arXiv subjects

Joon-Seok Kim

Publications and source records attributed to Joon-Seok Kim.

At least 19 recordsLinked to original sources

arXiv2026

Synthetic Wastewater Epidemiology Data Generation using Patterns-of-Life Simulation

Wastewater contains rich biological signals that can be used to monitor population health, track infectious diseases, and detect emerging outbreaks. Pathogens and other biomarkers in sewage provide a unique, noninvasive view of disease prevalence at the community level. However, extracting these signals requires extensive field sampling, laboratory analysis, and expert interpretation. Consequently, wastewater-based epidemiology (WBE) datasets are scarce, geographically fragmented, and rarely released as open data. Even when available, existing datasets typically cover short time periods and limited geographic regions, restricting their usefulness for method development, benchmarking, and large-scale modeling. To address this gap, we present an application of an existing patterns-of-life simulation framework for generating synthetic infectious disease and wastewater pathogen datasets for wastewater-based epidemiology. Our approach extends a patterns-of-life simulation framework by using it as a model of human mobility and behavior while incorporating disease transmission and pathogen shedding dynamics. The resulting framework generates high-resolution spatial and temporal datasets capturing infection dynamics, mobility behavior, and wastewater-associated pathogen signals. We release a fully simulated dataset containing check-in records, social network links, infection states, pathogen loads, and ground-truth disease transmission information. These data support controlled experimentation for outbreak detection, source localization, resource allocation, surveillance strategy design, mobility-aware wastewater analysis, and targeted public health interventions. We provide datasets for Fulton County and demonstrate that the framework generalizes to other regions by generating data for any city with available OpenStreetMap information.

q-bio.PE↗

arXiv2026

Staypoint Detection from Noisy Trajectory Data [Experiment Paper]

Detecting staypoints from raw trajectory data is fundamental to numerous spatial computing applications. This process transforms raw numeric sequences of geolocations into semantically meaningful locations, such as homes, workplaces, or restaurants. Despite its importance for semantic trajectory analysis, staypoint detection lacks standard benchmarks, and existing algorithms have never been systematically evaluated. This gap persists because no publicly available datasets provide both raw individual trajectories and ground-truth staypoint annotations. This benchmark paper addresses this limitation with two key contributions: (1) we introduce 16 large-scale simulated datasets capturing thousands of agents with annotated staypoints across varying trajectory noise levels, and (2) we evaluate nine staypoint detection algorithms-including both state-of-the-art and novel methods-to analyze their robustness to noise. Our evaluation reveals that existing state-of-the-art algorithms perform poorly under realistic noise conditions. Conversely, our proposed unsupervised methods yield substantial improvements, while supervised approaches drastically outperform existing baselines. While these results are very promising, these datasets and methods are only meant as starting points for future research in staypoint detection.

cs.LG↗

arXiv2026

Stigmergic Graph Memory: An Environment-Aware Approach for Many-to-Many Multi-Agent Pickup and Delivery

Automated fulfillment warehouses must continuously assign and execute pickup-and-delivery work while avoiding congestion. In many-to-many Multi-Agent Pickup and Delivery (MAPD), a request specifies a stock-keeping unit rather than fixed endpoints, requiring the controller to select an agent, source, and destination before path planning. Existing graph-guidance methods primarily influence routing after goals are fixed, leaving endpoint instantiation uninformed by recent traffic. We introduce Stigmergic Graph Memory (SGM), a bounded, decaying memory layer that records recent execution signals on warehouse nodes and directed edges to rank feasible endpoints and route preferences without altering collision constraints or planner validity. Across paired request streams on five layouts, three load levels, and 25 seeds per condition, SGM outperforms two reconstructed many-to-many allocation baselines in all 15 map-load conditions, with paired throughput gains of 20.5-36.7%. These results show that recent execution memory can improve warehouse throughput by shaping which feasible goals enter the planner, not only how agents travel to already fixed goals.

cs.MA↗

arXiv2026

Simulating Public Transit Fare Policies in NYC: An Efficient, Socioeconomic-Aware Framework

Designing equitable and effective public transit fare policies is challenging due to complex interactions among traveler behavior, multimodal networks, and socioeconomic heterogeneity. This paper presents a scalable, data-driven simulation framework for evaluating transit fare policies in New York City (NYC), integrating a synthetic population, agent-based simulation, multimodal travel-time estimation, and fare-sensitive mode choice modeling. We evaluate multiple fare scenarios, including distance-based pricing, fare increases, and fare-free bus policies. Results show that pricing changes modestly affect total ridership but significantly alter modal composition and produce heterogeneous impacts across income groups. In particular, fare-free bus policies generate substantial benefits for lower-income riders by increasing bus usage and reducing fare burden, while introducing trade-offs in revenue. To support city-scale analysis, we introduce a sampling-based approach that reduces computational cost while preserving aggregate accuracy. The proposed framework provides a practical tool for assessing trade-offs between ridership, revenue, and equity, enabling more informed and equitable transit policy design.

cs.CE↗

arXiv2026

Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints

Although the study of human trajectory anomalies is critical for advancing spatial data mining, empirical research remains severely hindered by a pervasive lack of ground-truth datasets. Despite the availability of several real-world and simulated human trajectory collections, these datasets exclusively capture normal mobility patterns and lack annotated anomalies. This specific scarcity is fundamentally driven by the inherent statistical rarity of anomalous events, precluding the feasibility of conventional observational methods. Compounding this challenge, the systematic acquisition of large-scale mobility data is strictly bottlenecked by prohibitive costs and stringent privacy regulations. To overcome these fundamental limitations and establish a reliable human trajectory anomalies dataset with annotated ground truth, we introduce a novel, end-to-end generative framework designed to synthesize realistic trajectory anomalies at scale. Our architecture bridges the gap between purely synthetic mobility data and complex real-world physical constraints by operating directly on baseline simulated trajectories. We employ Large Language Model (LLM) agents to systematically inject semantically meaningful behavioral anomalies such as irregular out-of-distribution check-ins and skipped routine visits. To ensure rigorous spatial validity, the system leverages map-constrained routing reconstruction to recalculate the physical transitions between these LLM agent-modified staypoints. Moreover, to narrow the simulation-to-reality gap, we augment the resulting trajectories with a context-aware spatial noise model, parameterized by environmental and location-specific variables, to accurately emulate heterogeneous GPS sensor degradation.

cs.AI↗

arXiv2026

Spatial Disparities in Fire Shelter Accessibility: Capacity Challenges in the Palisades and Eaton Fires

The increasing frequency and severity of wildfire in California, exacerbated by prolonged drought and environmental changes, pose significant challenges to urban community resilience and equitable emergency response. The study investigates issues of accessibility to shelters during the Palisades and Eaton Fires which started in January 2025 in Southern California that led to over 180,000 displacements and the loss of 16,000 structures. Despite coordinated efforts of many organizations' emergency assistance, shelter shortages left many evacuees without safety or accessible refuge. This research aims to measure shelter accessibility during the fires' peak, evaluate whether existing shelter capacity met the demand, and identify spatial disparities in access. Findings reveal severe shelter shortages and pronounced inequities in access to shelters, particularly in geographically isolated regions and mountainous areas. To address these challenges, we implemented shelter placement strategies using both capacity-based and distance-based approaches, demonstrating potential improvements in accessibility and equity. The findings underscore the critical need for strategic shelter planning and infrastructure development to enhance disaster readiness and reduce vulnerability in regions that frequently experience wildfires.

cs.CY↗

arXiv2026

HD-GEN: A High-Performance Software System for Human Mobility Data Generation Based on Patterns of Life

Understanding individual-level human mobility is critical for a wide range of applications. Real-world trajectory datasets provide valuable insights into movement behaviors and patterns of life but are often constrained by data sparsity and participation bias. Synthetic data, by contrast, offers scalability and flexibility but frequently lacks realism. % To address this gap, we introduce a comprehensive software pipeline for generating, calibrating, processing, and visualizing large-scale individual-level human mobility datasets that combine the realism of empirical data with the control and extensibility simulations. % Our system consists of four integrated components: (1) a data generation engine that constructs geographically grounded simulations using OpenStreetMap data to produce diverse mobility logs; (2) a genetic algorithm--based calibration module that fine-tunes simulation parameters to align with real-world mobility characteristics; (3) a data processing suite that transforms raw simulation logs into structured formats suitable for downstream applications; and (4) a visualization module that extracts and presents key mobility patterns and insights from the processed datasets for improved interpretability. Evaluation of generated trajectory datasets for the Atlanta, Georgia, USA region show realistic behavior that, despite emerging from a simulation without any reference to real human individuals, exhibits realistic human behavior that closely matches aggregate metrics of real-world datasets. We also provide a sensitivity analysis to study what simulation parameters affect simulation runtime. Code and simulated datasets are shared to provide the broad research community with large-scale dataset that, albeit not real, exhibit realistic human behavior while being orders of magnitudes larger than any open real-world mobility dataset.

cs.SE↗

arXiv2025

Training Machine Learning Models on Human Spatio-temporal Mobility Data: An Experimental Study [Experiment Paper]

Individual-level human mobility prediction has emerged as a significant topic of research with applications in infectious disease monitoring, child, and elderly care. Existing studies predominantly focus on the microscopic aspects of human trajectories: such as predicting short-term trajectories or the next location visited, while offering limited attention to macro-level mobility patterns and the corresponding life routines. In this paper, we focus on an underexplored problem in human mobility prediction: determining the best practices to train a machine learning model using historical data to forecast an individuals complete trajectory over the next days and weeks. In this experiment paper, we undertake a comprehensive experimental analysis of diverse models, parameter configurations, and training strategies, accompanied by an in-depth examination of the statistical distribution inherent in human mobility patterns. Our empirical evaluations encompass both Long Short-Term Memory and Transformer-based architectures, and further investigate how incorporating individual life patterns can enhance the effectiveness of the prediction. We show that explicitly including semantic information such as day-of-the-week and user-specific historical information can help the model better understand individual patterns of life and improve predictions. Moreover, since the absence of explicit user information is often missing due to user privacy, we show that the sampling of users may exacerbate data skewness and result in a substantial loss in predictive accuracy. To mitigate data imbalance and preserve diversity, we apply user semantic clustering with stratified sampling to ensure that the sampled dataset remains representative. Our results further show that small-batch stochastic gradient optimization improves model performance, especially when human mobility training data is limited.

cs.LG↗

arXiv2024

Comparative Analysis of Human Mobility Patterns: Utilizing Taxi and Mobile (SafeGraph) Data to Investigate Neighborhood-Scale Mobility in New York City

Numerous researchers have utilized GPS-enabled vehicle data and SafeGraph mobility data to analyze human movements. However, the comparison of their ability to capture human mobility remains unexplored. This study investigates differences in human mobility using taxi trip records and the SafeGraph dataset in New York City neighborhoods. The analysis includes neighborhood clustering to identify population characteristics and a comparative analysis of mobility patterns. Our findings show that taxi data tends to capture human mobility to and from locations such as Lower Manhattan, where taxi demand is consistently high, while often underestimating the volume of trips originating from areas with lower taxi demand, particularly in the suburbs of NYC. In contrast, SafeGraph data excels in capturing trips to and from areas where commuting by driving one's own car is common, but underestimates trips in pedestrian-heavy areas. The comparative analysis also sheds new light on transportation mode choices for trips across various neighborhoods. The results of this study underscore the importance of understanding the representativeness of human mobility big data and highlight the necessity for careful consideration when selecting the most suitable dataset for human mobility research.

cs.CY↗

arXiv2024

The Patterns of Life Human Mobility Simulation

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.

cs.MA↗

arXiv2024

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.

cs.OH↗

arXiv2021

Change of human mobility during COVID-19: A United States case study

With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the delta Time Spent in Public Places (Delta-TSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the Delta-TSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. We observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.

cs.LG↗

arXiv2021

Strain-Modulated Interlayer Charge and Energy Transfers in MoS2/WS2 Heterobilayer

Excitonic properties in 2D heterobilayers are closely governed by charge transfer (CT) and excitonic energy transfer (ET) at van der Waals interfaces. Various means have been employed to modulate the interlayer CT and ET, including electrical gating and modifying interlayer spacing, but with limited extent in their controllability. Here, we report a novel method to modulate these transfers in MoS2/WS2 heterobilayer by applying compressive strain under hydrostatic pressure. Raman and photoluminescence measurements, combined with density functional theory calculations show pressure-enhanced interlayer interaction of the heterobilayer. Photoluminescence enhancement factor η of WS2 in heterobilayer decreases by five times up to ~4 GPa, suggesting a strong ET, whereas it increases by an order of magnitude at higher pressures and reaches almost unity, indicating enhanced CT. Theoretical calculations show that orbital switching in the conduction bands is responsible for the modulation of the transfers. Our findings provide a compelling approach towards effective mechanical control of CT and ET in 2D excitonic devices.

cond-mat.mtrl-sci↗

arXiv2020

Simplification of Indoor Space Footprints

Simplification is one of the fundamental operations used in geoinformation science (GIS) to reduce size or representation complexity of geometric objects. Although different simplification methods can be applied depending on one's purpose, a simplification that many applications employ is designed to preserve their spatial properties after simplification. This article addresses one of the 2D simplification methods, especially working well on human-made structures such as 2D footprints of buildings and indoor spaces. The method simplifies polygons in an iterative manner. The simplification is segment-wise and takes account of intrusion, extrusion, offset, and corner portions of 2D structures preserving its dominant frame.

cs.CG↗

arXiv2016

A Review on Mechanics and Mechanical Properties of 2D Materials - Graphene and Beyond

Since the first successful synthesis of graphene just over a decade ago, a variety of two-dimensional (2D) materials (e.g., transition metal-dichalcogenides, hexagonal boron-nitride, etc.) have been discovered. Among the many unique and attractive properties of 2D materials, mechanical properties play important roles in manufacturing, integration and performance for their potential applications. Mechanics is indispensable in the study of mechanical properties, both experimentally and theoretically. The coupling between the mechanical and other physical properties (thermal, electronic, optical) is also of great interest in exploring novel applications, where mechanics has to be combined with condensed matter physics to establish a scalable theoretical framework. Moreover, mechanical interactions between 2D materials and various substrate materials are essential for integrated device applications of 2D materials, for which the mechanics of interfaces (adhesion and friction) has to be developed for the 2D materials. Here we review recent theoretical and experimental works related to mechanics and mechanical properties of 2D materials. While graphene is the most studied 2D material to date, we expect continual growth of interest in the mechanics of other 2D materials beyond graphene.

cond-mat.mes-hall↗

arXiv2016

Origin of the superconductivity of WTe2 under pressure

Tungsten ditelluride (WTe2) has attracted significant attention due to its interesting electronic properties, such as the unsaturated magnetoresistance and superconductivity. Recently, it has been proposed to be a new type of Weyl semimetal, which is distinguished from other transition metal dichalcogenides (TMDs) from a topological prospective. Here, we study the structure of WTe2 under pressure with a crystal structure prediction and ab initio calculations combined with high pressure synchrotron X-ray diffraction and Raman spectroscopy measurements. We find that the ambient orthorhombic structure (Td) transforms into a monoclinic structure (1T') at around 4-5 GPa. As the transition pressure is very close to the critical point in recent high-pressure electrical transport measurements, the emergence of superconductivity in WTe2 under pressure is attributed to the Td-1T' structure phase transition, which associates with a sliding mechanism of the TMD layers and results in a shorter Te-Te interlayer distance compared to the intralayer ones. These results highlight the critical role of the interlayer stacking and chalcogen interactions on the electronic and superconducting properties of multilayered TMDs under hydrostatic strain environments.

cond-mat.mtrl-sci↗

arXiv2016

Laser-assisted Oxidation of Multi-layer Tungsten Diselenide Nanosheets

We report the structural and electrical characterization of tungsten oxides formed by illuminating multi-layer tungsten diselenide (WSe2) nanosheets with an intense laser beam in the ambient environment. A noninvasive microwave impedance microscope (MIM) was used to perform electrical imaging of the samples. The local conductivity ~100 S/m of the oxidized product, measured by the MIM and conventional transport experiments, is much higher than that of the pristine WSe2, suggesting the formation of sub-stoichiometric WO3-x polycrystals with n-type carriers. With further efforts to improve the conductivity of the oxides, the laser-assisted oxidation process may be useful for patterning conductive features on WSe2 or forming electrical contacts to various transition metal dichalcogenides.

cond-mat.mtrl-sci↗

arXiv2015

Thermal Oxidation of WSe2 Nano-sheets Adhered on SiO2/Si Substrates

Due to the drastically different intralayer versus interlayer bonding strengths, the mechanical, thermal, and electrical properties of two-dimensional (2D) materials are highly anisotropic between the in-plane and out-of-plane directions. The structural anisotropy may also play a role in chemical reactions, such as oxidation, reduction, and etching. Here, the composition, structure, and electrical properties of mechanically exfoliated WSe2 nano- sheets on SiO2/Si substrates were studied as a function of the extent of thermal oxidation. A major component of the oxidation, as indicated from optical and Raman data, starts from the nano-sheet edges and propagates laterally towards the center. Partial oxidation also occurs in certain areas at the surface of the flakes, which are shown to be highly conductive by microwave impedance microscopy. Using secondary ion mass spectroscopy, we also observed extensive oxidation at the WSe2/SiO2 interface. The combination of multiple microcopy methods can thus provide vital information on the spatial evolution of chemical reactions on 2D materials and the nanoscale electrical properties of the reaction products.

cond-mat.mtrl-sci↗