Friday, September 21, 2018

Exodus 2.0: Crowdsourcing Geographical and Social Trails of Mass Migration

Readers of the blog might know we have an interest in volunteered geographic information, social media and Web 2.0 technologies and how they can be used to explore urban systems. Recently however, we turned our focus on how such information and technologies can be used to explore and understand mass migrations.

To this end we recently had a paper published in the Journal of Geographical Systems entitled "Exodus 2.0: Crowdsourcing Geographical and Social Trails of Mass Migration". We adopt the term Exodus 2.0 to refer to this new migration paradigm in the digital age, whereby information is a commodity in the migration process.

Given the nature of migration processes, it is possible to explore them across two key dimensions: geographical and situational. The geographical dimension is associated with the physical migration pathways migrants take from a country of origin to a destination site (often through a number of intermediate “stop” sites). The situational dimension is associated with the social connectivity of moving migrant populations, the conditions on the ground, and the activities that take place as part of migration efforts (including the root conditions, proximate conditions and triggering events).
Factors that potentially cause refugee production and
 mass movement based on identified factors detailed by
Clark (1989) and Zottarelli (1998).
In the paper, we use the ongoing Syrian humanitarian crisis as a case study to to explore how the factors that potentially causes refugee production and mass movement  can be gleamed from new sources of data. Specifically, the potential of crowd-generated data—especially open data, volunteered geographic information and social media content (e.g. OpenStreetMap, Flickr, Twitter and Instagram)  to provide information about migration processes.  Through a series of case studies  we show how such data (when combined with more traditional data sources) offers a new lens to study such the geographical and situational dimensions of mass migration. Finally we discuss  how such data could be used to inform migration modeling. If we have not bored you yet and you are interested in finding out more about this line of inquiry, below we provide the abstract to the paper, some of the figures which go along with our analysis for studying the refugee production and movement. Finally, we also provide the full reference and a link to the paper. 

Abstract:
The exodus of displaced populations is a recurring historical phenomenon, and the ongoing Syrian humanitarian crisis is its latest incarnation. During such mass migration events, information is an essential commodity. Of particular importance is geographical (e.g., pathways and refugee camps) and social (e.g., refugee activities and networking) information. Traditionally, such information had been produced and disseminated by authorities, but a new paradigm is emerging: Web 2.0 and mobile computing technologies enable the involved stakeholder communities to produce, access, and consume migration-related information. The purpose of this article is to put forward a new typology for understanding the factors around migration and to examine the potential of crowd-generated data—especially open data and volunteered geographic information—to study such events. Using the recent wave of migration to Europe from the Middle East and northern Africa as a case study, we examine how migration-related information can be dynamically mined and analyzed to study the migrants’ pathways from their home countries to their destination sites, as well as the conditions and activities that evolve during the migration process. These new data sources can provide a deeper and more fine-grained understanding of the migration process, often in real-time, and often through the eyes of the communities affected by it. Nevertheless, this also raises significant methodological and technical challenges for their future use associated with potential biases, data quality issues, and data processing.

Keywords: Refugees, Forced migration, Humanitarian crisis, Volunteered geographic information, Crowdsourcing, Social media, GIS, Web 2.0.
Cumulative flow (2011–2015) illustrating Syrian forced migration to neighboring countries and other destination countries. Line thickness indicates increasing number of persons migrating.

Retweet network of geolocated Twitter microblogs that are discussing opinions, news and retweeting information related to “refugee” in multiple languages from May to August 2017.

A concept graph illustrating the associations between a keyword related to root factors of mass migration such as poverty (“welfare”) to other keywords, as they appear in our Twitter data corpus. The color of the node refers to specific themes: locations (green), actors (dark red), topics (red), entities and individuals (blue), concepts (white), and events (yellow). Red edges represent active associations between terms; gray edges represent inactive associations between terms.

An agent-based model of migration: top: the spatial environment, where the lines represent migration pathways, and the nodes represent number of migrants. Purple nodes represent final destination sites, red nodes show migrant deaths, and green nodes show migrants en route (source: Hu 2016).

Full Reference: 
Curry, T., Croitoru, A., Crooks, A.T. and Stefanidis, A. (in press), Exodus 2.0: Crowdsourcing Geographical and Social Trails of Mass Migration, Journal of Geographical Systems. DOI: https://doi.org/10.1007/s10109-018-0278-1 (pdf)

Wednesday, September 19, 2018

An Agent-Based Model of Rural Household Adaptation to Climate Change

Geographical location of the South Omo Zone of Ethiopia
While 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 you can probably gather from the title of this post).  To this end, Ates Hailegiorgis, Claudio Cioff-Revilla and myself recently had a paper published in the Journal of Artificial Societies and Social Simulation entitled: An Agent-Based Model of Rural Household Adaptation to Climate Change

The purpose of the model is to explore how climate change could impact rural societies in less developed countries whose livelihoods rely on subsistence agriculture. It has been suggested that climate change will place unprecedented stress on rural communities, as it will alter their resource base without giving them sufficient time for adaptation. While rural systems have developed various adaptive strategies over many generations in order to survive, the alteration of any resources can significantly affect even highly regarded and accepted customs, and may lead to the displacement of populations along with other severe humanitarian consequences.

In this paper we focus on the South Omo Zone of Ethiopia which covers an area of 2.3 million hectares and is located in the southern part of Ethiopia. Climate change is expected to play a significant role in shaping the future socio-ecological setting of the region and to explore this we devlepd a model  in the MASON simulation system, including its geographical information system (GIS) extension, GeoMASON called OMOLAND-CA (OMOLAND Climate Change Adaptation). Results from the model show that successive episodes of extreme events (e.g., droughts) can affect the adaptive capacity of households in the region, causing them to migrate from the region. While at the same time the rural communities manage to endure in spite of such harsh climatic change conditions.

Below you can read the abstract of the paper, see some of the figures including the models high-level architecture, along with the household decision-making process, some results from various scenarios and a link to the model and the full reference of the paper.

Abstract: 
Future climate change is expected to have greater impacts on societies whose livelihoods rely on subsistence agricultural systems. Adaptation is essential for mitigating adverse effects of climate change, to sustain rural livelihoods, and ensure future food security. We present an agent-based model, called OMOLAND-CA, which explores the impact of climate change on the adaptive capacity of rural communities in the South Omo Zone of Ethiopia. The purpose of the model is to answer research questions on the resilience and adaptive capacity of rural households with respect to variations in climate, socioeconomic factors, and land-use at the local level. Our model explicitly represents the socio-cognitive behavior of rural households toward climate change and resource flows that prompt agents to diversify their production strategy under different climatic conditions. Results from the model show that successive episodes of extreme events (e.g., droughts) affect the adaptive capacity of households, causing them to migrate from the region. Nonetheless, rural communities in the South Omo Zone, and in the model, manage to endure in spite of such harsh climatic change conditions.

Keywords: Climate Change Adaptation, Agent-Based Modeling, Socio-Cognitive Behavior

High-level architecture of the OMOLAND-CA model.

Household decision-making sequence for each time period in the model.

Population migration over time with different climatic conditions: a) 50% reduction, b) 70% reduction, c) 90% reduction of rainfall with different drought frequencies.

Livestock growth over time with different climatic conditions: a) 50% reduction, b) 70% reduction, c) 90% reduction of rainfall with different drought frequencies.
Simulation results of the frequency of crop planted per hectare.

In keeping with many of our agent-based models that we have created, a full description of the model (using the Overview, Design concepts, and Details plus Decision (ODD+D) protocol), along with its source code and data needed to run the model can be found at: https://www.openabm.org/model/5734/ .

Full Reference:
Hailegiorgis, A.B., Crooks, A.T. and Cioff-Revilla, C. (2018), An Agent-Based Model of Rural Households’ Adaptation to Climate Change, Journal of Artificial Societies and Social Simulation, 21 (4): 4. Available at http://jasss.soc.surrey.ac.uk/21/4/4.html.
 

Friday, August 31, 2018

A Million Page Views: Thank you

While I started blogging during my PhD (actually the first real post was from February 21st 2006), for some reason I only started recording statistics about the blog in May 2010. This month marks the milestone of over 1,000,000 page views. So I thought I would write a post that reflects this milestone. 

Initially, I started blogging as a way to keep track of agent-based models (ABM), example applications and toolkits I found interesting and this trend has continued over the years (with a few variations along the way). Many of my initial posts where focused on ways of utilizing agent-based models and integrating geographical information into such models.  However, over time I have also branched out into writing about other areas such as the utility of volunteered geographical information and social media to monitor, analyze and model urban systems and how one can use such data to study the connections between people via social networks

From looking at the statistics, since 2010 the most popular post (as you can see from the image below) is that of  Agent-based modeling in ArcGIS  (unfortunately this work is currently not being updated:( ) but it does show an interest in agent-based modeling in more of the mainstream GIS (or at least from some people). The other posts in the top 10 relate to modeling and analyzing urban systems and the people within them in some shape or form including a book book review I did for  JASSS. Perhaps my favorite post in this top 10 is that of Modeling Human Behavior   inspired by a book chapter written Bill Kennedy entitled 'Modelling Human Behavior in Agent-Based Models'.
With respect to the audience of the blog, nearly 48% of page views come from the United States while the reminder come from all around the world (as you can see from from the figure to the left, including France, Russia and the Ukraine). The most popular search terms for people coming to the blog include "agent based modeling", "NetLogo GIS" "NetLogo Examples" along with terms such such as urban analytics and big data. 
Looking at what web browsers and which operating systems people are using to access the site (which takes me back to my Masters thesis when I was working on developing web-mapping features for the Gazetteer for Scotland), Chrome makes up 43% of all page views  followed by Firefox (29%) and IE (16%). While for operating systems, 54% of visitors are using Windows, followed by Macintosh (27%) and Linux (8%).

While I mentioned above my favorite post in the top 10, reflecting on which post I refer most people to, it has to be the one entitled Applications of Agent-based Models because it shows how agent-based models are being used in a variety of settings. Looking back on the evolution of GIS and agent-based modeling since I started blogging, its impressive to see how different toolkits have started to utilize GIS. For example my first post was a hack on how to integrate GIS into NetLogo, from backspaces.net. Since then NetLogo, MASON and other platforms such as GAMA have evolved to allow making it (relatively) easier for the integration and exploration of geographical information and agent-based models. 

Moreover, when I started writing about this, there were very few example GIS and agent-based models (expect from Repast ones) or resources to get up and running with agent-based models but over time this has changed with more and more people sharing their models (thanks to things like GitHub (e.g. mason models, OpenABM)). There has also been a number of good text books written on GIS and ABM (and there is a great one coming soon from us) along with more blogs (e.g. Simulating Complexity) and courses being taught (e.g. Agent-Based Modeling Short Course at SESYNC). Lets hope this growth continues and thank you for reading and visiting this blog. If you would like to share your work on ABM and GIS please feel free to contact me or leave a comment a below.

Wednesday, July 18, 2018

Online Vaccination Discussion and Communities in Twitter

Continuing on our work of exploring health related issues in social media, Xiaoyi Yuan and myself had a paper accepted at the 9th International Conference on Social Media and Society. In our paper entitled: "Examining Online Vaccination Discussion and Communities in Twitter"  we examined the communication patterns of anti-vaccine and pro-vaccine users on Twitter by studying the retweet network from 660,892 tweets related to the measles, mumps, and rubella (MMR) vaccine published by 269,623 users using supervised learning to identify clusters of users based on their opinions (i.e. a pro-vaccine, anti-vaccine, or neutral user). 

The overall methodology can be seen in Figure 1 and more details can be found in the paper. Our data was collected using the GeoSocial Gauge System, however, since tweets are short and their content diverse, the data corpus needed to be cleaned so that the tweets could then be converted to features (e.g., unigrams or bigrams). After which we were able to use such features for training a variety of classifiers (i.e., logistic regression, support vector machine (linear and non-linear kernel), k-nearest neighbors, nearest centroid, and Na├»ve Bayes) to identify opinion groups. After this, we moved from on from identifying each user’s opinion to construct a retweet network in order to understand how in-group and cross-group communicate in the committees detected via retweet network. By carrying out this analysis we discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group, while users with neutral opinions are distributed across communities. Below you can read our abstract, see some results from our study and the full reference (and link) to the paper.


Figure1: Steps used in our study to unveil the communication patterns of pro-vaccine and anti-vaccine users on Twitter
 Abstract:
Many states in the US allow a “belief exemption” for measles, mumps, and rubella (MMR) vaccines. People’s opinion on whether or not to take the vaccine could have direct consequences in public health— once the vaccine refusal of a group within a population is higher than what herd immunity can tolerate, a disease can transmit fast causing large scale of disease outbreaks. Social media has been one of the dominant communication channels for people to express their opinions of vaccination. Despite governmental organizations’ effects of disseminating information of vaccination benefits, anti-vaccine sentiment is still gaining its momentum, especially on social media. This research investigates the communicative patterns of anti-vaccine and pro-vaccine users on Twitter by studying the retweet network from 660,892 tweets related to MMR vaccine published by 269,623 users after the 2015 California Disneyland measles outbreak. Using supervised learning, we classified the users into anti-vaccination, neutral to vaccination, and pro-vaccination groups. Using a combination of opinion groups and retweet network structural community detection, we discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group, while users with neutral opinions are distributed across communities. For most cross-group communication, it was found that pro-vaccination users were retweeting anti-vaccination users than vice-versa. The paper concludes that anti-vaccine Twitter users are highly clustered and enclosed communities, and this makes it difficult for health organizations to penetrate and counter opinionated information. We believe that this finding may be useful in developing strategies for health communication of vaccination and overcome some the limits of current strategies.

Key Words: Anti-vaccine movement, Twitter, social media, opinion classification
Figure 2: Network visualizations of the four largest communities. A: is colored by the belonging to a specific structural community and; B: is colored by belonging to opinion groups

Figure 3: Distributions of opinion groups in the four largest structural community

Full Reference:
Yuan, X. and Crooks, A.T. (2018), Examining Online Vaccination Discussion and Communities in Twitter, Proceedings of the 9th International Conference on Social Media and Society, Copenhagen, Denmark, pp 197-206. (pdf)

Wednesday, July 04, 2018

MASON Update

At the upcoming Multi-Agent-Based Simulation (MABS) workshop, we have a paper entitled "The MASON Simulation Toolkit: Past, Present, and Future" in which we discuss MASON's development history, its design and (probably more interesting) where MASON is going. This includes:
  1. Making it more robust (i.e. easier to run parameter tests), 
  2. Making it distributed in order to  run large scale models including geographical explicit ones along for optimization and validation purposes.
  3. Making it more coder-friendly by adding code templates that allow users to generate code skeletons for common MASON patterns and a way to easily record outputs and statistics.
  4. Making it more community-friendly by hopefully developing a special online repository to enable researchers to distribute models as jar files along with education aids and examples. Relating to this last point we have added a number of example models (code and data) from our own research to GitHub, see: https://github.com/eclab/mason/tree/master/contrib/geomason/sim/app/geo and the data to run the models is either there or here https://cs.gmu.edu/~eclab/projects/mason/extensions/geomason/geodemodata.zip (note this is 1.5 GB).
Below you can read the abstract from the paper along with a link to the paper itself.

Example Applications of MASON

Abstract
MASON is a widely-used open-source agent-based simulation toolkit that has been in constant development since 2002. MASON’s architecture was cutting-edge for its time, but advances in computer technology now offer new opportunities for the ABM community to scale models and apply new modeling techniques. We are extending MASON to provide these opportunities in response to community feedback. In this paper we discuss MASON, its history and design, and how we plan to improve and extend it over the next several years. Based on user feedback will add distributed simulation, distributed GIS, optimization and sensitivity analysis tools, external language and development environment support, statistics facilities, collaborative archives, and educational tools.

Keywords: Agent-Based Simulation, Open Source, Library

Full Reference:
Luke, S., Simon, R., Crooks, A.T., Wang, H., Wei, E., Freelan, D., Spagnuolo, C., Scarano, V., Cordasco, G. and Cioffi-Revilla, C. (2018), The MASON Simulation Toolkit: Past, Present, and Future, 19th International Workshop on Multi-Agent-Based Simulation (MABS2018), Stockholm, Sweden. (pdf)

Available on Github


This research is supported by the National Science Foundation (Grant 1727303).