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. 

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 »

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 »

      International Congress on Agent Computing

      Between the 29th and 30th of November, the International Congress on Agent Computing was held at George Mason University. It was organized to celebrate the 20th anniversary of the publication of Growing Artificial Societies by Robert Axtell and Joshua Epstein. The congress brought together a great line up of interdisciplinary keynote speakers: Brian Arthur, Mike Batty, Stuart Kauffman and  David Krakauer and a  panel discussion entitled “Barriers to Progress in Agent Computing—Technical and Social” with Chris Barrett, Steven Kimbrough, Blake LeBaron, Dawn ParkerFlaminio Squazzoni and Leigh Tesfatsion. Along with the keynotes and there panel there were also over 19 posters and 59 presentations which showcased and demonstrated the theme of the congress, that of the:
      “explosive growth of agent modeling over the past two decades in the social sciences, in business and government, and related areas, and offer a tour d’horizon of its present state and myriad applications. Looking forward, we will identify challenges and opportunities — Hilbert Problems, if you will — to shape the future of agent-based computational modeling.”

      Joshua Epstein and Robert Axtell presenting their works.

      Josh and Rob each gave really impressive talks entitled “Agent-­based modeling: From Napkins to Nations” and “The Adoption of Agent Computing over Time by Social Scientists as Compared to Game Theory and Experimental/ Behavioral Economics” respectively. Which reflected how agent computing has evolved over the last 20 years with plenty of funny anecdotes along the way including references and critiques of their works such as “masculine gods of their cyberspace creations” and where the field is going.
      What really impressed me about the congress was the atmosphere. That of like minded individuals from many different disciplines coming together and discussing agent computing, complexity and modeling more generally.  Some of this can be seen via photos and tweets of the event.
      Alison Heppenstall, Nick Malleson and myslef also participated at the congress with a talk entitled “ABM for Simulating Spatial Systems: How are we doing?” which assessed how has agent-based modeling within the geographical sciences advanced over the last 20 years. Below one can read a brief outline of the talk and a movie of presentation.

      Abstract:

      While great advances in modeling have been made, one of the greatest challenges we face is that of understanding human behavior and how people perceive and behave in physical spaces. Can new sources of data (i.e. “big data”) be used to explore the connections between people and places?   In this paper we will review of the current state of art of modeling geographical systems.  We highlight the challenges and opportunities through a series of examples that new data can be used to better understand and simulate how individuals behave within geographical systems.

      Key Words: Agent-based Modeling, Geographical Information Science, Networks, Cities, Geographical Systems.

      Reference:

      Heppenstall, A., Crooks A.T. and Malleson, N. (2016), ABM for Simulating Spatial Systems: How are we doing? International Congress on Agent Computing, 29th-30th, November, Fairfax, VA.

      The Growth of Geographical  ABM (selected examples).

      Continue reading »

      International Congress on Agent Computing

      Between the 29th and 30th of November, the International Congress on Agent Computing was held at George Mason University. It was organized to celebrate the 20th anniversary of the publication of Growing Artificial Societies by Robert Axtell and Joshua Epstein. The congress brought together a great line up of interdisciplinary keynote speakers: Brian Arthur, Mike Batty, Stuart Kauffman and  David Krakauer and a  panel discussion entitled “Barriers to Progress in Agent Computing—Technical and Social” with Chris Barrett, Steven Kimbrough, Blake LeBaron, Dawn ParkerFlaminio Squazzoni and Leigh Tesfatsion. Along with the keynotes and there panel there were also over 19 posters and 59 presentations which showcased and demonstrated the theme of the congress, that of the:
      “explosive growth of agent modeling over the past two decades in the social sciences, in business and government, and related areas, and offer a tour d’horizon of its present state and myriad applications. Looking forward, we will identify challenges and opportunities — Hilbert Problems, if you will — to shape the future of agent-based computational modeling.”

      Joshua Epstein and Robert Axtell presenting their works.

      Josh and Rob each gave really impressive talks entitled “Agent-­based modeling: From Napkins to Nations” and “The Adoption of Agent Computing over Time by Social Scientists as Compared to Game Theory and Experimental/ Behavioral Economics” respectively. Which reflected how agent computing has evolved over the last 20 years with plenty of funny anecdotes along the way including references and critiques of their works such as “masculine gods of their cyberspace creations” and where the field is going.
      What really impressed me about the congress was the atmosphere. That of like minded individuals from many different disciplines coming together and discussing agent computing, complexity and modeling more generally.  Some of this can be seen via photos and tweets of the event.
      Alison Heppenstall, Nick Malleson and myslef also participated at the congress with a talk entitled “ABM for Simulating Spatial Systems: How are we doing?” which assessed how has agent-based modeling within the geographical sciences advanced over the last 20 years. Below one can read a brief outline of the talk and a movie of presentation.

      Abstract:

      While great advances in modeling have been made, one of the greatest challenges we face is that of understanding human behavior and how people perceive and behave in physical spaces. Can new sources of data (i.e. “big data”) be used to explore the connections between people and places?   In this paper we will review of the current state of art of modeling geographical systems.  We highlight the challenges and opportunities through a series of examples that new data can be used to better understand and simulate how individuals behave within geographical systems.

      Key Words: Agent-based Modeling, Geographical Information Science, Networks, Cities, Geographical Systems.

      Reference:

      Heppenstall, A., Crooks A.T. and Malleson, N. (2016), ABM for Simulating Spatial Systems: How are we doing? International Congress on Agent Computing, 29th-30th, November, Fairfax, VA.

      The Growth of Geographical  ABM (selected examples).

      Continue reading »

      Modeling the Emergence of Riots: A Geosimulation Approach

      As you might of guessed the paper is about riots but that is not all. In the paper we have a highly detailed cognitive model implemented through the PECS (Physical conditions, Emotional state, Cognitive capabilities, and Social status) framework based around identity theory. The purpose of the model (and paper) is to explore how the unique socioeconomic variables underlying Kibera, a slum in Nairobi, coupled with local interactions of its residents, and the spread of a rumor, may trigger a riot such as those seen in 2007. 
      In order to explore this question from the “bottom up” we have developed a novel agent-based model that integrates social network analysis (SNA) and geographic information systems (GIS) for this purpose. In the paper we argue that this integration facilitates the modeling of dynamic social networks created through the agents’ daily interactions. The GIS is used to develop a realistic environment for agents to move and interact that includes a road network and points of interest which impact their daily lives.
      Below is the abstract and a summary of its highlights in order to give you a sense of what our research contribution is. In addition to this we also provide some images either from the paper itself or the from Overview, Design Concepts, and Details (ODD) protocol. Finally at the bottom of this post you can see one of the simulation runs, details of where the model can be downloaded along with the full citation.

      Paper Abstract:

      Immediately after the 2007 Kenyan election results were announced, the country erupted in protest. Riots were particularly severe in Kibera, an informal settlement located within the nations capital, Nairobi. Through the lens of geosimulation, an agent-based model is integrated with social network analysis and geographic information systems to explore how the environment and local interactions underlying Kibera, combined with an external trigger, such as a rumor, led to the emergence of riots. We ground our model on empirical data of Kibera’s geospatial landscape, heterogeneous population, and daily activities of its residents. In order to effectively construct a model of riots, however, we must have an understanding of human behavior, especially that related to an individual’s need for identity and the role rumors play on a person’s decision to riot. This provided the foundation to develop the agents’ cognitive model, which created a feedback system between the agents’ activities in physical space and interactions in social space. Results showed that youth are more susceptible to rioting. Systematically increasing education and employment opportunities, however, did not have simple linear effects on rioting, or even on quality of life with respect to income and activities. The situation is more complex. By linking agent-based modeling, social network analysis, and geographic information systems we were able to develop a cognitive framework for the agents, better represent human behavior by modeling the interactions that occur over both physical and social space, and capture the nonlinear, reinforcing nature of the emergence and dissolution of riots.

      Keywords: agent-based modeling; geographic information systems; social network analysis; riots; social influence; rumor propagation.

      Paper Highlights:

      • An agent-based model integrates geographic information systems and social network analysis to model the emergence of riots. 
      • The physical environment and agent attributes are developed using empirical data, including GIS and socioeconomic data. 
      • The agent’s cognitive framework allowed for modeling their activities in physical space and interactions in social space. 
      • Through the integration of the three techniques, we were able to capture the complex, nonlinear nature of riots. 
      • Results show that youth are most vulnerable, and, increasing education and employment has nonlinear affects on rioting.

      The high-level UML diagram of the model
      A high-level representation of the model’s agent behavior incorporated into the PECS framework

      An example of the evolution of social networks of ten Residents across the first two days of a simulation run.

      The movie below shows the agent-based model which explores ethnic clashes in the Kenyan slum. The environment is made up of households, businesses, and service facilities (such data comes from OpenStreetMap). Agents within the model use a transportation network to move across the environment. As agents go about their daily activities, they interact with other agents – building out an evolving social network. Agents seek to meet their identity standard. Failure to reach their identity standard increases the agents frustration which can lead to an aggressive response (i.e. moving from blue to red color) such as rioting.

      As with many of our models, we provide the data, model code and detailed model description in the form of the ODD protocol for others to use, learn more or to extend. Click here for more information.

      Full Reference:

      Pires, B. and Crooks, A.T. (2017), Modeling the Emergence of Riots: A Geosimulation Approach, Computers, Environment and Urban Systems, 61: 66-80. (pdf)

      As normal, any thoughts or comments are most appreciated.
       

      Continue reading »

      Modeling the Emergence of Riots: A Geosimulation Approach

      As you might of guessed the paper is about riots but that is not all. In the paper we have a highly detailed cognitive model implemented through the PECS (Physical conditions, Emotional state, Cognitive capabilities, and Social status) framework based around identity theory. The purpose of the model (and paper) is to explore how the unique socioeconomic variables underlying Kibera, a slum in Nairobi, coupled with local interactions of its residents, and the spread of a rumor, may trigger a riot such as those seen in 2007. 
      In order to explore this question from the “bottom up” we have developed a novel agent-based model that integrates social network analysis (SNA) and geographic information systems (GIS) for this purpose. In the paper we argue that this integration facilitates the modeling of dynamic social networks created through the agents’ daily interactions. The GIS is used to develop a realistic environment for agents to move and interact that includes a road network and points of interest which impact their daily lives.
      Below is the abstract and a summary of its highlights in order to give you a sense of what our research contribution is. In addition to this we also provide some images either from the paper itself or the from Overview, Design Concepts, and Details (ODD) protocol. Finally at the bottom of this post you can see one of the simulation runs, details of where the model can be downloaded along with the full citation.

      Paper Abstract:

      Immediately after the 2007 Kenyan election results were announced, the country erupted in protest. Riots were particularly severe in Kibera, an informal settlement located within the nations capital, Nairobi. Through the lens of geosimulation, an agent-based model is integrated with social network analysis and geographic information systems to explore how the environment and local interactions underlying Kibera, combined with an external trigger, such as a rumor, led to the emergence of riots. We ground our model on empirical data of Kibera’s geospatial landscape, heterogeneous population, and daily activities of its residents. In order to effectively construct a model of riots, however, we must have an understanding of human behavior, especially that related to an individual’s need for identity and the role rumors play on a person’s decision to riot. This provided the foundation to develop the agents’ cognitive model, which created a feedback system between the agents’ activities in physical space and interactions in social space. Results showed that youth are more susceptible to rioting. Systematically increasing education and employment opportunities, however, did not have simple linear effects on rioting, or even on quality of life with respect to income and activities. The situation is more complex. By linking agent-based modeling, social network analysis, and geographic information systems we were able to develop a cognitive framework for the agents, better represent human behavior by modeling the interactions that occur over both physical and social space, and capture the nonlinear, reinforcing nature of the emergence and dissolution of riots.

      Keywords: agent-based modeling; geographic information systems; social network analysis; riots; social influence; rumor propagation.

      Paper Highlights:

      • An agent-based model integrates geographic information systems and social network analysis to model the emergence of riots. 
      • The physical environment and agent attributes are developed using empirical data, including GIS and socioeconomic data. 
      • The agent’s cognitive framework allowed for modeling their activities in physical space and interactions in social space. 
      • Through the integration of the three techniques, we were able to capture the complex, nonlinear nature of riots. 
      • Results show that youth are most vulnerable, and, increasing education and employment has nonlinear affects on rioting.

      The high-level UML diagram of the model
      A high-level representation of the model’s agent behavior incorporated into the PECS framework

      An example of the evolution of social networks of ten Residents across the first two days of a simulation run.

      The movie below shows the agent-based model which explores ethnic clashes in the Kenyan slum. The environment is made up of households, businesses, and service facilities (such data comes from OpenStreetMap). Agents within the model use a transportation network to move across the environment. As agents go about their daily activities, they interact with other agents – building out an evolving social network. Agents seek to meet their identity standard. Failure to reach their identity standard increases the agents frustration which can lead to an aggressive response (i.e. moving from blue to red color) such as rioting.

      As with many of our models, we provide the data, model code and detailed model description in the form of the ODD protocol for others to use, learn more or to extend. Click here for more information.

      Full Reference:

      Pires, B. and Crooks, A.T. (2017), Modeling the Emergence of Riots: A Geosimulation Approach, Computers, Environment and Urban Systems, 61: 66-80. (pdf)

      As normal, any thoughts or comments are most appreciated.
       

      Continue reading »

      New Paper: User-Generated Big Data and Urban Morphology

      Continuing our work with crowdsourcing and geosocial analysis we recently had a paper published in a special issue of the  Built Environment journal entitled “User-Generated Big Data and Urban Morphology.”
      The theme of the special issue is: “Big Data and the City” which was guest edited by Mike Batty and includes 12 papers.  To quote from the website

      “This cutting edge special issue responds to the latest digital revolution, setting out the state of the art of the new technologies around so-called Big Data, critically examining the hyperbole surrounding smartness and other claims, and relating it to age-old urban challenges. Big data is everywhere, largely generated by automated systems operating in real time that potentially tell us how cities are performing and changing. A product of the smart city, it is providing us with novel data sets that suggest ways in which we might plan better, and design more sustainable environments. The articles in this issue tell us how scientists and planners are using big data to better understand everything from new forms of mobility in transport systems to new uses of social media. Together, they reveal how visualization is fast becoming an integral part of developing a thorough understanding of our cities.”

      Table of Contents

      In our paper we discuss and show how crowdsourced data is leading to the emergence of alternate views of urban morphology that better capture the intricate nature of urban environments and their dynamics. Specifically how such data can provide us information pertaining to linked spaces and geosocial neighborhoods. We argue that a geosocial neighborhood is not defined by its administrative boundaries, planning zones, or physical barriers, but rather by its emergence as an organic self-organized social construct that is embedded in geographical spaces that are linked by human activity. Below is the abstract of the paper and some of the figures we have in it which showcase our work.
      “Traditionally urban morphology has been the study of cities as human habitats through the analysis of their tangible, physical artefacts. Such artefacts are outcomes of complex social and economic forces, and their study is primarily driven by traditional modes of data collection (e.g. based on censuses, physical surveys, and mapping). The emergence of Web 2.0 and through its applications, platforms and mechanisms that foster user-generated contributions to be made, disseminated, and debated in cyberspace, is providing a new lens in the study of urban morphology. In this paper, we showcase ways in which user-generated ‘big data’ can be harvested and analyzed to generate snapshots and impressionistic views of the urban landscape in physical terms. We discuss and support through representative examples the potential of such analysis in revealing how urban spaces are perceived by the general public, establishing links between tangible artefacts and cyber-social elements. These links may be in the form of references to, observations about, or events that enrich and move beyond the traditional physical characteristics of various locations. This leads to the emergence of alternate views of urban morphology that better capture the intricate nature of urban environments and their dynamics.”

      Keywords: Urban Morphology, Social Media, GeoSocial, Cities, Big Data.

      City Infoscapes – Fusing Data from Physical (L1, L2), Social, Perceptual (L3) Spaces to Derive Place Abstractions (L4) for Different Locations (N1, N2).
      Recreational Hotspots Composed of “Locals” and “Tourists” with Perceived Artifacts Indicating “Use” and “Need”. (A) High Line Park (B) Madison Square Garden.




      Moving from Spatial Neighborhoods to Geosocial Neighborhoods via Links.

      The Emergence of Geosocial Neighborhoods after the in the
      Aftermath of the 2013 Boston Marathon Bombing

      Full  Reference: 

      Crooks, A.T., Croitoru, A., Jenkins, A., Mahabir, R., Agouris, P. and Stefanidis A. (2016). “User-Generated Big Data and Urban Morphology,”  Built Environment, 42 (3): 396-414. (pdf)

      Continue reading »

      New Paper: User-Generated Big Data and Urban Morphology

      Continuing our work with crowdsourcing and geosocial analysis we recently had a paper published in a special issue of the  Built Environment journal entitled “User-Generated Big Data and Urban Morphology.”
      The theme of the special issue is: “Big Data and the City” which was guest edited by Mike Batty and includes 12 papers.  To quote from the website

      “This cutting edge special issue responds to the latest digital revolution, setting out the state of the art of the new technologies around so-called Big Data, critically examining the hyperbole surrounding smartness and other claims, and relating it to age-old urban challenges. Big data is everywhere, largely generated by automated systems operating in real time that potentially tell us how cities are performing and changing. A product of the smart city, it is providing us with novel data sets that suggest ways in which we might plan better, and design more sustainable environments. The articles in this issue tell us how scientists and planners are using big data to better understand everything from new forms of mobility in transport systems to new uses of social media. Together, they reveal how visualization is fast becoming an integral part of developing a thorough understanding of our cities.”

      Table of Contents

      In our paper we discuss and show how crowdsourced data is leading to the emergence of alternate views of urban morphology that better capture the intricate nature of urban environments and their dynamics. Specifically how such data can provide us information pertaining to linked spaces and geosocial neighborhoods. We argue that a geosocial neighborhood is not defined by its administrative boundaries, planning zones, or physical barriers, but rather by its emergence as an organic self-organized social construct that is embedded in geographical spaces that are linked by human activity. Below is the abstract of the paper and some of the figures we have in it which showcase our work.
      “Traditionally urban morphology has been the study of cities as human habitats through the analysis of their tangible, physical artefacts. Such artefacts are outcomes of complex social and economic forces, and their study is primarily driven by traditional modes of data collection (e.g. based on censuses, physical surveys, and mapping). The emergence of Web 2.0 and through its applications, platforms and mechanisms that foster user-generated contributions to be made, disseminated, and debated in cyberspace, is providing a new lens in the study of urban morphology. In this paper, we showcase ways in which user-generated ‘big data’ can be harvested and analyzed to generate snapshots and impressionistic views of the urban landscape in physical terms. We discuss and support through representative examples the potential of such analysis in revealing how urban spaces are perceived by the general public, establishing links between tangible artefacts and cyber-social elements. These links may be in the form of references to, observations about, or events that enrich and move beyond the traditional physical characteristics of various locations. This leads to the emergence of alternate views of urban morphology that better capture the intricate nature of urban environments and their dynamics.”

      Keywords: Urban Morphology, Social Media, GeoSocial, Cities, Big Data.

      City Infoscapes – Fusing Data from Physical (L1, L2), Social, Perceptual (L3) Spaces to Derive Place Abstractions (L4) for Different Locations (N1, N2).
      Recreational Hotspots Composed of “Locals” and “Tourists” with Perceived Artifacts Indicating “Use” and “Need”. (A) High Line Park (B) Madison Square Garden.




      Moving from Spatial Neighborhoods to Geosocial Neighborhoods via Links.

      The Emergence of Geosocial Neighborhoods after the in the
      Aftermath of the 2013 Boston Marathon Bombing

      Full  Reference: 

      Crooks, A.T., Croitoru, A., Jenkins, A., Mahabir, R., Agouris, P. and Stefanidis A. (2016). “User-Generated Big Data and Urban Morphology,”  Built Environment, 42 (3): 396-414. (pdf)

      Continue reading »

      The study of slums as social and physical constructs: challenges and emerging research opportunities

      Conceptual model for integrating social
      and physical constructs to monitor,
      analyze and model slums.

      Continuing our research on slums, we have just had a paper published in the journal Regional Studies, Regional Science entitled “The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities“. In this open access publication we review past lines of research with respect to studying slums which often focus on one of three constructs: (1) exploring the socio-economic and policy issues; (2) exploring the physical characteristics; and, lastly, (3) those modelling slums. We argue that while such lines of inquiry have proved invaluable with respect to studying slums, there is a need for  a  more  holistic  approach  for  studying  slums  to truly understand  them at the local, national and regional scales. Below you can read the abstract of our paper:

      “Over 1 billion people currently live in slums, with the number of slum dwellers only expected to grow in the coming decades. The vast majority of slums are located in and around urban centres in the less economically developed countries, which are also experiencing greater rates of urbanization compared with more developed countries. This rapid rate of urbanization is cause for significant concern given that many of these countries often lack the ability to provide the infrastructure (e.g., roads and affordable housing) and basic services (e.g., water and sanitation) to provide adequately for the increasing influx of people into cities. While research on slums has been ongoing, such work has mainly focused on one of three constructs: exploring the socio-economic and policy issues; exploring the physical characteristics; and, lastly, those modelling slums. This paper reviews these lines of research and argues that while each is valuable, there is a need for a more holistic approach for studying slums to truly understand them. By synthesizing the social and physical constructs, this paper provides a more holistic synthesis of the problem, which can potentially lead to a deeper understanding and, consequently, better approaches for tackling the challenge of slums at the local, national and regional scales.”

      Keywords: Slums; informal settlements; socio-economic; remote sensing; crowdsourced information; modelling.

      Framework for studying and understanding slums.

      We hope you enjoy this paper and we wound be interested in receiving any feedback.
      Full Reference:

      Mahabir, R., Crooks, A.T., Croitoru, A. and Agouris, P. (2016), “The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities”, Regional Studies, Regional Science, 3(1): 737-757. (pdf)

      Continue reading »

      The study of slums as social and physical constructs: challenges and emerging research opportunities

      Conceptual model for integrating social
      and physical constructs to monitor,
      analyze and model slums.

      Continuing our research on slums, we have just had a paper published in the journal Regional Studies, Regional Science entitled “The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities“. In this open access publication we review past lines of research with respect to studying slums which often focus on one of three constructs: (1) exploring the socio-economic and policy issues; (2) exploring the physical characteristics; and, lastly, (3) those modelling slums. We argue that while such lines of inquiry have proved invaluable with respect to studying slums, there is a need for  a  more  holistic  approach  for  studying  slums  to truly understand  them at the local, national and regional scales. Below you can read the abstract of our paper:

      “Over 1 billion people currently live in slums, with the number of slum dwellers only expected to grow in the coming decades. The vast majority of slums are located in and around urban centres in the less economically developed countries, which are also experiencing greater rates of urbanization compared with more developed countries. This rapid rate of urbanization is cause for significant concern given that many of these countries often lack the ability to provide the infrastructure (e.g., roads and affordable housing) and basic services (e.g., water and sanitation) to provide adequately for the increasing influx of people into cities. While research on slums has been ongoing, such work has mainly focused on one of three constructs: exploring the socio-economic and policy issues; exploring the physical characteristics; and, lastly, those modelling slums. This paper reviews these lines of research and argues that while each is valuable, there is a need for a more holistic approach for studying slums to truly understand them. By synthesizing the social and physical constructs, this paper provides a more holistic synthesis of the problem, which can potentially lead to a deeper understanding and, consequently, better approaches for tackling the challenge of slums at the local, national and regional scales.”

      Keywords: Slums; informal settlements; socio-economic; remote sensing; crowdsourced information; modelling.

      Framework for studying and understanding slums.

      We hope you enjoy this paper and we wound be interested in receiving any feedback.
      Full Reference:

      Mahabir, R., Crooks, A.T., Croitoru, A. and Agouris, P. (2016), “The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities”, Regional Studies, Regional Science, 3(1): 737-757. (pdf)

      Continue reading »

      Book Review: Rethinking Global Land Use in an Urban Era

      https://mitpress.mit.edu/books/rethinking-global-land-use-urban-era
      Recently I  reviewed a great book entitled “Rethinking Global Land Use in an Urban Era” edited  by Karen Seto and Anette Reenberg (2014) for the Journal of Regional Science. Readers can read my review below.
      The beginning of the 21st Century marked a milestone in human history. For the first time, more than half of the world’s population lived in urban areas (3.9 billion). This trend is expected to continue in the foreseeable future with 6.3 billion people living in cities by 2050 (United Nations, 2014). This growth will cause more urban land to be developed during the first 30 years of the 21st century than in all of human history (Angel et al., 2011). Combine this unprecedented urban expansion with global population growth, which is expected to grow from today’s 7.3 to 11.2 billion by 2100 (United Nations, 2015), and we are faced with unprecedented challenges and questions to be asked with respect to land-use in the 21st century. For example, how much living space will be needed to accommodate this growing population or how much land will be needed to feed such a population? Or how does urban growth in one country impact agricultural production and deforestation in the other parts of the world? To answer these questions, we need to understand the complexity of land competition from social, economic, and environmental perspectives at the local, national, and international levels and the connections between them.

      In their edited book, Rethinking Global Land Use in an Urban Era, Karen Seto and Anette Reenberg bring together 17 chapters from 50 experts from a variety of fields to explore global land dynamics in the 21st century. The first chapter acts as an introduction and scene-setting to the following chapters: it identifies current trends reshaping land-use locally and globally such as urbanization and the growing integration of economies and markets (e.g. telecoupling, see Seto et al., 2012), but also argues that there is a need to rethink land change science in a time when more and more people are living in cities. Specifically, they argue one should look at land-use through four lenses (which are the major sections in the book): land-use competition; distal land connections; decision making, governance, and institutions; and, finally, urbanization and land-use.

      The first section of the book focuses on land-use competition, specifically what types of land-use competition exist (such as forest vs. agricultural or urban vs. agriculture), and discusses how local land-use change is increasingly being caused by global factors (chapter 2). Chapter 3 addresses food security with respect to the growing population and discusses the need for intensification of production. Chapter 4 discusses the issue of finite land resources and competition for land—such as production vs. production (e.g. food vs. fuel) or production vs. conservation (e.g. food production vs. conservation). What is interesting about this chapter is that the competition for land is not just local but also global, due to the growing number of sovereign wealth funds and multi-national corporations and the increasing degree of interconnections between places. The section concludes with chapter 5, which offers an in-depth discussion of land-use competition between food production and urban expansion in China, specifically the effects of urbanization on the loss of cultivated land for food production.

      Source: Seto, K.C. and Reenberg, A. (eds.) (2014), Rethinking Global Land Use in an Urban Era, MIT Press, Cambridge, MA.
      The second section of the book explores distal land connections. It opens with a chapter that reviews the globalization of economic flows and the impact of these forces on land-use transitions (i.e. land-use and land-cover change). Chapter 7 introduces applications based on the telecoupling framework to land-change science. It makes a compelling argument considering not just coupled human-environmental systems (where the focus is on local conditions) but also causes that emanate from distant locations to truly understand land-change. This theme continues in chapter 8, which outlines analytical approaches to study telecoupling, while chapter 9 uses palm oil as a case study of distal land connections. In essence, the consumers of palm oil live far from the source; thus many consumers do not immediately feel the impacts of palm oil production on land-use change.

      In the third section of the book, the focus is on decision-making, governance, and institutions. Chapter 10 discusses the emergence of global land governance as a result of land grabbing by foreign investors or governments (see GRAIN, 2008), which is prompting states and global civil society to devise new global land governance instruments, while chapter 11 explores large-scale land (grabbing) transactions with a specific emphasis on the actors and their interactions. Chapter 12 focuses on private market-based regulations (such as the Forest Stewardship Council) and what they mean for land-use governance at the local and international level. The final chapter in this section focuses on changes in land-use governance in an urban era. It discusses how governance mechanisms that manage land-use are changing from territorial organizations to global industries that are tied to specific resource flows between urban and rural areas.

      The final section of the book turns to urbanization and land-use. Chapter 14 reviews major contemporary urban patterns and processes related to urbanization, such as central place theory, and shows how advances in technology and infrastructure challenge such established theories. The next chapter discusses how urban land-use is unique in terms of form, size, and shape of cities and asks what will the future hold? Will cities be sprawling or compact? An interesting fact brought up in chapter 15 is that currently less than five percent of the earth’s surface is urban and with the urban population predicted to grow to 5 billion by 2030, the urban footprint will still be less than 10 percent (Seto et al., 2011). The final chapter in this section proposes a framework that moves away from looking at land as discrete categories but instead as a continuum with respect to sustainable development. The book concludes with a chapter written by the editors, which not only provides a summary of what was presented, but reemphasizes the interconnected nature of land-use and the need to study future global land change and urbanization from a multidisciplinary perspective.

      Overall this is a timely, relevant, and thought-provoking collection of papers which not only explores urbanization and food production using case studies from around the world as well as the connections between cities and distant places, but also lays the foundation for new ways of thinking about land-use sustainability in the coming decades. In my opinion, this book would be a great resource for scholars interested in current state of the art of land-use science and a good textbook for any course exploring land-use and land-cover change in the 21st century.

      References:

      • Angel, Shlomo, Jason Parent, Daniel L. Civco, Alexander Blei, and David Potere. 2011. “The Dimensions of Global Urban Expansion: Estimates and Projections for All Countries, 2000–2050,” Progress in Planning, 75(2): 53-107.
      • GRAIN. 2008.Seized: The 2008 Landgrab for Food and Financial Security,” Available at http://bit.ly/28Nc7xK [Accessed on September, 7th, 2015].
      • Seto, Karen C., Michail Fragkias, Burak Güneralp, and Michael K. Reilly. 2011. “A Meta-analysis of Global Urban Land Expansion,” PloS One, 6(8): e23777.
      • Seto, Karen C., Anette Reenberg, Christopher G. Boone, Michail Fragkias, Dagmar Haase, Tobias Langanke, Peter Marcotullio, Darla K. Munroe, Branislav Olah, and David Simon. 2012. “Urban Land Teleconnections and Sustainability,” Proceedings of the National Academy of Sciences, 109(20): 7687-7692.
      • United Nations. 2014. World Urbanization Prospects: The 2014 Revision, Department of Economic and Social Affairs, New York, NY.
      • United Nations. 2015. World Urbanization Prospects: The 2015 Revision, Department of Economic and Social Affairs, New York, NY.

      Citation: 

      Crooks, A.T. (2016), Crooks on Seto and Reenberg (eds.): Rethinking Global Land Use in an Urban Era, Journal of Regional Science, 56 (4): 723-725. (pdf)

      Continue reading »
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