A Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums

Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that …

Continue reading »

A Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums

Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that …

Continue reading »

A Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums

Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that …

Continue reading »

New Paper: Cancer and Social Media

Continuing our work on geosocial analysis we recently had a paper entitled “Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram” published in the Journal of Health Communication. In the paper we  present a comparative study of differences in messaging for women’s and men’s cancer campaigns on social media through three discrete approaches. 
  1. we directly compare the incident rates of women’s and men’s cancers in the United States to the corresponding levels of traffic that these cancers elicited during World Cancer Day across two social media platforms, Twitter and Instagram. 
  2. we examine social media activity for breast cancer versus prostate cancer on both Twitter and Instagram during the dedicated month-long campaigns (October and November, respectively). 
  3. we compare the top terms associated with each campaign on these two social media platforms to discover whether there are differences in the terms associated with these online discussions.
Below you can read the abstract to our paper, see some of our results and at the bottom of the post have the full citation and link to the paper.

Abstract: 

Social media are often heralded as offering cancer campaigns new opportunities to reach the public. However, these campaigns may not be equally successful, depending on the nature of the campaign itself, the type of cancer being addressed, and the social media platform being examined. This study is the first to compare social media activity on Twitter and Instagram across three time periods: #WorldCancerDay in February, the annual month-long campaigns of National Breast Cancer Awareness Month (NBCAM) in October and Movember in November, and during the full year outside of these campaigns. Our results suggest that women’s reproductive cancers – especially breast cancer – tend to outperform men’s reproductive cancer – especially prostate cancer – across campaigns and social media platforms. Twitter overall generates substantially more activity than Instagram for both cancer campaigns, suggesting Instagram may be an untapped resource. However, the messaging for both campaigns tends to focus on awareness and support rather than on concrete actions and behaviors. We suggest health communication efforts need to focus on effective messaging and building engaged communities for cancer communication across social media platforms.

A comparison of percentages of cancer cases (green bars) and references to corresponding cancers in Twitter (blue bar) and Instagram (orange bar) during World Cancer Day 2016.

 References to breast cancer (green line), prostate cancer (orange line), and Movember (blue line) over the full year 2015 in Instagram.

Full Reference: 

Vraga, E., Stefanidis, A., Lamprianidis, G., Croitoru, A., Crooks, A.T. Delamater, P.L., Pfoser, D., Radzikowski, J. and Jacobsen, K.H. (2018), Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram, Journal of Health Communication. 3(2), 181-189. (pdf)

Continue reading »

New Paper: Cancer and Social Media

Continuing our work on geosocial analysis we recently had a paper entitled “Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram” published in the Journal of Health Communication. In the paper we  present a comparative study of differences in messaging for women’s and men’s cancer campaigns on social media through three discrete approaches. 
  1. we directly compare the incident rates of women’s and men’s cancers in the United States to the corresponding levels of traffic that these cancers elicited during World Cancer Day across two social media platforms, Twitter and Instagram. 
  2. we examine social media activity for breast cancer versus prostate cancer on both Twitter and Instagram during the dedicated month-long campaigns (October and November, respectively). 
  3. we compare the top terms associated with each campaign on these two social media platforms to discover whether there are differences in the terms associated with these online discussions.
Below you can read the abstract to our paper, see some of our results and at the bottom of the post have the full citation and link to the paper.

Abstract: 

Social media are often heralded as offering cancer campaigns new opportunities to reach the public. However, these campaigns may not be equally successful, depending on the nature of the campaign itself, the type of cancer being addressed, and the social media platform being examined. This study is the first to compare social media activity on Twitter and Instagram across three time periods: #WorldCancerDay in February, the annual month-long campaigns of National Breast Cancer Awareness Month (NBCAM) in October and Movember in November, and during the full year outside of these campaigns. Our results suggest that women’s reproductive cancers – especially breast cancer – tend to outperform men’s reproductive cancer – especially prostate cancer – across campaigns and social media platforms. Twitter overall generates substantially more activity than Instagram for both cancer campaigns, suggesting Instagram may be an untapped resource. However, the messaging for both campaigns tends to focus on awareness and support rather than on concrete actions and behaviors. We suggest health communication efforts need to focus on effective messaging and building engaged communities for cancer communication across social media platforms.

A comparison of percentages of cancer cases (green bars) and references to corresponding cancers in Twitter (blue bar) and Instagram (orange bar) during World Cancer Day 2016.

 References to breast cancer (green line), prostate cancer (orange line), and Movember (blue line) over the full year 2015 in Instagram.

Full Reference: 

Vraga, E., Stefanidis, A., Lamprianidis, G., Croitoru, A., Crooks, A.T. Delamater, P.L., Pfoser, D., Radzikowski, J. and Jacobsen, K.H. (2018), Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram, Journal of Health Communication. 3(2), 181-189. (pdf)

Continue reading »

New Paper: Cancer and Social Media

Continuing our work on geosocial analysis we recently had a paper entitled “Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram” published in the Journal of Health Communication. In the paper we  present a comparative study of differences in messaging for women’s and men’s cancer campaigns on social media through three discrete approaches. 
  1. we directly compare the incident rates of women’s and men’s cancers in the United States to the corresponding levels of traffic that these cancers elicited during World Cancer Day across two social media platforms, Twitter and Instagram. 
  2. we examine social media activity for breast cancer versus prostate cancer on both Twitter and Instagram during the dedicated month-long campaigns (October and November, respectively). 
  3. we compare the top terms associated with each campaign on these two social media platforms to discover whether there are differences in the terms associated with these online discussions.
Below you can read the abstract to our paper, see some of our results and at the bottom of the post have the full citation and link to the paper.

Abstract: 

Social media are often heralded as offering cancer campaigns new opportunities to reach the public. However, these campaigns may not be equally successful, depending on the nature of the campaign itself, the type of cancer being addressed, and the social media platform being examined. This study is the first to compare social media activity on Twitter and Instagram across three time periods: #WorldCancerDay in February, the annual month-long campaigns of National Breast Cancer Awareness Month (NBCAM) in October and Movember in November, and during the full year outside of these campaigns. Our results suggest that women’s reproductive cancers – especially breast cancer – tend to outperform men’s reproductive cancer – especially prostate cancer – across campaigns and social media platforms. Twitter overall generates substantially more activity than Instagram for both cancer campaigns, suggesting Instagram may be an untapped resource. However, the messaging for both campaigns tends to focus on awareness and support rather than on concrete actions and behaviors. We suggest health communication efforts need to focus on effective messaging and building engaged communities for cancer communication across social media platforms.

A comparison of percentages of cancer cases (green bars) and references to corresponding cancers in Twitter (blue bar) and Instagram (orange bar) during World Cancer Day 2016.

 References to breast cancer (green line), prostate cancer (orange line), and Movember (blue line) over the full year 2015 in Instagram.

Full Reference: 

Vraga, E., Stefanidis, A., Lamprianidis, G., Croitoru, A., Crooks, A.T. Delamater, P.L., Pfoser, D., Radzikowski, J. and Jacobsen, K.H. (2018), Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram, Journal of Health Communication. 3(2), 181-189. (pdf)

Continue reading »

Come Work with Us: 2 Postdocs in Urban Simulation

The George Mason University Department of Geography and Geoinformation Science within the College of Science, has an immediate opening for two postdoctoral fellows (up to 2-years), subject to budgetary approval. These positions will be part of the “Urban simulation” project team conducting research as part of the DARPA’s “Ground Truth” program, a network of DARPA-funded teams across the USA. The GMU team is directed by Andreas Züfle, Dieter Pfoser, and Andrew Crooks and supported by Carola Wenk at Tulane University. George Mason University has a strong institutional commitment to the achievement of excellence and diversity among its faculty and staff, and strongly encourages candidates to apply who will enrich Mason’s academic and culturally inclusive environment.

Postdoc 1

Responsibilities:
The primary job responsibilities of this position consist of the design, development and refinement of an agent-based simulation framework for urban areas. Using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java), new agent logic will have to be implemented, thus creating agents that use socially plausible rules for mobility and interaction with other agents. A main goal is to create computationally efficient agent logic, thus allowing millions of agents to make decisions, find shortest paths between locations, and interact with their simulated world at the same time. For this purpose, implemented algorithms will need to be highly parallelizable, thus allowing to scale simulation via distribution among computing clusters located at GMU and Tulane. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

Required Qualifications:

  • Ph.D. in computer science, data science, or closely related field;
  • Strong programming skills in Java;
  • Excellent written communication skills demonstrated by prior publications;
  • A track record that demonstrates the ability to work well with interdisciplinary research teams.
Preferred Qualifications:
  • Solid knowledge of graph algorithms;
  • Experience with Agent-Based Modeling and social science simulation;
  • Experience in design and implementation of software systems.
Postdoc 2
Responsibilities:
The primary job responsibilities of this position will be the design of an agent-based model based on the first principles underlying human needs, social interactions, and mobility to define socially plausible causalities. This model will contribute towards the design, development and refinement of an agent-based simulation framework for urban areas. Using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java), new agent logic will have to be implemented, thus creating agents that use socially plausible rules for mobility and interaction with other agents. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

Required Qualifications:

  • Ph.D. in computer science, data science, or closely related field; 
  • Experience with Agent-Based Modeling and social science simulation; 
  • Excellent written communication skills demonstrated by prior publications; 
  • A track record that demonstrates the ability to work well with interdisciplinary research teams.
Preferred Qualifications:
  • Strong programming skills in Java;

More Information: https://jobs.gmu.edu/postings/42109

    Continue reading »

    Come Work with Us: 2 Postdocs in Urban Simulation

    The George Mason University Department of Geography and Geoinformation Science within the College of Science, has an immediate opening for two postdoctoral fellows (up to 2-years), subject to budgetary approval. These positions will be part of the “Urban simulation” project team conducting research as part of the DARPA’s “Ground Truth” program, a network of DARPA-funded teams across the USA. The GMU team is directed by Andreas Züfle, Dieter Pfoser, and Andrew Crooks and supported by Carola Wenk at Tulane University. George Mason University has a strong institutional commitment to the achievement of excellence and diversity among its faculty and staff, and strongly encourages candidates to apply who will enrich Mason’s academic and culturally inclusive environment.

    Postdoc 1

    Responsibilities:
    The primary job responsibilities of this position consist of the design, development and refinement of an agent-based simulation framework for urban areas. Using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java), new agent logic will have to be implemented, thus creating agents that use socially plausible rules for mobility and interaction with other agents. A main goal is to create computationally efficient agent logic, thus allowing millions of agents to make decisions, find shortest paths between locations, and interact with their simulated world at the same time. For this purpose, implemented algorithms will need to be highly parallelizable, thus allowing to scale simulation via distribution among computing clusters located at GMU and Tulane. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

    Required Qualifications:

    • Ph.D. in computer science, data science, or closely related field;
    • Strong programming skills in Java;
    • Excellent written communication skills demonstrated by prior publications;
    • A track record that demonstrates the ability to work well with interdisciplinary research teams.
    Preferred Qualifications:
    • Solid knowledge of graph algorithms;
    • Experience with Agent-Based Modeling and social science simulation;
    • Experience in design and implementation of software systems.
    Postdoc 2
    Responsibilities:
    The primary job responsibilities of this position will be the design of an agent-based model based on the first principles underlying human needs, social interactions, and mobility to define socially plausible causalities. This model will contribute towards the design, development and refinement of an agent-based simulation framework for urban areas. Using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java), new agent logic will have to be implemented, thus creating agents that use socially plausible rules for mobility and interaction with other agents. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

    Required Qualifications:

    • Ph.D. in computer science, data science, or closely related field; 
    • Experience with Agent-Based Modeling and social science simulation; 
    • Excellent written communication skills demonstrated by prior publications; 
    • A track record that demonstrates the ability to work well with interdisciplinary research teams.
    Preferred Qualifications:
    • Strong programming skills in Java;

    More Information: https://jobs.gmu.edu/postings/42109

      Continue reading »

      AAG2018: Innovations in Urban Analytics

      Call for Papers, AAG2018: Innovations in Urban Analytics

      We welcome paper submissions for our session at the Association of American Geographers Annual Meeting on 10-14 April, 2018, in New Orleans.

      Session Description

      New forms of data about people and cities, often termed ‘Big’, are fostering research that is disrupting many traditional fields. This is true in geography, and especially in those more technical branches of the discipline such as computational geography / geocomputation, spatial analytics and statistics, geographical data science, etc. These new forms of micro-level data have lead to new methodological approaches in order to better understand how urban systems behave. Increasingly, these approaches and data are being used to ask questions about how cities can be made more sustainable and efficient in the future.

      This session will bring together the latest research in urban analytics. We are particularly interested in papers that engage with the following domains:

      • Agent-based modelling (ABM) and individual-based modelling;
      • Machine learning for urban analytics;
      • Innovations in consumer data analytics for understanding urban systems;
      • Real-time model calibration and data assimilation;
      • Spatio-temporal data analysis;
      • New data, case studies, demonstrators, and tools for the study of urban systems;
      • Complex systems analysis;
      • Geographic data mining and visualization;
      • Frequentist and Bayesian approaches to modelling cities.

      Please e-mail the abstract and key words with your expression of intent to Nick Malleson (n.s.malleson@leeds.ac.uk) by 18 October, 2017 (one week before the AAG abstract 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://annualmeeting.aag.org/submit_an_abstract. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions.

      For those interested specifically in the interface between research and policy, they might consider submitting their paper to the session “Computation for Public Engagement in Complex Problems” (http://www.gisagents.org/2017/10/call-for-papers-computation-for-public.html).

      Key Dates
      • 18 October, 2017: 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.
      • 23 October, 2017: Session finalization and author notification.
      • 25 October, 2017: Final abstract submission to AAG, via the link above. 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 (n.s.malleson@leeds.ac.uk). Neither the organizers nor the AAG will edit the abstracts.
      • 8 November, 2017: AAG session organization deadline. Sessions submitted to AAG for approval.
      • 9-14 April, 2018: AAG Annual Meeting.
      Session Organizers
      Continue reading »

      AAG2018: Innovations in Urban Analytics

      Call for Papers, AAG2018: Innovations in Urban Analytics

      We welcome paper submissions for our session at the Association of American Geographers Annual Meeting on 10-14 April, 2018, in New Orleans.

      Session Description

      New forms of data about people and cities, often termed ‘Big’, are fostering research that is disrupting many traditional fields. This is true in geography, and especially in those more technical branches of the discipline such as computational geography / geocomputation, spatial analytics and statistics, geographical data science, etc. These new forms of micro-level data have lead to new methodological approaches in order to better understand how urban systems behave. Increasingly, these approaches and data are being used to ask questions about how cities can be made more sustainable and efficient in the future.

      This session will bring together the latest research in urban analytics. We are particularly interested in papers that engage with the following domains:

      • Agent-based modelling (ABM) and individual-based modelling;
      • Machine learning for urban analytics;
      • Innovations in consumer data analytics for understanding urban systems;
      • Real-time model calibration and data assimilation;
      • Spatio-temporal data analysis;
      • New data, case studies, demonstrators, and tools for the study of urban systems;
      • Complex systems analysis;
      • Geographic data mining and visualization;
      • Frequentist and Bayesian approaches to modelling cities.

      Please e-mail the abstract and key words with your expression of intent to Nick Malleson (n.s.malleson@leeds.ac.uk) by 18 October, 2017 (one week before the AAG abstract 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://annualmeeting.aag.org/submit_an_abstract. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions.

      For those interested specifically in the interface between research and policy, they might consider submitting their paper to the session “Computation for Public Engagement in Complex Problems” (http://www.gisagents.org/2017/10/call-for-papers-computation-for-public.html).

      Key Dates
      • 18 October, 2017: 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.
      • 23 October, 2017: Session finalization and author notification.
      • 25 October, 2017: Final abstract submission to AAG, via the link above. 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 (n.s.malleson@leeds.ac.uk). Neither the organizers nor the AAG will edit the abstracts.
      • 8 November, 2017: AAG session organization deadline. Sessions submitted to AAG for approval.
      • 9-14 April, 2018: AAG Annual Meeting.
      Session Organizers
      Continue reading »

      Call for Papers – Computation for Public Engagement in Complex Problems

      Call for Papers – Computation for Public Engagement in Complex Problems: From Big Data, to Modeling, to Action 

      We welcome paper submissions for our session(s) at the Association of American Geographers Annual Meeting on 9-14 April, 2018, in New Orleans.  

      Session Description: In line with one of the major themes of this conference, we explore the opportunities and challenges that geo-computational tools offer to support public engagement, deliberation and decision-making to address complex problems that link human, socioeconomic and biophysical systems at a variety of different spatial and temporal scales (e.g., climate change, resource depletion, and poverty). Modelers and data scientists have shown increasing interest in the intersection between science and policy, acknowledging that, for all the computational advances achieved to support policy and decision-making, these approaches remain frustratingly foreign to the public they are meant to serve. On one hand, there is a persistent gap in the public’s understanding of and reasoning about complex systems, resulting in unintended and undesirable consequences. On the other hand, there is significant public skepticism about the knowledge generated by the modeling community and its ability to inform policy and decision-making.

      We invite theoretical, methodological, and empirical papers that explore advances in geo-computational approaches, including part or all the process to address complex problems: from data collection and analysis, to the development and use of models, to supporting action with data analysis and modeling. We are interested in any work that contributes towards the overall goal of supporting public engagement and action around complex problems, including—but not limited to—the following topics:

      • epistemological perspectives; 
      • extracting behavioral rules from novel and established data sets; 
      • innovative applications of complex systems techniques, and 
      • addressing the challenge of complex systems model calibration and validation. 

      Please e-mail the abstract and key words with your expression of intent to Moira Zellner (mzellner@uic.edu) by October 18, 2017 (one week before the AAG abstract 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://annualmeeting.aag.org/submit_an_abstract. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions.

       Timeline summary: 

      • October 18, 2017: Abstract submission deadline. E-mail Moira Zellner (mzellner@uic.edu) by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent. 
      • October 23, 2017: Session finalization and author notification. 
      • October 25, 2017: Final abstract submission to AAG, via the link above. 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 Moira Zellner. Neither the organizers nor the AAG will edit the abstracts. 
      • November 8, 2017: AAG session organization deadline. Sessions submitted to AAG for approval. 
      • April 9-14, 2018: AAG Annual Meeting.  

      Organizers:

      Continue reading »

      Call for Papers – Computation for Public Engagement in Complex Problems

      Call for Papers – Computation for Public Engagement in Complex Problems: From Big Data, to Modeling, to Action 

      We welcome paper submissions for our session(s) at the Association of American Geographers Annual Meeting on 9-14 April, 2018, in New Orleans.  

      Session Description: In line with one of the major themes of this conference, we explore the opportunities and challenges that geo-computational tools offer to support public engagement, deliberation and decision-making to address complex problems that link human, socioeconomic and biophysical systems at a variety of different spatial and temporal scales (e.g., climate change, resource depletion, and poverty). Modelers and data scientists have shown increasing interest in the intersection between science and policy, acknowledging that, for all the computational advances achieved to support policy and decision-making, these approaches remain frustratingly foreign to the public they are meant to serve. On one hand, there is a persistent gap in the public’s understanding of and reasoning about complex systems, resulting in unintended and undesirable consequences. On the other hand, there is significant public skepticism about the knowledge generated by the modeling community and its ability to inform policy and decision-making.

      We invite theoretical, methodological, and empirical papers that explore advances in geo-computational approaches, including part or all the process to address complex problems: from data collection and analysis, to the development and use of models, to supporting action with data analysis and modeling. We are interested in any work that contributes towards the overall goal of supporting public engagement and action around complex problems, including—but not limited to—the following topics:

      • epistemological perspectives; 
      • extracting behavioral rules from novel and established data sets; 
      • innovative applications of complex systems techniques, and 
      • addressing the challenge of complex systems model calibration and validation. 

      Please e-mail the abstract and key words with your expression of intent to Moira Zellner (mzellner@uic.edu) by October 18, 2017 (one week before the AAG abstract 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://annualmeeting.aag.org/submit_an_abstract. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions.

       Timeline summary: 

      • October 18, 2017: Abstract submission deadline. E-mail Moira Zellner (mzellner@uic.edu) by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent. 
      • October 23, 2017: Session finalization and author notification. 
      • October 25, 2017: Final abstract submission to AAG, via the link above. 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 Moira Zellner. Neither the organizers nor the AAG will edit the abstracts. 
      • November 8, 2017: AAG session organization deadline. Sessions submitted to AAG for approval. 
      • April 9-14, 2018: AAG Annual Meeting.  

      Organizers:

      Continue reading »

      Agent-Based Modeling Chapter

      In the recently published “Comprehensive Geographic Information Systems” edited by Bo Huang, Alison Heppenstall, Nick Malleson and myself have a chapter entitled “Agent-based Modelling1. Within the chapter, we provide a overview of agent-based modeling (ABM) especially for the geographical sciences. This includes a section on how ABM emerged i.e. “The Rise of the (Automated) Machines“, along with a discussion on what constitutes an agent. This is followed with steps to building an agent-based model, including: 1) the preparation and design; 2) model implementation 3) and how one goes about evaluating a model (i.e. verification, calibration and validation and how these are particularity challenging with respect to spatial agent-based models). We then discuss how we can integrate space and GIS into agent-based models and review a number of open-source ABM toolkits (e.g. GAMA, MASON, NetLogo) before concluding with challenges and opportunities that we see ahead of us, such as adding more complex behaviors to agent-based models, and how “big data” offers new avenues for multiscale calibration and validation of agent-based models.  If you are still reading this, below you can read the abstract of the paper and find the full reference to the chapter.

      Abstract:

      Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems. In this article, we introduce ABM and its utility for studying geographical systems. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.

      Keywords: Agent-based modeling; Calibration; Complexity; Geographical information science; Modeling and simulation; Validation; Verification.

      Full Reference

      Crooks, A.T., Heppenstall, A. and Malleson, N. (2018), Agent-based Modelling, in Huang, B. (ed), Comprehensive Geographic Information Systems, Elsevier, Oxford, England. Volume 1, pp. 218-243 DOI: https://doi.org/10.1016/B978-0-12-409548-9.09704-9. (pdf)

      1. [Readers of this blog might of expected the chapter would be about Agent-based Modeling, but its still worth a read!]

      Continue reading »

      Agent-Based Modeling Chapter

      In the recently published “Comprehensive Geographic Information Systems” edited by Bo Huang, Alison Heppenstall, Nick Malleson and myself have a chapter entitled “Agent-based Modelling1. Within the chapter, we provide a overview of agent-based modeling (ABM) especially for the geographical sciences. This includes a section on how ABM emerged i.e. “The Rise of the (Automated) Machines“, along with a discussion on what constitutes an agent. This is followed with steps to building an agent-based model, including: 1) the preparation and design; 2) model implementation 3) and how one goes about evaluating a model (i.e. verification, calibration and validation and how these are particularity challenging with respect to spatial agent-based models). We then discuss how we can integrate space and GIS into agent-based models and review a number of open-source ABM toolkits (e.g. GAMA, MASON, NetLogo) before concluding with challenges and opportunities that we see ahead of us, such as adding more complex behaviors to agent-based models, and how “big data” offers new avenues for multiscale calibration and validation of agent-based models.  If you are still reading this, below you can read the abstract of the paper and find the full reference to the chapter.

      Abstract:

      Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems. In this article, we introduce ABM and its utility for studying geographical systems. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.

      Keywords: Agent-based modeling; Calibration; Complexity; Geographical information science; Modeling and simulation; Validation; Verification.

      Full Reference

      Crooks, A.T., Heppenstall, A. and Malleson, N. (2018), Agent-based Modelling, in Huang, B. (ed), Comprehensive Geographic Information Systems, Elsevier, Oxford, England. Volume 1, pp. 218-243 DOI: https://doi.org/10.1016/B978-0-12-409548-9.09704-9. (pdf)

      1. [Readers of this blog might of expected the chapter would be about Agent-based Modeling, but its still worth a read!]

      Continue reading »

      Big Data, Agents and the City

      In the recently published book “Big Data for Regional Science” edited by Laurie Schintler and  Zhenhua Chen, Nick Malleson, Sarah Wise, and Alison Heppenstall and myself have a chapter entitled: Big Data, Agents and the City. In the chapter we discuss how big data can be used with respect to building more powerful agent-based models. Specifically how data from say social media could be used to inform agents behaviors and their dynamics; along with helping with the calibration and validation of such models with a emphasis on urban systems. 
      Below you can read the abstract of the chapter, see some of the figures we used to support our discussion, along with the full reference and a pdf proof of the chapter. As always any thoughts or comments are welcome.

      Abstract:

      Big Data (BD) offers researchers the scope to simulate population behavior through vastly more powerful Agent Based Models (ABMs), presenting exciting opportunities in the design and appraisal of policies and plans. Agent-based simulations capture system richness by representing micro-level agent choices and their dynamic interactions. They aid analysis of the processes which drive emergent population level phenomena, their change in the future, and their response to interventions. The potential of ABMs has led to a major increase in applications, yet models are limited in that the individual-level data required for robust, reliable calibration are often only available in aggregate form. New (‘big’) sources of data offer a wealth of information about the behavior (e.g. movements, actions, decisions) of individuals. By building ABMs with BD, it is possible to simulate society across many application areas, providing insight into the behavior, interactions, and wider social processes that drive urban systems. This chapter will discuss, in context of urban simulation, how BD can unlock the potential of ABMs, and how ABMs can leverage real value from BD.  In particular, we will focus on how BD can improve an agent’s abstract behavioral representation and suggest how combining these approaches can both reveal new insights into urban simulation, and also address some of the most pressing issues in agent-based modeling; particularly those of calibration and validation.

      Keywords: Agent-based models, Big Data, Emergence, Cities.

      The growth in Agent-based modeling -from search results of Web of Science and Google Scholar.
      Hotspots of activity of Tweeter Users: Tweet locations and associated densities for a selection of prolific users.

      Full Reference:

      Crooks, A.T., Malleson, N., Wise, S. and Heppenstall, A. (2018), Big Data, Agents and the City, in Schintler, L.A. and Chen, Z. (eds.), Big Data for Urban and Regional Science, Routledge, New York, NY, pp. 204-213. (pdf)

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      Big Data, Agents and the City

      In the recently published book “Big Data for Regional Science” edited by Laurie Schintler and  Zhenhua Chen, Nick Malleson, Sarah Wise, and Alison Heppenstall and myself have a chapter entitled: Big Data, Agents and the City. In the chapter we discuss how big data can be used with respect to building more powerful agent-based models. Specifically how data from say social media could be used to inform agents behaviors and their dynamics; along with helping with the calibration and validation of such models with a emphasis on urban systems. 
      Below you can read the abstract of the chapter, see some of the figures we used to support our discussion, along with the full reference and a pdf proof of the chapter. As always any thoughts or comments are welcome.

      Abstract:

      Big Data (BD) offers researchers the scope to simulate population behavior through vastly more powerful Agent Based Models (ABMs), presenting exciting opportunities in the design and appraisal of policies and plans. Agent-based simulations capture system richness by representing micro-level agent choices and their dynamic interactions. They aid analysis of the processes which drive emergent population level phenomena, their change in the future, and their response to interventions. The potential of ABMs has led to a major increase in applications, yet models are limited in that the individual-level data required for robust, reliable calibration are often only available in aggregate form. New (‘big’) sources of data offer a wealth of information about the behavior (e.g. movements, actions, decisions) of individuals. By building ABMs with BD, it is possible to simulate society across many application areas, providing insight into the behavior, interactions, and wider social processes that drive urban systems. This chapter will discuss, in context of urban simulation, how BD can unlock the potential of ABMs, and how ABMs can leverage real value from BD.  In particular, we will focus on how BD can improve an agent’s abstract behavioral representation and suggest how combining these approaches can both reveal new insights into urban simulation, and also address some of the most pressing issues in agent-based modeling; particularly those of calibration and validation.

      Keywords: Agent-based models, Big Data, Emergence, Cities.

      The growth in Agent-based modeling -from search results of Web of Science and Google Scholar.
      Hotspots of activity of Tweeter Users: Tweet locations and associated densities for a selection of prolific users.

      Full Reference:

      Crooks, A.T., Malleson, N., Wise, S. and Heppenstall, A. (2018), Big Data, Agents and the City, in Schintler, L.A. and Chen, Z. (eds.), Big Data for Urban and Regional Science, Routledge, New York, NY, pp. 204-213. (pdf)

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