Generation of Reusable Synthetic Population and Social Networks

Building on our work on synthetic populations, Na (Richard) Jiang, Bill Kennedy, Hamdi Kavak, and myself have a new paper which was presented at the 2021 Annual Modeling and Simulation (ANNSIM) Conference entitled "Generation of Reusable Synthetic Population And Social Networks for Agent-Based Modeling." Rather than go into details about the paper, below is the abstract and as the conference was virtual, the presentation which accompanies the paper was prerecorded by Richard and is embedded below. If you want to find out more, at the bottom of the post there is a link to a draft of the paper.

Abstract: Within agent-based models, agents interact with each other (e.g., social networks) and their environment, and it is through such interactions more aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the popularity of agent-based modeling has grown, one challenge remains, that of creating and sharing realistic synthetic populations which incorporate social networks. To overcome this challenge, this paper introduces a new approach that creates a reusable synthetic population using the New York Metro Area as a study area. Our method directly incorporates social networks (i.e., connections within a family or workplace) when creating a synthetic population. To demonstrate the utility and reusability of the synthetic population and to highlight the role of social networks, we show two example applications: traffic dynamics and the spread of a disease. These applications demonstrate how our synthetic population method can be easily utilized for different modeling problems. 

Keywords: Synthetic Population, Agent-Based Modeling, New York, Traffic Dynamics, Disease Models.

  Full Reference: 

Jiang, N., Crooks, A.T., Kennedy, W.G., and Kavak, H. (2021), Generation of Reusable Synthetic Population And Social Networks for Agent-Based Modeling, in  Martin, C.R.,  Blas, M.J. and Psijas, A.I (eds.), Proceedings of the 2021 Annual Modeling and Simulation Conference (ANNSIM), Online, pp. 1-12.. (pdf)

Kinetic Action and Radicalization

In the past we had posted on models of radicalization, but such models were rather abstract.  However in a recent paper entitled "Kinetic Action and Radicalization: A Case Study of Pakistan" which was presented at the  International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMS for short) we take such work a step further. 

In the paper, Brandon Shapiro and myself develop and present a simple agent-based model informed by theory and calibrated using empirical data to explore the relationship between kinetic actions (i.e., drone strikes) and terrorist attacks in Pakistan from 2004 through 2018. The data itself came from the Bureau of Investigative Journalism data as our source for Pakistan drone strikes (i.e., kinetic actions) and the National Consortium for the Study of Terrorism and Responses to Terrorism( START)  Global Terrorism Database (GTD) as our source for terrorist incidents. 

Rather than try to pinpoint and define the motivating factors which might influence somebody down a path toward radicalization, our model that incorporated a distributed lag model to characterize the inter-dependencies between drone strikes and terrorist attacks observed in Pakistan. Based on parametric and validation tests, the model simulates a terrorist attack curve which approximates the rate and magnitude observed in Pakistan from 2007 through 2018. If this sounds of interest, below we provide the abstract to the paper, along with some images of model graphical user interface, the model logic and some of the results. The model itself was created in NetLogo and is available at: https://bit.ly/3qLJynv  (along with the data and detailed ODD of the model). At the bottom of the page you can find the full citation and a link to the paper.

Abstract. Drone strikes have been ongoing and there is a debate about their benefits. One major question is what is their role with respect to radicalization. This paper presents a data-driven approach to explore the relationship between drone strikes in Pakistan and subsequent responses, often in the form of terrorist attacks carried out by those in the communities targeted by these counter-terrorism measures. Our analysis of news reports which dis-cussed drone strikes and radicalization suggests that government-sanctioned drone strikes in Pakistan appear to drive terrorist events with a distributed lag that can be determined analytically. We then utilize these news reports to inform and calibrate an agent-based model which is ground-ed in radicalization and opinion dynamics theory. In doing so, we were able to simulate terrorist attacks that approximated the rate and magnitude ob-served in Pakistan from 2007 through 2018. We argue that this research effort advances the field of radicalization and lays the foundation for further work in the area of data-driven modeling and kinetic actions.

Keywords: Radicalization, Data-driven modeling, Drone strikes, Terrorism, Pakistan, Agent-based modeling.

Radicalization model’s graphical user interface.

The agent-based model flow diagram.

Terrorist attacks simulated by radicalization model qualitatively agree with real-world system.

Full Reference:

Shapiro, B. and Crooks, A.T. (2021), Kinetic Action and Radicalization: A Case Study of Pakistan, in Thomson, R., Hussain, M.N., Dancy, C.L. and Pyke, A. (eds), Proceedings of 2021 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp 321-330. (pdf)

Towards Large-Scale Agent-Based Geospatial Simulation

Running large scale spatial agent-based models is often a computational challenge. To address this challenge, 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), Umar Manzoor, Hamdi Kavak, Joon-Seok Kim, Dieter Pfoser, Andreas Zufle, and Carola Wenk and myself have a paper entitled "Towards Large-Scale Agent-Based Geospatial Simulation."

In the paper we propose a scalable and general agent-based modeling and simulation framework for geospatial simulations involving networks. Specifically we propose to a solution for the parallelization of the single-threaded GeoMASON tookit by employing the Java Agent Development Environment (JADE) for the communication between threads (essentially, we divide the space of our agents into partitions, each handled by a separate thread of execution). We evaluate the proposed framework an simple urban model (created in MASON), which simulates simple patterns of life within an urban setting (click here for the blog post). The model has spatial network for agent movement and social network for maintaining social links. We compared the performance of the proposed framework on different settings, and concluded from experimentation that the proposed framework is outperformed by GeoMason when the agent population is small whereas with an increasing agent population, our proposed framework outperforms GeoMason as the complexity and time taken in simulation step increases substantially. If this sounds of interest, below we provide the abstract to the paper, along with some images of the framework and and simulation architecture. At the bottom of the page you can find the full citation and a link to the paper.

Abstract. Agent-based geospatial simulations have become very popular and widely used in examining the social and cultural characteristics of populations. Well-known toolkits such as NetLogo or MASON generally have scalability limitations, especially when the model and underlying spatial infrastructure become complex. This paper presents a framework for simulating large-scale agent-based geospatial systems by integrating the multi-agent systems toolkit JADE with the MASON agent-based modeling framework and its GIS extension, GeoMASON. The proposed Java-based framework can simulate large areas with hundreds of thousands of agents. It allows for the studying the evolution of a population and its environment over time. Such a framework provides the essential first steps for scalable model execution without sacrificing the model generality. 

Keywords: Large-scale geospatial simulation, Agent-based Modeling, MASON, Jade, GIS.

System Architecture of Proposed Framework.

Agent transfer between Zones.

Simulation using Proposed Architecture.

Full Reference:

Manzoor, U., Kavak, H., Kim, J-S., Crooks, A.T., Pfoser, D., Zufle, A. and Wenk, C. (2021), Towards Large-Scale Agent-Based Geospatial Simulation, 2021 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Washington DC. (pdf)

Achieving Situational Awareness with Geolocated Social Media

Tuning back to our work on geosocial analysis we (Xiaoyi Yuan, Ron Mahabir, Arie Croitoru and myself) recently had a paper published in GeoJournal entitled "Achieving Situational Awareness of Drug Cartels with Geolocated Social Media." 

 

The overarching objective of this paper is to develop an approach that would enable the extraction of potentially relevant situational awareness-related information from geolocated raw data streams (in this example we use Twitter). We accomplish this goal by focusing on Named Entities (NEs) related to drug cartels rather than the raw text as a whole. Specifically, our analysis is performed on the NEs by first extracting them and then clustering them to identify relevant concepts/themes (using TextRazor). This approach gives rise to themes that can then be assessed for temporal and spatial patterns based on frequency in order to gain underlying insights into drug cartels. If is of interest to you below we provide the abstract to the paper, a diagram of our workflow and a sample of our results along with the link to the paper. Also the complete code for the analysis and results is available at https://bitbucket.org/xiaoyiyuan/cartel.

 

Abstract: Using geolocated tweets to achieve situational awareness is an often researched topic in disaster and emergency management. However, little has been done in the area of drug cartels, which, as transnational crime organizations, continue to pose great risk to the stability and safety of our communities. This paper made an initial effort in using geolocated social media (specifically Twitter) to achieve situational awareness of drug cartels through temporal and spatial analysis of derived named entity clusters. The results show that detecting peaks in the time series of frequently occurring entity clusters enabled the tracking of important events in public discourse surrounding drug cartels. Correlations between time series also provided valuable insights into the synchronicity between different events. Further examining the spatial distribution of key events for different countries, we identify thematic hotpots of public discourse on cartel activity. Our methodology also addresses issues of language ambiguity when working with noisy social media data in order to achieve situational awareness on drug cartels.

Keywords: Cartels, Social Media, Situational Awareness and Temporal and Spatial Analysis.

The workflow of achieving situational awareness of drug cartels using geolocated tweets.

Tweet and entity counts by language and geolocation.

An example of tweets of high frequency on peak day in Venezuela

Heat maps of frequencies of a Cluster for Day 14 and Days 18-21.

Full Reference:

Yuan, X., Mahabir, R., Crooks, A.T. and Croitoru, A. (2021), Achieving Situational Awareness of Drug Cartels with Geolocated Social Media, GeoJournal. DOI: https://doi.org/10.1007/s10708-021-10433-2 (pdf)

A Semester of Spatial Simulation

While at Mason, it was a tradition of mine to make  a post of some of the models developed by students in my classes as part of their end of semester projects.  So while I am not at Mason anymore, I thought I would keep this tradition when teaching agent-based modeling classes. To that end, this semester at UB I taught a class entitled “Spatial Simulation” (and the course description is below for these who are interested). 

 

For many, this was their first exposure to agent-based and cellular automata (CA) modeling. As part of the class the students were expected to complete an end of semester project, in this case, develop an agent-based or CA model that explores some aspect of the course themes.   The movie below shows a selection of these projects which ranged from what a exploring what 15 minute city would look like, to that of land use change over years and several other  topics in-between. Many of the astute readers might notice these models where created using NetLogo which was used in class to teach the basics of spatial simulation but at the same time could leverage our book  "Agent-based Modelling and Geographical Information Systems: A Practical Prime" and associated resources on GitHub.

Coarse Description:

This graduate course will introduce students in the geographical and environmental sciences to the use of spatial simulation methods (e.g., cellular automata, agent-based modeling) to explore complex geographical phenomena from the bottom up. For example, how the micro-movement of pedestrians lead to the emergence of crowds or how individuals buying and selling houses lead to property markets forming. We will cover geographical applications in areas such as agriculture, biodiversity, interactions between human populations and nonhuman species and cities. Emphasis will be placed on the notion that geographical systems are constantly changing at various spatiotemporal scales and how through spatial simulation we can gain an understanding of the processes that lead to patterns that we can observe through data. The course will combine taught classes, literature reviews and discussions with hands-on spatial simulation modeling. The format of the class will consist of both lecture and discussion, with substantial emphasis on student participation.