of GAMA are illustrated by the presentation of applications that take advantage of its new features.

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“…Although there is ample confirmation of these theories, real-world data paint a more complex picture of confounding relationships, such as global economic trade or group unrest. Exploration of these complex interactions and nonlinear relationships has been done in another agent-based model [227] that uses identity and social influence theory [228] to inform data collection and exploration of the emergence of riots. Other applications related to disasters and conflict apply the theory of planned behavior [229] to agent-based models of social-ecological systems, for example, Kniveton et al [230] and Schwarz and Ernst [231], and these are currently being fit into a framework for mapping and comparing behavioral theories [232].…”

Section: Interactions Among the Components Of Cssdmentioning

confidence: 99%

Computational Social Science of Disasters: Opportunities and Challenges

Burger

Kennedy

et al. 2019

Future Internet

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Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

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Modeling the emergence of riots: A geosimulation approach

Cited by 33 publications

References 43 publications

Representing Complex Evolving Spatial Networks: Geographic Network Automata

Representing Complex Evolving Spatial Networks: Geographic Network Automata

Building, composing and experimenting complex spatial models with the GAMA platform

Computational Social Science of Disasters: Opportunities and Challenges

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