The latest outputs from researchers, alumni and friends at the UCL Centre for Advanced Spatial Analysis (CASA).
Geographical location of the South Omo Zone of EthiopiaWhile many of the recent posts on the site have focused on social media, social networks and volunteered geographical information, we have not forgotten or moved away from agent-based modeling (as …
Continue reading »About a year ago, the Adam Mickiewicz University in Poznań, Poland, hosted the PPGIS 2017 workshop (here are my notes from the first day and the second day). Today, four papers from the workshop were published in the journal Quaestiones Geographicae which was established in 1974 as an annual journal of the Faculty of Geographical and Geological … Continue reading Papers from PPGIS 2017 meeting: state of the art and examples from Poland and the Czech Republic
Continue reading »Summary of the session on Digital Representation of Place at the RGS-IBG conference in Cardiff. The session aim was to address the following challenge: “Over the last few decades, our cities have become increasingly digital. Urban environments are layered with data and algorithms that fundamentally shape our geographic interactions: impacting how we perceive, move through, … Continue reading Digital Representations of Place: Urban Overlays and Digital Justice
Continue reading »The first outcome of the December 2016 workshop on apps, platforms, and portals for citizen science projects was the open access paper “Defining principles for mobile apps and platforms development in citizen science“, which came out in October 2017. The workshop, which was organised by Soledad Luna and Ulrike Sturm from the Berlin Museum for Natural History, has … Continue reading Developing mobile applications for environmental and biodiversity citizen science: considerations and recommendations
Continue reading »The Open GeoPortal is a Free and Open Source framework for rapidly discovering, previewing and retrieving curated geospatial data from multiple repositories. It implements a modular architecture, including a database, a search engine and several web applications. While it can … Continue reading →
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Normally, on this blog, the focus is on agent-based modeling and GIS. However, I am not agnostic to other modeling approaches especially cellular automata (CA) modeling (which I have written about in the past). To this end, Rui Zhang, Qing Tian, …
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Normally, on this blog, the focus is on agent-based modeling and GIS. However, I am not agnostic to other modeling approaches especially cellular automata (CA) modeling (which I have written about in the past). To this end, Rui Zhang, Qing Tian, …
Continue reading »In the 2018 State and Future of GEOINT Report published by the The United States Geospatial Intelligence Foundation (USGIF) we had a paper accepted entitled “The Future of GEOINT: Data Science Will Not Be Enough”. In the paper we discuss how there has …
Continue reading »In the 2018 State and Future of GEOINT Report published by the The United States Geospatial Intelligence Foundation (USGIF) we had a paper accepted entitled “The Future of GEOINT: Data Science Will Not Be Enough”. In the paper we discuss how there has …
Continue reading »Since the end of 2015, I’ve been using the following mapping of citizen science activities in a range of talks: The purpose of this way of presentation is to provide a way to guide my audience through the landscape of citizen science (see examples on SlideShare). The reason that I came up with it, is … Continue reading Citizen Science for Observing and Understanding the Earth
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Nick Malleson, Alison Heppenstall and myself recently had a chapter published in the Oxford Research Encyclopedia of Criminology and Criminal Justice, entitled “Place-Based Simulation Modelling: Agent-Based Modelling and Virtual Environments”. In…
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Nick Malleson, Alison Heppenstall and myself recently had a chapter published in the Oxford Research Encyclopedia of Criminology and Criminal Justice, entitled “Place-Based Simulation Modelling: Agent-Based Modelling and Virtual Environments”. In…
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Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that …
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Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that …
Continue reading »The first week of the “Introduction to Citizen Science and Scientific Crowdsourcing” course was dedicated to an introduction to the field of citizen science using the history, examples and typologies to demonstrate the breadth of the field. The second week was dedicated to the second half of the course name – crowdsourcing in general, and its … Continue reading Citizen Science & Scientific Crowdsourcing – week 2 – Google Local Guides
Continue reading »It’s a new year, and just the right time to announce that starting on the 11th January, UCL will run an 11 weeks hybrid (online and face to face) course called “Introduction to Citizen Science and Scientific Crowdsourcing“. This course aim is to introduce students to the theory and practice of citizen science and scientific crowdsourcing. … Continue reading Online course – Introduction to Citizen Science and Scientific Crowdsourcing
Hot on the heels of the Routledge Handbook of Environmental Justice is the Routledge Handbook of Mapping and Cartography. The handbook was edited by Alex Kent (Canterbury Christ Church University) who is currently the President of the British Cartographic Society and Editor of The Cartographic Journal; and Peter Vujakovic (also from Canterbury Christ Church University) who edited The Cartographic … Continue reading Chapter in Routledge Handbook of Mapping and Cartography – VGI and Beyond: From Data to Mapping
The Routledge Handbook of Environmental Justice has been published in mid-September. This extensive book, of 670 pages is providing an extensive overview of scholarly research on environmental justice. The book was edited by three experts in the area – Ryan Holifield from the University of Wisconsin-Milwaukee, Jayajit Chakraborty from the University of Texas at El Paso, and Gordon Walker … Continue reading Chapter in Routledge Handbook of Environmental Justice – Participatory GIS and community-based citizen science for environmental justice action
Call for Papers, AAG2018: Innovations in Urban Analytics
We welcome paper submissions for our session at the Association of American Geographers Annual Meeting on 10-14 April, 2018, in New Orleans.
New forms of data about people and cities, often termed ‘Big’, are fostering research that is disrupting many traditional fields. This is true in geography, and especially in those more technical branches of the discipline such as computational geography / geocomputation, spatial analytics and statistics, geographical data science, etc. These new forms of micro-level data have lead to new methodological approaches in order to better understand how urban systems behave. Increasingly, these approaches and data are being used to ask questions about how cities can be made more sustainable and efficient in the future.
This session will bring together the latest research in urban analytics. We are particularly interested in papers that engage with the following domains:
Please e-mail the abstract and key words with your expression of intent to Nick Malleson (n.s.malleson@leeds.ac.uk) by 18 October, 2017 (one week before the AAG abstract deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at: http://annualmeeting.aag.org/submit_an_abstract. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions.
For those interested specifically in the interface between research and policy, they might consider submitting their paper to the session “Computation for Public Engagement in Complex Problems” (http://www.gisagents.org/2017/10/call-for-papers-computation-for-public.html).
Call for Papers, AAG2018: Innovations in Urban Analytics
We welcome paper submissions for our session at the Association of American Geographers Annual Meeting on 10-14 April, 2018, in New Orleans.
New forms of data about people and cities, often termed ‘Big’, are fostering research that is disrupting many traditional fields. This is true in geography, and especially in those more technical branches of the discipline such as computational geography / geocomputation, spatial analytics and statistics, geographical data science, etc. These new forms of micro-level data have lead to new methodological approaches in order to better understand how urban systems behave. Increasingly, these approaches and data are being used to ask questions about how cities can be made more sustainable and efficient in the future.
This session will bring together the latest research in urban analytics. We are particularly interested in papers that engage with the following domains:
Please e-mail the abstract and key words with your expression of intent to Nick Malleson (n.s.malleson@leeds.ac.uk) by 18 October, 2017 (one week before the AAG abstract deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at: http://annualmeeting.aag.org/submit_an_abstract. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions.
For those interested specifically in the interface between research and policy, they might consider submitting their paper to the session “Computation for Public Engagement in Complex Problems” (http://www.gisagents.org/2017/10/call-for-papers-computation-for-public.html).
In December 2016, ECSA and the Natural History Museum in Berlin organised a workshop on analysing apps, platforms, and portals for citizen science projects. Now, the report from the workshop has evolved into an open peer review paper on RIO Journal. RIO is worth noticing: is “The Research Ideas and Outcomes (RIO) journal” and what … Continue reading Defining principles for mobile apps and platforms development in citizen science
At the upcoming 2017 Annual conference of the Computational Social Science Society of the Americas, Annetta Burger, Talha Oz, William Kennedy and myself have a paper entitled: “Generation of Realistic Mega-City Populations and Social Networks for Agent…
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At the upcoming 2017 Annual conference of the Computational Social Science Society of the Americas, Annetta Burger, Talha Oz, William Kennedy and myself have a paper entitled: “Generation of Realistic Mega-City Populations and Social Networks for Agent…
Continue reading »The book ‘Understanding Spatial Media‘ came out earlier this year. The project is the result of joint effort of the editors Rob Kitchin (NUI Maynooth, Ireland), Tracey P. Lauriault (Carleton University, Canada), and Matthew W. Wilson (University of Kentucky, USA). The book is filling the need to review and explain what happened in the part 20 years, with the increase use … Continue reading Chapter in ‘Understanding Spatial Media’ on VGI & Citizen Science
Abstract:
Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems. In this article, we introduce ABM and its utility for studying geographical systems. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.Keywords: Agent-based modeling; Calibration; Complexity; Geographical information science; Modeling and simulation; Validation; Verification.
Full Reference:
Crooks, A.T., Heppenstall, A. and Malleson, N. (2018), Agent-based Modelling, in Huang, B. (ed), Comprehensive Geographic Information Systems, Elsevier, Oxford, England. Volume 1, pp. 218-243 DOI: https://doi.org/10.1016/B978-0-12-409548-9.09704-9. (pdf)
1. [Readers of this blog might of expected the chapter would be about Agent-based Modeling, but its still worth a read!]↩
Continue reading »Abstract:
Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems. In this article, we introduce ABM and its utility for studying geographical systems. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.Keywords: Agent-based modeling; Calibration; Complexity; Geographical information science; Modeling and simulation; Validation; Verification.
Full Reference:
Crooks, A.T., Heppenstall, A. and Malleson, N. (2018), Agent-based Modelling, in Huang, B. (ed), Comprehensive Geographic Information Systems, Elsevier, Oxford, England. Volume 1, pp. 218-243 DOI: https://doi.org/10.1016/B978-0-12-409548-9.09704-9. (pdf)
1. [Readers of this blog might of expected the chapter would be about Agent-based Modeling, but its still worth a read!]↩
Continue reading »Last weekend, I updated my Linkedin page to indicate that I’ve now completed the move between departments at UCL – from the Department of Civil, Environmental, and Geomatic Engineering to the Department of Geography. It’s not just me – the Extreme Citizen Science group will be now based at the Department of Geography. With this move, … Continue reading Changing departments – the pros and cons of being away from home discipline(s)
Once you switch the smartphone off from email and social media network, you can notice better when and how you’re crowdsourced. By this, I mean that use of applications to contribute data is sometimes clearer as the phone becomes less of communication technology and more of information technology (while most of the time it is … Continue reading Crowdsourced: navigation & location-based services
Abstract:
Big Data (BD) offers researchers the scope to simulate population behavior through vastly more powerful Agent Based Models (ABMs), presenting exciting opportunities in the design and appraisal of policies and plans. Agent-based simulations capture system richness by representing micro-level agent choices and their dynamic interactions. They aid analysis of the processes which drive emergent population level phenomena, their change in the future, and their response to interventions. The potential of ABMs has led to a major increase in applications, yet models are limited in that the individual-level data required for robust, reliable calibration are often only available in aggregate form. New (‘big’) sources of data offer a wealth of information about the behavior (e.g. movements, actions, decisions) of individuals. By building ABMs with BD, it is possible to simulate society across many application areas, providing insight into the behavior, interactions, and wider social processes that drive urban systems. This chapter will discuss, in context of urban simulation, how BD can unlock the potential of ABMs, and how ABMs can leverage real value from BD. In particular, we will focus on how BD can improve an agent’s abstract behavioral representation and suggest how combining these approaches can both reveal new insights into urban simulation, and also address some of the most pressing issues in agent-based modeling; particularly those of calibration and validation.
Keywords: Agent-based models, Big Data, Emergence, Cities.
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| The growth in Agent-based modeling -from search results of Web of Science and Google Scholar. |
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| Hotspots of activity of Tweeter Users: Tweet locations and associated densities for a selection of prolific users. |
Full Reference:
Crooks, A.T., Malleson, N., Wise, S. and Heppenstall, A. (2018), Big Data, Agents and the City, in Schintler, L.A. and Chen, Z. (eds.), Big Data for Urban and Regional Science, Routledge, New York, NY, pp. 204-213. (pdf)
Abstract:
Big Data (BD) offers researchers the scope to simulate population behavior through vastly more powerful Agent Based Models (ABMs), presenting exciting opportunities in the design and appraisal of policies and plans. Agent-based simulations capture system richness by representing micro-level agent choices and their dynamic interactions. They aid analysis of the processes which drive emergent population level phenomena, their change in the future, and their response to interventions. The potential of ABMs has led to a major increase in applications, yet models are limited in that the individual-level data required for robust, reliable calibration are often only available in aggregate form. New (‘big’) sources of data offer a wealth of information about the behavior (e.g. movements, actions, decisions) of individuals. By building ABMs with BD, it is possible to simulate society across many application areas, providing insight into the behavior, interactions, and wider social processes that drive urban systems. This chapter will discuss, in context of urban simulation, how BD can unlock the potential of ABMs, and how ABMs can leverage real value from BD. In particular, we will focus on how BD can improve an agent’s abstract behavioral representation and suggest how combining these approaches can both reveal new insights into urban simulation, and also address some of the most pressing issues in agent-based modeling; particularly those of calibration and validation.
Keywords: Agent-based models, Big Data, Emergence, Cities.
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| The growth in Agent-based modeling -from search results of Web of Science and Google Scholar. |
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| Hotspots of activity of Tweeter Users: Tweet locations and associated densities for a selection of prolific users. |
Full Reference:
Crooks, A.T., Malleson, N., Wise, S. and Heppenstall, A. (2018), Big Data, Agents and the City, in Schintler, L.A. and Chen, Z. (eds.), Big Data for Urban and Regional Science, Routledge, New York, NY, pp. 204-213. (pdf)
“More than 900 million people or one third of the world’s urban population lives in either slum or squatter settlements. Urbanization rates in developing countries are often so rapid that formal housing development cannot meet the demand. In the past decades, international, national and local development communities have taken several policy actions in an attempt to improve the living conditions of people within slums or to eradicate them completely. However, such policies have largely failed and slum-free cities have remained a distant goal for many developing countries. This chapter argues that for informed policymaking, it is important to investigate questions related to slum formation such as: (1) How do slums form and expand? (2) Where and when are they formed? (3) What types of structural changes and/or policy interventions could improve housing conditions for the urban poor? In order to address these questions, this chapter develops a geosimulation model that is capable of exploring the spatio-temporal dynamics of slum formation and simulating future formation and expansion of slums within cities of the developing world. Our geosimulation model integrates agent-based modeling (ABM) and Geographic Information System (GIS), methods that are often applied separately to explore slums. In our model, ABM simulates human behavior and GIS provides a spatial environment for the housing market. GIS is also used to analyze empirical data using spatial analyses techniques, which is in turn used to validate the model outputs. The core of this framework is a linked dynamic model operating at both micro and macro geographic and demographic scales. The model explores the collective effect of many interacting inhabitants of slums as well as non-slum actors (e.g. local government) and how their interactions within the spatial environment of the city generate the emergent structure of slums at the macro scale. We argue that when empirical data is absent, geosimulation provides useful insights to study implications of various policies. The goal of this framework is to develop a decision support tool that could allow urban planners and policymakers to experiment with new policy ideas ex-ante in a simulated environment. We calibrate and validate the model using data from Ahmedabad, the sixth largest city of India, where 41% of its population lives in slums. This is one of the first attempts to develop an integrated and multi-scalar analytical framework to tackle slum issues in the developing world at multiple spatial scales.”
Keywords: Slums Agent-based modeling India Geosimulation
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| Integrated Simulation Framework |
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| Slum Locations and Slum Sizes in Ahmedabad, 2001 |
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| Spatial Sprawl Experiment |
Patel, A., Crooks, A.T. and Koizumi, N. (2018). “Spatial Agent-based Modeling to Explore Slum Formation Dynamics in Ahmedabad, India” in Thill J.C. and Drajicavic, S. (eds.), Geocomputational Analysis and Modeling of Regional Systems, Springer, New York, NY, pp 121-141. (pdf)
Further details of the model and project can be found here. As normal any thoughts and comments are most welcome.
Continue reading »
“More than 900 million people or one third of the world’s urban population lives in either slum or squatter settlements. Urbanization rates in developing countries are often so rapid that formal housing development cannot meet the demand. In the past decades, international, national and local development communities have taken several policy actions in an attempt to improve the living conditions of people within slums or to eradicate them completely. However, such policies have largely failed and slum-free cities have remained a distant goal for many developing countries. This chapter argues that for informed policymaking, it is important to investigate questions related to slum formation such as: (1) How do slums form and expand? (2) Where and when are they formed? (3) What types of structural changes and/or policy interventions could improve housing conditions for the urban poor? In order to address these questions, this chapter develops a geosimulation model that is capable of exploring the spatio-temporal dynamics of slum formation and simulating future formation and expansion of slums within cities of the developing world. Our geosimulation model integrates agent-based modeling (ABM) and Geographic Information System (GIS), methods that are often applied separately to explore slums. In our model, ABM simulates human behavior and GIS provides a spatial environment for the housing market. GIS is also used to analyze empirical data using spatial analyses techniques, which is in turn used to validate the model outputs. The core of this framework is a linked dynamic model operating at both micro and macro geographic and demographic scales. The model explores the collective effect of many interacting inhabitants of slums as well as non-slum actors (e.g. local government) and how their interactions within the spatial environment of the city generate the emergent structure of slums at the macro scale. We argue that when empirical data is absent, geosimulation provides useful insights to study implications of various policies. The goal of this framework is to develop a decision support tool that could allow urban planners and policymakers to experiment with new policy ideas ex-ante in a simulated environment. We calibrate and validate the model using data from Ahmedabad, the sixth largest city of India, where 41% of its population lives in slums. This is one of the first attempts to develop an integrated and multi-scalar analytical framework to tackle slum issues in the developing world at multiple spatial scales.”
Keywords: Slums Agent-based modeling India Geosimulation
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| Integrated Simulation Framework |
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| Slum Locations and Slum Sizes in Ahmedabad, 2001 |
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| Spatial Sprawl Experiment |
Patel, A., Crooks, A.T. and Koizumi, N. (2018). “Spatial Agent-based Modeling to Explore Slum Formation Dynamics in Ahmedabad, India” in Thill J.C. and Drajicavic, S. (eds.), Geocomputational Analysis and Modeling of Regional Systems, Springer, New York, NY, pp 121-141. (pdf)
Further details of the model and project can be found here. As normal any thoughts and comments are most welcome.
Continue reading »Just a short post to highlight Rohan Fisher’s excellent website demonstrating and sharing a number of NetLogo models. One such example is shown below, which integrates GIS cost distance analysis to explore access to services via travel times (click her…
Continue reading »Just a short post to highlight Rohan Fisher’s excellent website demonstrating and sharing a number of NetLogo models. One such example is shown below, which integrates GIS cost distance analysis to explore access to services via travel times (click her…
Continue reading »The late Roger Tomlinson is considered the “Father of Geographic Information Systems” and he completed his PhD in the UCL Department of Geography in 1974. Tomlinson pioneered digital mapping – every map created using a computer today still uses the principles he laid down in his thesis and its associated work creating the “The Canada Geographic […]
Continue reading »Deadline 28th July 2017 UCL Department of Geography and the Ordnance Survey are inviting applications for a PhD studentship to explore the internal systematic biases in crowd-sourced geographic information datasets (also known as Volunteered Geographic Information – VGI). The studentship provides an exciting opportunity for a student to work with Ordnance Survey on understanding the … Continue reading PhD studentship in collaboration with the Ordnance Survey – identifying systematic biases in crowdsourced geographic information
The second day of the PPGIS 2017 symposium (see Day 1 here) started with a session on METHODS AND TOOLS. The session opened with a keynote from Peter Nijkamp (Vrije Universiteit Amsterdam, Amsterdam, Netherlands, Adam Mickiewicz University, Poznań, Poland) . The talk is titled “A big data dashboard architecture for computable intelligent city policy“. Peter noted that the … Continue reading PPGIS 2017 – Poznan, Poland (Day 2) – Geodesign, applications and discussion
These notes are from the workshop Modern Methods and Tools for Public Participation in Urban Planning 2017, held in Palac Obrzycko near Poznan, Poland on 22nd and 23rd June 2017 – the outline of the workshop stated “Researchers and practitioners of urban planning have had a variable interest in developing and applying methods of public participation … Continue reading PPGIS 2017 – Poznan, Poland (Day 1) – different notions and tools of public participation GIS
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.
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| Figure 1: Two-Dimensional Cellular Automata Neighborhoods |
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| Figure 2: Voronoi Tessellations Of Space Where Each Polygon Has A Different Number Of Neighbors Based On A Shared Edge. |
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| Figure 3: Example of Cells Changing State from Dead (White) To Alive (Black) Over Time Depending On The States of its Neighboring Cells. |
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| Figure 4: A One-Dimensional CA Model Implementing “Rule 30” Where Successive Iterations Are Presented Below Each Other. |
Full Reference:
Continue reading »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)
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.
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| Figure 1: Two-Dimensional Cellular Automata Neighborhoods |
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| 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:
Continue reading »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)