Spatio-temporal data captures complex dynamics across both space and time, yet traditional visualizations are complex, require domain expertise and often fail to resonate with broader audiences. Here, we propose MapMuse, a storytelling-based framework for interpreting spatio-temporal datasets, transforming them into compelling, narrative-driven experiences. We utilize large language models and employ retrieval augmented generation (RAG) and agent-based techniques to generate comprehensive stories. Drawing on principles common in cinematic storytelling, we emphasize clarity, emotional connection, and audience-centric design. As a case study, we analyze a dataset of taxi trajectories. Two perspectives are presented: a captivating story based on a heat map that visualizes millions of taxi trip endpoints to uncover urban mobility patterns; and a detailed narrative following a single long taxi journey, enriched with city landmarks and temporal shifts. By portraying locations as characters and movement as plot, we argue that data storytelling drives insight, engagement, and action from spatio-temporal information. The case study illustrates how MapMuse can bridge the gap between data complexity and human understanding. The aim of this short paper is to provide a glimpse to the potential of the cinematic storytelling technique as an effective communication tool for spatio-temporal data, as well as to describe open problems and opportunities for future research.
Given a collection of Boolean spatial features, Super-Colocation Pattern Discovery identifies subsets of features that are not only frequently located together but also have dense interactions. For example, the presence of multiple immune cells around cancer cells is more interesting to oncologists than a simple colocation between immune and cancer cells. This problem is important due to its societal applications, including oncology, transportation, and economic analysis. The problem is challenging due to the need to model interaction density among a subset of Boolean spatial features. Related work on colocation pattern mining is limited due to a lack of conceptual, logical, and physical models that accurately represent interaction density. Traditional interest measures (e.g., participation index) largely focus on the mere presence of another spatial feature type and overlook the number or density of neighboring instances. We propose a novel interest measure, termed Super-Colocation Density, which utilizes a matrix or tensor along with a utility-based index to quantify the interaction density among subsets of spatial features. We also introduce novel Super-Colocation Mining algorithms and evaluate the proposed methods through both theoretical analysis and experiments with real and synthetic data.
Community detection stands as a pivotal process in network analysis, having undergone extensive examination over the past 25 years within static network contexts. This task involves partitioning networks based on structural characteristics, specifically into classes of nodes exhibiting denser connections than the overall network. Moreover, in recent years, an additional layer of complexity has arisen, particularly in networks characterized by a static nature where each node and edge is assigned a valid time interval. Such historical graphs, unlike traditional graphs, incorporate a temporal dimension, allowing for the analysis of how connections between entities evolve and change over different time intervals. In this study, we present a distributed algorithm for static community detection within a query time interval in historical graphs. Specifically, when provided with a designated query time interval, our proposed method identifies communities by assessing the individual contributions of each edge and node within the graph during that specified time interval. To the best of our knowledge, this setting has not been considered before in the literature.
The adoption of electric trucks in freight transportation introduces new operational challenges, including limited battery range, sparse charging infrastructure, real-time traffic variability, and heavy-duty vehicle constraints such as weight-restricted roads and limited maneuverability. Addressing these issues requires systems that combine fleet-level planning with behavior-aware, energy-efficient navigation. This paper presents ChargeNav, a system designed for electric truck fleets, composed of two integrated modules: (1) a fleet-level optimization engine that solves the Electric Truck Vehicle Routing Problem (ETVRP), and (2) a trip-level navigation module that provides real-time, personalized range predictions and routing. The fleet optimizer assigns vehicles to deliveries while minimizing energy consumption and satisfying time window and capacity constraints. The navigation module uses historical GPS data and real-time conditions to learn driver route preferences via machine learning, enabling accurate energy predictions based on road topology, elevation, load, traffic, and weather.
Spatiotemporal data modeling has long been a fundamental task across disciplines such as climate & environmental science, and transportation engineering. A typical goal is to estimate unknown information at specific spatiotemporal points based on partially observed data—for example, interpolating weather conditions at unmeasured locations, reconstructing missing historical records, or forecasting the future trajectories of financial markets. These are all core tasks within the broader scope of spatiotemporal modeling. This tutorial (1 hours) introduces a cohesive view of spatiotemporal data modeling, tracing the evolution from traditional statistical approaches to modern deep learning paradigms. We begin by revisiting Kriging and time series decomposition to highlight the essential assumptions and strengths of these classical methods. Next, we explore low-rank matrix and tensor completion techniques, which leverage the structured patterns of spatiotemporal data. We then elaborate on spatiotemporal graph neural networks, which characterize complex dependencies by integrating graph structures with dynamic temporal features. Finally, we discuss recent advances in applying large foundation models to spatiotemporal tasks, including their capabilities and current limitations. Throughout the tutorial, we emphasize how lessons from traditional methods—such as the importance of locality, periodicity, and smoothness priors—can inspire new directions for developing and fine-tuning foundation models in the spatiotemporal domain. We conclude by outlining key challenges and opportunities in bridging classical wisdom with emerging AI capabilities.
