Showing posts with label Urban Systems. Show all posts
Showing posts with label Urban Systems. Show all posts

Monday, August 16, 2021

Organizing Theories for Disasters into a Complex Adaptive System Framework

In past posts we have discussed or demonstrated how computational social science (CSS) (i.e. the study of social science through computational methods) and complexity theory can be utilized explore disasters or diseases but this has not really been  formalized.  To this end, Annetta Burger, William Kennedy and myself have a new review paper in Urban Science entitled "Organizing Theories for Disasters into a Complex Adaptive System Framework." In the paper we review over a century of disaster research and demonstrate the properties and dynamics of complex adaptive systems in such studies and argue how complexity theory is integral to understanding human behavior in disasters by addressing the interactions across systems (i.e., physical, social, and individual systems). We discuss the characteristics of a complex adaptive system (e.g., heterogeneity, webs of connections, relationships and interactions, and adaptations arising from individual actions, decisions, and learning) and how such characteristics can be applied to disaster research and explore implications for future disaster research with an eye on sustainable and resilient cities. If this sounds of interest, and you want to find out more, below we provide the abstract to the paper and a  link to the the paper itself.

Abstract: Increasingly urbanized populations and climate change have shifted the focus of decision1makers from economic growth to the sustainability and resilience of urban infrastructure and communities, especially when communities face multiple hazards and need to recover from recurring disasters. Understanding human behavior and its interactions with built-environments in disasters requires disciplinary crossover to explain its complexity, therefore we apply the lens of complex adaptive systems (CAS) to review disaster studies across disciplines. Disasters can be understood to consist of three interacting systems: 1) the physical system, consisting of geological, ecological, and human-built systems; 2) the social system, consisting of informal and formal human collective behavior; and 3) the individual actor system. Exploration of human behavior in these systems shows that CAS properties of heterogeneity, interacting subsystems, emergence, adaptation, and learning are integral, not just to cities, but to disaster studies and connecting them in the CAS framework provides us with a new lens to study disasters across disciplines. This paper explores the theories and models used in disaster studies, provides a framework to study and explain disasters, and discusses how complex adaptive systems can support theory-building in disaster science for promoting more sustainable and resilient cities.

Keywords: Cities; Complex Adaptive Systems; Computational Social Science, Disasters; Human Behavior.

Framework for Understanding the Intersecting Complex Adaptive Systems of Disaster.

Full Reference:

Burger, A., Kennedy, W.G. and Crooks A.T. (2021), Organizing Theories for Disasters into a Complex Adaptive System Framework, Urban Science, 5(3), 61; https://doi.org/10.3390/urbansci5030061 (pdf)

 

Friday, April 09, 2021

Agent-Based Modeling and the City

Turning our attention back to agent-based modeling, in the recently open access edited volume by Wenzhong Shi, Michael Goodchild, Michael Batty, Mei-Po Kwan and Anshu Zhang entitled Urban Informatics, Alison Heppenstall, Nick Malleson, Ed Manley and myself have a chapter entitled "Agent-Based Modeling and the City: A Gallery of Applications.

In the chapter we discuss cities through the lens of complex systems comprised of composed of people, places, flows, and activities. Moreover, we make the argument that as cities contain large numbers of discrete actors interacting within space and with other systems from nature, predicting what might happen in the future is a challenge. We base this argument on the fact that human behavior cannot be understood or predicted in the same way as in the physical sciences such as physics or chemistry. The actions and interactions of the inhabitants of a city, for example, cannot be easily described in a physical science theory such as that of Newton’s Laws of Motion. This notion is captured quite aptly by a quote by Nobel laureate Murray Gell-Mann: “Think how hard physics would be if particles could think.” Building on these arguments we introduce readers to agent-based modeling as it offers a way to explore the processes that lead to patterns we see in cities from the bottom up but also allows us to incorporate ideas from complex systems (e.g., feedbacks, path dependency, emergence) along with providing a gallery of applications of geographically explicit agent-based models. 

We then discuss how agent-based models can incorporate various decision-making processes within them and  how we can integrate data within such models with a specific emphasis on geographical and social information. This leads us to a discussion on how agent-based modelers are utilizing machine learning (such as genetic algorithms, artificial neural networks, Bayesian classifiers, decision trees, reinforcement learning, to name but a few) and data mining (i.e. finding patterns in the data) within their models: from the design of the model, the execution of the model to that of the evaluation of the model. Finally,  we conclude the chapter with a summary and discuss new opportunities with respect to agent-based modeling and the city. One such opportunity is dynamic data assimilation which could be transformative for the ways that some systems, for example “smart” cities, are modeled. Our argument is that agent-based models are often used to simulate the behavior of complex systems, these systems often diverge rapidly from initial starting conditions. One way to prevent a simulation from diverging from reality would be to occasionally incorporate more up-to-date data and adjust the model accordingly (i.e., data assimilation). Data, especially streaming data produced through near real time observational datasets (e.g., social media, vehicle routing counters) could be utilized in such a case. If what we have written above is of interest, below we provide the abstract to chapter along with some figures which we use to illustrate some key points or concepts (such as dynamic data assimilation). Finally at the bottom post, we provide the full reference and a link to the chapter.

Abstract:
Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which governs their interactions with each other and their environment. It is through these interactions that more macro phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the last two decades, with the growth of computational power and data, agent-based models have evolved into one of the main modeling paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter we will introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.

Key Words: Agent-based Modeling, Geographical Information Systems, Machine Learning, Urban Simulation.
Using geographical information as a foundation for artificial worlds.
A selection of GeoMason models across various spatial and temporal scales.
Dynamic data assimilation and agent-based modeling.

Full Reference:
Crooks, A.T., Heppenstall, A., Malleson, N. and Manley, E. (2021), Agent-Based Modeling and the City: A Gallery of Applications, in Shi, W., Goodchild, M., Batty, M., Kwan, M.-P., Zhang, A. (eds.), Urban Informatics, Springer, New York, NY, pp. 885-910. (pdf)

 

Friday, October 13, 2017

AAG2018: Innovations in Urban Analytics

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.

Session Description

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:
  • Agent-based modelling (ABM) and individual-based modelling;
  • Machine learning for urban analytics;
  • Innovations in consumer data analytics for understanding urban systems;
  • Real-time model calibration and data assimilation;
  • Spatio-temporal data analysis;
  • New data, case studies, demonstrators, and tools for the study of urban systems;
  • Complex systems analysis;
  • Geographic data mining and visualization;
  • Frequentist and Bayesian approaches to modelling cities.

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).

Key Dates
  • 18 October, 2017: Abstract submission deadline. E-mail Nick Malleson by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 23 October, 2017: Session finalization and author notification.
  • 25 October, 2017: Final abstract submission to AAG, via the link above. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Nick Malleson (n.s.malleson@leeds.ac.uk). Neither the organizers nor the AAG will edit the abstracts.
  • 8 November, 2017: AAG session organization deadline. Sessions submitted to AAG for approval.
  • 9-14 April, 2018: AAG Annual Meeting.

Session Organizers

Thursday, January 12, 2017

Transportation in Agent-Based Urban Modelling


Sarah Wise, Mike Batty and myself have recently had a chapter published in Agent Based Modelling of Urban Systems entitled "Transportation in Agent-Based Urban Modelling". In the chapter we provide a critique in how transportation has been included or omitted from agent-based models and suggest how it might be handled in future applications.

Our argument is that transportation plays an important role in nearly every aspect of our daily lives. However, within agent-based models that explore urban problems, transportation is often omitted. Using representative case studies (e.g. from crime, disease spread, and land use) we present different levels/tiers of complexity at which transportation systems are captured with agent-based models (as shown in Table 1). Table 2 shows how these tiers of complexity are captured within crime models.  For interested readers, below you can see the abstract to our chapter.  


Abstract:
As the urban population rapidly increases to the point where most of us will be living in cities by the end of this century, the need to better understand urban areas grows ever more urgent. Urban simulation modelling as a field has developed in response to this need, utilizing developing technologies to explore the complex inter-dependencies, feedback's, and heterogeneities which characterize and drive processes that link the functions of urban areas to their form. As these models grow more nuanced and powerful, it is important to consider the role of transportation within them. Transportation joins, divides, and structures urban areas, providing a functional definition of the geometry and the economic costs that determine urban processes accordingly. However, it has proved challenging to factor transportation into agent-based models (ABM); past approaches to such modelling have struggled to incorporate information about accessibility, demographics, or time costs in a significant way. ABM have not yet embraced alternative traditions such as that in land use transportation modelling that build on spatial interaction in terms of transport directly, nor have these alternate approaches been disaggregated to the level at which populations are represented as relatively autonomous agents. Where disaggregation of aggregate transport has taken place, it has led to econometric models of individual choice or microsimulaton models of household activity patterns which only superficially embody the key principles of ABM. But the explosion in the availability of movement data in recent years, combined with improvements in modelling technology, is easing this process dramatically. In particular, agent-based modelling as a methodology has grown ever more promising and is now capable of emulating the interplay of urban systems and transportation. Here, we explore the importance of this approach, review how transportation has been factored into or omitted from agent-based models of urban areas, and suggest how it might be handled in future applications. Our approach is to take snapshots of different applications and use these to illustrate how transportation is handled in such models.

Keywords: Agent-based modelling; urban systems; urban modelling






Full Reference:
Wise, S. Crooks, A.T. and Batty, M. (2017). Transportation in Agent-Based Urban Modelling, in Namazi-Rad, M., Padgham, L., Perez, P., Nagel, K. and Bazzan, A. (eds), Agent Based Modelling of Urban Systems, Springer, New York, NY, pp. 129-148. (pdf)


Friday, May 13, 2016

A Semester with Urban Analytics

This past semester I gave a new class at GMU entitled "Urban Analytics". In a nutshell the class was about introducing students to a broad interdisciplinary field that focuses on the use of data to study cities. More specifcally the emphasis of the class was to provide students with a understanding of what methods, tools and theory can be used to monitor, analyze and model cities. 

From my past research and also when preparing the class material,  I have come to the realization that to study cities (like many others, you know who you are) that there is no one general model, tool or dataset. Therefore, one needs to maintain a toolbox of specialized tools than can be applied to different aspects of urban problems and questions. 

The toolbox that we used in class included a variety of software such as ArcGIS, QGIS, GeoDa, SANET along with programing and scripting in Python and R to modeling  cities via UrbanSim, NetLogo and MASON. Data we used ranged from crowdsourced (e.g. volunteered geographical information) data such as from OpenStreetMap or Wikipedia, to crowd harvested (ambient geographical information) data such as Twitter and Flickr, as-well as more traditional sources of data such as the US Census.

The Urban Analytics Toolbox

As an introduction to urban analytics, the course had the following objectives:
  1. to understand the motivation for the use of data to study cities, including some historical aspects; 
  2. to learn about the variety of Urban Analytics research programs across the several disciplines (urban planning, regional science, public policy, geography, computational social science etc.), through a survey of the literature and case studies. 
  3. to understand the distinct contribution that Urban Analytics can make by providing specific insights about cities at multiple scales. 
  4. to provide the foundations for more advanced work in the area of Urban Analytics. 
As with many of my courses, students were expected to complete a end of semester project. Below is a selection of these projects which explored some aspect of urban life.



I would like to thank the students for participating in this new class. It was a fun trip.

Wednesday, March 09, 2016

AAG: Symposium on Human Dynamics Research - Urban Analytics

Urban Analytics Sessions @ the AAG 2016
 
As part of the Symposium on Human Dynamics Research we have organized three great sessions sessions relating to Urban Analytic which will take place on Thursday, 3/31/2016, from 1:20 PM - 7:00 PM in Union Square 18, Hilton Hotel, 4th Floor.

Session Description: A deluge of new data created by people and machines is changing the way that we understand, organize and model urban spaces. New analytics are required to make sense of these data and to usefully apply findings to real systems. This session seeks to bring together quantitative or mixed methods papers that develop or use new analytics in order to better understand the form, function and future of urban systems. We invite methodological, theoretical and empirical papers that engage with any aspect of urban analytics. Topics include, but are not limited to:
  • New methodologies for tackling large, complex or dirty data sets;
  • Case studies involving analysis of novel or unusual data sources;
  • Policy analysis, predictive analytics, other applications of data;
  • Intensive modelling or simulation applied to urban areas or processes;
  • Individual-level and agent-based models (ABM) of geographical systems;
  • Validating and calibrating models with novel data sources;
  • Ethics of data collected en masse and their use in simulation and analytics.

Organizers:

3445 Symposium on Human Dynamics Research: Urban Analytics (I)

Thursday, 3/31/2016, from 1:20 PM - 3:00 PM in Union Square 18, Hilton Hotel, 4th Floor

Chair: Nick Malleson

Talks:
Discussant: Mark Birkin

3545 Symposium on Human Dynamics Research: Urban Analytics (II)

Thursday, 3/31/2016, from 3:20 PM - 5:00 PM in Union Square 18, Hilton Hotel, 4th Floor

Chair: Paul Longley

Talks:

3645 Symposium on Human Dynamics Research: Urban Analytics (III) 

Thursday, 3/31/2016, from 5:20 PM - 7:00 PM in Union Square 18, Hilton Hotel, 4th Floor

Chair: Andrew Crooks

Talks:
Discussant: Andrew Crooks 

Thursday, September 17, 2015

Call for papers: Symposium on Human Dynamics Research: Urban Analytics at the 2016 AAG



Call for papers: AAG 2016. San Francisco. 29th March – 2nd April


Symposium on Human Dynamics Research: Urban Analytics

A deluge of new data created by people and machines is changing the way that we understand, organise and model urban spaces. New analytics are required to make sense of these data and to usefully apply findings to real systems. This session seeks to bring together quantitative or mixed methods papers that develop or use new analytics in order to better understand the form, function and future of urban systems. We invite methodological, theoretical and empirical papers that engage with any aspect of urban analytics. Topics include, but are not limited to:
  • New methodologies for tackling large, complex or dirty data sets;
  • Case studies involving analysis of novel or unusual data sources;
  • Policy analysis, predictive analytics, other applications of data;
  • Intensive modelling or simulation applied to urban areas or processes; 
  • Individual-level and agent-based models (ABM) of geographical systems; 
  • Validating and calibrating models with novel data sources; 
  • Ethics of data collected en masse and their use in simulation and analytics.

Please e-mail the abstract and key words with your expression of intent to Nick Malleson (n.s.malleson@leeds.ac.uk) by 22nd October, 2015 (one week before the AAG session 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://www.aag.org/cs/annualmeeting/call_for_papers

An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and  conclusions.

Timeline summary:

  • 22nd October, 2015: Abstract submission deadline. E-mail Nick Malleson by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 25th October, 2015: Session finalization and author notification
  • 28th October, 2015: Final abstract submission to AAG, via www.aag.org. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Nick Malleson. Neither the organizers nor the AAG will edit the abstracts.
  • 29th October, 2015: AAG registration deadline. Sessions submitted to AAG for approval.

Organizers

  • Nick Malleson, School of Geography, University of Leeds  
  • Alex Singleton, School of Environmental Sciences, University of Liverpool  
  • Mark Birkin, Director of the University of Leeds Institute for Data Analytics (LIDA)  
  • Paul Longley, Department of Geography, University College London  
  • Andrew Crooks, Department of Computational and Data Sciences, George Mason University.   
  • Seth Spielman, Geography Department, University of Colorado

Tuesday, March 31, 2015

Exploring Creativity and Urban Development with Agent-Based Modeling

There is considerable debate about "creative cities" and relatively few agent-based models that explore such ideas from the bottom up. To that end we have recently published a paper in the Journal of Artificial Societies and Social Simulation entitled: "Exploring Creativity and Urban Development through Agent-Based Modeling"

In the paper we introduce the Creative City Model, an exploratory ABM to simulate the theoretical relationship between land-use regulation, urban mobility and societal tolerance on the economic performance of cities. The model is based on simplified assumptions from our empirically informed understanding of urban morphology, economic geography and the diffusion of creativity from human interactions.  It contributes to the growing literature exploring the dynamic socioeconomic processes underlying urban economic growth through computer simulation. Specifically the model offers a new lens to view the diffusion of creativity through knowledge spillovers under various scenarios from the bottom up. Through experimentation, the model suggests the existence of tradeoffs between the desire for social equity, estimated via rent affordability, and the rapid diffusion of creativity. Below you can find the abstract of the paper.

Abstract:
Scholars and urban planners have suggested that the key characteristic of leading world cities is that they attract the highest quality human talent through educational and professional opportunities. They offer enabling environments for productive human interactions and the growth of knowledge-based industries which drives economic growth through innovation. Both through hard and soft infrastructure, they offer physical connectivity which fosters human creativity and results in higher income levels. When combined with population density, socioeconomic diversity and societal tolerance; the elevated interaction intensity improves productivity. In many developing country cities however, rapid urbanization is increasing sprawl and causing deteriorating in public service standards. We further explore these insights by creating a stylized agent-based model where heterogeneous and independent decision-making agents interact under the following scenarios: (1) improved urban transportation investments; (2) mixed land-use regulations; and (3) reduced residential segregation. We find that any combination of scenarios resulting in conditions of intense human interaction results in greater economic growth. However, model results also demonstrate a clear trade-off between rapid economic progress and socioeconomic equity mainly due to the crowding out of low- and middle-income households from clusters of creativity. 

Key Words: Agent-Based Modeling; Developing Countries; Urban; Segregation; Land-use; Transportation
The movie below shows a typical simulation run of the model.


Further details about the model along with its ODD is available from the OpenABM website (click here).

Full Reference:
Malik, A.A., Crooks, A.T., Root, H.L. and Swartz, M. (2015), Exploring Creativity and Urban Development through Agent-Based Modeling, Journal of Artificial Societies and Social Simulation. 18 (2): 12. Available at http://jasss.soc.surrey.ac.uk/18/2/12.html

Thursday, January 23, 2014

Creative Cities

Over the last year I have been working with Ammar Malik and Hilton Root on a small project which explores the relationship between human creativity and urban development via an agent-based model. We have recently just completed a working paper for this project entitled: "Can Pakistan have creative cities? An agent based modeling approach with preliminary application to Karachi" which was published by the International Food Policy Research Institute (IFPRI). For interested readers, below is the abstract to the paper:
Scholars and urban planners have suggested that the key characteristic of leading world cities is that they attract the best and brightest minds. As home to the creative classes, which consist of professionals working in knowledge-based industries, they are the bedrocks of prosperity and drivers of innovation. They not only provide unrivaled educational and professional opportunities, but also the best entertainment facilities such as art galleries, theaters and restaurants. Both through hard and soft infrastructure, residents of these cities enjoy seamless connectivity which fosters human creativity. When combined with population density, socio-economic diversity and societal tolerance, the elevated interaction intensity diffuses creativity and boosts economic productivity. However, rapidly urbanizing cities in the developing world are struggling to maintain adequate service delivery standards. The form and function of many cities are increasingly marred by congestion, sprawl and socioeconomic segregation, preventing them from experiencing expected productivity gains associated with urbanization. We operationalize these insights by creating a stylized agent-based model of a theoretical city, inspired by social complexity theory and the new urban literature. A virtual environment is designed where heterogeneous and independent decision-making agents interact under various policy scenarios, such as greater urban transportation investments and altered land-use regulations. By creating typical urban conditions, we conclude that the combination of mixed land-use, improved access to urban mobility and high societal tolerance levels foster creativity led urban economic growth.

For more details see:
Malik, A.A., Crooks, A.T. and Root, H.L. (2013), Can Pakistan have Creative Cities? An Agent Based Modeling Approach with Preliminary Application to Karachi. Pakistan Strategy Support Program Working Paper 13, International Food Policy Research Institute (IFPRI), Washington, DC. (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.


Monday, October 07, 2013

Interurban Simulation Models

Following on from a previous post about the rise of civilizations. I thought it was worth blogging of another publication which I just came across in Environment and Planning A which demonstrates the utility of agent-based modeling for looking at urban systems by Denise Pumain and Lena Sanders. While I have blogged about the SimPop models before (here), which explore a systems of cities and how they evolve in space and time. In this recent paper the authors compare and contrast ABM with other styles of modeling. To quote from the paper:
"Agent-based models are increasingly used by urban specialists, supplanting the simulation models using differential equations which were more popular earlier. These models already made reference to the theories of self-organisation and to mechanisms of evolution not so far from those used today to describe the emergence of macroscopic properties or structures in a bottom-up process from interactions operating at the microlevel. Moreover there is less difference than often suggested in the literature between the two forms of modelling – differential equations and multi-agent models—in the way they integrate principles of urban theory. To test this assumption, we compare models made of systems of differential equations (Allen’s model firmly rooted in self-organisation theory and the model developed by Weidlich and Haag, affiliated to synergetic theory) with multi-agent models (SIMPOP family) designed to meet the same task: simulating the differentiated dynamics of urban entities over the medium to long term from their functional economic specialisation. We show that multi-agent systems are providing interesting solutions for the modelling method, because of their greater ability to simulate the emergence of geographical macro structures from different levels of interaction." 


Full Reference:
Pumain, D. and  Sanders, L. (2013). Theoretical principles in interurban simulation models: a comparison. Environment and Planning A, 45(9), 2243-2260.
 

Thursday, May 31, 2012

Call for papers: Intelligent Agents in Urban Simulations and Smart Cities


Readers of the blog might be interested in the "Intelligent Agents in Urban Simulations and Smart Cities" workshop at the  ECAI-2012 Conference in Montpellier, France, August 27 or 28, 2012.

To quote from the call for papers:
In this workshop, we intend to address specific methodological and technological issues raised by the deployment of agents in rich environments such as virtual cities. We will welcome contributions tackling issues related to reactive agents, cognitive architectures, the capacity to scale up to handle thousands or hundreds of thousands of agents, the ability to simulate realistic group behaviors which might be judged non rational, etc., all in the context of urban agents. We will also welcome contributions showcasing original applications of agent and multi-agent technologies within urban simulations, be it for design, planning, education, training, or entertainment. 

Workshop Chairs: 
  • Vincent Corruble (contact), Université Pierre et Marie Curie (Paris 6), France 
  • Fabio Carrera, Worcester Polytechnic Institute (WPI), USA 
  • Stephen Guerin, Santa Fe Complex, USA 

Important Dates: 
  • *6 June 2012*: Workshop paper submission deadline 
  • 28 June 2012: Notifications to authors (subject to modification) 
  • 13 July 2012: Submissions of camera-ready copies of selected papers 
  • 27 or 28 August 2012: Workshop date 

Submission information: 

Monday, April 27, 2009

The Urban Experience


Reading up on American cities, I just come across a very interesting book entitled "The Urban Experience: Economics, Society, and Public Policy" by Barry Bluestone, Mary Huff Stevenson and Russell Williams.

The books description: The Urban Experience provides a study of metropolitan areas by combining economic principles, social insight, and political realities with an appreciation of public policy to understand how U.S. cities and suburbs function in the 21st century (click here to read more).