Thursday, September 22, 2016

The study of slums as social and physical constructs: challenges and emerging research opportunities

Conceptual model for integrating social
and physical constructs to monitor,
analyze and model slums.


Continuing our research on slums, we have just had a paper published in the journal Regional Studies, Regional Science entitled "The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities". In this open access publication we review past lines of research with respect to studying slums which often focus on one of three constructs: (1) exploring the socio-economic and policy issues; (2) exploring the physical characteristics; and, lastly, (3) those modelling slums. We argue that while such lines of inquiry have proved invaluable with respect to studying slums, there is a need for  a  more  holistic  approach  for  studying  slums  to truly understand  them at the local, national and regional scales. Below you can read the abstract of our paper:
"Over 1 billion people currently live in slums, with the number of slum dwellers only expected to grow in the coming decades. The vast majority of slums are located in and around urban centres in the less economically developed countries, which are also experiencing greater rates of urbanization compared with more developed countries. This rapid rate of urbanization is cause for significant concern given that many of these countries often lack the ability to provide the infrastructure (e.g., roads and affordable housing) and basic services (e.g., water and sanitation) to provide adequately for the increasing influx of people into cities. While research on slums has been ongoing, such work has mainly focused on one of three constructs: exploring the socio-economic and policy issues; exploring the physical characteristics; and, lastly, those modelling slums. This paper reviews these lines of research and argues that while each is valuable, there is a need for a more holistic approach for studying slums to truly understand them. By synthesizing the social and physical constructs, this paper provides a more holistic synthesis of the problem, which can potentially lead to a deeper understanding and, consequently, better approaches for tackling the challenge of slums at the local, national and regional scales."

Keywords: Slums; informal settlements; socio-economic; remote sensing; crowdsourced information; modelling.
Framework for studying and understanding slums.


We hope you enjoy this paper and we wound be interested in receiving any feedback.

Full Reference:
Mahabir, R., Crooks, A.T., Croitoru, A. and Agouris, P. (2016), “The Study of Slums as Social and Physical Constructs: Challenges and Emerging Research Opportunities”, Regional Studies, Regional Science, 3(1): 737-757. (pdf)

Wednesday, September 07, 2016

Book Review: Rethinking Global Land Use in an Urban Era

https://mitpress.mit.edu/books/rethinking-global-land-use-urban-era
Recently I  reviewed a great book entitled “Rethinking Global Land Use in an Urban Era” edited  by Karen Seto and Anette Reenberg (2014) for the Journal of Regional Science. Readers can read my review below.

The beginning of the 21st Century marked a milestone in human history. For the first time, more than half of the world’s population lived in urban areas (3.9 billion). This trend is expected to continue in the foreseeable future with 6.3 billion people living in cities by 2050 (United Nations, 2014). This growth will cause more urban land to be developed during the first 30 years of the 21st century than in all of human history (Angel et al., 2011). Combine this unprecedented urban expansion with global population growth, which is expected to grow from today’s 7.3 to 11.2 billion by 2100 (United Nations, 2015), and we are faced with unprecedented challenges and questions to be asked with respect to land-use in the 21st century. For example, how much living space will be needed to accommodate this growing population or how much land will be needed to feed such a population? Or how does urban growth in one country impact agricultural production and deforestation in the other parts of the world? To answer these questions, we need to understand the complexity of land competition from social, economic, and environmental perspectives at the local, national, and international levels and the connections between them.

In their edited book, Rethinking Global Land Use in an Urban Era, Karen Seto and Anette Reenberg bring together 17 chapters from 50 experts from a variety of fields to explore global land dynamics in the 21st century. The first chapter acts as an introduction and scene-setting to the following chapters: it identifies current trends reshaping land-use locally and globally such as urbanization and the growing integration of economies and markets (e.g. telecoupling, see Seto et al., 2012), but also argues that there is a need to rethink land change science in a time when more and more people are living in cities. Specifically, they argue one should look at land-use through four lenses (which are the major sections in the book): land-use competition; distal land connections; decision making, governance, and institutions; and, finally, urbanization and land-use.

The first section of the book focuses on land-use competition, specifically what types of land-use competition exist (such as forest vs. agricultural or urban vs. agriculture), and discusses how local land-use change is increasingly being caused by global factors (chapter 2). Chapter 3 addresses food security with respect to the growing population and discusses the need for intensification of production. Chapter 4 discusses the issue of finite land resources and competition for land—such as production vs. production (e.g. food vs. fuel) or production vs. conservation (e.g. food production vs. conservation). What is interesting about this chapter is that the competition for land is not just local but also global, due to the growing number of sovereign wealth funds and multi-national corporations and the increasing degree of interconnections between places. The section concludes with chapter 5, which offers an in-depth discussion of land-use competition between food production and urban expansion in China, specifically the effects of urbanization on the loss of cultivated land for food production.

Source: Seto, K.C. and Reenberg, A. (eds.) (2014), Rethinking Global Land Use in an Urban Era, MIT Press, Cambridge, MA.

The second section of the book explores distal land connections. It opens with a chapter that reviews the globalization of economic flows and the impact of these forces on land-use transitions (i.e. land-use and land-cover change). Chapter 7 introduces applications based on the telecoupling framework to land-change science. It makes a compelling argument considering not just coupled human-environmental systems (where the focus is on local conditions) but also causes that emanate from distant locations to truly understand land-change. This theme continues in chapter 8, which outlines analytical approaches to study telecoupling, while chapter 9 uses palm oil as a case study of distal land connections. In essence, the consumers of palm oil live far from the source; thus many consumers do not immediately feel the impacts of palm oil production on land-use change.

In the third section of the book, the focus is on decision-making, governance, and institutions. Chapter 10 discusses the emergence of global land governance as a result of land grabbing by foreign investors or governments (see GRAIN, 2008), which is prompting states and global civil society to devise new global land governance instruments, while chapter 11 explores large-scale land (grabbing) transactions with a specific emphasis on the actors and their interactions. Chapter 12 focuses on private market-based regulations (such as the Forest Stewardship Council) and what they mean for land-use governance at the local and international level. The final chapter in this section focuses on changes in land-use governance in an urban era. It discusses how governance mechanisms that manage land-use are changing from territorial organizations to global industries that are tied to specific resource flows between urban and rural areas.

The final section of the book turns to urbanization and land-use. Chapter 14 reviews major contemporary urban patterns and processes related to urbanization, such as central place theory, and shows how advances in technology and infrastructure challenge such established theories. The next chapter discusses how urban land-use is unique in terms of form, size, and shape of cities and asks what will the future hold? Will cities be sprawling or compact? An interesting fact brought up in chapter 15 is that currently less than five percent of the earth’s surface is urban and with the urban population predicted to grow to 5 billion by 2030, the urban footprint will still be less than 10 percent (Seto et al., 2011). The final chapter in this section proposes a framework that moves away from looking at land as discrete categories but instead as a continuum with respect to sustainable development. The book concludes with a chapter written by the editors, which not only provides a summary of what was presented, but reemphasizes the interconnected nature of land-use and the need to study future global land change and urbanization from a multidisciplinary perspective.

Overall this is a timely, relevant, and thought-provoking collection of papers which not only explores urbanization and food production using case studies from around the world as well as the connections between cities and distant places, but also lays the foundation for new ways of thinking about land-use sustainability in the coming decades. In my opinion, this book would be a great resource for scholars interested in current state of the art of land-use science and a good textbook for any course exploring land-use and land-cover change in the 21st century.


References:
  • Angel, Shlomo, Jason Parent, Daniel L. Civco, Alexander Blei, and David Potere. 2011. “The Dimensions of Global Urban Expansion: Estimates and Projections for All Countries, 2000–2050,” Progress in Planning, 75(2): 53-107.
  • GRAIN. 2008.Seized: The 2008 Landgrab for Food and Financial Security,” Available at http://bit.ly/28Nc7xK [Accessed on September, 7th, 2015].
  • Seto, Karen C., Michail Fragkias, Burak Güneralp, and Michael K. Reilly. 2011. “A Meta-analysis of Global Urban Land Expansion,” PloS One, 6(8): e23777.
  • Seto, Karen C., Anette Reenberg, Christopher G. Boone, Michail Fragkias, Dagmar Haase, Tobias Langanke, Peter Marcotullio, Darla K. Munroe, Branislav Olah, and David Simon. 2012. “Urban Land Teleconnections and Sustainability,” Proceedings of the National Academy of Sciences, 109(20): 7687-7692.
  • United Nations. 2014. World Urbanization Prospects: The 2014 Revision, Department of Economic and Social Affairs, New York, NY.
  • United Nations. 2015. World Urbanization Prospects: The 2015 Revision, Department of Economic and Social Affairs, New York, NY.

Citation: 
Crooks, A.T. (2016), Crooks on Seto and Reenberg (eds.): Rethinking Global Land Use in an Urban Era, Journal of Regional Science, 56 (4): 723-725. (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. (2016), Generating and Analyzing Spatial Social Networks. Computational and Mathematical Organization Theory, DOI: 10.1007/s10588-016-9232-2 (pdf)


Thursday, August 25, 2016

Call for Papers - Symposium on Human Dynamics in Smart and Connected Communities: Agents - the ‘atomic unit’ of social systems?



Call for Papers - Symposium on Human Dynamics in Smart and Connected Communities: Agents - the ‘atomic unit’ of social systems?

We welcome paper submissions for our session(s) at the Association of American Geographers Annual Meeting on 5-9 April, 2017, in Boston.

Session Description:

By defining a social system as a collection of agents, individuals and their behaviors/decisions become the driving force of these systems. Complex global phenomena such as collective behaviors, extensive spatial patterns, and hierarchies are manifested through agent interaction in such a way that the actions of the parts do not simply sum to the activity of the whole. This allows unique perspectives into the inner workings of social systems, making agent-based modelling (ABM) a powerful and appealing tool for understanding the drivers of these systems and how they may change in the future.

What is noticeable from recent applications of ABM is the increase in complexity (richness and detail) of the agents, a factor made possible through new data sources and increased computational power. While there has always been ‘resistance’ to the notion that social scientists should search for some ‘atomic element or unit’ of representation that characterizes the geography of a place, the shift from aggregate to individual mark agents as a clear contender to fulfill the role of ‘atom’ in social simulation modelling. However, there are a number of methodological challenges that need to be addressed if ABM is to fully realize its potential and be recognized as a powerful tool for policy modelling in key societal issues. Most pressing are methods to accurately identify, represent, and evaluate key behaviors and their drivers in ABM.

We invite any papers that contribute towards this wide discussion ranging from epistemological perspectives of the place of ABM, extracting behavior from novel and established data sets to new, intriguing applications to establishing robustness in calibrating and validating ABMs.

Please e-mail the abstract and key words with your expression of intent to Andrew Crooks (acrooks2@gmu.edu) by 22nd October, 2016 (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:
An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions.

Timeline summary:
  • 20th October, 2016: Abstract submission deadline. E-mail Andrew Crooks by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 24th October, 2016: Session finalization and author notification
  • 26th October, 2016: 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 Andrew Crooks. Neither the organizers nor the AAG will edit the abstracts.
  • 27th October, 2016: AAG registration deadline. Sessions submitted to AAG for approval.
  • 5-9th April, 2017: AAG Annual Meeting.

Organizers:
  • Andrew Crooks, Department of Computational and Data Sciences, George Mason University.
  • Alison Heppenstall, School of Geography, University of Leeds.
  • Nick Malleson, School of Geography, University of Leeds
  • Paul Torrens, Department of Computer Science and Engineering, Tandon School of Engineering, New York University.
  • Sarah Wise, Centre for Advanced Spatial Analysis (CASA), University College London.

Monday, August 15, 2016

Summer Projects

Over the summer, Arie Croitoru and myself took part in the George Mason University Aspiring Scientists Summer Internship Program. We worked with three very talented high-school students who over the course of the seven and a half week program produced some excellent research around the areas of agent-based modeling and social media analysis. An overview of their work can be seen in the posters and abstracts that the students produced at the end of the internship.

Lawrence Wang explored how social media could be used with respect to predicting election results under a project entitled "And the Winner Is? Predicting Election Results using Social Media". Below you can read Lawrence's abstract and see his poster.

"The 2012 U.S. presidential election demonstrated how Twitter can serve as a widely accessible forum of political discourse. Recently, researchers have investigated whether social media, particularly Twitter, can function as a predictive tool. In the past decade, multiple studies have claimed to successfully predict the results of elections using Twitter data. However, many of these studies fail to account for the inherent population bias present in Twitter data, leading to ungeneralizable results. In this project, I investigate the prospects of using Twitter data as an alternative to poll data for predicting the 2012 presidential election. The tweet corpus consisted of tweets published one month before the November election day. Using VADER, a sentiment analysis tool, I analyzed over 140,000 tweets for political sentiment. I attempted to circumvent the Twitter population bias by comparing age, race, and gender metrics of the Twitter population with that of the U.S. population. Furthermore, I utilized Bayesian inference with prior distributions from the results of the 2008 presidential election in order to mitigate the effects of limited tweet data in certain states. The resulting model correctly predicted the likely outcomes of 46 of the 50 states and predicted that President Obama would be reelected with a probability of 0.945. Such a model could be used to explore the forthcoming elections. " 


In a second project, Varun Talwar, explored how knowledge bases could be utilized to better contextualize social media discussions with a project entitled "Context Graphs: A Knowledge-Driven Model for Contextualizing Twitter Discourse." Below you can read Varun's project abstract and his end of project poster.

"Introduction: User posted content through online social media (SM) platforms in recent years has emerged as a rich field for narrative analysis of topics captured during the discussion discourse. In particular, collective discourse has been used to manually contextualize public perception of health related events.

Objective: As SM feeds tend to be noisy, automated detection of the context of a given SM discourse stream has proven to be a challenging task. The primary objective of this research is to explore how existing knowledge bases could be utilized to better contextualize SM discussions through topic modeling and mining. By utilizing such existing knowledge it would then be possible to explore to what extent a given discourse is related to a known or a new context, as well as compare and contrast SM discussions through their respective contexts.

Methods: In order to accomplish these goals this research proposes a novel approach for contextualizing SM discourse. In this approach, topic modeling is combined with a knowledgebase in a two-step process. First, key topics are extracted from a SM data corpus by applying a statistical topic-modeling algorithm, a process that also results in data dimensionality reduction. Once a set of salient topics are extracted, each topic is then used to mine the knowledge base for sub graphs that represent the contextual linkages between knowledge elements. Such sub-graphs can then further disambiguate the topic modeling results, and be utilized for qualifying context similarity across SM discussions.

Results: The time-series analysis of the Twitter discourse via graph-matching algorithms reveals the change in topics as evidenced by the emergence of the terms “pregnancy” and “abortion” as information about the virus propagated through the Twitter community. "




Elizabeth Hu explored the current migration crisis in Europe in a project entitled "Across the Sea: A Novel Agent-Based Model for the Migratory Patterns of the European Refugee Crisis". Below is Elizabeth's abstract, poster and an example model run.

"Since 2010, a growing number of refugees have sought asylum in European nations, fleeing violence and military conflict in their home countries. Most of the refugees originate from Syria, Iraq, Afghanistan, and African nations. The vast majority of refugees risk their lives in the popular yet perilous Mediterranean Sea Route often prone to boat accidents and subsequent deaths of migrants.  The flow of millions of refugees has introduced a humanitarian crisis not seen since World War II. European nations are struggling to cope with the influx of refugees through various border policies.

In order to explore this crisis, a geographically explicit agent-based model has been developed to study the past and future patterns of refugee flows. Traditional migration models, which represent the population as an aggregate, fail to consider individual decision-making processes based on personal status and intervening opportunities. However, the novel agent-based model developed here of migration allows population behavior to emerge as the result of individual decisions. Initial population, city, and route attributes are based upon data from the UNHCR, EU agencies, crowd-sourced databases, and news articles. The agents, refugees, select goal destinations in accordance with the Law of Intervening Opportunities. Thus, goals are prone to change with fluctuating personal needs. Agents choose routes not only based on distance, but also other relevant route attributes. The resulting migration flows generated by the model under various circumstances could provide crucial guidance for policy and humanitarian aid decisions."



The movie below gives a sense of the migration paths the refugees are taking.