Thursday, July 04, 2019

Challenges and Opportunities of Social Media Data for Socio-environmental Systems Research

SES diagram with examples of topics that
have been researched using social media data
While I have written about how one can use social media data to study cities, health issues etc... more recently we have been looking into how such data can be used to aid  Socio-environmental Systems (SES) research. SES are defined as tightly linked social and biophysical subsystems that mutually influence one another through positive and negative feedbacks.  To this end, Bianca Lopez, Nick Magliocca and myself just ahd a paper published in Land entitled "Challenges and Opportunities of Social Media Data for Socio-environmental Systems Research." 

In this paper we discuss SES and how research into them poses many challenges, not least of which are collecting or compiling data at the appropriate scales and aligning social and environmental data to address SES questions.  We discuss how SES have been studied using more traditional sources of data (e.g. census data, remote sensing etc.) and explore how social media can be used in the context of SES research. Specifically we ask three specific questions. 1) How can feedback between social and environmental systems be meaningfully studied using social media data? 2) How can using social media data re-frame or compliment current SES research questions and methods? and 3) Are there best practices for collecting and validating social media data for use in SES research? If these questions sound interesting to you, we encourage you to read the abstract below or the full paper.

Abstract:
Social media data provide an unprecedented wealth of information on people’s perceptions, attitudes, and behaviors at fine spatial and temporal scales and over broad extents. Social media data produce insight into relationships between people and the environment at scales that are generally prohibited by the spatial and temporal mismatch between traditional social and environmental data. These data thus have great potential for use in socio-environmental systems (SES) research. However, biases in who uses social media platforms and what they use them for create uncertainty in the potential insights from these data. Here, we describe ways that social media data have been used in SES research, including tracking land-use and environmental changes, natural resource use, and ecosystem service provisioning. We also highlight promising areas for future research and present best practices for SES research using social media data.

Keywords: social media; socio-ecological systems; human-environment interactions; geospatial analysis; crowdsourced data.
Example of information provided by social media posts and how it is used in analyses. A single post from a social media user.

Example of information on people’s use of natural resources from social media data based on key word searches fish and oyster from Twitter, Instagram and Foursquare.

Full Reference:
Lopez, B., Magliocca, N. and Crooks, A.T. (2019), Challenges and Opportunities of Social Media Data for Socio-environmental Systems Research, Land, 8(7), 107; https://www.mdpi.com/2073-445X/8/7/107/htm  (pdf)

Monday, July 01, 2019

Modeling Society Reacting to a Nuclear Weapon of Mass Destruction Event

https://www.dropbox.com/s/mid39pfgvu1vr8l/SBPBrims_2019_Poster.pdf?dl=0
Over the last couple of years we have been working on generating synthetic human populations with realistic social networks with respect to the New York mega-city and surrounding region. This is being done for a variety of modeling applications such as the spread of a disease or exploring peoples reactions to disasters (which was a topic of a recent post on Computational Social Science of Disasters).

To this end, at the upcoming International Conference on Social Computing, Behavioral-Cultural Modeling and; Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMS for short) we have a short working paper outlining some of our initial efforts to how people might react following a Nuclear Weapon of Mass Destruction (NWMD) event. In the paper we show some preliminary simulation results relating on  how we are able to simulate basic commuting patterns and initial movement away from the affected area after the NWMD event (like those in the movies below). By using a synthetic population we are able to create an artificial world populated by agents with sufficient heterogeneity to create realistic movement patterns and the social networks which play a vital role in disaster situations. If you want to know more about this work, feel free to read the abstract blow or read the paper. 

Abstract:
Individual connections between human beings often dictate where people go and how they behave, yet their representation through social networks are rarely used as measures of human behavior in agent-based models. Social networks are increasingly used for study of human behavior in disasters, and empirical work has shown that human beings prioritize the safety of themselves and loved ones (i.e., households) before helping neighbors and coworkers. Based on this assumption we have created a set of heuristics for modeling how agents behave in an emergency event and how the individual behavior aggregates into a variety of patterns of life. In this paper will present briefly our agent-based model being used to characterize the population’s reaction to a Nuclear Weapon of Mass Destruction (NWMD) event in the New York City region. Agents are modeled commuting on work-day schedules before the explosion of a small (10Kt) nuclear device. After the explosion, agents respond to signals in their environment and make decisions based on prioritization of safety for themselves and those in their networks. The model methodology demonstrates how social networks can be integrated into an agent-based model and act as a basis for decision-making, and preliminary simulations show how agents potentially respond to a NWMD event with measurable changes in location and network formations over space and time. 

Keywords: Agent-Based Model, Human Behavior, Social Networks, Emergency, Disaster Response, Nuclear Weapon of Mass Destruction.
Various patterns of commuting behavior representing daily routines of the individual agents.





Full Reference:
Burger, A. G., Kennedy, W.G., Crooks, A.T., Jiang, N. and Guillen-Piazza, D. (2019), Modeling Society Reacting to a Nuclear Weapon of Mass Destruction Event, 2019 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Washington DC. (pdf)

Saturday, June 08, 2019

A Semester of CSS 645: Spatial Agent-based Models of Human-Environment Interactions

This last Spring semester I taught a class entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students were 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.  For several of the students this was their first exposure to either agent-based modeling or utilizing geographical information in the modeling process. In the movie below a selection of these projects can be seen. The projects ranged from migration, evacuation modeling during a natural disaster, gerrymandering, the spread of diseases, recidivism, Commons problems to that of urban decline. As can be seen the models ranged from abstract spatial representations to those utilizing geographical information as a foundation of their artificial worlds. Many of the models where created using NetLogo.



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

Wednesday, May 29, 2019

Postdoctoral Research Fellow in Urban Simulation

The George Mason University, Department of Geography and Geoinformation Science (GGS), in the College of Science invites application for a Postdoctoral Research Fellow position beginning August 1st, 2019. The project, which is supported by the Defense Advanced Research Projects Agency (DARPA), is jointly conducted by Andreas Z├╝fle, Dieter Pfoser, and Andrew Crooks at GMU, and by Carola Wenk at Tulane. George Mason University has a strong institutional commitment to the achievement of excellence and diversity among its faculty and staff, and strongly encourages candidates to apply who will enrich Mason’s academic and culturally inclusive environment.

Responsibilities: 

The primary job responsibilities of this position consist of the design, development and refinement of an agent-based simulation framework of urban areas. For this purpose, we are using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java) that has been developed at GMU. Using MASON, new agent logic will have to be implemented, thus creating agents that use socially plausible rules to traverse their simulated world, and to interact with other agents. This project has started in Spring 2018, and such a simulation has already been developed. A main responsibility will be to implement more complex agent logic efficiently, thus allowing more agents to make more complex decisions, find shortest paths between locations, and interact with their simulated world, at the same time. For this purpose, implemented algorithms will need to be highly parallelizable, thus allowing to scale simulation via distribution among computing clusters located at GMU and Tulane. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

Required Qualifications:
  • Ph.D. in computer science, modeling and simulation, or closely related field;
  • Experience with Agent-Based Modeling and social science simulation;
  • Excellent written communication skills demonstrated by prior publications; and
  • A track record that demonstrates the ability to work well with interdisciplinary research teams.
Preferred Qualifications:
  • Strong programming skills in Java.
More Details: 

For more details and how to apply see: https://jobs.gmu.edu/postings/45722

Friday, May 24, 2019

Call for Papers: GeoSim 2019

https://www.geosim.org/

The 2nd International Workshop on Geospatial Simulation (GeoSim) 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. Example topics include, but are not limited to:
  • Urban 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
  • GIS 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
  • Applications for Spatial Simulation
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 (which will be in Chicago, Illinois), as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library. 

This workshop should also be 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.

More information about the workshop along with key dates is available at: https://www.geosim.org/  

https://www.geosim.org/