Friday, October 19, 2018

New Paper: Scalability in the MASON Multi-agent Simulation System

Previously we posted about our work on advancing MASON, part of which we briefly discussed making it distributed in order to  run large scale models including geographical explicit ones along for optimization and validation purposes. To this end we recently had a paper accepted and presented at the  22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2018),  entitled "Scalability in the MASON Multi-agent Simulation System". 

In this paper we describe a distributed version of the MASON, and use three existing MASON models: HeatBugs, Flockers, and CampusWorld, to demonstrate how Distributed MASON achieves highly scalable performance, in terms of linear performance increases as the size of the simulations grow using Amazon Web Services.  Below you can read the abstract of the paper, see  some figures relating to how we go about data management and some of the results. Finally, at the bottom of the post you can see the full reference and access the paper itself.

Abstract:
This paper describes Distributed MASON, a distributed version of the MASON agent-based simulation tool. Distributed MASON is architected to take advantage of well known principles from Parallel and Discrete Event Simulation, such as the use of Logical Processes (LP) as a method for obtaining scalable and high performing simulation systems. We first explain data management and sharing between LPs and describe our approach to load balancing. We then present both a local greedy approach and a global hierarchical approach. Finally, we present the results of our implementation of Distributed MASON on an instance in the Amazon Cloud, using several standard multi-agent models. The results indicate that our design is highly scalable and achieves our expected levels of speed-up.




Full Reference:
Wang, H., Wei, E., Simon, R., Luke, S., Crooks, A.T., Freelan, D. and Spagnuolo, C. (2018), Scalability in the MASON Multi-agent Simulation System, The 22nd International Symposium on Distributed Simulation and Real Time Applications, Madrid, Spain. (pdf)

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

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 Naïve 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).

Wednesday, June 13, 2018

Call for Papers: GeoSim’18



The GeoSim’18 workshop focuses on all aspects of simulation as a general paradigm to model and predict spatial systems and generate spatial data. New simulation methodologies and frameworks, not necessarily coming from the SIGSPATIAL community, are encouraged to participate. Also, this workshop is of interest to everyone who works with spatial data. The simulation methods that will be presented and discussed in the workshop should find a wide application across the community by producing benchmark datasets that can be parameterized and scaled. Simulated data sets will be made available to the community via the website.

The workshop seeks high-quality full (8 pages) and short (4 pages) papers that will be peer-reviewed. Once accepted, at least one author is required to register for the workshop and the ACM SIGSPATIAL conference, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.

Example topics include, but not limited to:
  • Applications for Spatial Simulation
  • Agent Based Models for Spatial Simulation
  • Multi-Agent based Spatial Simulation
  • Big Spatial Data Simulation
  • Spatial Data/Trajectory Generators
  • Road Traffic Simulation
  • Environmental Simulation
  • Geoinformation Systems using Spatial Simulation
  • Interactive Spatial Simulation
  • Spatial Simulation Parallelization and Distribution
  • Geo-Social Simulation and Data Generators
  • Social Unrest and Riot Prediction using Simulation
  • Spatial Analysis based on Simulation
  • Behavioral Simulation
  • Verifying, and Validating Spatial Simulations
  • Urban Simulation
  •  
Important Dates:
  • Submission deadline: August 20, 2018
  • Notification: September 20, 2018
  • Workshop date: November 06, 2018
For more information please visit www.geosim.org

https://www.dropbox.com/s/lgt6ip1u9lxvgwa/GeoSim18_cfp_final.pdf?dl=0

Tuesday, May 29, 2018

Spatial Agent-based Models of Human-Environment Interactions: Spring 2018

During the past spring semester I taught a class entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students are expected to complete a end of semester project, in this case, develop an agent-based model that explores some aspect of related to the course theme of human-environment interactions. In the movie below is a selection of these projects can be seen. The projects ranged from urban growth, housing markets, the adoption of solar energy, employment opportunities, populations at risk from terrorism, commuting, to the spread of diseases. Many of the models were done in NetLogo, MASON and some in Python including using MESA.




I would like to thank the Students of CSS 645: Spatial Agent-based Models of Human-Environment Interactions for their participation in the class.

Monday, April 09, 2018

Predicting Rice Cropping Patterns around Poyang Lake, China using a Cellular Automata Model

http://mason.gmu.edu/~qtian2/QingTianSummary.html
Normally, on this blog, the focus is on agent-based modeling and GIS. However, I am not agnostic to other modeling approaches especially cellular automata (CA) modeling (which I have written about in the past).  To this end, Rui Zhang, Qing Tian, Luguang Jiang, Shuhua Qi, Ruixin Yang and myself recently had a paper published in Land Use Policy entitled: "Projecting Cropping Patterns around Poyang Lake and Prioritizing Areas for Policy Intervention to Promote Rice: A Cellular Automata Model" In the paper we explore current land use patterns in the Poyang Lake Region (PLR) of China. Specifically, we focus on current rice production in the region and what this might look like in the future (especially the impact of farmland consolidation) by using an CA model (built on the DINAMICA EGO platform). Below you can read the abstract to our paper, along with some figures, outlining our study area, the model design and development, along with observed current day and predicted rice cropping patterns around Poyang Lake. Finally at the bottom of the post I provide the full reference and a link to the paper.

Abstract:
Rural households’ cropping choices are increasingly influenced by nonfarm activities across the developing world, raising serious concerns about food security locally and globally. In China, rapid urbanization has led to agricultural decline in some regions. To stimulate agriculture, the Chinese government has recently increased its effort in farmland consolidation by providing special support to large farms in an attempt to address land-use inefficiency associated with small farming operations in rural China. Focusing on the Poyang Lake Region (PLR), we develop a Cellular Automata (CA) model to explore future agricultural land use and examine the impact of farmland consolidation. PLR is an important rice production base in Jiangxi Province and China. In PLR rice can be grown once a year on a plot, called one-season rice, or twice a year on the same plot, called two-season rice. Our CA model simulates the transition between one-season and two-season rice. Emphasizing distributional differences in the region, we use the modeling results to identify five areas where rice cultivation is (i) relatively stable for one-season rice, (ii) more likely to be one-season rice, (iii) of equal probability for either type, (iv) more likely to be two-season rice, and (v) relatively stable for two-season rice. We then explore the characteristics of these areas in terms of biophysical and geographical environments to provide further insights into how the government may prioritize areas for interventions to effectively promote food production and environmental sustainability. The analysis also indicates a positive effect of farmland consolidation on promoting rice production.

Keywords: Agricultural Land Use; Cellular Automata; Food Security; Environmental Sustainability; Farmland Consolidation; China.
Poyang Lake Region. The left map shows its location in China. Rice cropping patterns shown on the right map were interpreted from Landsat images in 2013.
Model design and development

Rice cropping patterns around Poyang Lake. The map on the left is observed land use in 2013 and on the right prediction for 2033.

Full Reference:
Zhang, R., Tian, Q., Jiang, L., Crooks, A.T., Qi, S. and Yang, R. (2018), Projecting Cropping Patterns around Poyang Lake and Prioritizing Areas for Policy Intervention to Promote Rice: A Cellular Automata Model, Land Use Policy, 74: 248-260. (pdf)
As always, any thoughts or comments are most welcome.

Saturday, April 07, 2018

Innovations in Urban Analytics @ the AAG

Symposium on New Horizons in Human Dynamics Research: Innovations in Urban Analytics Sessions

As part of the Symposium on New Horizons in Human Dynamics Research we have organized 5 sessions around Innovations in Urban Analytics. These sessions will take place on Thursday 12th of April from 8am to 7pm in the Bayside A, Sheraton, 4th Floor.

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.

These sessions will bring together the latest research in urban analytics. In particular the papers will engage in 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 visualisation;
  • Frequentist and Bayesian approaches to modelling cities.


Symposium on New Horizons in Human Dynamics Research: Innovations in Urban Analytics I - Agent-Based Modelling and Machine Learning

Time: 8:00 AM
Location: Bayside A, Sheraton, 4th Floor

Chair: Nick Malleson.

Andrew Crooks, Annetta Burger, Xiaoyi Yuan and William Kennedy:
Title: The Generation and Application of Large Scale Synthetic Populations for Disease Outbreaks and Disasters.
Achilleas Psyllidis and Hendra Hadhil Choiri:
Title: A Convolutional Neural Network-based Model for Predicting the Perceived Attractiveness of Urban Places
Jonathan Reades, Jordan de Souza and Elizabeth Sklar:
Title: Predicting Neighbourhood Change in London with Random Forests  
Nick Malleson, Tomas CrolsJonathan Ward and Andrew Evans:
Title: Forecasting Short-Term Urban Dynamics: Data Assimilation for Agent-Based Modelling
Tomas Crols and Nick Malleson:
Title: Calibrating an Agent-Based Model of the Ambient Population using Big Data  

Symposium on New Horizons in Human Dynamics Research: Innovations in Urban Analytics II - Transport and Accessibility 


Time: 10:00 AM
Location: Bayside A, Sheraton, 4th Floor

Chair: Andrew Crooks

Ed Manley:
Title: Analysing Cities through Cognitive Models of Geographic Space.
Alison Heppenstall, Yuanxuan Yang and Alexis Comber:
Title: Who, why and when? Using smart card and social media data to reveal flows through urban spaces. 
Kerry Nice, Jason Thompson, Jasper Wijnands, Gideon Aschwanden and Mark Stevenson:
Title: The Paris end of town? Urban typology through machine learning.
Henrikki Tenkanen, Olle JärvMaria Salonen, Rein Ahas and  Tuuli Toivonen:
Title: Dynamic cities: Spatial accessibility as a function of time.
Thomas Redfern, Nicolas MallesonGillian Harrison, Frances Hodgson, Alexis Comber and Susan Grant-Muller:
Title: Monitoring, modelling and understanding the complex spatiotemporal dynamics of air pollution exposure, transport policies, and health burdens. 

Symposium on New Horizons in Human Dynamics Research: Innovations in Urban Analytics III - Data Synergies and Emerging Insights


Time: 1:20 PM
Location: Bayside A, Sheraton, 4th Floor

Chair: Alison Heppenstall

Tuuli Toivonen, Henrikki Tenkanen, Vuokko HeikinheimoOlle Järv and Tuomo Hiippala:
Title: Social media content for understanding the spatial patterns of urban leisure time 
Emmanouil Tranos:
Title: Doing internet archaeology to reveal the evolution of the digital economy in the UK.
Daniel Arribas-Bel:
Title: "Nowcasting" house prices at high spatiotemporal resolution.
Nik Lomax and Andrew Smith:
Title: High resolution demographic projections for infrastructure planning.

Discussant: Alison Heppenstall


Symposium on New Horizons in Human Dynamics Research: Innovations in Urban Analytics IV


Time: 3:20 PM
Location: Bayside A, Sheraton, 4th Floor

Chair: Ed Manley.

Boyana Buyuklieva and Adam Dennett:
Title: Making Metrics Meaningful: A Discussion of Implementation and Reproducibility Using Measures of Migration
Marina Toger, Ian Shuttleworth and John Östh:
Title: How average is average? Temporal patterns and variability in mobile phone data
Alec Davies, Mark Green and Alex Singleton
Title: Using new forms of data to investigate self-medication.
Ellen Talbot:
Title: Estimating Energy Consumption Through Smart Meter and Socio-demographic Datasets.
Discussant Ed Manley.


Symposium on New Horizons in Human Dynamics Research: Innovations in Urban Analytics V: Panel Session

Time: 5:20 PM
Location: Bayside A, Sheraton, 4th Floor

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 panel session concludes the 'Innovations in Urban Analytics' paper theme.

Panelists:
Alex Singleton, Andrew Crooks, Boyana Buyuklieva, Tuuli Toivonen and Moira Zellner


Session Sponsors:
Organizers: 

Monday, March 12, 2018

Call for Papers: ABMUS 2018

The ABMUS2018 workshop on Agent-based modelling of urban systems will held on 14/15 July 2018 in Stockholm, Sweden. The workshop is part of the Federated AI Meeting (FAIM2018), which includes the AAMAS2018 conference and IJCAI-ECAI (International Joint Conference on Artificial Intelligence and the European Conference on Artificial Intelligence). It is the follow-up of ABMUS2016, ABMUS2017.

The central goal of this workshop is to bring together the community of researchers and practitioners who use agent-based models and multi-agent systems to understand and manage cities and urban infrastructure systems. Through the exchange of ideas and state-of-the-art within this area, we will pool together current thinking to discuss avenues of fruitful research and methodological challenges we face in building robust, realistic, and trusted models of urban systems.

Agent-based modelling has proven itself to be a useful technique for understanding and predicting changes and impact of urban form and policy on urban systems. However, recognised challenges remain in designing, developing and implementing trusted models that can be used by industry and governments to enhance decision-making. This workshop invites submissions from researchers and practitioners who use agent-based models and agent systems to understand, explore, and manage cities and urban infrastructure systems.

In particular, we invite presentations that describe efforts and challenges in design, development and deployment of urban system models that have balanced the provision of mechanistic insight into complex challenges facing urban systems vs practical challenges of producing 'numbers' for real-world decision support for industry and government.

Workshop topics include, but are not limited to, the following:
  • Large scale urban simulation applications
  • Spatially explicit micro-simulation modelling
  • Agent-based modelling of urban transport, land-use, housing, energy, health, etc.
  • Simulation of household behaviour and technology adoption
  • Localized population synthesis
  • Multi-scale urban systems (temporal and spatial)
  • Social simulation of demographic transitions
  • Model development and co-development processes and protocols
  • Data structures for simulating urban environments
  • (Multi-)agent systems to provide decision support in e.g. transport, energy and air quality
  • Connection of simulation models to social and geographical theory
  • Government and industry engagement in model development and uptake
  • Processes of model co-development to enhance decision-making in urban systems
  • Development in model interfaces and engagement that enhance model uptake
If accepted, each presenter will be given a short time slot (max 10 minutes) to introduce their paper and/or case study, followed by 5-10 minutes in which presenters will share their views on the balancing insight and numbers theme. After three presentations there will be 20-30 minutes of group discussion in which presenters will act as panel members.

Papers should be submitted as an extended abstract (2-4 pages) through the workshop website. Your abstract should include a Title as well as all authors and affiliations. It should articulate the objectives of the paper and provide a brief but thorough description of the research related to the theme of the workshop and the expected gain by those attending the presentation. Accepted authors will be invited to submit a full paper after the workshop to be included in the post-workshop proceedings.

For details on how to submit please see http://modelling-urban-systems.com/abmus2018 and for more information

Thursday, February 22, 2018

Call for papers: GIScience 2018 Workshop: Rethinking the ABCs: Agent-Based Models and Complexity in the age of Big Data

At the upcoming GIScience 2018 conference, Raja Sengupta and Liliana Perez are organizing a workshop entitled: Rethinking the ABCs: Agent-Based Models and Complexity in the age of Big Data.  To quote from the workshop website:
"The scope of this workshop is to explore novel complexity science approaches to dynamic geographic phenomena and their applications, addressing challenges and enriching research methodologies in geography in a Big Data Era."
For more information, check out the workshop homepage:  https://ledgeumontreal.org/bigcomplexitygisci2018. Note the deadline for papers is May 1st 2018.

Thursday, February 01, 2018

The Future of GEOINT: Data Science Will Not Be Enough







In the 2018 State and Future of GEOINT Report published by the The United States Geospatial Intelligence Foundation (USGIF) we had a paper accepted entitled "The Future of GEOINT: Data Science Will Not Be Enough". In the paper we discuss how there has been a deluge of spatial-temporally enabled data in the last several years with no signs of slowing down (e.g. by the year 2020, many experts predict the global universe of accessible data to be on the order of 44 zettabytes—44 trillion gigabytes). With this growth in data there has been steady uptake of data scientists in the GEOINT community because of their  ability to navigate petabytes of raw and unstructured data, then clean, analyze, and visualize the data. However,  we argue that we must go beyond just statistically analyzing data collected on the world around us to truly gain an understanding of the people who inhabit the world.  In order to do this, we suggest that future GEOINT analysts should not only have skills in data science but also be able to apply advanced computational methods, such as agent-based modeling, social network analysis, geographic information science, and deep learning algorithms (i.e. geospatial computational social science) to explore and test hypotheses based on social and geographic theory to truly achieve an understanding of human interactions.  

Full Reference: 
Parrett, C.M., Crooks, A.T. and Pike, T. (2018), The Future of GEOINT: Data Science Will Not Be Enough. The State and Future of GEOINT 2018 Report, The United States Geospatial Intelligence Foundation, Herndon, VA. pp 12-15. (pdf)
As always, any thoughts or comments are welcome. 

Thursday, January 25, 2018

Place-Based Simulation Modelling

Nick Malleson, Alison Heppenstall and myself recently had a chapter published in the Oxford Research Encyclopedia of Criminology and Criminal Justice, entitled "Place-Based Simulation Modelling:  Agent-Based Modelling and Virtual Environments". In the chapter, we discuss how agent-based modeling (ABM) can be used for modeling spatial patterns of crime.

Specifically we discuss the motivation for ABM in criminology. We then introduce the core components of ABM and how one can structure such models and represent human behavior in them (e.g. using Beliefs-Desires-Intentions (BDI) or Physical conditions, Emotional states, Cognitive capabilities and Social status" (PECS) frameworks). Next, we discuss space can be represented in agent-based models, both from an abstract sense but also accurate spatial environments (i.e. by using GIS data). The chapter then moves onto briefly discussing tools for the implementation of agent-based models (e.g. MASON, NetLogo, Repast) before we provide a critique of ABM for modeling spatial crime, including its appeal (e.g. the emergence of crime from the bottom up), the difficulties of using ABM (e.g adequately defining behavior, access to data etc.) and finally the ethical implications of using agent-based models for studying crime. Below you can read the official summary of the chapter along with its full citation.

Chapter Summary: 
Since the earliest geographical explorations of criminal phenomena, scientists have come to the realization that crime occurrences can often be best explained by analysis at local scales. For example, the works of Guerry and Quetelet—which are often credited as being the first spatial studies of crime—analyzed data that had been aggregated to regions approximately similar to US states. The next major seminal work on spatial crime patterns was from the Chicago School in the 20th century and increased the spatial resolution of analysis to the census tract (an American administrative area that is designed to contain approximately 4,000 individual inhabitants). With the availability of higher-quality spatial data, as well as improvements in the computing infrastructure (particularly with respect to spatial analysis and mapping), more recent empirical spatial criminology work can operate at even higher resolutions; the “crime at places” literature regularly highlights the importance of analyzing crime at the street segment or at even finer scales. These empirical realizations—that crime patterns vary substantially at micro places—are well grounded in the core environmental criminology theories of routine activity theory, the geometric theory of crime, and the rational choice perspective. Each theory focuses on the individual-level nature of crime, the behavior and motivations of individual people, and the importance of the immediate surroundings. For example, routine activities theory stipulates that a crime is possible when an offender and a potential victim meet at the same time and place in the absence of a capable guardian. The geometric theory of crime suggests that individuals build up an awareness of their surroundings as they undertake their routine activities, and it is where these areas overlap with crime opportunities that crimes are most likely to occur. Finally, the rational choice perspective suggests that the decision to commit a crime is partially a cost-benefit analysis of the risks and rewards. To properly understand or model these three decisions it is important to capture the motivations, awareness, rationality, immediate surroundings, etc., of the individual and include a highly disaggregate representation of space (i.e. “micro-places”). Unfortunately one of the most common methods for modeling crime, regression, is somewhat poorly suited capturing these dynamics. As with most traditional modeling approaches, regression models represent the underlying system through mathematical aggregations. The resulting models are therefore well suited to systems that behave in a linear fashion (e.g., where a change in model input leads to a predictable change in the model output) and where low-level heterogeneity is not important (i.e., we can assume that everyone in a particular group of people will behave in the same way). However, as alluded to earlier, the crime system does not necessarily meet these assumptions. To really understand the dynamics of crime patterns, and to be able to properly represent the underlying theories, it is necessary to represent the behavior of the individual system components (i.e. people) directly. For this reason, many scientists from a variety of different disciplines are turning to individual-level modeling techniques such as agent-based modeling.

Keywords: agent-based modelling, crime simulation, travel to crime, virtual environment, NetLogo, virtual laboratory.

Figure 1: The process of initializing, running, and analyzing an agent-based model.

Full Reference:
Malleson, N., Heppenstall, A. and Crooks, A.T. (2018). Place-Based Simulation Modelling: Agent-Based Modelling and Virtual Environments, Oxford Research Encyclopedia of Criminology and Criminal Justice, Oxford University Press. DOI: 10.1093/acrefore/9780190264079.013.319 (pdf)

Wednesday, January 24, 2018

A Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums

Regular readers of this site might of noticed that we have an interest in slums. In the past this has focused on modeling them from an agent-based perspective, comparing volunteered geographical information to more authoritative data on slums, to that of attempting to come up with a Slum Severity Index. However, more recently we have taken to looking at how remote sensing approaches have been and can be used to detect and map slums.

To this end we recently had a review paper accepted in Urban Systems entitled "A Critical Review of High and Very High Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities". In this paper we carry out a comprehensive review of studies that have used high and very high resolution (H/VH-R) remote sensing techniques to detect and map slums (along with their global footprint). We discuss approaches used (e.g. multi-scale, image texture analysis, landscape analysis, object-based image analysis, building feature extraction, data mining, socio-economic measures) using H/VH-R imagery for identifying and mapping slums, listing what are the limitations and advantages of each. After this, we  discuss emerging sources of geospatial data that should we thing should be considered (e.g., volunteer geographic information, VGI, social media) in conjunction with growing trends and advancements in technology (e.g., geosensor networks, unmanned aerial vehicles (UAVs) or “drones) when trying to map and monitor slums. We argue that it is only through such data integration and analysis that we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality. Below you can read the abstract of the paper and see some of the figures we use to support our discussion, along with the full reference.

Abstract: Slums are a global urban challenge, with less developed countries being particularly impacted. To adequately detect and map them, data is needed on their location, spatial extent and evolution. High- and very high-resolution remote sensing imagery has emerged as an important source of data in this regard. The purpose of this paper is to critically review studies that have used such data to detect and map slums. Our analysis shows that while such studies have been increasing over time, they tend to be concentrated to a few geographical areas and often focus on the use of a single approach (e.g., image texture and object-based image analysis), thus limiting generalizability to understand slums, their population, and evolution within the global context. We argue that to develop a more comprehensive framework that can be used to detect and map slums, other emerging sourcing of geospatial data should be considered (e.g., volunteer geographic information) in conjunction with growing trends and advancements in technology (e.g., geosensor networks). Through such data integration and analysis we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality, thus fostering the creation of new approaches to address this challenge.
Keywords: high and very high resolution imagery; remote sensing, slums; geosensor networks; image analysis.

Global distribution of urban and slum populations.

Country level distribution of H/VH-R studies (studies published between 1997-2016).

OSM and Google Maps views of Kibera slum (a) Top:Left OSM and right Google Maps (b) Bottom:Left OSM and right Google Maps.

Full Reference:
Mahabir, R., Croitoru, A., Crooks, A.T., Agouris, P. and Stefanidis, A. (2018), A Critical Review of High and Very High Resolution Remote Sensing  Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities, Urban Science. 2(1), 8; doi:10.3390/urbansci2010008 (pdf)
As always, any thoughts or comments are most welcome.