Category: ABM

New Publication: GIS and Agent-Based models for Humanitarian Assistance

Posted on by
Inputs to the model
 
As the readers of the blog know, we have an interest in GIS, agent-based modeling and crowdsourcing. Now we have a paper that combines all these three elements. Its entitled “GIS and Agent-Based models for Humanitarian Assistance” and is published in Computers, Environment and Urban Systems. 
 
The model itself was written in MASON and uses extensively GeoMASON. Data comes from several different sources (both raster and vector) including OpenStreetMap and LandScan. Below you can read an abstract of the paper and see a movie of one of the scenarios.

“Natural disasters such as earthquakes and tsunamis occur all over the world, altering the physical landscape and often severely disrupting people’s daily lives. Recently researchers’ attention has focused on using crowds of volunteers to help map the damaged infrastructure and devastation caused by natural disasters, such as those in Haiti and Pakistan. This data is extremely useful, as it is allows us to assess damage and thus aid the distribution of relief, but it tells us little about how the people in such areas will react to the devastation. This paper demonstrates a prototype spatially explicit agent-based model, created using crowdsourced geographic information and other sources of publicly available data, which can be used to study the aftermath of a catastrophic event. The specific case modelled here is the Haiti earthquake of January 2010. Crowdsourced data is used to build the initial populations of people affected by the event, to construct their environment, and to set their needs based on the damage to buildings. We explore how people react to the distribution of aid, as well as how rumours relating to aid availability propagate through the population. Such a model could potentially provide a link between socio-cultural information about the people affected and the relevant humanitarian relief organizations.”

Full Reference: 

Crooks, A.T. and Wise, S. (2013), GIS and Agent-Based models for Humanitarian Assistance, Computers, Environment and Urban Systems, 41: 100-111.

Continue reading »
ABM, ABM Examples, Crowdsourcing, GeoMason, GIS, MASON, social media, VGI

New Publication: GIS and Agent-Based models for Humanitarian Assistance

Posted on by
Inputs to the model
 
As the readers of the blog know, we have an interest in GIS, agent-based modeling and crowdsourcing. Now we have a paper that combines all these three elements. Its entitled “GIS and Agent-Based models for Humanitarian Assistance” and is published in Computers, Environment and Urban Systems. 
 
The model itself was written in MASON and uses extensively GeoMASON. Data comes from several different sources (both raster and vector) including OpenStreetMap and LandScan. Below you can read an abstract of the paper and see a movie of one of the scenarios.

“Natural disasters such as earthquakes and tsunamis occur all over the world, altering the physical landscape and often severely disrupting people’s daily lives. Recently researchers’ attention has focused on using crowds of volunteers to help map the damaged infrastructure and devastation caused by natural disasters, such as those in Haiti and Pakistan. This data is extremely useful, as it is allows us to assess damage and thus aid the distribution of relief, but it tells us little about how the people in such areas will react to the devastation. This paper demonstrates a prototype spatially explicit agent-based model, created using crowdsourced geographic information and other sources of publicly available data, which can be used to study the aftermath of a catastrophic event. The specific case modelled here is the Haiti earthquake of January 2010. Crowdsourced data is used to build the initial populations of people affected by the event, to construct their environment, and to set their needs based on the damage to buildings. We explore how people react to the distribution of aid, as well as how rumours relating to aid availability propagate through the population. Such a model could potentially provide a link between socio-cultural information about the people affected and the relevant humanitarian relief organizations.”

Full Reference: 

Crooks, A.T. and Wise, S. (2013), GIS and Agent-Based models for Humanitarian Assistance, Computers, Environment and Urban Systems, 41: 100-111.

Continue reading »
ABM, ABM Examples, Crowdsourcing, GeoMason, GIS, MASON, social media, VGI

15,000 Agents: A* Pathfinding

Posted on by

Games engines such as Unity are the perfect platform for agent based modelling, they allow a combination of 3D urban cityscapes and navmeshes/grid graphs/point graphs and local avoidance systems. The A* Pathfinding project features an array of techniques for rapid pathfinding or AI development using a low memory footprint. We…

Continue reading »
ABM, Unity

Urban Ball – CityEngine, Unity and Agent Based Modelling on the iPad

Posted on by

CityEngine combined with Unity and a tablet – iOS requires a developers account, Android simple needs the 30 days Unity trial – allows city wide models to run with a touch based interface. We have used our recent work on Unity and Agent Based modelling to create a simple app…

Continue reading »
ABM, CityEngine, Game Engines, iOS App, iOS Virtual City, iPad, NavMesh, Unity, Unity iOS App

Modeling Human Behavior

Posted on by
I have recently been thinking about how do people go about implementing human behavior within agent-based models. There are several good papers out their including Bill Kennedy’s (2012) paper entitled ‘Modelling Human Behavior in Agent-Based Models‘. I thought I would attempt to sum up some of these readings in a blog post but also add to how it links to the main properties of agent-based models.

The reason I do this is that modeling human behavior is not as simple as it sounds. This is because, humans do not just make random decisions, but base their actions upon their knowledge and their abilities. Moreover, it might be nice to think that human behavior is rationale but this is not always the case, decisions can also be based on emotions (e.g. interest, happiness anger, and fear; see Izard, 2007). Moreover, emotions can influence ones decision making by altering our perceptions about the environment and future evaluations (Loewenstein and Lerner, 2003). The question therefore is how do we model human behavior? Over the last decade, one of the dominant ways of modeling human behavior in its many shapes and forms is through agent-based modeling (ABM). ABM allows us to focus on individuals or groups of individuals and give them diverse knowledge and abilities which is not possible in other modeling methodologies (see Crooks and Heppenstall, 2012). This is possible through the unique properties one can endow upon the agents (e.g. people) within such models (see Wooldridge and Jennings, 1995; Franklin and Graesser, 1996; Castle and Crooks, 2006). These properties include:

    • Autonomy: In sense that we can model individual autonomous units which are not centrally governed. Through this property agents are able to process and exchange information with other agents in order to make independent decisions.
    • Heterogeneity: Through using autonomous agents the notion of the average individual is redundant. Each agent can have their own properties and it’s these unique properties of individuals that cause more aggregate phenomena to develop.
    • Activity: As agents are autonomous individuals with heterogeneous properties, they can exert active independent influence within a simulation. There are several ways agents can do this from being proactive (goal directed) for example trying to solve a specific problem. Or they can be reactive, in the sense agents can be designed to perceive their surroundings and given prior knowledge based on experiences (e.g. learning) or observation and take actions accordingly.
      The primary strength of ABMs is as a testing ground for a variety of theoretical assumptions and concepts about human behavior (Stanilov, 2012) within the safe environment of a computer simulation. For example, we know humans process sensory information about the environment, their own current state, and their remembered history to decide what actions to take (Kennedy, 2012) all of which can be incorporated within ABMs. Through the ability to model heterogeneity within ABMs we can capture the uniqueness that makes us human, in the sense that all humans have diverse personality traits (e.g. motivation, emotion, risk avoidance) and complex psychology (Bonabeau, 2002). We also know that human behavior is influenced by others (Friedkin and Johnsen, 1999) say via their social networks which can introduce positive and negative feedbacks into the system and when people form groups, results from such groups can be greater than the sum of the group (Hong and Page, 2004). These properties again can be captured through the agent’s heterogeneity and active status. However, what drives humans? What motivates us to take certain actions? By agents being active we can test ideas and theories (e.g. Maslow’s “Hierarchy of Needs” (Maslow, 1943)) on what motivates people and why do they do certain things.

      Maslow’s Hierarchy of Needs (Source: Wikipedia)
      The question of how to model decision-making within an agent-based model is another important consideration. Kennedy lists three main approaches to capturing such cognitive processes within ABMs (Kennedy, 2012). The first, being a mathematical approach such as the use of ad hoc direct and custom coding of behaviors within the simulation such as using random number generators to select a predefined possible choice (e.g. to buy or sell) (Gode and Sunder, 1993). But, as noted above, people are not random which has lead researchers to develop other methods such as directly incorporating threshold-based rules, i.e. when an environment parameter passes a certain threshold a specific agent behavior will result (e.g. move to a new location when the neighborhood composition reaches a certain percentage) (Crooks, 2010). One could argue that these approaches of modeling are appropriate when behavior can be well specified. The second approach to modeling human behavior uses conceptual cognitive frameworks. Within such models, instead of using thresholds, more abstract concepts such as beliefs, desires, and intentions (BDI, (Rao and Georgeff, 1991)) or physical, emotional, cognitive, and social factors (PECS, (Schmidt, 2002)) are given to individual agents. Both the BDI and PECS frameworks have been successively applied to modeling human behavior in a number of applications such as what drives people to crime (see (Brantingham et al., 2005) and (Malleson, 2012) respectively). These conceptual cognitive frameworks and mathematical approaches for representing behavior can both be considered as rule based systems and are often applied to tens to millions of agents. The third approach, that of cognitive architectures, (e.g. Soar (Laird, 2012) and ACT-R (Anderson and Lebiere, 1998)) focus on abstract or theoretical cognition of one agent at a time with a strong emphasis on artificial intelligence compared to the other two approaches.

      Determining the strongest motive before planing an action (Source: Malleson, 2012).
      ABM offers a new lens to explore human behavior allowing us to move away from more traditional methods such as rational choice theory (Coleman, 1990), where it is assumed that humans behave in ways to maximize their benefits or minimize their costs. However, people rarely meet the requirements of rational choice models (Axelrod, 1997) in the sense that most, if not all people have limited cognitive abilities and limited time to make decisions (Simon, 1996). The incorporation of this bounded rationality (e.g. limited access to information) within agent-based models addresses this issue and can be used to explore many application domains where human behavior is important.

      Any thoughts or comments on what I have written here would be most appreciated.

      References

      • Anderson, J.R. and Lebiere, C. (1998), The Atomic Components of Thought, Mahwah, NJ.
      • Axelrod, R. (1997), ‘Advancing the Art of Simulation in the Social Sciences’, in Conte, R., Hegselmann, R. and Terno, P. (eds.), Simulating Social Phenomena, Springer, Berlin, Germany, pp. 21-40.
      • Bonabeau, E. (2002), ‘Agent-Based Modelling: Methods and Techniques for Simulating Human Systems’, Proceedings of the National Academy of Sciences of the United States of America, 99(3): 7280-7287.
      • Brantingham, P., Glasser, U., Kinney, B., Singh, K. and Vajihollahi, M. (2005), ‘A Computational Model for Simulating Spatial Aspects of Crime in Urban Environments.’ 2005 IEEE International Conference on Systems, Man and Cybernetics (Vol. 4), pp. 3667-3674.
      • Coleman, J.S. (1990), Foundations of Social Theory, Harvard University Press, Cambridge, MA.
      • Crooks, A.T. (2010), ‘Constructing and Implementing an Agent-Based Model of Residential Segregation through Vector GIS’, International Journal of GIS, 24(5): 661-675.
      • Friedkin, N.E. and Johnsen, E.C. (1999), ‘Social Influence Networks and Opinion Change’, Advances in Group Processes, 16(1-29).
      • Gode, D.K. and Sunder, S. (1993), ‘Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality’, The Journal of Political Economy, 101: 119-137.
      • Hong, L. and Page, S.E. (2004), ‘Groups of Diverse Problem Solvers Can Outperform Groups of High-ability Problem Solvers’, Proceedings of the National Academic Sciences, 101(46): 16385-16389.
      • Kennedy, W. (2012), ‘Modelling Human Behaviour in Agent-Based Models’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 167-180.
      • Laird, J.E. (2012), The Soar Cognitive Architecture, The MIT Press, Cambridge, MA.
      • Malleson, N. (2012), ‘Using Agent-Based Models to Simulate Crime’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 411-434.
      • Maslow, A.H. (1943), ‘A Theory of Human Motivation’, Psychological Review, 50(4): 370-396.
      • Rao, A.S. and Georgeff, M.P. (1991), ‘Modeling Rational Agents within a BDI-architecture’, in Allen, J., Fikes, R. and Sandewall, E. (eds.), Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning, San Mateo, CA.
      • Schmidt, B. (2002), ‘The Modelling of Human Behaviour: The PECS Reference Model’, Proceedings 14th European Simulation Symposium, Dresden, Germany.
      • Simon, H.A. (1996), The Sciences of the Artificial (3rd Edition), MIT Press, Cambridge, M. A.
      • Stanilov, K. (2012), ‘Space in Agent-Based Models’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 253-271.
        Continue reading »
        ABM, Agent Based Models

        Modeling Human Behavior

        Posted on by
        I have recently been thinking about how do people go about implementing human behavior within agent-based models. There are several good papers out their including Bill Kennedy’s (2012) paper entitled ‘Modelling Human Behavior in Agent-Based Models‘. I thought I would attempt to sum up some of these readings in a blog post but also add to how it links to the main properties of agent-based models.

        The reason I do this is that modeling human behavior is not as simple as it sounds. This is because, humans do not just make random decisions, but base their actions upon their knowledge and their abilities. Moreover, it might be nice to think that human behavior is rationale but this is not always the case, decisions can also be based on emotions (e.g. interest, happiness anger, and fear; see Izard, 2007). Moreover, emotions can influence ones decision making by altering our perceptions about the environment and future evaluations (Loewenstein and Lerner, 2003). The question therefore is how do we model human behavior? Over the last decade, one of the dominant ways of modeling human behavior in its many shapes and forms is through agent-based modeling (ABM). ABM allows us to focus on individuals or groups of individuals and give them diverse knowledge and abilities which is not possible in other modeling methodologies (see Crooks and Heppenstall, 2012). This is possible through the unique properties one can endow upon the agents (e.g. people) within such models (see Wooldridge and Jennings, 1995; Franklin and Graesser, 1996; Castle and Crooks, 2006). These properties include:

          • Autonomy: In sense that we can model individual autonomous units which are not centrally governed. Through this property agents are able to process and exchange information with other agents in order to make independent decisions.
          • Heterogeneity: Through using autonomous agents the notion of the average individual is redundant. Each agent can have their own properties and it’s these unique properties of individuals that cause more aggregate phenomena to develop.
          • Activity: As agents are autonomous individuals with heterogeneous properties, they can exert active independent influence within a simulation. There are several ways agents can do this from being proactive (goal directed) for example trying to solve a specific problem. Or they can be reactive, in the sense agents can be designed to perceive their surroundings and given prior knowledge based on experiences (e.g. learning) or observation and take actions accordingly.
            The primary strength of ABMs is as a testing ground for a variety of theoretical assumptions and concepts about human behavior (Stanilov, 2012) within the safe environment of a computer simulation. For example, we know humans process sensory information about the environment, their own current state, and their remembered history to decide what actions to take (Kennedy, 2012) all of which can be incorporated within ABMs. Through the ability to model heterogeneity within ABMs we can capture the uniqueness that makes us human, in the sense that all humans have diverse personality traits (e.g. motivation, emotion, risk avoidance) and complex psychology (Bonabeau, 2002). We also know that human behavior is influenced by others (Friedkin and Johnsen, 1999) say via their social networks which can introduce positive and negative feedbacks into the system and when people form groups, results from such groups can be greater than the sum of the group (Hong and Page, 2004). These properties again can be captured through the agent’s heterogeneity and active status. However, what drives humans? What motivates us to take certain actions? By agents being active we can test ideas and theories (e.g. Maslow’s “Hierarchy of Needs” (Maslow, 1943)) on what motivates people and why do they do certain things.

            Maslow’s Hierarchy of Needs (Source: Wikipedia)
            The question of how to model decision-making within an agent-based model is another important consideration. Kennedy lists three main approaches to capturing such cognitive processes within ABMs (Kennedy, 2012). The first, being a mathematical approach such as the use of ad hoc direct and custom coding of behaviors within the simulation such as using random number generators to select a predefined possible choice (e.g. to buy or sell) (Gode and Sunder, 1993). But, as noted above, people are not random which has lead researchers to develop other methods such as directly incorporating threshold-based rules, i.e. when an environment parameter passes a certain threshold a specific agent behavior will result (e.g. move to a new location when the neighborhood composition reaches a certain percentage) (Crooks, 2010). One could argue that these approaches of modeling are appropriate when behavior can be well specified. The second approach to modeling human behavior uses conceptual cognitive frameworks. Within such models, instead of using thresholds, more abstract concepts such as beliefs, desires, and intentions (BDI, (Rao and Georgeff, 1991)) or physical, emotional, cognitive, and social factors (PECS, (Schmidt, 2002)) are given to individual agents. Both the BDI and PECS frameworks have been successively applied to modeling human behavior in a number of applications such as what drives people to crime (see (Brantingham et al., 2005) and (Malleson, 2012) respectively). These conceptual cognitive frameworks and mathematical approaches for representing behavior can both be considered as rule based systems and are often applied to tens to millions of agents. The third approach, that of cognitive architectures, (e.g. Soar (Laird, 2012) and ACT-R (Anderson and Lebiere, 1998)) focus on abstract or theoretical cognition of one agent at a time with a strong emphasis on artificial intelligence compared to the other two approaches.

            Determining the strongest motive before planing an action (Source: Malleson, 2012).
            ABM offers a new lens to explore human behavior allowing us to move away from more traditional methods such as rational choice theory (Coleman, 1990), where it is assumed that humans behave in ways to maximize their benefits or minimize their costs. However, people rarely meet the requirements of rational choice models (Axelrod, 1997) in the sense that most, if not all people have limited cognitive abilities and limited time to make decisions (Simon, 1996). The incorporation of this bounded rationality (e.g. limited access to information) within agent-based models addresses this issue and can be used to explore many application domains where human behavior is important.

            Any thoughts or comments on what I have written here would be most appreciated.

            References

            • Anderson, J.R. and Lebiere, C. (1998), The Atomic Components of Thought, Mahwah, NJ.
            • Axelrod, R. (1997), ‘Advancing the Art of Simulation in the Social Sciences’, in Conte, R., Hegselmann, R. and Terno, P. (eds.), Simulating Social Phenomena, Springer, Berlin, Germany, pp. 21-40.
            • Bonabeau, E. (2002), ‘Agent-Based Modelling: Methods and Techniques for Simulating Human Systems’, Proceedings of the National Academy of Sciences of the United States of America, 99(3): 7280-7287.
            • Brantingham, P., Glasser, U., Kinney, B., Singh, K. and Vajihollahi, M. (2005), ‘A Computational Model for Simulating Spatial Aspects of Crime in Urban Environments.’ 2005 IEEE International Conference on Systems, Man and Cybernetics (Vol. 4), pp. 3667-3674.
            • Coleman, J.S. (1990), Foundations of Social Theory, Harvard University Press, Cambridge, MA.
            • Crooks, A.T. (2010), ‘Constructing and Implementing an Agent-Based Model of Residential Segregation through Vector GIS’, International Journal of GIS, 24(5): 661-675.
            • Friedkin, N.E. and Johnsen, E.C. (1999), ‘Social Influence Networks and Opinion Change’, Advances in Group Processes, 16(1-29).
            • Gode, D.K. and Sunder, S. (1993), ‘Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality’, The Journal of Political Economy, 101: 119-137.
            • Hong, L. and Page, S.E. (2004), ‘Groups of Diverse Problem Solvers Can Outperform Groups of High-ability Problem Solvers’, Proceedings of the National Academic Sciences, 101(46): 16385-16389.
            • Kennedy, W. (2012), ‘Modelling Human Behaviour in Agent-Based Models’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 167-180.
            • Laird, J.E. (2012), The Soar Cognitive Architecture, The MIT Press, Cambridge, MA.
            • Malleson, N. (2012), ‘Using Agent-Based Models to Simulate Crime’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 411-434.
            • Maslow, A.H. (1943), ‘A Theory of Human Motivation’, Psychological Review, 50(4): 370-396.
            • Rao, A.S. and Georgeff, M.P. (1991), ‘Modeling Rational Agents within a BDI-architecture’, in Allen, J., Fikes, R. and Sandewall, E. (eds.), Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning, San Mateo, CA.
            • Schmidt, B. (2002), ‘The Modelling of Human Behaviour: The PECS Reference Model’, Proceedings 14th European Simulation Symposium, Dresden, Germany.
            • Simon, H.A. (1996), The Sciences of the Artificial (3rd Edition), MIT Press, Cambridge, M. A.
            • Stanilov, K. (2012), ‘Space in Agent-Based Models’, in Heppenstall, A., Crooks, A.T., See, L.M. and Batty, M. (eds.), Agent-based Models of Geographical Systems, Springer, New York, NY, pp. 253-271.
              Continue reading »
              ABM, Agent Based Models

              Fusing remote sensing with demographic data for synthetic population generation

              Posted on by
              When building agent-based models related to “real” world locations and people, the challenge is to build agents which resemble people on the ground. I have blogged about microsimulation approaches before and their utility with respect to agent-based models. A new paper in the International Journal of Geographical Information Science by alumnae from the Department of Computational Social Science here at GMU have developed a new algorithm which could prove useful. Below is abstract of the paper:

              We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.

              Full Reference:

              Rizi, S.M.M., Łatek, M.M. and Geller, A. (2012), ‘Fusing Remote Sensing with Sparse Demographic Data for Synthetic Population Generation: An Algorithm and Application to Rural Afghanistan’, International Journal of Geographical Information Science, DOI:10.1080/13658816.2012.734825.

              Continue reading »
              ABM, Microsimulation, Remote Sensing

              Fusing remote sensing with demographic data for synthetic population generation

              Posted on by
              When building agent-based models related to “real” world locations and people, the challenge is to build agents which resemble people on the ground. I have blogged about microsimulation approaches before and their utility with respect to agent-based models. A new paper in the International Journal of Geographical Information Science by alumnae from the Department of Computational Social Science here at GMU have developed a new algorithm which could prove useful. Below is abstract of the paper:

              We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.

              Full Reference:

              Rizi, S.M.M., Łatek, M.M. and Geller, A. (2012), ‘Fusing Remote Sensing with Sparse Demographic Data for Synthetic Population Generation: An Algorithm and Application to Rural Afghanistan’, International Journal of Geographical Information Science, DOI:10.1080/13658816.2012.734825.

              Continue reading »
              ABM, Microsimulation, Remote Sensing

              Research Update

              Posted on by
              With the semester now well underway, I have been reflecting on some of the recent work we have been doing at George Mason University. This is currently taking two strands, the first being agent-based modeling and the second being deriving information from social media. Hopefully by the end of the semester, these two strands will be merged together.
              One of the models we are working on is the movement of people across national boarders. Below is a visualization of our work looking at the movment of people across the US/Mexico border which a specific focus on Arizona.

              Moreover, we have continued to work diseases and refugee camps. We are scaling up the model to represent the entire population of the Dadaab refugee camps along with verifying the model and exploring the spatial characteristics of the model (i.e the spread of cholera). If anyone will be at the annual North American Meetings of the Regional Science Association International in Ottowa  Canada feel free to come and listen to our presentation. The movie directly below shows the spread of cholera in one camp, while the second movie shows how cholera can be spread throughout the camps by people becoming infected and moving between the different camps.



              Some of this work has been feated in UPMagazine and Trajectory Magazine:

              Metcalfe, M. (2012), The Bounds of Rationality, UP Magazine, May, Issue 5: 40-43.

              Quinn, K. (2012), Visualizing the Invisible:GMU Pioneers a New Approach to Harvesting GEOINT, Trajectory Magazine, Fall, 11-12.

              Continue reading »
              ABM, social media, Social network analysis

              Research Update

              Posted on by
              With the semester now well underway, I have been reflecting on some of the recent work we have been doing at George Mason University. This is currently taking two strands, the first being agent-based modeling and the second being deriving information from social media. Hopefully by the end of the semester, these two strands will be merged together.
              One of the models we are working on is the movement of people across national boarders. Below is a visualization of our work looking at the movment of people across the US/Mexico border which a specific focus on Arizona.
              Moreover, we have continued to work diseases and refugee camps. We are scaling up the model to represent the entire population of the Dadaab refugee camps along with verifying the model and exploring the spatial characteristics of the model (i.e the spread of cholera). If anyone will be at the annual North American Meetings of the Regional Science Association International in Ottowa  Canada feel free to come and listen to our presentation. The movie directly below shows the spread of cholera in one camp, while the second movie shows how cholera can be spread throughout the camps by people becoming infected and moving between the different camps.

              Some of this work has been feated in UPMagazine and Trajectory Magazine:

              Metcalfe, M. (2012), The Bounds of Rationality, UP Magazine, May, Issue 5: 40-43.

              Quinn, K. (2012), Visualizing the Invisible:GMU Pioneers a New Approach to Harvesting GEOINT, Trajectory Magazine, Fall, 11-12.

              Continue reading »
              ABM, social media, Social network analysis

              New paper: Agent-based modeling for community resource management: Acequia-based agriculture

              Posted on by
              We have just got a paper accepted in Computers, Environment and Urban Systems entitled “Agent-based modeling for community resource management: Acequia-based agriculture.” In the paper we explore the complex social interactions of water management, which involves landowners collectively maintaining and managing ditches which distribute water among the properties.

              This system of the physical ditches and the maintaining organization together is known as an acequia, and the landowners who maintain it are called Parciantes. Acequias are interesting to researchers because of the developed common property regimes they require to function. The water carried by the ditches is a shared resource, and the complex management system of the acequia has evolved to avoid Hardin’s tragedy of the commons with regard to natural resources in the sense that it prevents the resource from being overused or under-maintained to the detriment of everyone. Ostrom has extensively studied the process of sharing such resources, investigating the structures set in place to preserve them. In ‘‘Governing the Commons’’, her book on common pool resources and human–ecosystem interactions, she suggests a set of characteristics that define stable communal social mechanisms. These characteristics include the presence of environment-appropriate rules governing the use of collective goods and the efficacy of individuals in the system.

              Below is the abstract from the paper:

              Water management is a major concern across the world. From northern China to the Middle East to Africa to the United States, growing populations can stress local water resources as they demand more water for both direct consumption and agriculture. Irrigation based agriculture draws especially heavily on these resources and usually cannot survive without them; however, irrigation systems must be maintained, a task individual agriculturalists cannot bear alone. We have constructed an agent-based model to investigate the significant interaction and cumulative impact of the physical water system, local social and institutional structures which regulate water use, and the real estate market on the sustainability of traditional farming as a lifestyle in the northern New Mexico area. The regional term for the coupled social organization and physical system of irrigation is ‘‘acequias’’. The results of the model show that depending on the future patterns of weather and government regulations, acequia-based farming may continue at near current rates, shrink significantly but continue to exist, or disappear altogether.
              In the figure below we show some of our efforts in verification of the model, specifically, the water network, after 100 years of regular maintenance (A) and after 100 years of no maintenance (B). The darker the line, the more clear the segment is of sedimentation; only unmaintained acequias are impacted by sedimentation in this model, and appear in lighter shades.

              Below is a movie are a few sample model runs showing how different scenarios play out, specifically with respect to land-use change.

              Full reference:

              Wise, S. and Crooks, A. T. (2012), Agent Based Modelling and GIS for Community Resource Management: Acequia-based Agriculture, Computers, Environment and Urban Systems. Doi http://dx.doi.org/10.1016/j.compenvurbsys.2012.08.004.
              Continue reading »
              ABM, CSS, GIS

              New paper: Agent-based modeling for community resource management: Acequia-based agriculture

              Posted on by
              We have just got a paper accepted in Computers, Environment and Urban Systems entitled “Agent-based modeling for community resource management: Acequia-based agriculture.” In the paper we explore the complex social interactions of water management, which involves landowners collectively maintaining and managing ditches which distribute water among the properties.

              This system of the physical ditches and the maintaining organization together is known as an acequia, and the landowners who maintain it are called Parciantes. Acequias are interesting to researchers because of the developed common property regimes they require to function. The water carried by the ditches is a shared resource, and the complex management system of the acequia has evolved to avoid Hardin’s tragedy of the commons with regard to natural resources in the sense that it prevents the resource from being overused or under-maintained to the detriment of everyone. Ostrom has extensively studied the process of sharing such resources, investigating the structures set in place to preserve them. In ‘‘Governing the Commons’’, her book on common pool resources and human–ecosystem interactions, she suggests a set of characteristics that define stable communal social mechanisms. These characteristics include the presence of environment-appropriate rules governing the use of collective goods and the efficacy of individuals in the system.

              Below is the abstract from the paper:

              Water management is a major concern across the world. From northern China to the Middle East to Africa to the United States, growing populations can stress local water resources as they demand more water for both direct consumption and agriculture. Irrigation based agriculture draws especially heavily on these resources and usually cannot survive without them; however, irrigation systems must be maintained, a task individual agriculturalists cannot bear alone. We have constructed an agent-based model to investigate the significant interaction and cumulative impact of the physical water system, local social and institutional structures which regulate water use, and the real estate market on the sustainability of traditional farming as a lifestyle in the northern New Mexico area. The regional term for the coupled social organization and physical system of irrigation is ‘‘acequias’’. The results of the model show that depending on the future patterns of weather and government regulations, acequia-based farming may continue at near current rates, shrink significantly but continue to exist, or disappear altogether.
              In the figure below we show some of our efforts in verification of the model, specifically, the water network, after 100 years of regular maintenance (A) and after 100 years of no maintenance (B). The darker the line, the more clear the segment is of sedimentation; only unmaintained acequias are impacted by sedimentation in this model, and appear in lighter shades.

              Below is a movie are a few sample model runs showing how different scenarios play out, specifically with respect to land-use change.

              Full reference:

              Wise, S. and Crooks, A. T. (2012), Agent Based Modelling and GIS for Community Resource Management: Acequia-based Agriculture, Computers, Environment and Urban Systems. Doi http://dx.doi.org/10.1016/j.compenvurbsys.2012.08.004.
              Continue reading »
              ABM, CSS, GIS
              1 2 3 4 5 6