Continuing and building upon our previous work on Location-Based Social Networks (LBSNs) at the The 21st IEEE International Conference on Mobile Data Management we have a paper entitled "Location-Based Social Network Data Generation Based on Patterns of Life." In the paper we discuss how LBSNs research has become an active research topic in a variety of areas describing mobility patterns, location recommendation and friend recommendation systems. However we make the argument that real-world LBSN data sets (e.g., Gowalla, BrightKite) are a rather scarce resource due to privacy implications of making such data public available. Furthermore, in many publicly available LBSN data sets, the vast majority of users have less than ten check-ins or the number of locations visited by a user
is usually only a small portion of all locations that user
has visited (as shown in the table below).
Publicly Available Real-World LBSN Data Sets.
To overcome these weaknesses in this paper we present a LBSN simulation (an agent-based model created in MASON) capable of creating multiple
artificial but socially plausible, large-scale LBSN data sets. If this sounds of interest to you, below we provide a little more information about the paper, Specifically, its abstract, a depiction of LBSNs, our case studies and the resulting simulations we used to develop LBSN data based on patterns of life (PoL) and some sample results. In addition to this, as the conference was virtual, Joon-Seok Kim also made a great movie of the conference paper. At the bottom of this post we provide the full reference and link to the paper.
Location-based social networks (LBSNs) have been
studied extensively in recent years. However, utilizing real-world
LBSN data sets in such studies yields several weaknesses: sparse
and small data sets, privacy concerns, and a lack of authoritative
ground-truth. To overcome these weaknesses, we leverage a large scale
LBSN simulation to create a framework to simulate human
behavior and to create synthetic but realistic LBSN data based on
human patterns of life. Such data not only captures the location
of users over time but also their interactions via social networks.
Patterns of life are simulated by giving agents (i.e., people) an
array of “needs” that they aim to satisfy, e.g., agents go home
when they are tired, to restaurants when they are hungry, to
work to cover their financial needs, and to recreational sites
to meet friends and satisfy their social needs. While existing
real-world LBSN data sets are trivially small, the proposed
framework provides a source for massive LBSN benchmark
data that closely mimics the real-world. As such it allows us
to capture 100% of the (simulated) population without any
data uncertainty, privacy-related concerns, or incompleteness.
It allows researchers to see the (simulated) world through the
lens of an omniscient entity having perfect data. Our framework
is made available to the community. In addition, we provide a
series of simulated benchmark LBSN data sets using different
real-world urban environments obtained from OpenStreetMap.
The simulation software and data sets which comprise gigabytes
of spatio-temporal and temporal social network data are made
available to the research community.
LBSN Overview
Case Studies: A: New Orleans, Louisiana (NOLA), Mississippi River, Lake Pontchartrain, and the ‘French Quarter’. B: George Mason University (GMU), Fairfax, VA. C: Synthetic Villages - Small (Left) and Large (Right).
Environments Populated with Agents. Clockwise from Top Left: GMU, NOLA, Large and Small Synthetic Villages.
Data Sets Resulting from Location-Based Social Network Simulation
Average Social Network Degree over Time (1K).
Social Network
Full Reference:
Kim, J-S., Jin, H., Kavak, H., Rouly, O.C., Crooks, A.T., Pfoser, D., Wenk, C. and Züfle, A. (2020), Location-Based Social Network Data Generation Based on Patterns of Life, The 21st IEEE International Conference on Mobile Data Management, Versailles, France. (pdf)
The 3rd GeoSim workshop will focus 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. Also, this workshop is 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.
The workshop seeks high-quality full (8-10 pages) and short (up to 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, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.
We solicit novel and previously unpublished research on all topics related to geospatial simulation including, but not limited to:
Disease Spread Simulation
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
Modeling and Simulation of COVID-19
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
Special Topic
The special topic for GeoSim 2020 brings focus to current trends in disease spread simulations, their practicality in predictive and prescriptive analytics, and the challenges they face in their use.
Building upon our work on volunteered geographical information (VGI) and ambient geographic information (AGI) and how such data (e.g. social media) can be used to understand place, Xiaoyi Yuan, Andreas Züfle and myself have a new paper entitled: "A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information" in the ISPRS International Journal of Geo-Information. In this paper we use textual data from crowdsourced reviews originating with TripAdvisor and geo-located Twitter data and leverage this unstructured geographical information to comprehend the complexity of places at scale. Specifically we explore the connectedness and relationships of places through thematic (i.e., topical) similarity networks using Manhattan, New York as a case study. If such work sounds of interest to you, below we provide the abstract to the paper in order for you to gain a greater understanding of work, along with some figures that show our workflow and how communities where connected, before presenting some of our results. Finally at the bottom of the post, the full reference and a link to the paper is provided. For those interested in extending or utilizing this work. The python code for presented in our analysis is available at: https://bitbucket.org/xiaoyiyuan/network_vgi/
Abstract:
The research presented in this paper proposes a thematic network approach to explore rich relationships between places. We connect places in networks through their thematic similarities by applying topic modeling to the textual volunteered geographic information (VGI) pertaining to the places. The network approach enhances previous research involving place clustering using geo-textual information, which often simplifies relationships between places to be either in-cluster or out-of-cluster. To demonstrate our approach, we use as a case study in Manhattan (New York) that compares networks constructed from three different geo-textural data sources --TripAdvisor attraction reviews, TripAdvisor restaurant reviews, and Twitter data. The results showcase how the thematic similarity network approach enables us to conduct clustering analysis as well as node-to-node and node-to-cluster analysis, which is fruitful for understanding how places are connected through individuals’ experiences. Furthermore, by enriching the networks with geodemographic information as node attributes, we discovered that some low-income communities in Manhattan have distinctive restaurant cultures. Even though geolocated tweets are not always related to place they are posted from, our case study demonstrates that topic modeling is an efficient method to filter out the place-irrelevant tweets and therefore refining how of places can be studied.
Work flow from data input to the construction of the thematic similarity network and analysis (i.e., community detection and unique nodes discovery).
A stylized network demonstrating the process of community detection from a fully-connected similarity network.
Network visualization of all communities from the thematic similarity networks with major communities highlighted. Only the major communities are shown on the map for the sake of clarity. Major communities in Network visualization and mapping for each network are colored the same and thus the legend applies for both.
Two examples of communities with boundary nodes and their respective topics.
Full Reference:
Yuan X., Crooks, A.T. and Züfle, A. (2020), A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information, ISPRS International Journal of Geo-Information, 9(6), 385, https://doi.org/10.3390/ijgi9060385. (pdf)
In this article we discuss a geographically explicit agent-based model that we have been developing that is capable not only of simulating human behavior but also able to create synthetic
but realistic LBSN data based on human patterns-of-life. Furthermore, in the article we discuss how such data and models can be used to explore the parameter space
of possible prescriptions to find optimal strategies (or policies) to achieve a desired system state and outcome. We refer to such a search for optimal policies as prescriptive analytics. (for readers wishing to learn more about prescriptive analytics please see the 1st ACM KDD Workshop on Prescriptive Analytics for the Physical World).
To give an example of such prescriptions, in the article we make use of a simple hypothetical disease model and explore two prescribed policies
to mitigate the spread of the disease. The first policy requires all agents to wear simulated Personal Protective
Equipment (PPE) that reduce the chance of infection by 50%. The second policy enforces strict social distancing
measures onto a fixed proportion of 50% of the population. Those who follow the social distancing order avoid
recreational site visits from meeting people although they still go to restaurants. In addition to these two policies, as a baseline, we also ran
a “null-prescription” in which no intervention was prescribed. We find that
the social distancing prescription was extremely effective. On the other hand, our simulation results for PPE policy showed that merely wearing protective gear
without any change in behavior has no significant effect (for the case of this disease).
If this type of research is of interest to you, below we provide the abstract to the paper, a movie of a representative simulation run, some of our results of the prescriptions described above and a link to the paper itself. Further information about the model and data can be found at https://geosocial.joonseok.org/p/epidemic.html and the data is available at https://osf.io/e24th/. Also as we are currently going through COVID-19, we thought a a brief write up and links to some disease models and discussions of modeling efforts related to it was also appropriate to include.
Abstract:
Human mobility and social networks have received considerable attention from researchers in recent years. What has been sorely missing is a comprehensive data set that not only addresses geometric movement patterns derived from trajectories, but also provides social networks and causal links as to why movement happens in the first place. To some extent, this challenge is addressed by studying location-based social networks (LBSNs). However, the scope of real-world LBSN data sets is constrained by privacy concerns, a lack of authoritative ground-truth, their sparsity, and small size. To overcome these issues we have infused a novel geographically explicit agent-based simulation framework to simulate human behavior and to create synthetic but realistic LBSN data based on human patterns-of-life (i.e., a geo-social simulation). Such data not only captures the location of users over time, but also their motivation, and interactions via temporal social networks. We have open sourced our framework and released a set of large data sets for the SIGSPATIAL community. In order to showcase the versatility of our simulation framework, we added disease a model that simulates an outbreak and allows us to test different policy measures such as implementing mandatory mask use and various social distancing measures. The produced data sets are massive and allow us to capture 100% of the (simulated) population over time without any data uncertainty, privacy-related concerns, or incompleteness. It allows researchers to see the (simulated) world through the lens of an omniscient entity having perfect data.
Screenshot of the epidemic simulator depicting the French Quarter, New Orleans, LA, USA.
New cases and SEIR epidemic course.
Full Reference:
Kim, J-S., Kavak, H., Rouly, C.O., Jin, H., Crooks, A.T., Pfoser, D., Wenk, C. and Zufle, A. (2020), Location-Based Social Simulation for Prescriptive Analytics of Disease Spread, SIGSPATIAL Special, 12(1): 53-61. (pdf)
