Showing posts with label Geography. Show all posts
Showing posts with label Geography. Show all posts

Thursday, December 08, 2022

Simulating Geographical Systems using CA and ABMs

 
In the chapter we discuss how thinking and studying of geographical systems like cities has changed over time from top down aggregate analysis to more bottom up approaches which captures the complex nature of such systems. We then discuss how we can model such systems from a cellular automata and agent-based perspectives. and how these styles of models have evolved and how they can be used to model future systems. If this sounds of interest below we provide the abstract to the chapter, some of the figures that accompany it and at the  bottom of the page we provide the full reference to the paper along with a link to the chapter itself.
"Abstract: How we view and understand the processes driving and shaping geographical systems is constantly evolving. This is due to the appearance of new rich data sources, increased computing power and storage, and the development of individual-level approaches. This allows us to explore geographical systems (from the bottom up) at scales not possible in the past. In this chapter, we examine the utility of two of the most commonly used individual-level modelling approaches, cellular automata and agent-based modelling. We outline their key differences and how these models are being used to further our understanding of geographical systems through simulation. We conclude with a discussion about the challenges that both approaches need to meet to continue developing into the future.
Keywords: Cellular automata; Agent-based models; Geographical systems; Machine learning
 

A SLEUTH like model stylized on Santa Fe, New Mexico denoting how land use charges over time from undeveloped (grey) to urban (red).

Example applications of agent-based models at different spatial and temporal scales

Full reference:

Heppenstall, A., Crooks, A.T., Manley, E. and Malleson, N. (2022) Simulating Geographical Systems using Cellular Automata and Agent-based Models, in Rey S. and Franklin, R. (eds.), Handbook of Spatial Analysis in the Social Sciences, Edward Elgar Publishing, Cheltenham, UK, pp. 142-157. (pdf)

Friday, December 04, 2020

Future Developments in Geographical Agent-Based Models: Challenges and Opportunities

Its been a while since (to say the least), that we wrote a position paper about agent-based modeling. But with agent-based modeling becoming more widely accepted  and the growth of machine learning within the geographical sciences we thought we would revisit some of the existing challenges  (e.g. validation, representing behavior) and discuss how machine learning and data might help here. To this end, Alison HeppenstallNick Malleson, Ed Manley, Jiaqi Ge and Mike Batty, have recently published a paper entitled "Future Developments in Geographical Agent-Based Models: Challenges and Opportunities" in Geographical Analysis.  Below we provide the abstract to the paper, and if this is of interest please follow the links to the paper itself.

Abstract

Despite reaching a point of acceptance as a research tool across the geographical and social sciences, there remain significant methodological challenges for agent-based models. These include recognizing and simulating emergent phenomena, agent representation, construction of behavioral rules, calibration and validation. Whilst advances in individual-level data and computing power have opened up new research avenues, they have also brought with them a new set of challenges. This paper reviews some of the challenges that the field has faced, the opportunities available to advance the state-of-the-art, and the outlook for the field over the next decade. We argue that although agent-based models continue to have enormous promise as a means of developing dynamic spatial simulations, the field needs to fully embrace the potential offered by approaches from machine learning to allow us to fully broaden and deepen our understanding of geographical systems.

Full Reference:

Heppenstall, A., Crooks, A.T., Malleson, N., Manley, E., Ge, J. and Batty, M. (2021), Future Developments in Geographical Agent-Based Models: Challenges and Opportunities, Geographical Analysis. https://doi.org/10.1111/gean.12267 (pdf)

Thursday, December 19, 2019

New Working Paper: Agent-Based Models for Geographical Systems: A Review

Its been a while since we published a working paper, especially a CASA one, but this has now changed with the release of a new one entitled "Agent-Based Models for Geographical Systems: A Review." In the paper Alison Heppenstall, Nick Malleson, Ed Manley, Jiaqi Ge, Michael Batty and myself reflect back on the agent-based modeling and their use in geographical systems. 

In the paper we revisit challenges that we first explored back in 2008 (which an earlier version was another CASA working paper) and progress that has been made to address them. We then  explore new challenges within the field of agent-based models especially in light of new new (big) data along with new opportunities (such as data assimilation). If you want to find out more about the paper, below is the abstract and a ling to the paper.

Abstract:
This paper charts the progress made since agent-based models (ABMs) of geographical systems emerged from more aggregative approaches to spatial modeling in the early 1990s. We first set the context by noting that ABM explicitly represent the spatial system by individual objects, usually people in the social science domain, with behaviors that we simulate here mainly as decisions about location and movement. Key issues pertaining to the way in which temporal dynamics characterize these models are noted and we then pick up the challenges from the review of this field conducted by Crooks, et al. (2008) some 12 years ago which was also published as a CASA working paper. We then define key issues from this past review as pertaining to a series of questions involving: the rationale for modeling; the way in which theory guides models and vice versa; how models can be compared; questions of model replication,experiment, verification and validation; how dynamics are incorporated in models; how agent behaviors can be simulated; how such ABMs are communicated and disseminated; and finally the data challenges that still dominate the field. This takes us to the current challenges emerging from this discussion. Big data, the way it is generated, and its relevance for ABM is explored with some important caveats as to the relevance of such data for these models, the way these models might be integrated with one another and with different genera of models are noted, while new ways of testing such models through ensemble forecasting and data assimilation are described. The notion about how we model human behaviors through agents learning in complex environment is presented and this then suggests that ABM still have enormous promise for effective simulations of how spatial systems evolve and change.
Full Reference:
Heppenstall, A., Crooks, A.T., Malleson, N., Manley, E., Ge J. and Batty, M. (2019), Agent-Based Models for Geographical Systems: A Review. Centre for Advanced Spatial Analysis (University College London): Working Paper 214, London, England. (pdf)

Tuesday, October 04, 2016

Agent-based Modeling in Geographical Systems

Recently Alison Heppenstall and myslef wrote a short introductory chapter entitled "Agent-based Modeling in Geographical Systems" for AccessScience (a online version of McGraw-Hill Encyclopedia of Science and Technology).

In the chapter we trace the rise in agent-based modeling within geographical systems with a specific emphasis of cities. We briefly outline how thinking and modeling cities has changed and how agent-based models align with this thinking along with giving a selection of example applications. We discuss the current limitations of agent-based models and ways of overcoming them and how such models can and have been used to support real world decision-making.

Conceptualization of an agent-based model where people are connected to each other and take actions when a specific condition is met

 Full Reference:
Heppenstall, A. and Crooks, A.T. (2016). Agent-based Modeling in Geographical Systems, AccessScience, McGraw-Hill Education, Columbus, OH. DOI: http://dx.doi.org/10.1036/1097-8542.YB160741. (pdf)
 

Saturday, September 03, 2016

New Paper: Generating and Analyzing Spatial Social Networks


We recently had a paper entitled "Generating and Analyzing Spatial Social Networks" accepted in Computational and Mathematical Organization Theory. In the paper we proposed and explored spatial versions of three well known networks, that of the Erdös-Rényi, Watts-Strogatz, and Barabási-Albert. Further details about the paper can be seen in the abstract below:
"In this paper, we propose a class of models for generating spatial versions of three classic networks: Erdös-Rényi (ER), Watts-Strogatz (WS), and Barabási-Albert (BA). We assume that nodes have geographical coordinates, are uniformly distributed over an m × m Cartesian space, and long-distance connections are penalized. Our computational results show higher clustering coefficient, assortativity, and transitivity in all three spatial networks, and imperfect power law degree distribution in the BA network. Furthermore, we analyze a special case with geographically clustered coordinates, resembling real human communities, in which points are clustered over k centers. Comparison between the uniformly and geographically clustered versions of the proposed spatial networks show an increase in values of the clustering coefficient, assortativity, and transitivity, and a lognormal degree distribution for spatially clustered ER, taller degree distribution and higher average path length for spatially clustered WS, and higher clustering coefficient and transitivity for the spatially clustered BA networks."
Keywords: Spatial social networks, Network properties, Random network, Small-world network, Scale-free network.



The Python code for the models can be found here.


Full Reference: 
Alizadeh, M., Cioffi-Revilla, C. and Crooks, A. (2017), Generating and Analyzing Spatial Social Networks. Computational and Mathematical Organization Theory, 23(3): 362-390. (pdf)


Tuesday, January 26, 2016

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Recently, Alison Heppenstall, Nick Malleson  and myself have just had a paper accepted in Systems entitled: “Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?" In the paper we critically examine how well agent-based models have  simulated a variety of urban processes. We discus what considerations are needed when choosing the appropriate level of spatial analysis and time frame to model urban phenomena and what role Big Data can play in agent-based modeling. Below you can read the abstract of the paper and see a number of example applications discussed.
Abstract: Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.
Keywords: cities; agent-based modeling; big data; crime; retail; space; simulation

Figure 1. (A) System structure; (B) System hierarchy; and (C) Related subsystems/processes (adapted from Batty, 2013).



Reference cited:
Batty, M. (2013).  The New Science of Cities; MIT Press: Cambridge, MA, USA.

Full reference to the open access paper:
Heppenstall, A., Malleson, N. and Crooks A.T. (2016). “Space, the Final Frontier”: How Good are Agent-based Models at Simulating Individuals and Space in Cities?, Systems, 4(1), 9; doi: 10.3390/systems4010009 (pdf)
 

Wednesday, December 11, 2013

Book review: A Framework for Geodesign:

Recently I had the pleasure of reading and reviewing the book entitled "A Framework for Geodesign: Changing Geography by Design" by Carl Steinitz. The full review can be found in Environment and Planning B. However, I thought I would share the review to readers of the blog (with some added images).

"People have designed and changed the geography of their landscapes for thousands of years, for the better or for the worse. But with more pressure being placed on the world’s resources, with increasing numbers of people, the question that we are now faced with is, what are the best sustainable design solutions to mitigate these challenges? For example, urban growth is inevitable given the increasing concentration of people living within in cities, and as a trend is expected to continue into the foreseeable future. The question that designers and planners are therefore faced with is what scenarios would lead to say the least amount of loss in biodiversity. But this is a multi-faceted problem ranging in scale from how do people build there homes, to where should new industry be located, or how should land be conserved etc? These are all questions involving spatial decision-making, and where geographic information systems can play an important role. Over the last forty years, geographic information systems (GIS) have increasingly been used to assist in such complex decisions, from modelling urban growth projections through to assessing the spread of pollution (see Longley et al., 2010 for a extensive list of applications). However, one of the original visions for GIS, which is often overlooked, is that of a tool for design (Goodchild, 2010).

In his book “A Framework for Geodesign: Changing Geography by Design” Karl Steinitz brings his vast experience as a landscape architect and planner to such an issue. For those not familiar to the term geodesign, Steinitz (2012) writes in his preference to the book that it “is an invented word, and a very useful term to describe an activity that is not the territory of any single design profession, geographic science or information technology” (p ix). More generally Steinitz (2012) frames geodesign as “the development and application of design-related processes intended to change the geographical study areas in which they are applied and realised” (p1). Or another way of putting it, the merging of geography and design through computers. This is reiterated later on by a quote from Michael Flaxman were he states “Geodesign is a design and planning method which tightly couples the creation of design proposals with impact simulations informed by geographic contexts, systems thinking, and digital technology” (Flaxman quoted in Steinitz, 2012 p 12).

Moreover, geodesign can be considered both as a verb and as a noun which Steinitz relates to design more generally (see Steinitz, 1995). In the sense as a verb, geodesign is about asking questions and as a noun, geodesign is the content of the answers. In this book Steinitz not only clears up the meaning of geodesign but more importantly provides a comprehensive framework (based on his past work) for thinking about strategies of geodesign, and for organising and operationalizing these meanings.

The book is made up of twelve chapters and split into four parts. The first part is spent on framing geodesign and to set the scene for the remainder of book. For example, chapter 1 notes that for geodesign to be successful, one requires collaboration between the design professions (e.g. architects, planners, urban designers, etc.), geographical sciences (e.g. geographers, ecologists, etc), information technologies and those people living within the communities where geodesign is being applied. This is reiterated throughout the book. Chapter 1 also traces the history of geodesign, and how the advent of computer methods for the acquisition, management and display of digital data can be used to link many participants, thus making design not a solitary activity. Chapter 2 introduces the reader to the context for geodesign in the sense that 1) geography matters and that different societies think differently about their geography, 2) scale maters in the sense of what scale should a geodesign project be applied at (e.g., local, regional or global), and what are the appropriate considerations that need to incorporated at each scale, and finally 3) size matters, if the size of the geographic study area increases, there is a high risk of a harmful impact if one makes a mistake.

Part 2 of the book lays out a framework for geodesign. It is important to note that Steinitz does not call this a methodology for geodesign, as he argues one cannot have a singular methodology as the approaches, principles and methods are applied to projects across a range of geographies, scales and sizes. He therefore introduces a framework as a verb, specifically for asking questions, choosing among many methods and seeking possible answers. In order to develop this framework Steinitz walks the reader through six different questions and types of models common in geodesign projects.

Chapter 3 focuses on components of the framework and the questions one needs to address for a successful geodesign project. These questions broadly range from: 1) How should the study area be described? 2) How does the study area operate? 3) Is the current study area working well? 4) How might the study area be altered? 5) What differences might the changes cause?, and finally, 6) How should the study area be changed? As posed by Steinitz, these questions are not a linear progression, but have several iterative loops and feedbacks both with the geodesign team and the application stakeholders. Moreover, Steinitz argues that such questions should be asked three times during the geodesign study, the first to treat them as why questions (e.g. to understand the geographic study area and the scope of the study). Secondly, the questions are asked in reverse order to identify the how questions (e.g. to define the methods of the study, therefore geodesign becomes a decision rather than data driven process) and finally, the questions are asked in sequential order to address the what, where and when questions as the geodesign study is being implemented. Once these three iterations are complete, there can be three possible decisions, yes, no and maybe. If maybe or no, more feedback is needed between the geodesign team and the stakeholders. These iterations highlight how geodesign is an on-going process of changing geography by design.


Using this framework, Chapter 4 discusses the first iteration of questions, that of scoping the geodesign study. The emphasis of this iteration is ensuring that it is being decision-driven as opposed to data-driven. Moreover, it goes over the six questions in an attempt to identity the intended scope for the study before looking at a feasible methodological plan. Chapter 5, moves onto the second iteration, that of designing the study methodology. Having identified the scope of the study (the why) from the first iteration, the geodesign team must then explore how it will be carried out and what are the evaluation criteria. Chapter 6 discusses the third iteration, that of carrying out the geodesign study. That of the what, where and when questions. Throughout these chapters, Steinitz reiterates the need for stakeholder input and feedback from the geodesign team. Moreover, at the end of chapter 6, Steinitz reiterates that the choices mater. The why questions provide a sense of the scope and objectives of the design application: the problem, the study area and those scales required for operationalizing a successful project.

Part 3 of the book looks at nine case studies in geodesign from around the world. Ranging in temporal scale from days to years, from no financial budget to a large budget, and from a small to large numbers of participants. These case studies helped solidify many of the concepts identified in the preceding chapters. They range from urban growth, to urban change to that of fire management. The case studies focus on specific places and utilize GIS with a variety of different techniques, from anticipatory modelling to that of participatory modelling and rule based models (e.g. cellular automata). They also show the importance of visualisation, as Steinitz (2012) notes “spatial visualisation can significantly influence decision making” (p 168). These examples have details but not depth (however, references are given to the full case study report), but this reiterates the purpose of the book, in the sense it is not a “how to” textbook or listing technologies that enable geodesign. It is a discussion with examples of geodesign. Or to quote from the last page of the book “you cannot copy an example but you can gain experience by joining the collaborative activities of geodesign and changing geography by design” (Steinitz, 2012, p 201). The same goes for the applications, they give a valuable insight into what is possible with geodesign. The book concludes by discussing the future applications for geodesign which range from looking at the implications for research in geodesign, and sketching out a geodesign support system (see Ervin, 2011); and in a sense, one could relate this to other planning support systems (see Brail, 2008), but with a greater emphasis on design.

Overall the book is extremely well written and Steinitz provides a critical and personal account of geodesign, which shows his expertise in the area from his years of teaching and carrying out geodesign work. The use of figures and real world examples really helps support the discussion. But if you are looking for a textbook for “how to” do geodesign, or a list of technologies that enable geodesign, you need to look elsewhere. This is a book the principles and practice of geodesign in a general sense, and which provides a valuable resource for those interested in this topic."

References:
Brail, R.K. (ed.) (2008), Planning Support Systems for Cities and Regions, Lincoln Institute of Land Policy, Cambridge, MA.
Ervin, S. (2011), 'A System for GeoDesign', Proceedings of Digital Landscape Architecture, Anhalt University of Applied Science, Dessau, Germany, pp. 145-154.
Goodchild, M.F. (2010), 'Towards Geodesign: Repurposing Cartography and GIS?.' Cartographic Perspectives, 66(7-22).
Longley, P.A., Goodchild, M.F., Maguire, D.J. and Rhind, D.W. (2010), Geographical Information Systems and Science (3rd Edition), John Wiley & Sons, New York, NY.
Steinitz, C. (1995), 'Design is a Verb; Design is a Noun', Landscape Journal, 14(2): 188-200.
Steinitz, C. (2012), A Framework for Geodesign: Changing Geography by Design, ESRI Press, Redlands. CA.
Full reference to the book:
Review of Steinitz, C. (2012), A Framework for Geodesign: Changing Geography by Design, ESRI Press, Redlands. CA.
Full reference to the review:

Crooks, A.T. (2013), Crooks on Steinitz: A Framework for Geodesign: Changing Geography by Design, Environment and Planning B, 40 (6): 1122-1124.