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London’s Street Trees

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Following a data release of Southwark’s publically maintained trees a couple of years back, the Greater London Authority recently published a map which shows street trees (trees along roads and public paths, and trees in public open spaces, such as small parks and other minor green areas). Each borough has its own tree database and […]

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boroughs, data, gla, green, species, street, street tress, trees

Cellular Automata

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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)

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CA, Cell Space, Complexity Science, GIS

Cellular Automata

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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)

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CA, Cell Space, Complexity Science, GIS

Call for Papers in a special issue of Remote Sensing in Ecology and Conservation on citizen science

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Remote Sensing in Ecology and Conservation is a new open access journal, addressing the interdisciplinary field that links different aspects of remote sensing (the use of different imaging and sensing technologies) and the field of ecology and conservation. It is publishing its papers in Open Access, so the papers are free to download and share. … Continue reading Call for Papers in a special issue of Remote Sensing in Ecology and Conservation on citizen science

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citizen science, Conservation, Conservation biology, DIY, Ecology, nature conservation, PPGIS, Remote Sensing, Research projects, RSEC, VGI

Mapping 5,000 Years of City Growth

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I recently stumbled upon a great dataset. It’s the first to provide comprehensive data for world city sizes as far back as 3700BC. The authors (Meredith Reba, Femke Reitsma & Karen Seto) write: How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional […]

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ggplot2, historic population, map, mapping, R, R spatial, spatial analysis, stats, visualisation

Paper: GeoKey – open infrastructure for community mapping and science

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The special issue of the Human Computation Journal (see the details of the editorial here), summarises the result from the EU FP7 “Citizen Cyberlab” project. One of the outcomes of the project is the development of the GeoKey platform for participatory mapping. Therefore, a paper that was written with Oliver Roick and Claire Ellul explains … Continue reading Paper: GeoKey – open infrastructure for community mapping and science

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citizen cyberlab, citizen science, EU FP7, GeoKey, Papers, participatory GIS, Participatory Mapping, PPGIS, Public Participation GIS, VGI

Evolution of London’s Rush Hour Traffic Mix

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My latest London data visualisation crunches an interesting dataset from the Department of Transport. The data is available across England, although I’ve chosen London in particular because of its more interesting (i.e. not just car dominated) traffic mix. I’ve also focused on just the data for 8am to 9am, to examine the height of the … Continue reading Evolution of London’s Rush Hour Traffic Mix →

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Data Graphics, london

Big Data and Design: More Baboon, Less Unicorn

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I recently had the pleasure of giving a Creative Mornings talk. Each month there is a new theme that the presenters need to refer to – mine was “fantasy” so I chose to open with one of my favourite fantasy creatures: the unicorn. It’s a talk about the creative process behind Oliver Uberti and I’s […]

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baboon, Big Data, Resources, unicorn, Video, visualisation, where the animals go

Competition Winner: Exceptional contribution to (GIS/statistical) software

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The GIScRG (Geographic Information Science Research Group) and QMRG (Quantitative Methods Research Group) are pleased to announce the winner of our 2017 Exceptional contribution to (GIS/statistical) software competition. Zhaoya Gong has been awarded £400 for your development of the ARTMAP-based GeoComputation Toolbox. Further details: The ARTMAP-based GeoComputation Toolbox is a set of ARTMAP-based neural networks tools … Continue reading Competition Winner: Exceptional contribution to (GIS/statistical) software →

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Applications of Agent-based Models

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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, Legion,  Steps 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 »
    ABM Examples, Agent Based Models

    Applications of Agent-based Models

    Posted on by
    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, Legion,  Steps 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 »
      ABM Examples, Agent Based Models
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