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.
  • Helbing, D. and Balietti, S. (2011), How to do Agent-based Simulations in the Future: From Modeling Social Mechanisms to Emergent Phenomena and Interactive Systems Design, Santa Fe Institute, Working Paper 11-06-024, Santa Fe, NM.
  • North, M.J., Macal, C.M., Aubin, J.S., Thimmapuram, P., Bragen, M., Hahn, J., J., K., Brigham, N., Lacy, M.E. and Hampton, D. (2010), 'Multiscale Agent-based Consumer Market Modeling', Complexity, 15(5): 37-47.
  • Railsback, S.F. and Harvey, B.C. (2002), 'Analysis of Habitat Selection Rules using an Individual-based Model', Ecology, 83(7): 1817-1830.
  • Seibel, F. and Thomas, C. (2000), 'Manifest Destiny: Adaptive Cargo Routing at Southwest Airlines', Perspectives on Business Innovation, 4: 27-33.

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.