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)
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)
At the ACM SIGSPATIAL'19 conference, Joon-Seok Kim, Hamdi Kavak, Umar Manzoor, Dieter Pfoser, Carola Wenk, Andreas Züfle and myself have a paper entitled "Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework." The general idea behind the paper is that while trajectory data is being used to capture human mobility in many applications (e.g. traffic
prediction, ride-sharing applications), the use of real-world trajectory data raises serious concerns with respect to the privacy of users who contribute such information.
To overcome privacy concerns we have created a geo-social data generator by utilizing agent-based modeling. The notion behind this generator is to allow users to develop and
customize the logic of agent behaviors for different applications domains (e.g. commuting around a city). Once the basic model is created, the simulation can then be run and geo-social data is generated which can then be used as a substitute to real-world trajectory data to study human mobility. If you wish to find out more about this paper, below is the abstract to the paper, along with some figures of the framework architecture and a link to the paper. Further supplementary materials including a demo video (which is also below) and sample
data can be found at: http://sigspatial19demo.joonseok.org.
Abstract:
Data generators have been heavily used in creating massive trajectory
datasets to address common challenges of real-world datasets,
including privacy, cost of data collection, and data quality. However,
such generators often overlook social and physiological characteristics
of individuals and as such their results are often limited to
simple movement patterns. To address these shortcomings, we
propose an agent-based simulation framework that facilitates the
development of behavioral models in which agents correspond to
individuals that act based on personal preferences, goals, and needs
within a realistic geographical environment. Researchers can use
a drag-and-drop interface to design and control their own world
including the geospatial and social (i.e. geo-social) properties. The
framework is capable of generating and streaming very large data
that captures the basic patterns of life in urban areas. Streaming
data from the simulation can be accessed in real time through a
dedicated API.
Keywords: Agent-based simulation, trajectory data, data generator, spatial
network, human behavior.
Causality in human behavior
Architecture of framework
Layout of model builder and sample model
Full Reference:
Kim, J-S., Kavak, H., Manzoor, U., Crooks, A.T., Pfoser, D., Wenk C. and Züfle, A (2019), Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework, in Banaei-Kashani, F., Trajcevski, G., Güting, R.H., Kulik, L. and Newsam, S. (eds.), Proceedings of the 27th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019), Chicago, IL. (pdf)
At the upcoming 16th International Symposium on Spatial and Temporal Databases (SSTD) we have vision paper entitled "Location-Based Social Simulation" accepted. In the paper we discuss issues such as data sparsity and privacy concerns with using real world location-based social networks (LBSNs) like Foursquare and Yelp. To overcomes these issues, we describe how one can employ geospatial simulation (i.e. an agent-based model) to create artificial, but socially plausible LBSN data sets which overcomes some of the limitations with respect to LBSNs.
ABSTRACT:
Location-based social networks (LBSNs) have been studied extensively
in recent years. However, utilizing real-world LBSN datasets
in such studies has severe weaknesses: sparse and small datasets,
privacy concerns, and a lack of authoritative ground-truth. Our vision
is to create a large scale geosimulation framework to simulate
human behavior and to create synthetic but realistic LBSN data
that captures the location of users over time as well as social interactions
of users in a social network. While existing LBSN datasets
are trivially small, such a framework would provide the first source
of very large LBSN benchmark data which would closely mimic
the real world, containing high-fidelity information of location,
and social connections of millions of simulated agents over several
years of simulated time. Therefore, it would serve the research
community by revitalizing and reshaping research on LBSNs by
allowing researchers to see the (simulated) world through the lens
of an omniscient entity having perfect data. These evaluations will
guide future research allowing us to develop solutions to improve
LBSN applications such as user-location recommendation, friend
recommendation, location prediction, and location privacy.
KEYWORDS:
Agent-based simulation, location-based social network, data generator,
spatial network, human behavior
Full Reference:
Kavak, H., Kim, J-S., Crooks, A.T., Pfoser, D., Wenk C. and Züfle, A (2019), Location-Based Social Simulation, Proceedings of the 16th International Symposium on Spatial and Temporal Databases, Vienna, Austria, pp 218-221. (pdf)
Update: Our paper was selected as runner-up for best Vision Paper.
