Tuesday, May 25, 2021

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)

Wednesday, May 19, 2021

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.