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

Recently, Alison Heppenstall, Nick Malleson  and myself have just had a paper accepted in Systems entitled: “Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?” In the paper we critically examine how well agent-based models have  simulated a variety of urban processes. We discus what considerations are needed when choosing the appropriate level of spatial analysis and time frame to model urban phenomena and what role Big Data can play in agent-based modeling. Below you can read the abstract of the paper and see a number of example applications discussed.

Abstract: 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.

Keywords: cities; agent-based modeling; big data; crime; retail; space; simulation

Figure 1. (A) System structure; (B) System hierarchy; and (C) Related subsystems/processes (adapted from Batty, 2013).

Reference cited:

Batty, M. (2013).  The New Science of Cities; MIT Press: Cambridge, MA, USA.

Full reference to the open access paper:

Heppenstall, A., Malleson, N. and Crooks A.T. (2016). “Space, the Final Frontier”: How Good are Agent-based Models at Simulating Individuals and Space in Cities?, Systems, 4(1), 9; doi: 10.3390/systems4010009 (pdf)

 

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

Recently, Alison Heppenstall, Nick Malleson  and myself have just had a paper accepted in Systems entitled: “Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?” In the paper we critically examine how well agent-based models have  simulated a variety of urban processes. We discus what considerations are needed when choosing the appropriate level of spatial analysis and time frame to model urban phenomena and what role Big Data can play in agent-based modeling. Below you can read the abstract of the paper and see a number of example applications discussed.

Abstract: 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.

Keywords: cities; agent-based modeling; big data; crime; retail; space; simulation

Figure 1. (A) System structure; (B) System hierarchy; and (C) Related subsystems/processes (adapted from Batty, 2013).

Reference cited:

Batty, M. (2013).  The New Science of Cities; MIT Press: Cambridge, MA, USA.

Full reference to the open access paper:

Heppenstall, A., Malleson, N. and Crooks A.T. (2016). “Space, the Final Frontier”: How Good are Agent-based Models at Simulating Individuals and Space in Cities?, Systems, 4(1), 9; doi: 10.3390/systems4010009 (pdf)

 

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Measles Vaccination Narrative in Twitter

A summary of our approach
Continuing our work with respects to GeoSocial analysis we have recently published a paper in JMIR Public Health and Surveillance entitled “The Measles Vaccination Narrative in Twitter: A Quantitative Analysis“. In this paper we explore how social media can be quantitatively studied to explore the narrative behind measles vaccinations. Below you can read the abstract to the paper which includes the background to why we chose to study this topic, the study objective, our methodology, a summary of our results and conclusions. 

Background: The emergence of social media is providing an alternative avenue for information exchange and opinion formation on health-related issues. Collective discourse in such media leads to the formation of a complex narrative, conveying public views and perceptions.

Objective: This paper presents a study of Twitter narrative regarding vaccination in the aftermath of the 2015 measles outbreak, both in terms of its cyber and physical characteristics. The contributions of this work are the analysis of the data for this particular study, as well as presenting a quantitative interdisciplinary approach to analyze such open-source data in the context of health narratives.

Methods: 669,136 tweets were collected in the period February 1 through March 9, 2015 referring to vaccination. These tweets were analyzed to identify key terms, connections among such terms, retweet patterns, the structure of the narrative, and connections to the geographical space.

Results: The data analysis captures the anatomy of the themes and relations that make up the discussion about vaccination in Twitter. The results highlight the higher impact of stories contributed by news organizations compared to direct tweets by health organizations in communicating health-related information. They also capture the structure of the anti-vaccination narrative and its terms of reference. Analysis also revealed the relationship between community engagement in Twitter and state policies regarding child vaccination. Residents of Vermont and Oregon, the two states with the highest rates of non-medical exemption from school-entry vaccines nationwide, are leading the social media discussion in terms of participation.

Conclusions: The interdisciplinary study of health-related debates in social media across the cyber-physical debate nexus leads to a greater understanding of public concerns, views, and responses to health-related issues. Further coalescing such capabilities shows promise towards advancing health communication, supporting the design of more effective strategies that take into account the complex and evolving public views of health issues.

Global distribution of tweets in our data corpus
The paper is open access and can be viewed and downloaded from here.
Full reference:

Radzikowski, J., Stefanidis, A., Jacobsen K.H., Croitoru, A., Crooks, A.T. and Delamater, P.L. (2016). “The Measles Vaccination Narrative in Twitter: A Quantitative Analysis”, JMIR Public Health and Surveillance, 2(1):e1. 

Hashtag associations: clustering based on co-occurrences of hashtags in individual tweets
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Measles Vaccination Narrative in Twitter

A summary of our approach
Continuing our work with respects to GeoSocial analysis we have recently published a paper in JMIR Public Health and Surveillance entitled “The Measles Vaccination Narrative in Twitter: A Quantitative Analysis“. In this paper we explore how social media can be quantitatively studied to explore the narrative behind measles vaccinations. Below you can read the abstract to the paper which includes the background to why we chose to study this topic, the study objective, our methodology, a summary of our results and conclusions. 

Background: The emergence of social media is providing an alternative avenue for information exchange and opinion formation on health-related issues. Collective discourse in such media leads to the formation of a complex narrative, conveying public views and perceptions.

Objective: This paper presents a study of Twitter narrative regarding vaccination in the aftermath of the 2015 measles outbreak, both in terms of its cyber and physical characteristics. The contributions of this work are the analysis of the data for this particular study, as well as presenting a quantitative interdisciplinary approach to analyze such open-source data in the context of health narratives.

Methods: 669,136 tweets were collected in the period February 1 through March 9, 2015 referring to vaccination. These tweets were analyzed to identify key terms, connections among such terms, retweet patterns, the structure of the narrative, and connections to the geographical space.

Results: The data analysis captures the anatomy of the themes and relations that make up the discussion about vaccination in Twitter. The results highlight the higher impact of stories contributed by news organizations compared to direct tweets by health organizations in communicating health-related information. They also capture the structure of the anti-vaccination narrative and its terms of reference. Analysis also revealed the relationship between community engagement in Twitter and state policies regarding child vaccination. Residents of Vermont and Oregon, the two states with the highest rates of non-medical exemption from school-entry vaccines nationwide, are leading the social media discussion in terms of participation.

Conclusions: The interdisciplinary study of health-related debates in social media across the cyber-physical debate nexus leads to a greater understanding of public concerns, views, and responses to health-related issues. Further coalescing such capabilities shows promise towards advancing health communication, supporting the design of more effective strategies that take into account the complex and evolving public views of health issues.

Global distribution of tweets in our data corpus
The paper is open access and can be viewed and downloaded from here.
Full reference:

Radzikowski, J., Stefanidis, A., Jacobsen K.H., Croitoru, A., Crooks, A.T. and Delamater, P.L. (2016). “The Measles Vaccination Narrative in Twitter: A Quantitative Analysis”, JMIR Public Health and Surveillance, 2(1):e1. 

Hashtag associations: clustering based on co-occurrences of hashtags in individual tweets
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Mesa: An Agent-Based Modeling Framework in Python

Just a short post to say two of our PhD students, David Masad and Jackie Kazil have been developing an agent-based modeling framework in Python called Mesa.
To quote from David’s talk abstract:

“Agent-based modeling is currently a hole in in Python’s robust and growing scientific ecosystem. Mesa is a new open-source package meant to fill that gap. It allows users to quickly create agent-based models using built-in core components (such as agent schedulers and spatial grids) or customized implementations; visualize them using an innovative browser-based interface; and analyze their results using Python’s robust data analysis tools. Its goal is to be a Python 3-based alternative to other popular frameworks based in other languages such as NetLogo, Repast, or MASON.”

Below is short presentation outlining Mesa from SciPy 2015:

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Mesa: An Agent-Based Modeling Framework in Python

Just a short post to say two of our PhD students, David Masad and Jackie Kazil have been developing an agent-based modeling framework in Python called Mesa.
To quote from David’s talk abstract:

“Agent-based modeling is currently a hole in in Python’s robust and growing scientific ecosystem. Mesa is a new open-source package meant to fill that gap. It allows users to quickly create agent-based models using built-in core components (such as agent schedulers and spatial grids) or customized implementations; visualize them using an innovative browser-based interface; and analyze their results using Python’s robust data analysis tools. Its goal is to be a Python 3-based alternative to other popular frameworks based in other languages such as NetLogo, Repast, or MASON.”

Below is short presentation outlining Mesa from SciPy 2015:

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Call for papers: Symposium on Human Dynamics Research: Urban Analytics at the 2016 AAG

Call for papers: AAG 2016. San Francisco. 29th March – 2nd April

Symposium on Human Dynamics Research: Urban Analytics

A deluge of new data created by people and machines is changing the way that we understand, organise and model urban spaces. New analytics are required to make sense of these data and to usefully apply findings to real systems. This session seeks to bring together quantitative or mixed methods papers that develop or use new analytics in order to better understand the form, function and future of urban systems. We invite methodological, theoretical and empirical papers that engage with any aspect of urban analytics. Topics include, but are not limited to:

  • New methodologies for tackling large, complex or dirty data sets;
  • Case studies involving analysis of novel or unusual data sources;
  • Policy analysis, predictive analytics, other applications of data;
  • Intensive modelling or simulation applied to urban areas or processes; 
  • Individual-level and agent-based models (ABM) of geographical systems; 
  • Validating and calibrating models with novel data sources; 
  • Ethics of data collected en masse and their use in simulation and analytics.

Please e-mail the abstract and key words with your expression of intent to Nick Malleson (n.s.malleson@leeds.ac.uk) by 22nd October, 2015 (one week before the AAG session deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at:

http://www.aag.org/cs/annualmeeting/call_for_papers

An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and  conclusions.

Timeline summary:

  • 22nd October, 2015: Abstract submission deadline. E-mail Nick Malleson by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 25th October, 2015: Session finalization and author notification
  • 28th October, 2015: Final abstract submission to AAG, via www.aag.org. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Nick Malleson. Neither the organizers nor the AAG will edit the abstracts.
  • 29th October, 2015: AAG registration deadline. Sessions submitted to AAG for approval.

Organizers

  • Nick Malleson, School of Geography, University of Leeds  
  • Alex Singleton, School of Environmental Sciences, University of Liverpool  
  • Mark Birkin, Director of the University of Leeds Institute for Data Analytics (LIDA)  
  • Paul Longley, Department of Geography, University College London  
  • Andrew Crooks, Department of Computational and Data Sciences, George Mason University.   
  • Seth Spielman, Geography Department, University of Colorado
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Call for papers: Symposium on Human Dynamics Research: Urban Analytics at the 2016 AAG

Call for papers: AAG 2016. San Francisco. 29th March – 2nd April

Symposium on Human Dynamics Research: Urban Analytics

A deluge of new data created by people and machines is changing the way that we understand, organise and model urban spaces. New analytics are required to make sense of these data and to usefully apply findings to real systems. This session seeks to bring together quantitative or mixed methods papers that develop or use new analytics in order to better understand the form, function and future of urban systems. We invite methodological, theoretical and empirical papers that engage with any aspect of urban analytics. Topics include, but are not limited to:

  • New methodologies for tackling large, complex or dirty data sets;
  • Case studies involving analysis of novel or unusual data sources;
  • Policy analysis, predictive analytics, other applications of data;
  • Intensive modelling or simulation applied to urban areas or processes; 
  • Individual-level and agent-based models (ABM) of geographical systems; 
  • Validating and calibrating models with novel data sources; 
  • Ethics of data collected en masse and their use in simulation and analytics.

Please e-mail the abstract and key words with your expression of intent to Nick Malleson (n.s.malleson@leeds.ac.uk) by 22nd October, 2015 (one week before the AAG session deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at:

http://www.aag.org/cs/annualmeeting/call_for_papers

An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and  conclusions.

Timeline summary:

  • 22nd October, 2015: Abstract submission deadline. E-mail Nick Malleson by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 25th October, 2015: Session finalization and author notification
  • 28th October, 2015: Final abstract submission to AAG, via www.aag.org. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Nick Malleson. Neither the organizers nor the AAG will edit the abstracts.
  • 29th October, 2015: AAG registration deadline. Sessions submitted to AAG for approval.

Organizers

  • Nick Malleson, School of Geography, University of Leeds  
  • Alex Singleton, School of Environmental Sciences, University of Liverpool  
  • Mark Birkin, Director of the University of Leeds Institute for Data Analytics (LIDA)  
  • Paul Longley, Department of Geography, University College London  
  • Andrew Crooks, Department of Computational and Data Sciences, George Mason University.   
  • Seth Spielman, Geography Department, University of Colorado
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