Pattern of Life Human Mobility Simulation (Demo)

While in the past we have written about how we can use agent-based models to capture basic patterns of life, and even developed a simulations, but until now we have never really demonstrated how we go about this. However, at the  SIGSPATIAL 2024 conference  we (Hossein Amiri, Will Kohn, Shiyang Ruan, Joon-Seok Kim, Hamdi Kavak, Dieter Pfoser, Carola Wenk, Andreas Zufle and myslf) have a demonstration paper entitled "The Pattern of Life Human Mobility Simulation." in which we show: 

1. How to run the Patterns of Life Simulation with the graphical user interface (GUI) to visually explore the mobility patterns of a region.
2. How to run the Patterns of Life Simulation headless (without GUI) for large-scale data generation.
3. How to adapt the simulation to any region in the world using OpenStreetMap data,
4. Showcase how recent scalability improvements allow us to simulate hundreds of thousands of agents.

If this sounds of interest, below we show the GUI to the model, along with the steps to generate a trajectory dataset or a new map for the simulation. At the bottom of the post you can actually see the papers full reference and a link to download it. While at https://github.com/onspatial/generate-mobility-dataset you can find the source code for the enhanced simulation and data-processing tools for you to experiment with.

Abstract: 

We demonstrate the Patterns of Life Simulation to create realistic simulations of human mobility in a city. This simulation has recently been used to generate massive amounts of trajectory and check-in data. Our demonstration focuses on using the simulation twofold: (1) using the graphical user interface (GUI), and (2) running the simulation headless by disabling the GUI for faster data generation. We further demonstrate how the Patterns of Life simulation can be used to simulate any region on Earth by using publicly available data from OpenStreetMap. Finally, we also demonstrate recent improvements to the scalability of the simulation allows simulating up to 100,000 individual agents for years of simulation time. During our demonstration, as well as offline using our guides on GitHub, participants will learn: (1) The theories of human behavior driving the Patters of Life simulation, (2) how to simulate to generate massive amounts of synthetic yet realistic trajectory data, (3) running the simulation for a region of interest chosen by participants using OSM data, (4) learn the scalability of the simulation and understand the properties of generated data, and (5) manage thousands of parallel simulation instances running concurrently.

Keywords: Patterns of Life, Simulation, Trajectory, Dataset, Customization

A screenshot of the graphical user interface of the Patterns of Life Simulation. The GUI shows the map and the movements of agents on the left side and the social network of agents and their statistical properties on the right side. 

Steps to generate the one trajectory dataset.

Steps to generate a new map for the simulation.

Full referece: 

Amiri, H., Kohn, W., Ruan, S., Kim, J-S., Kavak, H., Crooks, A.T., Pfoser, D., Wenk, C. and Zufle, A. (2024) The Pattern of Life Human Mobility Simulation (Demo Paper), ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Atlanta, GA. (pdf)

In Silico Human Mobility Data Science

In the past we have wrote about using simulation to build synthetic datasets for trajectory analysis due to the limited availability of real world comprehensive datasets. In relation to this work we  (Andreas Züfle, Dieter Pfoser, Carola Wenk, Hamdi Kavak, Taylor Anderson, Joon-Seok Kim, Nathan Holt, Andrew DiAntonio and myself) have a new vision paper entitled "In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data" published in Transactions on Spatial Algorithms and Systems. 

In the paper we sketch out a framework  for in silico mobility data science. The rationale being in someway that mobility data alone does not tell us much about why people do what do and to quote from the paper "but imagine a world where we can go back in time to ask people about the purpose of their mobility to understand why an individual visited a place of interest." By building models (aka, agent-based models) we can do just that which therefore allows us to build in silico human mobility data  

To build this argument, in the paper we review existing data sets of individual human mobility and their limitations in terms of size and representativeness. We then survey existing simulation frameworks that generate individual human mobility data and comment on their limitations before presenting our vision of a scalable in silico world that captures realistic human patterns of life and allows us to generate massive datasets as sandboxes for human mobility data science. Building off this we describe a small sample of applications and research directions that would be enabled by such massive individual human mobility datasets if our vision came true.

If this sounds of interest, below we provide the abstract to the paper, some of the figures we use to highlight our argument and our envisioned framework that could exhibit both realistic behavior and realistic movement. Finally at the bottom of the post we provide a reference and a link to the paper itself. As always, any thoughts or comments are most welcome. 

Abstract:

Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals while data sets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this paper is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform “in silico” experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.

Key Words: Spatial Simulation, Mobility Data Science, Trajectory Data, Location Based Social Network Data, In Silico

The envisioned in silico mobility data science process- (let:) A massive microsimulation is created to simulate realistic human behavior specified by a user through an AI-supported builder tool. (middle:) The microsimulation generates massive datasets, including high-fidelity trajectories of all individuals over years of simulation time. This data, which is 100% accurate and complete (in the simulated world) is then sampled to generate realistic datasets. (right:) These datasets are then used to perform mobility data science tasks in the simulated in silico world as if it was the real world. The results of these tasks can then be compared to the ground truth data (of the simulated in silico world) for validation.

The Patterns of Life Simulation. A video of the simulation can be found at: https://www.youtube.com/watch?v=rP1PDyQAQ5M.

Envisioned framework for a simulation that exhibits both realistic behavior and realistic movement.

Full reference: 

Züfle, A., Pfoser, D., Wenk, C., Crooks, A.T., Kavak, H., Anderson, T., Kim, J-S., Holt, N. and Diantonio, A. (2024), In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper), Transactions on Spatial Algorithms and Systems (pdf).

Massive Trajectory Data Based on Patterns of Life

Following on from the last post, we (Hossein Amiri, Shiyang Ruan, Joon-Seok Kim, Hyunjee Jin, Hamdi Kavak, Dieter Pfoser, Carola Wenk and Andreas Zufle and myself) have a paper in the Data and Resources track at the 2023 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems entitled "Massive Trajectory Data Based on Patterns of Life".  

This Data and Resources paper introduces readers to a large sets of simulated individual-level trajectory and location-based social network data we have generated from our Urban Life Model (click here to find out more about the model). The data comprises of 4 suburban and urban regions, including 1) the George Mason University Campus area, Fairfax, Virginia, 2) the French Quarter of New Orleans, Louisiana, 3) San Francisco, California, and 4) Atlanta, Georgia. For each of the 4 study regions, we run the simulation with 1K, 3K, 5K, and 10K agents for 15 months of simulation time. We also provide simulations for 10 years and 20 years, having 1K agents for each of the 4 regions of interest. For each dataset, three items are provided: 1) Check-ins, and 2) social network links and 3) trajectory information per agent per five-minute tick. As such we argue in the paper that our datasets are orders of magnitude larger than existing real-world trajectory and location-based social network (LBSN) data sets. 

If this sounds of interest we encourage readers to check out the paper (see the bottom of this post), while the datasets, as well as additional documentation, can be found at OSF (https://osf.io/gbhm8/) and the data generator (model) can be found at https://github.com/azufle/pol.

Abstract: Individual human location trajectory and check-in data have been the driving force for human mobility research in recent years. However, existing human mobility datasets are very limited in size and representativeness. For example, one of the largest and most commonly used datasets of individual human location trajectories, GeoLife, captures fewer than two hundred individuals. To help fill this gap, this Data and Resources paper leverages an existing data generator based on fine-grained simulation of individual human patterns of life to produce large-scale trajectory, check-in, and social network data. In this simulation, individual human agents commute between their home and work locations, visit restaurants to eat, and visit recreational sites to meet friends. We provide large datasets of months of simulated trajectories for two example regions in the United States: San Francisco and New Orleans. In addition to making the datasets available, we also provide instructions on how the simulation can be used to re-generate data, thus allowing researchers to generate the data locally without downloading prohibitively large files.

Full Referece: 

Amiri, H., Ruan, S., Kim, J., Jin, H., Kavak, H., Crooks, A.T., Pfoser, D., Wenk, C. and Züfle, A. (2023), Massive Trajectory Data Generation using a Patterns of Life Simulation, Proceedings of the 2023 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Hamburg, Germany. (pdf)

Urban life: A model of people and places

We have just wrapped up project that created a simple agent-based simulation of urban life as part of DARPA's Ground Truth Program. To this end we have just published a  new paper entitled "Urban life: a model of people and places" published in Computational and Mathematical Organization Theory, with Andreas Züfle, Carola Wenk, Dieter Pfoser, Joon-Seok Kim, Hamdi Kavak, Umar Manzoor, Hyunjee Jin  and myself. In the paper we provide an overview of the model and how it was used to test and validate human domain research. For interested readers, below you can find the abstract  to the paper along with some images that will give you a sense of our simulation model (which for interested readers was created with MASON and its GIS extension (GeoMason). While at the bottom of the post you can find the full reference and a link to the paper. 

 Abstract

We introduce the Urban Life agent-based simulation used by the Ground Truth program to capture the innate needs of a human-like population and explore how such needs shape social constructs such as friendship and wealth. Urban Life is a spatially explicit model to explore how urban form impacts agents’ daily patterns of life. By meeting up at places agents form social networks, which in turn affect the places the agents visit. In our model, location and co-location affect all levels of decision making as agents prefer to visit nearby places. Co-location is necessary (but not sufficient) to connect agents in the social network. The Urban Life model was used in the Ground Truth program as a virtual world testbed to produce data in a setting in which the underlying ground truth was explicitly known. Data was provided to research teams to test and validate Human Domain research methods to an extent previously impossible. This paper summarizes our Urban Life model’s design and simulation along with a description of how it was used to test the ability of Human Domain research teams to predict future states and to prescribe changes to the simulation to achieve desired outcomes in our simulated world.

Our generated maps colored based on different aggregation levels.

A screenshot of the graphical user interface from a representative model run. Top-Left: The spatial network and agents. Bottom left: Simulation parameters that can be specified prior to simulation start. Top-middle: the social network. Bottom-middle: Summary statistics of the simulation during tun-time such as friendship. Right: Profiles of recreational sites.

Screenshot of the epidemic simulator depicting the French Quarter, New Orleans, LA, USA.

Full Reference:

Züfle, A., Wenk, C., Pfoser, D., Crooks, A.T., Kavak, H., Kim, J-S. and Jin, H. (2021), Urban Life: A Model of People and Places, Computational and Mathematical Organization Theory. Available at https://doi.org/10.1007/s10588-021-09348-7 (pdf)

Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework

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)