Generation of Realistic Mega-City Populations and Social Networks for Agent-Based Modeling

Burger, Annetta; Oz, Talha; Crooks, Andrew; Kennedy, William G.

doi:10.1145/3145574.3145593

Cited by 15 publications

(7 citation statements)

References 22 publications

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“…Or census data can be used to create a specified number of agents for a given location with associated socio-economic characteristics (e.g. Burger et al 2017). Raster data such as those from the national land-cover dataset (Wickham et al 2014) can be used for initialization of an urban growth simulation as they provide details on urban and non-urban land extents which affect where cities can and cannot grow (see Crooks et al 2019 for further details and examples of how one can use such data in models).…”

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

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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.

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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

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“…The representation and analysis of social networks and the processes occurring on or within them also lend naturally to network‐based GAS modelling approaches. Burger, Oz, Crooks, and Kennedy (2017) acknowledge that the observed characteristics of social networks used to inform and influence human interactions are typically absent from the ABMs that seek to model these types of systems, prompting the integration of the two approaches for the study of social phenomena. In another example, Pires and Crooks (2017) integrate social networks and ABM to model the flow of information through a population to simulate rioting behavior.…”

Section: Toward Network‐based Geographic Automatamentioning

confidence: 99%

Complex spatial networks: Theory and geospatial applications

Anderson

Dragićević

2020

Geography Compass

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Complex systems modeling approaches offer the means to examine the way in which local interactions between system components form emergent systems. Using these bottom‐up modeling approaches in combination with geographic information systems (GIS) and geospatial data, the complexity inherent to spatial phenomena including geographical, urban, ecological, or geophysical systems can be captured and represented. Scientific research in the field of network science also uses a complex systems approach to conceptualize, model, and analyze geospatial systems as networks. Despite having common characteristics, complexity, geographic information, and network sciences are not yet fully integrated. Therefore, the main objective of this article is to provide a comprehensive review of scientific research related to network theory and to evaluate the potential of their integration with complex systems modeling approaches originating in the field of geographic information science (GISc). This article finds that existing literature focuses on characterizing static spatial network structures to better understand the dynamics that take place on or within them. This article argues for a necessity in research advancements to explore the way in which real spatial network structures evolve in response to spatial dynamics and advocates for the integration of geographic automata systems (GAS) modeling approaches with networks to do so. The mathematical foundation for graph theory, including the measures that are used to describe networks and the theoretical graph types, are introduced. Geospatial applications of networks and graph theory are also presented. Examples of network‐based automata models are presented as avenues for future research work in evolving spatial networks as part of GISc and computational geography.

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“…Given all of the above, we ask: Is it possible that by explicitly modeling the movement of offenders, their direction choices, and distances traveled inferred from real-world open data, and by comparing random walks to more realistic non-random human movement, we might discover that a simple mobility rule could be used together with other behavior rules to reproduce crime patterns that allow for a better predictive result? In contrast to more traditional methods for generating a synthetic population representative of a city (Beckman, Baggerly, & McKay, 1996;Adigaa, Agashea, Arifuzzamana, Barretta, Beckmana, Bisseta, Chena, Chungbaeka, Eubanka, Guptaa, Khana, Kuhlmana, Mortveita, Nordberga, Riversa, Stretza, Swarupa, Wilsona, & Xiea, 2015;Burger, Oz, Crooks, & Kennedy, 2017), we rely on activity and mobility data to build strategies for offenders only, accounting for factors relevant to crime. Moreover, researchers have already shown the potential of using novel types of data in order to account for population at risk (also referred to as ambient population) rather than residential population for the purpose of crime analysis and prediction.…”

Section: Related Workmentioning

confidence: 99%

Simulating Offender Mobility: Modeling Activity Nodes from Large-Scale Human Activity Data

Brüngger

Kadar

Gerritsen³

et al. 2020

jair

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In recent years, simulation techniques have been applied to investigate the spatiotemporal dynamics of crime. Researchers have instantiated mobile offenders in agent-based simulations for theory testing, experimenting with crime prevention strategies, and exploring crime prediction techniques, despite facing challenges due to the complex dynamics of crime and the lack of detailed information about offender mobility. This paper presents a simulation model to explore offender mobility, focusing on the interplay between the agent's awareness space and activity nodes. The simulation generates patterns of individual mobility aiming to cumulatively match crime patterns. To instantiate a realistic urban environment, we use open data to simulate the urban structure, location-based social networks data to represent activity nodes as a proxy for human activity, and taxi trip data as a proxy for human movement between regions of the city. We analyze and systematically compare 35 different mobility strategies and demonstrate the benefits of using large-scale human activity data to simulate offender mobility. The strategies combining taxi trip data or historic crime data with popular activity nodes perform best compared to other strategies, especially for robbery. Our approach provides a basis for building agent-based crime simulations that infer offender mobility in urban areas from real-world data.

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Generation of Realistic Mega-City Populations and Social Networks for Agent-Based Modeling

Cited by 15 publications

References 22 publications

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

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

Complex spatial networks: Theory and geospatial applications

Simulating Offender Mobility: Modeling Activity Nodes from Large-Scale Human Activity Data

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