A Computational Model for Simulating Spatial Aspects of Crime in Urban Environments

Brantingham, Paul; Glässer, Uwe; Kinney, Bryan; Singh, Komal; Vajihollahi, Mona

doi:10.1109/icsmc.2005.1571717

Cited by 40 publications

(29 citation statements)

References 23 publications

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“…It thus helps to leave the design space open as much as possible. 18 Experience in the following domains has confirmed that the ASM language is understandable for domain experts without computer science education: railway, control and telephony systems [31,41,49], business and aviation security processes [34,7,13,75,76], linguistics [83,90,91,54], biology [87], social sciences [47,46].…”

Section: Asm Ground Model Techniquementioning

confidence: 99%

The Abstract State Machines Method for High-Level System Design and Analysis

Börger¹

2009

Formal Methods: State of the Art and New Directions

282

518

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Section: Asm Ground Model Techniquementioning

confidence: 99%

The Abstract State Machines Method for High-Level System Design and Analysis

Börger¹

2009

Formal Methods: State of the Art and New Directions

282

518

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“…Within such models, instead of using thresholds, more abstract concepts such as beliefs, desires, and intentions (BDI, [67]) or physical, emotional, cognitive, and social factors (PECS, [68]) are imbued in individual agents. Both the BDI and PECS frameworks have been successively applied to modeling human behavior in a number of applications such as what drives people to crime-for example see [69] for an a-spatial model that uses the BDI framework and [54] for a geographically explicit model that uses the PECS framework). These conceptual cognitive frameworks and mathematical approaches for representing behavior can be considered as a rule based systems and are often applied to tens to millions of agents.…”

Section: Abm For City Simulationmentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

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Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.

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“…In 2008 Brantingham, P., Glässer, U., Jackson, P., Kinney, B., & Vajihollahi, M. gave Mastermind: Computational modeling and simulation of spatiotemporal aspects of crime in urban environments [11]. The paper is shows in Section-I described the introduction and review of literatures.…”

Section: In 1993mentioning

confidence: 99%

“…In 2005 Brantingham, P., Glasser, U., Kinney, B., Singh, K., & Vajihollahi, M. gave A computational model for simulating spatial aspects of crime in urban environments [10].…”

Section: In 1993mentioning

confidence: 99%