Understanding individual human mobility is critical for a wide range of applications. Real-world trajectory datasets provide valuable insights into actual movement behaviors but are often constrained by data sparsity and participant bias. Synthetic data, by contrast, offer scalability and flexibility but frequently lack realism. To address this gap, we introduce a comprehensive software pipeline for generating, calibrating, and processing large-scale human mobility datasets that integrate the realism of empirical data with the control and extensibility of Patterns-of-Life simulations. Our system consists of three integrated components. First, a genetic algorithm-based calibration module fine-tunes simulation parameters to align with real-world mobility characteristics, such as daily trip counts and radius of gyration, enabling realistic behavioral modeling. Second, a data generation engine constructs geographically grounded simulations using OpenStreetMap data to produce diverse mobility logs. Third, a data processing suite transforms raw simulation logs into structured formats suitable for downstream applications, including model training and benchmarking.

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.

This study addresses the challenge of equifinality in agent-based modeling (ABM) by introducing a novel sequential calibration approach. Equifinality arises when multiple models equally fit observed data, risking the selection of an inaccurate model. In the context of ABM, such a situation might arise due to limitations in data, such as aggregating observations into coarse spatial units. It can lead to situations where successfully calibrated model parameters may still result in reliability issues due to uncertainties in accurately calibrating the inner mechanisms. To tackle this, we propose a method that sequentially calibrates model parameters using diverse outcomes from multiple datasets. The method aims to identify optimal parameter combinations while mitigating computational intensity. We validate our approach through indoor pedestrian movement simulation, utilizing three distinct outcomes: (1) the count of grid cells crossed by individuals, (2) the number of people in each grid cell over time (fine grid) and (3) the number of people in each grid cell over time (coarse grid). As a result, the optimal calibrated parameter combinations were selected based on high test accuracy to avoid overfitting. This method addresses equifinality while reducing computational intensity of parameter calibration for spatially explicit models, as well as ABM in general.

The notion of physical space has long been central in geographical theories. However, the widespread adoption of information and communication technologies (ICTs) has freed human dynamics from purely physical to also relational and cyber spaces. While researchers increasingly recognize such shifts, rarely have studies examined how the information propagates in these hybrid spaces (ie physical, relational, and cyber). By exploring the vaccine opinion dynamics through agent-based modeling, this study is the first that combines all hybrid spaces and explores their distinct impacts on human dynamics from an individual’s perspective. Our model captures the temporal dynamics of vaccination progress with small errors (MAE = 2.45). Our results suggest that all hybrid spaces are indispensable in vaccination decision-making. However, in our model, most of the agents tend to give more emphasis to the information that is spread in the physical instead of other hybrid spaces. Our study not only sheds light on human dynamics research but also offers a new lens to identifying vaccinated individuals which has long been challenging in disease-spread models. Furthermore, our study also provides responses for practitioners to develop vaccination outreach policies and plan for future outbreaks.

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.

Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN data sets 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 synthetic towns and 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.

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.

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 geo-simulation 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 massive 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 enabling us to develop solutions to improve LBSN applications such as user-location recommendation, friend recommendation, location prediction, and location privacy.

Urban form and function have been studied extensively in urban planning and geographical information science. However, gaining a greater understanding of how they merge to define the urban morphology remains a substantial scientific challenge. Toward this goal, this paper addresses the opportunities presented by the emergence of crowdsourced data to gain novel insights into form and function in urban spaces. We are focusing in particular on information harvested from social media and other open-source and volunteered datasets (e.g. trajectory and OpenStreetMap data). These data provide a first-hand account of form and function from the people who define urban space through their activities. This novel bottom-up approach to study these concepts complements traditional urban studies to provide a new lens for studying urban activity. By synthesizing recent advancements in the analysis of open-source data, we provide a new typology for characterizing the role of crowdsourcing in the study of urban morphology. We illustrate this new perspective by showing how social media, trajectory, and traffic data can be analyzed to capture the evolving nature of a city’s form and function. While these crowd contributions may be explicit or implicit in nature, they are giving rise to an emerging research agenda for monitoring, analyzing, and modeling form and function for urban design and analysis.

The remarkable success of online social media sites marks a shift in the way people connect and share information. Much of this information now contains some form of geographical content because of the proliferation of location-aware devices, thus fostering the emergence of geosocial media – a new type of user-generated geospatial information. Through geosocial media we are able, for the first time, to observe human activities in scales and resolutions that were so far unavailable. Furthermore, the wide spectrum of social media data and service types provides a multitude of perspectives on real-world activities and happenings, thus opening new frontiers in geosocial knowledge discovery. However, gleaning knowledge from geosocial media is a challenging task, as they tend to be unstructured and thematically diverse. To address these challenges, this article presents a system prototype for harvesting, processing, modeling, and integrating heterogeneous social media feeds towards the generation of geosocial knowledge. Our article addresses primarily two key components of this system prototype: a novel data model for heterogeneous social media feeds and a corresponding general system architecture. We present these key components and demonstrate their implementation in our system prototype, GeoSocial Gauge.

In this article, we present a geographically explicit agent-based model (ABM), loosely coupled with vector geographical information systems (GISs), which explicitly captures and uses geometric data and socioeconomic attributes in the simulation process. The ability to represent the urban environment as a series of points, lines, and polygons not only allows one to represent a range of different-sized features such as buildings or larger areas portrayed as the urban environment but is a move away from many ABMs utilizing GIS that are rooted in grid-based structures. We apply this model to the study of residential segregation, specifically creating a Schelling (Citation1971) type of model within a hypothetical cityscape, thus demonstrating how this approach can be used for linking vector-based GIS and agent-based modeling. A selection of simulation experiments are presented, highlighting the inner workings of the model and how aggregate patterns of segregation can emerge from the mild tastes and preferences of individual agents interacting locally over time. Furthermore, the article suggests how this model could be extended and demonstrates the importance of explicit geographical space in the modeling process.