Friday, June 30, 2017

From Cyber Space to Physical Space Disease Outbreaks


At the upcoming 2017 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation  conference (or SBP-BRiMS 2017 for short), Xiaoyi Yuan and myself will present a paper entitled: "From Cyber Space Opinion Leaders and the Diffusion of Anti-vaccine Extremism to Physical Space Disease Outbreaks". In the paper we explore how online discussions with respect to vaccinations can potentially impact on the spread of a disease. Below you can read the abstract to our paper, see the basic model logic and movie of a single simulation. If you are interested in finding out more about the model or running it yourself, you can do so here: https://www.openabm.org/model/5509/


Measles is one of the leading causes of death among young children. In many developed countries with high measles, mumps, and rubella (MMR) vaccine coverage, measles outbreaks still happen each year. Previous research has demonstrated that what underlies the paradox of high vaccination coverage and measles outbreaks is the ineffectiveness of “herd immunity”, which has the false assumption that people are mixing randomly and there’s equal distribution of vaccinated population. In reality, the unvaccinated population is often clustered instead of not equally distributed. Meanwhile, the Internet has been one of the dominant information sources to gain vaccination knowledge and thus has also been the locus of the “anti-vaccine movement”. In this paper, we propose an agent-based model that explores sentiment diffusion and how this process creates anti-vaccination opinion clusters that leads to larger scale disease outbreaks. The model separates cyber space (where information diffuses) and physical space (where both information diffuses and diseases transmit). The results show that cyber space anti-vaccine opinion leaders have such an influence on anti-vaccine sentiments diffusion in the information network that even if the model starts with the majority of the population being pro-vaccine, the degree of disease outbreaks increases significantly. 

Keywords: Agent-based modeling Information networks Infectious disease transmission.



Full Reference:  
Yuan, X. and Crooks, A.T. (2017), From Cyber Space Opinion Leaders and the Spread of Anti-Vaccine Extremism to Physical Space Disease Outbreaks, in Lee, D., Lin, Y., Osgood, N. and Thomson, R. (eds.) Proceedings of the 2017 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Springer, New York, NY., pp. 114-119. (PDF).

Monday, June 05, 2017

Comparing four modeling approaches using a Susceptible-Infected-Recovered (SIR) epidemic model

Over the years several modeling styles have been developed but often it is unclear what are the differences between them. In this joint post, we, (Yang Zhou and myself) would like to compare and contrast four modeling approaches widely used in Computational Social Science, namely: System Dynamics (SD) models, Agent-based Models (ABM), Cellular Automata (CA) models, and Discrete Event Simulation (DES). For a review of their undying mechanisms and core components of each readers are referred to Gilbert and Troitzsch's (2005) "Simulation for the Social Scientist"

To compare and contrast the differences in how these models work and how their underlying mechanisms generate outputs, we needed a common problem to test them against with the same set of model parameters. While one could choose a more complex example, here we decided to chose one of the simplest models we know. Specifically, we chose to model the spread of a disease specifically using a Susceptible-Infected-Recovered (SIR) epidemic model. Our inspiration for this came from the SD model outlined in the great book “Introduction to Computational Science: Modeling and Simulation for the Sciences” by Shiflet and Shiflet (2014) which was implemented in NetLogo from the accompanying website. For the remaining models (i.e. the ABM, CA, and DES) we created models from scratch in NetLogo. Below we will introduce how we built each model, before showing the results from the four models with the same set of parameters, which allows us to compare the results of the models. The source code, further documentation for the four models can be found over at Yang Zhou's website and GitHub page.


The System Dynamics Model

In the system dynamics model from Shiflet and Shiflet (2014), one person is infected at start. Infected people can infect susceptible people. The population of infected will always increase by (number of infected * number of susceptible * InfectionRate * change in time dt). The infected people may recover. The amount of people that will recover in an iteration is always equal to (number of infected * RecoveryRate * change in time dt). Figure 1 illustrates the system dynamics process while Figure 2 shows the SIR process as a flowchart.

Figure 1. System Dynamics process (source: Shiflet and Shiflet, 2014)



Figure 2. System Dynamics flowchart


The Agent-based Model

As in the case for the SD model, at the beginning of the simulation, one agent is infected. Agents are randomly distributed on the landscape, and in the beginning of each iteration, they turn to a random direction and move forward by one cell. During each iteration, an infected agent may infect other agents on the same cell. This is different from how the SD model works, specifically the probability of getting infected. In the SD model, the infection rate is the infection rate on the entire population. In the ABM, the probability of becoming infected is equal to the infection rate divided by the probability of an agent to be in the same cell, multiplied by the change in time. Each infected agent has a probability to recover in each time period, which equals to the recovery rate times the change in time. The equations in the ABM are the following:

Where P(same cell) = probability to be on the same cell, equals 1 divided by total number of cells; dt = change in time. Figure 3 illustrates the agent decision process while Figure 4 shows the display of the ABM

Figure 3. Agent-based Modeling: agent decision process

Figure 4. Display of the ABM. Green = susceptible. Red = infected. Blue = recovered.

The Cellular Automata Model

At the beginning of the simulation, one cell is infected. During each iteration (dt), the infected cell can infect other cells in its Moore neighborhood (i.e. 8 surrounding cells). The landscape will be a n by n square, and n is equal to the square root of the number of people to be created at the beginning of the simulation. Wrapping is enabled both horizontally and vertically. Similar to the ABM, we would like to map the probability of becoming infected to the one in the SD model. In the CA model, the probability of becoming infected is equal to the infection rate divided by the probability to be in the Moore neighborhood, multiplied by the change in time. Each infected cell has a probability to recover in each time period, which is based on the recovery rate multiplied by the change in time. The equations here are:


Figure 5 shows the changing process of the cells while Figure 6 shows the display of the CA model.

Figure 5. Cellular Automata cell changing process

Figure 6. Display of the CA model. Green = susceptible. Red = infected. Blue = recovered.

The Discrete Event Simulation Model

In a Discrete Event Simulation model (aka. queuing model), there are three abstract types of objects: 1) servers, 2) customers, 3) queues, which is quite different from the CA and ABMs.

So to implement a SIR model as a DES Servers are the processes of becoming infected and recovering. The durations people stay with the servers represent the process of becoming infected and becoming recovered. Customers are susceptible people to be infected, and infected people are waiting to recover. We assume there are two queues in this model. As susceptible objects (i.e. individuals) are created, queues for infection are formed while people are waiting to be infected. On the other hand, as people get infected, they form a second queue waiting to recover. During each iteration (dt), each object in queue has a probability to get become infected. Each infected agent object has a probability to recover which is equal to RecoveryRate. After agents recover, they enter the sink of recovered people. The equations can be written as follow:


While the whole process is illustrated in Figure 7.
Figure 7. Discrete Event Simulation process.

Results from the Implementations


Now that the models have been briefly described. We turn to how using the same set of parameters lead to different results. The default parameters being used in each model are: number of susceptible people at setup = 2500, Infection Rate = 0.002, Recovery Rate = 0.5, change of time (dt) = 0.001, and the numbers of people in each status are recorded. Since the SD model has no randomness and will always give the same result, it is run only once. Each of the other three models were run for 10 times (feel free to run them more if you wish), and then we took the average of the ten results and show them in Figure 8. The stop condition is that no individual left to be infected.

Figure 8. Results for the different models. Clockwise from top left: SD model, ABM, DES and CA



In the four models, we observe the same pattern: the number of susceptible people decreases, the number of infected people increases first and then decrease again, and the number of recovered people increase over time. However, each model realization also shows a lot of differences in how such patterns play out.

First of all, the SD model has the smallest number of iterations before no one is infected. The number of iterations shown on the graph are the average of the ten runs, since the runs range from smaller to larger numbers (except for the SD model, which only has one run). The SD model only took 17451 iterations to stop, while the ABM took 19145 iterations (on average), the DES model took 18645 iterations (on average). The CA model took the longest time on average for no more individuals to be infected, it took 25680 iterations (on average).

The results of the SD, ABM and DES models while appearing to be very similar to each other. In the sense, that the number of infected people increase fast at first and reaches a peak number of over 1500 at more than 2000 iterations (2272 for SD, 2403 for ABM, 2538 for DES). On the other hand, in the CA model, the number of infected people increases much slower due to the diffusion mechanism of the CA model and never reaches an amount as high as in the former models.

An important characteristic of the SD model is that there is no randomness in the model, so no matter how many times you run this model, you will get the same result. In the other three models, getting infected or recover always depend on a probability function, so there is difference in every run.

Furthermore, people in the SD model and the DES model are homogeneous, and everyone has the same probability to becoming infected or recovering from an infection, although these rates change over time, they do not vary among the different people in the population. On the other hand, in the ABM and the CA model, people (represented by moving agents or static cells) are heterogenous in the sense that they have different locations. Only susceptible people around an infected individual can be infected. It is interesting that when people can move around, like in the ABM, the result is similar to the SD model, though the ABM takes a little more time to recover (19145 iterations in ABM vs. 17451 iterations in SD). When people are static and the number of people on the same space is limited (one cell in one space in this case), like in the CA model, the infection process becomes slower and it takes longer for everyone to recover.

To test how the models are sensitive to a specific parameter we now present what happens if we increase the infection rate in each model from 0.002 to 0.02 and show the results shown in Figure 9. As to be expected as the infection rate increased, the number of susceptible people decrease at a much faster rate. However, the SD, the ABM, and the DES models are still similar to each other, while the infection in the CA model is slower. The average number of iterations for these models are: 15807 (SD), 15252 (ABM), 16937 (CA), 16677 (DES). By increasing the infection rate the total number of iterations of each model has decreased, with the CA model still taking the longest time to converge. The peak of infected people in each model are on average: 2363 people at 255 iterations (SD), 2310 people at 363 iterations (ABM), 2035 people at 1019 iterations (CA), 2340 people at 286 iterations (DES). The CA model takes a longer time and reaches a lower peak.

Figure 9. Results for the different models with infection rate = 0.02. Clockwise from top left: SD model, ABM, DES and CA.

These models are only simple examples of how a SIR model can be implemented in different modeling techniques, but in reality, if we were to model disease propagation in more detail we would need to consider many other things such as people could be both moving through space (i.e. traveling to work) and static (i.e. staying at home), and the capacity of each cell is always limited to some amount.


References: 
Gilbert, N. and Troitzsch, K.G. (2005), Simulation for the Social Scientist (2nd Edition), Open University Press, Milton Keynes, UK.

Shiflet, A.B. and Shiflet, G.W. (2014), Introduction to Computational Science: Modeling and Simulation for the Sciences (2nd Edition), Princeton University Press, Princeton, NJ.
More information about the models and to download them please visit Yang Zhou's website.

Thursday, April 20, 2017

Zika in Twitter: Health Narratives


In the paper we explored how health narratives and event storylines pertaining to the recent Zika outbreak emerged in social media and how it related to news stories and actual events.

Specifically we combined actors (e.g. twitter uses), locations (e.g. where the tweets originated) and concepts (e.g. emerging narratives such as pregnancy) to gain insights on the mechanisms that drive participation, contributions, and interactions on social media  during a disease outbreak. Below you can read a summary of our paper along with some of the figures which highlight our methodology and findings.  

An overview of the Twitter narrative analysis approach, starting with data collection, and proceeding with preprocessing and data analysis to identify narrative events, which can be used to build an event storyline.


Abstract:
 
Background: The recent Zika outbreak witnessed the disease evolving from a regional health concern to a global epidemic. During this process, different communities across the globe became involved in Twitter, discussing the disease and key issues associated with it. This paper presents a study of this discussion in Twitter, at the nexus of location, actors, and concepts.
Objective: Our objective in this study was to demonstrate the significance of 3 types of events: location related, actor related, and concept- related for understanding how a public health emergency of international concern plays out in social media, and Twitter in particular. Accordingly, the study contributes to research efforts toward gaining insights on the mechanisms that drive participation, contributions, and interaction in this social media platform during a disease outbreak. 
Methods: We collected 6,249,626 tweets referring to the Zika outbreak over a period of 12 weeks early in the outbreak (December 2015 through March 2016). We analyzed this data corpus in terms of its geographical footprint, the actors participating in the discourse, and emerging concepts associated with the issue. Data were visualized and evaluated with spatiotemporal and network analysis tools to capture the evolution of interest on the topic and to reveal connections between locations, actors, and concepts in the form of interaction networks. 
Results: The spatiotemporal analysis of Twitter contributions reflects the spread of interest in Zika from its original hotspot in South America to North America and then across the globe. The Centers for Disease Control and World Health Organization had a prominent presence in social media discussions. Tweets about pregnancy and abortion increased as more information about this emerging infectious disease was presented to the public and public figures became involved in this. 
Conclusions: The results of this study show the utility of analyzing temporal variations in the analytic triad of locations, actors, and concepts. This contributes to advancing our understanding of social media discourse during a public health emergency of international concern.

Keywords: Zika Virus; Social Media; Twitter Messaging; Geographic Information Systems.

Spatiotemporal participation patterns and identifiable clusters over 4 of our twelve week study. The top left panel shows the data during the first week, and time progresses from left to right and from top to bottom towards .

Subsets of the full retweet network pertaining to the WHO (left) and CDC (right), and clusters identified within them. Magenta clusters are centered upon health entities, green upon news organizations, orange upon political entities.

Visualizing a narrative storyline across locations (blue), actors (red), and concepts (green).

Full Reference:
Stefanidis, A., Vraga, E., Lamprianidis, G., Radzikowski, J., Delamater, P.L., Jacobsen, K.H., Pfoser, D., Croitoru, A. and Crooks, A.T. (2017). “Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts”, JMIR Public Health and Surveillance, 3 (2): e22. (pdf)

As normal, any feedback or comments are most welcome. 

Saturday, April 08, 2017

Talk from the AAG

The last few days I have been attending the  Association of American Geographers (AAG) Annual Meeting in Boston. A common theme at the AAG sessions I attended  (to me at least) seemed to  be the rise of new sources of data which give us new ways to explore geographical problems and the challenges of working with bigger data sets. Perhaps where this was most explicitly expressed were in the Geographic Data Science sessions which was pitched to be at the nexus of data science and geography.

While at the meeting I participated in a panel under the theme of "Geographic Data Science", and as part of the Symposium on Human Dynamics in Smart and Connected Communities, I co-organized two sessions entitled Agents - the 'atomic unit' of social systems? which also included Agent-Bingo.  Finally I and gave a presentation of our current research at Mason, entitled "Megacities through the Lens of Computational Social Science", more details can be seen below. For those wanting to know more on the synthetic population generation, click here.

Geographic Data Science Panel


Megacities through the Lens of Computational Social Science

Abstract:

Currently there are over 35 megacities, cities with over 10 million inhabitants, and the number of such cities are expected to grow in the coming years. These habitats represent many challenges from an agent-based modeling perspective. Their size and density, the diverse behaviors of their inhabitants, and their evolving social network of communities along with multiple interacting subsystems need to be understood, captured and modeled. To capture and link the dynamics that shape and form these systems, we must grapple with them in their entirety. While there have been many models applied to specific subsystems of megacities (e.g. traffic, disease spread, urban growth etc.) their interactions often go untouched.

The lens of computational social science (CSS), the interdisciplinary science of complex social systems and their investigation through computational modeling and related techniques can be used to understand and model megacities. Given the advances in computational power and the availability of fine scale datasets, what are the opportunities offered to us with respect to exploring megacities? In an attempt to answer this question we will demonstrate how new sources of data (e.g. volunteered geographical information) can be fused with more traditional data (e.g. census data) to create the basis of a megacity model both in terms of its physical environment and its social environment. We will then show results from a simulated disaster explores how people potentially react and behave to the evolving crisis within a megacity.

Keywords: Megacities, GIS, Agent-based modeling, Social Networks, Behavior



Full References:
Crooks A.T., Kennedy W.G., Burger, A. Oz, T. and Heppenstall, A. (2017), Megacities through the Lens of Computational Social Science, The Association of American Geographers (AAG) Annual Meeting, 5th-9th, April, Boston, MA. (pdf)



Tuesday, April 04, 2017

Smart Cities in IEEE Pervasive Computing


We are excited to announce that the special issue that we organized for IEEE Pervasive Computing is now out. In the special issue entitled "Smart Cities" and demonstrates the state of the art of pervasive computing technologies that collect, monitor, and analyze various aspects of urban life. The articles and departments in the special issue highlight the coming revolution in urban data via some of the different approaches researchers are taking to build tools and applications to better inform decision making (to reduce energy consumption or improve visitor flows, for example). Such research will be critical to setting goals for sustainable urban development within different global contexts. We need to better understand cities and their underlying systems if we want to improve the quality of urban life. To this end, in the special issue we have an introduction (editorial) followed by a number of articles, an interview and a research spotlight:
We hope you enjoy them. Thank you for the authors who submitted papers, the reviewers, Rob Kitchen for giving an interview and Barbara Lenz and Dirk Heinrichs for discussing their research. Lastly, we would also like to thank the IEEE Pervasive Computing team for ensuring that the special issue came to fruition.

Full Reference to the Introduction: 
Crooks, A.T., Schechtner, K., Day, A.K and Hudson-Smith, A (2017), Creating Smart Buildings and Cities, IEEE Pervasive Computing, 16 (2): 23-25. (pdf)

Friday, March 10, 2017

Geovisualization of Social Media

Figure 1: Map Mashup of Twitter data, where eachdot
represents a tweet, the text corresponds to the selected
 tweet marked with a star
In the recently released "The International Encyclopedia of Geography: People, the Earth, Environment, and Technology" we were asked to write a brief entry entitled "geovisualization of social media". Below is a summary of  our chapter:

The proliferation of social media over the last decade is presenting substantial computational challenges associated with the management, processing, analysis and visualization of the corresponding massive volumes of data. Furthermore, this new form of information also imposes new-found challenges upon the geographical community due to the unique nature of its content, as analyzing such data calls for a hybrid mix of spatial and social analysis. The spatial content of social media comprises primarily coordinates from which the contributions originate, or references to specific locations. At the same time, these data have a strong social component, as they can reveal the underlying social structure of the user community through manifestations of their interactions. Analyzing both the spatial and social content of social media feeds is referred to as geosocial analysis. Within this entry we explore the geovisualization opportunities and challenges that are emerging as social media are becoming the subject of study of the geographical community.
In more detail, we start off discussing how the geographic content of social media feeds represents a new type of geographic information. It transcends the early definitions of crowdsourcing or volunteered geographic information as it is not the product of a process through which citizens explicitly and purposefully contribute geographic information to update or expand geographic databases. Instead, the type of geographic information that can be harvested from social media feeds can be referred to as Ambient Geographic Information; it is embedded in the content of these feeds, often across the content of numerous entries rather than within a single one, and has to be somehow extracted. Nevertheless, it is of great importance as it communicates instantaneously information about emerging issues. At the same time, it provides an unparalleled view of the complex social networking and cultural dynamics within a society, and captures the temporal evolution of the human landscape.

In many cases, the geovisualization of social media feeds predominately take the appearance of web map mashups, in essence portraying the location of social media usage on a map. Such an early attempt to visualize social media is shown Figure 1. We argue that while this approach is informative, it often falls short of capturing the depth, richness, and complexity of the information that can be gleaned from social data. As a result, a need for more advanced geovisualization approaches that are capable of better capturing and communicating the complexity and multidimensionality of social media arises. And this is the focus of our chapter. We discuss briefly the geovisualization of network structures (such as shown in Figure 2), the geovisualization of network structure dynamics, the geovisualization of social media content (such as shown in Figure 3) along with the visualization of social media analysis (Figure 4) and conclude the chapter with a list of emerging research challenges.

Figure 2: Visualizing communities: a social network of an interest group (a), and the geovisualization of the  largest community shown over the contiguous U.S (B).

Figure 3: Visualizing social media content dynamics by coupling a Twitter stream viewer (A), a Twitter activity density map (B), and a ranked list top hash-tags (C) and top authors (E), a time slider (D), and author/hash-tags time series graphs.
Figure 4: Visualizing spatiotemporal clusters of tweets following the 2013 Boston bombing. Red circles indicate the approximate radius of each cluster, and color is used to indicate time.


We hope you enjoy. As always any feedback or comments most welcome. Please note this chapter was written a couple of years ago and more recent work by us has been done, click here to see some.

Full Reference:
Croitoru, A., Crooks, A.T., Radzikowski, J. and Stefanidis, A. (2017), Geovisualization of Social Media, in Richardson, D., Castree, N., Goodchild, M. F., Kobayashi, A. L., Liu, W. and Marston, R. (eds.), The International Encyclopedia of Geography: People, the Earth, Environment, and Technology, Wiley Blackwell. DOI: 10.1002/9781118786352.wbieg0605 (PDF)

Thursday, March 09, 2017

Cellular Automata


In the recently released "The International Encyclopedia of Geography: People, the Earth, Environment, and Technology" I was asked to write a brief entry on "Cellular Automata". Below is the abstract to my chapter, along some of the images I used in my discussion, the full reference to the chapter.

Abstract: 
Cellular Automata (CA) are a class of models where one can explore how local actions generate global patterns through well specified rules. In such models, decisions are made locally by each cell which are often arranged on a regular lattice and the patterns that emerge, be it urban growth or deforestation are not coordinated centrally but arise from the bottom up. Such patterns emerge through the cell changing its state based on specific transition rules and the states of their surrounding cells. This entry reviews the principles of CA models, provides a background on how CA models have developed, explores a range of applications of where they have been used within the geographical sciences, prior to concluding with future directions for CA modeling. 

The figures below are a sample from the entry, for example, we outline different types of spaces within CA models such as those shown in Figures 1 and 2. We also show how simple rules can lead to the emergence of patterns such as the Game of Life as shown in Figure 3 or  Rule 30 as shown in Figure 4.

Figure 1: Two-Dimensional Cellular Automata Neighborhoods

Figure 2: Voronoi Tessellations Of Space Where Each Polygon Has A Different Number Of Neighbors Based On A Shared Edge.

Figure 3: Example of Cells Changing State from Dead (White) To Alive (Black) Over Time Depending On The States of its Neighboring Cells.

Figure 4: A One-Dimensional CA Model Implementing “Rule 30” Where Successive Iterations Are Presented Below Each Other.

Full Reference:
Crooks, A.T. (2017), Cellular Automata, in Richardson, D., Castree, N., Goodchild, M. F., Kobayashi, A. L., Liu, W. and Marston, R.  (eds.), The International Encyclopedia of Geography: People, the Earth, Environment, and Technology, Wiley Blackwell. DOI: 10.1002/9781118786352.wbieg0578. (pdf)


Monday, February 27, 2017

Agents - the 'atomic unit' of social systems? @AAG 2017

As part of the Symposium on Human Dynamics in Smart and Connected Communities at the forthcoming AAG Annual Meeting in Boston we have organized 2 sessions under the title of "Agents - the 'atomic unit' of social systems?" (session IDs 4169 & 4269). These will be held on on Saturday, 4/8/2017, from 8:00 am to 11.40 (we did not chose this time slot). Below you can see the session description and the list of speakers and titles. We hope some of the readers of this blog can make it to the sessions.

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.

This session will present 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. 

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.



4169 Symposium on Human Dynamics in Smart and Connected Communities: Agents - the 'atomic unit' of social systems? 1 

Saturday, 4/8/2017, from 8:00 AM - 9:40 AM in Regis, Marriott, Third Floor

Chair: Nick Malleson

Presentations:

4269 Symposium on Human Dynamics in Smart and Connected Communities: Agents - the 'atomic unit' of social systems? 2 

Saturday, 4/8/2017, from 10:00 AM - 11:40 AM in Regis, Marriott, Third Floor

Chair: Alison Heppenstall 

Presentations:

We hope you will stay around and attend these sessions. See you in Boston.

Wednesday, February 22, 2017

Applications of Agent-based Models

Often I get asked the question along the lines of: "how are agent-based models are being used outside academia, especially in government and private industry?" So I thought it was about time I briefly write something about this.

Let me start with a question I ask my students when I first introduce agent-based modeling: "Have you ever seen an agent-based model before?" Often the answer is NO, but then I show them the following clip from MASSIVE (Multiple Agent Simulation System in Virtual Environment) where agent-based models are used in a variety of movies and TV shows. But apart from TV shows and movies where else have agent-based models been used?




There are two specific application domains where agent-based modeling has taken off. The first being pedestrian simulation for example, LegionSteps and EXODUS simulation platforms. The second is the area of traffic modeling for example, there are several microsimulation/agent-based model platforms such as PTV Visum, TransModeler and Paramics. Based on these companies websites they have clients in industry, government and academia.

If we move away from the areas discussed above, there is a lot of writing about the potential of agent-based modeling. For example, the Bank of England had a article entitled "Agent-based models: understanding the economy from the bottom up" which to quote from the summary:
"considers the strengths of agent-based modelling, which explains the behaviour of a system by simulating the behaviour of each individual ‘agent’ in it, and the ways that it can be used to help central banks understand the economy."
Similar articles can be seen in the New York Times and the Guardian to name but a few. But where else have agent-based models been used? A sample (and definitely not an exhaustive list) of applications and references are provided below for interested readers:
  • Southwest Airlines used an agent-based model to improve how it handled cargo (Seibel and Thomas, 2000).
  • Eli Lilly used an agent-based model for drug development (Bonabeau, 2003a).
  • Pacific Gas and Electric: Used an agent based model to see how energy flows through the power grid (Bonabeau, 2003a).
  • Procter and Gamble used an agent-based model to understand its consumer markets (North et al., 2010) while Hewlett-Packard used an agent-based model to understand how hiring strategies effect corporate culture (Bonabeau, 2003b).
  • Macy’s have used agent-based models for store design (Bonabeau, 2003b).
  • NASDAQ used and agent based model to explore changes to Stock Market's decimalization (Bonabeau, 2003b; Darley and Outkin, 2007).
  • Using a agent-based model to explore capacity and demand in theme parks (Bonabeau, 2000).
  • Traffic and pedestrian modeling (Helbing and Balietti, 2011).
  • Disease dynamics (e.g. Eubank et al., 2004).
  • Agent-based modeling has also been used for wild fire training, incident command and community outreach (Guerin and Carrera, 2010). For example SimTable was used in the  2016 Sand Fire in California. 
  • InSTREAM: Explores how river salmon populations react to changes (Railsback and Harvey, 2002).
While not a comprehensive list, it is hoped that these examples and links will be useful if someone asks the question I started this post with. If anyone else knows of any other real world applications of agent-based modeling please let me know (preferably with a link to a paper or website).
 
References
  • Bonabeau, E. (2000), 'Business Applications of Social Agent-Based Simulation', Advances in Complex Systems, 3(1-4): 451-461.
  • Bonabeau, E. (2003a), 'Don’t Trust Your Gut', Harvard Business Review, 81(5): 116-123.
  • Bonabeau, E. (2003b), 'Predicting the Unpredictable', Harvard Business Review, 80(3): 109-116.
  • Darley, V. and Outkin, A.V. (2007), NASDAQ Market Simulation: Insights on a Major Market from the Science of Complex Adaptive Systems, World Scientific Publishing, River Edge, NJ.
  • Eubank, S., Guclu, H., Kumar, A.V.S., Marathe, M.V., Srinivasan, A., Toroczkai, Z. and Wang, N. (2004), 'Modelling Disease Outbreaks in Realistic Urban Social Networks', Nature, 429: 180-184.
  • Guerin, S. and Carrera, F. (2010), 'Sand on Fire: An Interactive Tangible 3D Platform for the Modeling and Management of Wildfires.' WIT Transactions on Ecology and the Environment, 137: 57-68.
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Friday, January 20, 2017

Authoritative and VGI in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi


The motivation behind the paper was that while there are numerous studies comparing VGI to authoritative data in the developed world, there are very few that do so in developing world. In order to address this issue in the paper we compare the quality of authoritative road data (i.e. from the Regional Center for Mapping of Resources for Development - RCMRD) and non-authoritative crowdsourced road data (i.e. from OpenStreetMap (OSM) and Google’s Map Maker) in conjunction with population data in and around Nairobi, Kenya.

Results from our analysis show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards more rural areas. Further information including the abstract to our paper, some figures and full reference is given below.

Abstract:
With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development - RCMRD) and non-authoritative (data from OSM and Google’s Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.

Keywords: Volunteered Geographic Information; Crowdsourcing; Road Networks; Population Data; Kenya  
Road Coverage per km2
Pairwise difference in road coverage. Clockwise from top left: i) RCMRD 2011 versus Map Maker 2014; ii) RCMRD 2011 versus OSM 2011; iii) RCMRD 2011 versus OSM 2014; iv) OSM 2014 versus Map Maker 2014 (Red cells: first layer has higher coverage; Green cells: second layer has higher coverage).

Full Reference:
Mahabir, R., Stefanidis, A., Croitoru, A., Crooks, A.T. and Agouris, P. (2017), “Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya”, ISPRS International Journal of Geo-Information, 6(1): 24, doi:10.3390/ijgi6010024.
As always any thoughts or comments about this work are welcome.

Thursday, January 12, 2017

Transportation in Agent-Based Urban Modelling


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

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


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

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






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


Friday, January 06, 2017

ABMUS2017: The 2nd International Workshop on Agent-based modelling of urban systems

Call for Papers: The 2nd International Workshop on Agent-based modelling of urban systems

The ABMUS2017 workshop on Agent-based modelling of urban systems will be held at the AAMAS2017 conference in Sao Paulo, Brazil on 8-9 May 2017. It is the follow-up of ABMUS2016 held in Singapore during AAMAS2016 on the 10th of May 2016.


Researchers and practitioners who use agent-based models and agent systems to understand, explore, and manage cities and urban infrastructure systems are invited to submit papers to ABMUS2017. The overarching theme for the workshop is data for agent-based models. Data is essential for building, calibrating, and validating agent-based city and urban infrastructure models. But which approaches are optimal for what purposes? We invite presentations that describe data collection and data management approaches in agent-based models, as well as the use of data sets and methodologies that can be translated and re-used between researchers, sectors and countries.

Workshop topics include, but are not limited to, the following:
  • Large scale urban simulation applications
  • Agent-based modelling of urban transport, land-use, housing, energy, health, etc.
  • Spatially explicit micro-simulation modelling
  • Simulation of household behaviour and technology adoption
  • Localized population synthesis
  • Multi-scale urban systems (temporal and spatial)
  • Social simulation of demographic transitions
  • Use of mobile technology to validate activity patterns
  • Techniques for integrating independently developed components
  • Agent-based platforms for urban simulation
  • Data structures for simulating urban environments
  • (Multi-)agent systems for decision support in e.g. transport, energy use and air quality
  • Connection of simulation models to social and geographical theory
  • Development of 'master' city datasets for model validation
At the workshop each presenter will be given 10 minutes to introduce their paper and/or case study, followed by 5-10 minutes in which presenters will share their views on the data for agent-based models theme. After three presentations there will be 20-30 minutes of group discussion in which presenters will act as panel members.

Important dates:
  • 7 February 2017: Deadline for paper submissions
  • 2 March 2017: Notification of acceptance following the review process
  • 17 March 2017: Deadline for submitting camera-ready papers (including LaTeX files)
  • 8-9 May 2017: ABMUS workshop at the AAMAS2017 conference in Sao Paulo, Brazil

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

The organizing committee consists of: