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)

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)

Continue reading »

MAIS in CSS

It is always a great pleasure to teach and work with students, and see them complete their academic program. Over this last academic year, I supervised 3 masters students and served on the committee of another one, who all successfully completed their Master of Arts in Interdisciplinary Studies (MAIS) with a concentration in Computational Social Science (CSS). To quote from the MAIS in CSS website:

“Computational Social Science (CSS) is an interdisciplinary science in which social science questions are investigated with modern computational tools. Our program was the first CSS MA in the world, and continues to advance the study of social science through computational methods (e.g. agent-based modeling, social network analysis, and big data).

Our faculty members are internationally recognized for their pioneering work in CSS, including authoring the first textbook in the field, and have written numerous books and articles on topics such as growing artificial societies, modeling geographical systems, and sustainability. Research in the program is and has been funded by the National Science Foundation, United States Agency for International Development, National Geospatial-Intelligence Agency, the Defense Threat Reduction Agency, the Defense Advanced Research Projects Agency, and NASA.

Besides taking introductory classes in theories and practices of social, geo-social, economic, and network modeling, you will have the opportunity to work one-on-one with faculty on your project or thesis of interest, as well as directed readings. Additionally, Mason’s proximity to the Washington, D.C., area provides an excellent opportunity to attend seminars offered by NGOs, visiting professors, and government employees.

Students range from recent college graduates to mid-career professionals who bring diverse knowledge that enhances the classroom experience. Graduates have gone on to pursue their doctorates at Mason and other Carnegie Classification Research 1 universities. Others have pursued or continued their career in government or the private sector, in organizations such as the U.S. Army, MapR Technologies, CACI, Logistics Management Institute, and Ninja Analytics, Inc.

To get the latest information on our program, visit us on Facebook or our program page.”

Below is a selection of projects from this academic year. Eric Hansen project was entitled “An Agent-Based Model of British And Boer Small Arms and Tactics During the Second Anglo-Boer War” in which he explored different military technology had an impact on the military victories.
In another project, Paul Cummings  explored different strategies for combating radicalism (i.e. Security Risk model and Socio-Economic Hardship model) via an agent-based model under the title of “Modeling the Characteristics of Radical Ideological Growth using an Agent based Model Methodology

Marta Hansen’s final project was entitled “Positive Affect And Prospect Theory In Agent_Zero: A Model Extension” which extends Joshua Epstein’s Agent_Zero model to allow for cooperative events to take place.
Just to highlight that not all students opt for  agent-based models. Devin Bright undertook a project entitled “Mapping the Human Terrain of a Modern Megacity with the use of Social Media.” In which he explored how a years worth of social media data can be mined and analyzed via GIS and social network analysis (SNA) to to give insights into the dynamics of New York City in the United States and Lagos in Nigeria.
Continue reading »

MAIS in CSS

It is always a great pleasure to teach and work with students, and see them complete their academic program. Over this last academic year, I supervised 3 masters students and served on the committee of another one, who all successfully completed their Master of Arts in Interdisciplinary Studies (MAIS) with a concentration in Computational Social Science (CSS). To quote from the MAIS in CSS website:

“Computational Social Science (CSS) is an interdisciplinary science in which social science questions are investigated with modern computational tools. Our program was the first CSS MA in the world, and continues to advance the study of social science through computational methods (e.g. agent-based modeling, social network analysis, and big data).

Our faculty members are internationally recognized for their pioneering work in CSS, including authoring the first textbook in the field, and have written numerous books and articles on topics such as growing artificial societies, modeling geographical systems, and sustainability. Research in the program is and has been funded by the National Science Foundation, United States Agency for International Development, National Geospatial-Intelligence Agency, the Defense Threat Reduction Agency, the Defense Advanced Research Projects Agency, and NASA.

Besides taking introductory classes in theories and practices of social, geo-social, economic, and network modeling, you will have the opportunity to work one-on-one with faculty on your project or thesis of interest, as well as directed readings. Additionally, Mason’s proximity to the Washington, D.C., area provides an excellent opportunity to attend seminars offered by NGOs, visiting professors, and government employees.

Students range from recent college graduates to mid-career professionals who bring diverse knowledge that enhances the classroom experience. Graduates have gone on to pursue their doctorates at Mason and other Carnegie Classification Research 1 universities. Others have pursued or continued their career in government or the private sector, in organizations such as the U.S. Army, MapR Technologies, CACI, Logistics Management Institute, and Ninja Analytics, Inc.

To get the latest information on our program, visit us on Facebook or our program page.”

Below is a selection of projects from this academic year. Eric Hansen project was entitled “An Agent-Based Model of British And Boer Small Arms and Tactics During the Second Anglo-Boer War” in which he explored different military technology had an impact on the military victories.
In another project, Paul Cummings  explored different strategies for combating radicalism (i.e. Security Risk model and Socio-Economic Hardship model) via an agent-based model under the title of “Modeling the Characteristics of Radical Ideological Growth using an Agent based Model Methodology

Marta Hansen’s final project was entitled “Positive Affect And Prospect Theory In Agent_Zero: A Model Extension” which extends Joshua Epstein’s Agent_Zero model to allow for cooperative events to take place.
Just to highlight that not all students opt for  agent-based models. Devin Bright undertook a project entitled “Mapping the Human Terrain of a Modern Megacity with the use of Social Media.” In which he explored how a years worth of social media data can be mined and analyzed via GIS and social network analysis (SNA) to to give insights into the dynamics of New York City in the United States and Lagos in Nigeria.
Continue reading »

Zika in Twitter: Health Narratives

In the paper we explored how health narratives and event storylines pertaining to the recent Zika outbreak emerged in social media and how it related to news stories and actual events.

Specifically we combined actors (e.g. twitter uses), locations (e.g. where the tweets originated) and concepts (e.g. emerging narratives such as pregnancy) to gain insights on the mechanisms that drive participation, contributions, and interactions on social media  during a disease outbreak. Below you can read a summary of our paper along with some of the figures which highlight our methodology and findings.  

An overview of the Twitter narrative analysis approach, starting with data collection, and proceeding with preprocessing and data analysis to identify narrative events, which can be used to build an event storyline.

Abstract:
 

Background: The recent Zika outbreak witnessed the disease evolving from a regional health concern to a global epidemic. During this process, different communities across the globe became involved in Twitter, discussing the disease and key issues associated with it. This paper presents a study of this discussion in Twitter, at the nexus of location, actors, and concepts.

Objective: Our objective in this study was to demonstrate the significance of 3 types of events: location related, actor related, and concept- related for understanding how a public health emergency of international concern plays out in social media, and Twitter in particular. Accordingly, the study contributes to research efforts toward gaining insights on the mechanisms that drive participation, contributions, and interaction in this social media platform during a disease outbreak. 

Methods: We collected 6,249,626 tweets referring to the Zika outbreak over a period of 12 weeks early in the outbreak (December 2015 through March 2016). We analyzed this data corpus in terms of its geographical footprint, the actors participating in the discourse, and emerging concepts associated with the issue. Data were visualized and evaluated with spatiotemporal and network analysis tools to capture the evolution of interest on the topic and to reveal connections between locations, actors, and concepts in the form of interaction networks. 

Results: The spatiotemporal analysis of Twitter contributions reflects the spread of interest in Zika from its original hotspot in South America to North America and then across the globe. The Centers for Disease Control and World Health Organization had a prominent presence in social media discussions. Tweets about pregnancy and abortion increased as more information about this emerging infectious disease was presented to the public and public figures became involved in this. 

Conclusions: The results of this study show the utility of analyzing temporal variations in the analytic triad of locations, actors, and concepts. This contributes to advancing our understanding of social media discourse during a public health emergency of international concern.

Keywords: Zika Virus; Social Media; Twitter Messaging; Geographic Information Systems.

Spatiotemporal participation patterns and identifiable clusters over 4 of our twelve week study. The top left panel shows the data during the first week, and time progresses from left to right and from top to bottom towards .

Subsets of the full retweet network pertaining to the WHO (left) and CDC (right), and clusters identified within them. Magenta clusters are centered upon health entities, green upon news organizations, orange upon political entities.

Visualizing a narrative storyline across locations (blue), actors (red), and concepts (green).

Full Reference:

Stefanidis, A., Vraga, E., Lamprianidis, G., Radzikowski, J., Delamater, P.L., Jacobsen, K.H., Pfoser, D., Croitoru, A. and Crooks, A.T. (2017). “Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts”, JMIR Public Health and Surveillance, 3 (2): e22. (pdf)

As normal, any feedback or comments are most welcome. 

Continue reading »

Zika in Twitter: Health Narratives

In the paper we explored how health narratives and event storylines pertaining to the recent Zika outbreak emerged in social media and how it related to news stories and actual events.

Specifically we combined actors (e.g. twitter uses), locations (e.g. where the tweets originated) and concepts (e.g. emerging narratives such as pregnancy) to gain insights on the mechanisms that drive participation, contributions, and interactions on social media  during a disease outbreak. Below you can read a summary of our paper along with some of the figures which highlight our methodology and findings.  

An overview of the Twitter narrative analysis approach, starting with data collection, and proceeding with preprocessing and data analysis to identify narrative events, which can be used to build an event storyline.

Abstract:
 

Background: The recent Zika outbreak witnessed the disease evolving from a regional health concern to a global epidemic. During this process, different communities across the globe became involved in Twitter, discussing the disease and key issues associated with it. This paper presents a study of this discussion in Twitter, at the nexus of location, actors, and concepts.

Objective: Our objective in this study was to demonstrate the significance of 3 types of events: location related, actor related, and concept- related for understanding how a public health emergency of international concern plays out in social media, and Twitter in particular. Accordingly, the study contributes to research efforts toward gaining insights on the mechanisms that drive participation, contributions, and interaction in this social media platform during a disease outbreak. 

Methods: We collected 6,249,626 tweets referring to the Zika outbreak over a period of 12 weeks early in the outbreak (December 2015 through March 2016). We analyzed this data corpus in terms of its geographical footprint, the actors participating in the discourse, and emerging concepts associated with the issue. Data were visualized and evaluated with spatiotemporal and network analysis tools to capture the evolution of interest on the topic and to reveal connections between locations, actors, and concepts in the form of interaction networks. 

Results: The spatiotemporal analysis of Twitter contributions reflects the spread of interest in Zika from its original hotspot in South America to North America and then across the globe. The Centers for Disease Control and World Health Organization had a prominent presence in social media discussions. Tweets about pregnancy and abortion increased as more information about this emerging infectious disease was presented to the public and public figures became involved in this. 

Conclusions: The results of this study show the utility of analyzing temporal variations in the analytic triad of locations, actors, and concepts. This contributes to advancing our understanding of social media discourse during a public health emergency of international concern.

Keywords: Zika Virus; Social Media; Twitter Messaging; Geographic Information Systems.

Spatiotemporal participation patterns and identifiable clusters over 4 of our twelve week study. The top left panel shows the data during the first week, and time progresses from left to right and from top to bottom towards .

Subsets of the full retweet network pertaining to the WHO (left) and CDC (right), and clusters identified within them. Magenta clusters are centered upon health entities, green upon news organizations, orange upon political entities.

Visualizing a narrative storyline across locations (blue), actors (red), and concepts (green).

Full Reference:

Stefanidis, A., Vraga, E., Lamprianidis, G., Radzikowski, J., Delamater, P.L., Jacobsen, K.H., Pfoser, D., Croitoru, A. and Crooks, A.T. (2017). “Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts”, JMIR Public Health and Surveillance, 3 (2): e22. (pdf)

As normal, any feedback or comments are most welcome. 

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Smart Cities in IEEE Pervasive Computing

We are excited to announce that the special issue that we organized for IEEE Pervasive Computing is now out. In the special issue entitled “Smart Cities” and demonstrates the state of the art of pervasive computing technologies that collect, monitor, and analyze various aspects of urban life. The articles and departments in the special issue highlight the coming revolution in urban data via some of the different approaches researchers are taking to build tools and applications to better inform decision making (to reduce energy consumption or improve visitor flows, for example). Such research will be critical to setting goals for sustainable urban development within different global contexts. We need to better understand cities and their underlying systems if we want to improve the quality of urban life. To this end, in the special issue we have an introduction (editorial) followed by a number of articles, an interview and a research spotlight:
We hope you enjoy them. Thank you for the authors who submitted papers, the reviewers, Rob Kitchen for giving an interview and Barbara Lenz and Dirk Heinrichs for discussing their research. Lastly, we would also like to thank the IEEE Pervasive Computing team for ensuring that the special issue came to fruition.

Full Reference to the Introduction: 

Crooks, A.T., Schechtner, K., Day, A.K and Hudson-Smith, A (2017), Creating Smart Buildings and Cities, IEEE Pervasive Computing, 16 (2): 23-25. (pdf)

Continue reading »

Smart Cities in IEEE Pervasive Computing

We are excited to announce that the special issue that we organized for IEEE Pervasive Computing is now out. In the special issue entitled “Smart Cities” and demonstrates the state of the art of pervasive computing technologies that collect, monitor, and analyze various aspects of urban life. The articles and departments in the special issue highlight the coming revolution in urban data via some of the different approaches researchers are taking to build tools and applications to better inform decision making (to reduce energy consumption or improve visitor flows, for example). Such research will be critical to setting goals for sustainable urban development within different global contexts. We need to better understand cities and their underlying systems if we want to improve the quality of urban life. To this end, in the special issue we have an introduction (editorial) followed by a number of articles, an interview and a research spotlight:
We hope you enjoy them. Thank you for the authors who submitted papers, the reviewers, Rob Kitchen for giving an interview and Barbara Lenz and Dirk Heinrichs for discussing their research. Lastly, we would also like to thank the IEEE Pervasive Computing team for ensuring that the special issue came to fruition.

Full Reference to the Introduction: 

Crooks, A.T., Schechtner, K., Day, A.K and Hudson-Smith, A (2017), Creating Smart Buildings and Cities, IEEE Pervasive Computing, 16 (2): 23-25. (pdf)

Continue reading »

Cellular Automata

In the recently released “The International Encyclopedia of Geography: People, the Earth, Environment, and Technology” I was asked to write a brief entry on “Cellular Automata“. Below is the abstract to my chapter, along some of the images I used in my discussion, the full reference to the chapter.

Abstract: 

Cellular Automata (CA) are a class of models where one can explore how local actions generate global patterns through well specified rules. In such models, decisions are made locally by each cell which are often arranged on a regular lattice and the patterns that emerge, be it urban growth or deforestation are not coordinated centrally but arise from the bottom up. Such patterns emerge through the cell changing its state based on specific transition rules and the states of their surrounding cells. This entry reviews the principles of CA models, provides a background on how CA models have developed, explores a range of applications of where they have been used within the geographical sciences, prior to concluding with future directions for CA modeling. 
The figures below are a sample from the entry, for example, we outline different types of spaces within CA models such as those shown in Figures 1 and 2. We also show how simple rules can lead to the emergence of patterns such as the Game of Life as shown in Figure 3 or  Rule 30 as shown in Figure 4.

Figure 1: Two-Dimensional Cellular Automata Neighborhoods

Figure 2: Voronoi Tessellations Of Space Where Each Polygon Has A Different Number Of Neighbors Based On A Shared Edge.

Figure 3: Example of Cells Changing State from Dead (White) To Alive (Black) Over Time Depending On The States of its Neighboring Cells.

Figure 4: A One-Dimensional CA Model Implementing “Rule 30” Where Successive Iterations Are Presented Below Each Other.

Full Reference:

Crooks, A.T. (2017), Cellular Automata, in Richardson, D., Castree, N., Goodchild, M. F., Kobayashi, A. L., Liu, W. and Marston, R.  (eds.), The International Encyclopedia of Geography: People, the Earth, Environment, and Technology, Wiley Blackwell. DOI: 10.1002/9781118786352.wbieg0578. (pdf)

Continue reading »

Cellular Automata

In the recently released “The International Encyclopedia of Geography: People, the Earth, Environment, and Technology” I was asked to write a brief entry on “Cellular Automata“. Below is the abstract to my chapter, along some of the images I used in my discussion, the full reference to the chapter.

Abstract: 

Cellular Automata (CA) are a class of models where one can explore how local actions generate global patterns through well specified rules. In such models, decisions are made locally by each cell which are often arranged on a regular lattice and the patterns that emerge, be it urban growth or deforestation are not coordinated centrally but arise from the bottom up. Such patterns emerge through the cell changing its state based on specific transition rules and the states of their surrounding cells. This entry reviews the principles of CA models, provides a background on how CA models have developed, explores a range of applications of where they have been used within the geographical sciences, prior to concluding with future directions for CA modeling. 
The figures below are a sample from the entry, for example, we outline different types of spaces within CA models such as those shown in Figures 1 and 2. We also show how simple rules can lead to the emergence of patterns such as the Game of Life as shown in Figure 3 or  Rule 30 as shown in Figure 4.

Figure 1: Two-Dimensional Cellular Automata Neighborhoods

Figure 2: Voronoi Tessellations Of Space Where Each Polygon Has A Different Number Of Neighbors Based On A Shared Edge.

Figure 3: Example of Cells Changing State from Dead (White) To Alive (Black) Over Time Depending On The States of its Neighboring Cells.

Figure 4: A One-Dimensional CA Model Implementing “Rule 30” Where Successive Iterations Are Presented Below Each Other.

Full Reference:

Crooks, A.T. (2017), Cellular Automata, in Richardson, D., Castree, N., Goodchild, M. F., Kobayashi, A. L., Liu, W. and Marston, R.  (eds.), The International Encyclopedia of Geography: People, the Earth, Environment, and Technology, Wiley Blackwell. DOI: 10.1002/9781118786352.wbieg0578. (pdf)

Continue reading »

Applications of Agent-based Models

Often I get asked the question along the lines of: “how are agent-based models are being used outside academia, especially in government and private industry?” So I thought it was about time I briefly write something about this.

Let me start with a question I ask my students when I first introduce agent-based modeling: “Have you ever seen an agent-based model before?” Often the answer is NO, but then I show them the following clip from MASSIVE (Multiple Agent Simulation System in Virtual Environment) where agent-based models are used in a variety of movies and TV shows. But apart from TV shows and movies where else have agent-based models been used?
There are two specific application domains where agent-based modeling has taken off. The first being pedestrian simulation for example, LegionSteps and EXODUS simulation platforms. The second is the area of traffic modeling for example, there are several microsimulation/agent-based model platforms such as PTV Visum, TransModeler and Paramics. Based on these companies websites they have clients in industry, government and academia.
If we move away from the areas discussed above, there is a lot of writing about the potential of agent-based modeling. For example, the Bank of England had a article entitled “Agent-based models: understanding the economy from the bottom up” which to quote from the summary:

“considers the strengths of agent-based modelling, which explains the behaviour of a system by simulating the behaviour of each individual ‘agent’ in it, and the ways that it can be used to help central banks understand the economy.”

Similar articles can be seen in the New York Times and the Guardian to name but a few. But where else have agent-based models been used? A sample (and definitely not an exhaustive list) of applications and references are provided below for interested readers:
  • Southwest Airlines used an agent-based model to improve how it handled cargo (Seibel and Thomas, 2000).
  • Eli Lilly used an agent-based model for drug development (Bonabeau, 2003a).
  • Pacific Gas and Electric: Used an agent based model to see how energy flows through the power grid (Bonabeau, 2003a).
  • Procter and Gamble used an agent-based model to understand its consumer markets (North et al., 2010) while Hewlett-Packard used an agent-based model to understand how hiring strategies effect corporate culture (Bonabeau, 2003b).
  • Macy’s have used agent-based models for store design (Bonabeau, 2003b).
  • NASDAQ used and agent based model to explore changes to Stock Market’s decimalization (Bonabeau, 2003b; Darley and Outkin, 2007).
  • Using a agent-based model to explore capacity and demand in theme parks (Bonabeau, 2000).
  • Traffic and pedestrian modeling (Helbing and Balietti, 2011).
  • Disease dynamics (e.g. Eubank et al., 2004).
  • Agent-based modeling has also been used for wild fire training, incident command and community outreach (Guerin and Carrera, 2010). For example SimTable was used in the  2016 Sand Fire in California. 
  • InSTREAM: Explores how river salmon populations react to changes (Railsback and Harvey, 2002).

While not a comprehensive list, it is hoped that these examples and links will be useful if someone asks the question I started this post with. If anyone else knows of any other real world applications of agent-based modeling please let me know (preferably with a link to a paper or website).
 
References

  • Bonabeau, E. (2000), ‘Business Applications of Social Agent-Based Simulation’, Advances in Complex Systems, 3(1-4): 451-461.
  • Bonabeau, E. (2003a), ‘Don’t Trust Your Gut’, Harvard Business Review, 81(5): 116-123.
  • Bonabeau, E. (2003b), ‘Predicting the Unpredictable’, Harvard Business Review, 80(3): 109-116.
  • Darley, V. and Outkin, A.V. (2007), NASDAQ Market Simulation: Insights on a Major Market from the Science of Complex Adaptive Systems, World Scientific Publishing, River Edge, NJ.
  • Eubank, S., Guclu, H., Kumar, A.V.S., Marathe, M.V., Srinivasan, A., Toroczkai, Z. and Wang, N. (2004), ‘Modelling Disease Outbreaks in Realistic Urban Social Networks’, Nature, 429: 180-184.
  • Guerin, S. and Carrera, F. (2010), ‘Sand on Fire: An Interactive Tangible 3D Platform for the Modeling and Management of Wildfires.’ WIT Transactions on Ecology and the Environment, 137: 57-68.
  • Helbing, D. and Balietti, S. (2011), How to do Agent-based Simulations in the Future: From Modeling Social Mechanisms to Emergent Phenomena and Interactive Systems Design, Santa Fe Institute, Working Paper 11-06-024, Santa Fe, NM.
  • North, M.J., Macal, C.M., Aubin, J.S., Thimmapuram, P., Bragen, M., Hahn, J., J., K., Brigham, N., Lacy, M.E. and Hampton, D. (2010), ‘Multiscale Agent-based Consumer Market Modeling’, Complexity, 15(5): 37-47.
  • Railsback, S.F. and Harvey, B.C. (2002), ‘Analysis of Habitat Selection Rules using an Individual-based Model’, Ecology, 83(7): 1817-1830.
  • Seibel, F. and Thomas, C. (2000), ‘Manifest Destiny: Adaptive Cargo Routing at Southwest Airlines’, Perspectives on Business Innovation, 4: 27-33.

    Continue reading »

    Applications of Agent-based Models

    Often I get asked the question along the lines of: “how are agent-based models are being used outside academia, especially in government and private industry?” So I thought it was about time I briefly write something about this.

    Let me start with a question I ask my students when I first introduce agent-based modeling: “Have you ever seen an agent-based model before?” Often the answer is NO, but then I show them the following clip from MASSIVE (Multiple Agent Simulation System in Virtual Environment) where agent-based models are used in a variety of movies and TV shows. But apart from TV shows and movies where else have agent-based models been used?
    There are two specific application domains where agent-based modeling has taken off. The first being pedestrian simulation for example, LegionSteps and EXODUS simulation platforms. The second is the area of traffic modeling for example, there are several microsimulation/agent-based model platforms such as PTV Visum, TransModeler and Paramics. Based on these companies websites they have clients in industry, government and academia.
    If we move away from the areas discussed above, there is a lot of writing about the potential of agent-based modeling. For example, the Bank of England had a article entitled “Agent-based models: understanding the economy from the bottom up” which to quote from the summary:

    “considers the strengths of agent-based modelling, which explains the behaviour of a system by simulating the behaviour of each individual ‘agent’ in it, and the ways that it can be used to help central banks understand the economy.”

    Similar articles can be seen in the New York Times and the Guardian to name but a few. But where else have agent-based models been used? A sample (and definitely not an exhaustive list) of applications and references are provided below for interested readers:
    • Southwest Airlines used an agent-based model to improve how it handled cargo (Seibel and Thomas, 2000).
    • Eli Lilly used an agent-based model for drug development (Bonabeau, 2003a).
    • Pacific Gas and Electric: Used an agent based model to see how energy flows through the power grid (Bonabeau, 2003a).
    • Procter and Gamble used an agent-based model to understand its consumer markets (North et al., 2010) while Hewlett-Packard used an agent-based model to understand how hiring strategies effect corporate culture (Bonabeau, 2003b).
    • Macy’s have used agent-based models for store design (Bonabeau, 2003b).
    • NASDAQ used and agent based model to explore changes to Stock Market’s decimalization (Bonabeau, 2003b; Darley and Outkin, 2007).
    • Using a agent-based model to explore capacity and demand in theme parks (Bonabeau, 2000).
    • Traffic and pedestrian modeling (Helbing and Balietti, 2011).
    • Disease dynamics (e.g. Eubank et al., 2004).
    • Agent-based modeling has also been used for wild fire training, incident command and community outreach (Guerin and Carrera, 2010). For example SimTable was used in the  2016 Sand Fire in California. 
    • InSTREAM: Explores how river salmon populations react to changes (Railsback and Harvey, 2002).

    While not a comprehensive list, it is hoped that these examples and links will be useful if someone asks the question I started this post with. If anyone else knows of any other real world applications of agent-based modeling please let me know (preferably with a link to a paper or website).
     
    References

    • Bonabeau, E. (2000), ‘Business Applications of Social Agent-Based Simulation’, Advances in Complex Systems, 3(1-4): 451-461.
    • Bonabeau, E. (2003a), ‘Don’t Trust Your Gut’, Harvard Business Review, 81(5): 116-123.
    • Bonabeau, E. (2003b), ‘Predicting the Unpredictable’, Harvard Business Review, 80(3): 109-116.
    • Darley, V. and Outkin, A.V. (2007), NASDAQ Market Simulation: Insights on a Major Market from the Science of Complex Adaptive Systems, World Scientific Publishing, River Edge, NJ.
    • Eubank, S., Guclu, H., Kumar, A.V.S., Marathe, M.V., Srinivasan, A., Toroczkai, Z. and Wang, N. (2004), ‘Modelling Disease Outbreaks in Realistic Urban Social Networks’, Nature, 429: 180-184.
    • Guerin, S. and Carrera, F. (2010), ‘Sand on Fire: An Interactive Tangible 3D Platform for the Modeling and Management of Wildfires.’ WIT Transactions on Ecology and the Environment, 137: 57-68.
    • Helbing, D. and Balietti, S. (2011), How to do Agent-based Simulations in the Future: From Modeling Social Mechanisms to Emergent Phenomena and Interactive Systems Design, Santa Fe Institute, Working Paper 11-06-024, Santa Fe, NM.
    • North, M.J., Macal, C.M., Aubin, J.S., Thimmapuram, P., Bragen, M., Hahn, J., J., K., Brigham, N., Lacy, M.E. and Hampton, D. (2010), ‘Multiscale Agent-based Consumer Market Modeling’, Complexity, 15(5): 37-47.
    • Railsback, S.F. and Harvey, B.C. (2002), ‘Analysis of Habitat Selection Rules using an Individual-based Model’, Ecology, 83(7): 1817-1830.
    • Seibel, F. and Thomas, C. (2000), ‘Manifest Destiny: Adaptive Cargo Routing at Southwest Airlines’, Perspectives on Business Innovation, 4: 27-33.

      Continue reading »

      Authoritative and VGI in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi

      The motivation behind the paper was that while there are numerous studies comparing VGI to authoritative data in the developed world, there are very few that do so in developing world. In order to address this issue in the paper we compare the quality of authoritative road data (i.e. from the Regional Center for Mapping of Resources for Development – RCMRD) and non-authoritative crowdsourced road data (i.e. from OpenStreetMap (OSM) and Google’s Map Maker) in conjunction with population data in and around Nairobi, Kenya.

      Results from our analysis show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards more rural areas. Further information including the abstract to our paper, some figures and full reference is given below.

      Abstract:

      With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development – RCMRD) and non-authoritative (data from OSM and Google’s Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.

      Keywords: Volunteered Geographic Information; Crowdsourcing; Road Networks; Population Data; Kenya  
      Road Coverage per km2
      Pairwise difference in road coverage. Clockwise from top left: i) RCMRD 2011 versus Map Maker 2014; ii) RCMRD 2011 versus OSM 2011; iii) RCMRD 2011 versus OSM 2014; iv) OSM 2014 versus Map Maker 2014 (Red cells: first layer has higher coverage; Green cells: second layer has higher coverage).

      Full Reference:

      Mahabir, R., Stefanidis, A., Croitoru, A., Crooks, A.T. and Agouris, P. (2017), “Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya”, ISPRS International Journal of Geo-Information, 6(1): 24, doi:10.3390/ijgi6010024.

      As always any thoughts or comments about this work are welcome.

      Continue reading »

      Authoritative and VGI in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi

      The motivation behind the paper was that while there are numerous studies comparing VGI to authoritative data in the developed world, there are very few that do so in developing world. In order to address this issue in the paper we compare the quality of authoritative road data (i.e. from the Regional Center for Mapping of Resources for Development – RCMRD) and non-authoritative crowdsourced road data (i.e. from OpenStreetMap (OSM) and Google’s Map Maker) in conjunction with population data in and around Nairobi, Kenya.

      Results from our analysis show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards more rural areas. Further information including the abstract to our paper, some figures and full reference is given below.

      Abstract:

      With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development – RCMRD) and non-authoritative (data from OSM and Google’s Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.

      Keywords: Volunteered Geographic Information; Crowdsourcing; Road Networks; Population Data; Kenya  
      Road Coverage per km2
      Pairwise difference in road coverage. Clockwise from top left: i) RCMRD 2011 versus Map Maker 2014; ii) RCMRD 2011 versus OSM 2011; iii) RCMRD 2011 versus OSM 2014; iv) OSM 2014 versus Map Maker 2014 (Red cells: first layer has higher coverage; Green cells: second layer has higher coverage).

      Full Reference:

      Mahabir, R., Stefanidis, A., Croitoru, A., Crooks, A.T. and Agouris, P. (2017), “Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya”, ISPRS International Journal of Geo-Information, 6(1): 24, doi:10.3390/ijgi6010024.

      As always any thoughts or comments about this work are welcome.

      Continue reading »
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