The recent waves of COVID-19 highlighted the importance of understanding and quantifying spatiotemporal interactions to infer, model, and predict disease spread in real time. In this demonstration paper, we present a robust infrastructure for interactive ...

Socio-demographic information is usually only accessible at relatively coarse spatial resolutions. However, its availability at thinner granularities is of substantial interest for several stakeholders, since it enhances the formulation of informed ...

With the rise of data-driven methods for traffic forecasting, accident prediction, and profiling driving behavior, personal GPS trajectory data has become an essential asset for businesses and emerging data markets. However, as personal data, GPS ...

In this paper, we develop a set of normalized spatial temporal properties from which to assess road performance and detect abnormal traffic stream environments. Data is provided by non-instrumented retail vehicle telemetry data, which captures timestamps,...

Given a collection of Boolean spatial feature-types, their instances, a neighborhood relation (e.g., proximity), and a hierarchical taxonomy on the feature-types, taxonomy-aware colocation pattern discovery finds the subsets of feature-types or their ...

We introduce Zebra regression, a novel streaming regression task. A stream of periodically-created point particles travels along a finite, 1-dimensional trajectory, and we observe a stream of sporadic, noisy reports that estimate the location of some ...

Planning of cellular networks is a process in which network engineers select locations for the cellular antennas and adjust parameters like transmission frequency, transmission power and tilt. The goal of network planning is to provide cellular coverage ...

An address, a textual description of a physical location, plays an important role in location-based services such as on-demand delivery and e-commerce. However, abnormal addresses (i.e., an address without detailed information representing a spatial ...

A growing number of research areas such as location-based social networks, intelligent transportation systems, and urban computing utilize large amounts of trajectory data for benchmarking data management approaches and analysis methods. Given the ...

The touring problem aims to find an "interesting" (round) trip of a given length. Here, what is considered interesting depends on the type of the desired route, e.g., a user may be looking for an off-road cycling trip or fast running route.

This problem ...

Many online trip planning and navigation software need to routinely solve the problem of deciding where to take stops during a journey for various services such as refueling (or EV charging), rest stops, food, etc. The goal is to minimize the overhead of ...

The availability of GPS sensors in vehicles has enabled the collection of trajectory data that can be utilized to improve the quality of location-based services. However, mostly due to privacy concerns, many data sets are published without containing ...

Trip purpose plays a critical role in reflecting human mobility behavior. However, it is relatively difficult to determine. With the rapid growth of urban mobility and big mobile data, utilizing these data for trip purpose classification has been a long-...

App-based ride-hailing services, such as Uber and Lyft, have become popular thanks to technology advancements including smartphones and 4G/5G network. However, little is known about to what degree their operations impact urban traffic since ...

Accurate and timely mapping of flood extent plays a crucial role in disaster management such as damage assessment and relief activities. In recent years, high-resolution optical imagery becomes increasingly available with the wide deployment of ...

Geocoding, associating textual addresses with corresponding GPS coordinates, is vital for many location-based services (e.g., logistics, ridesharing, and social networks). One of the most common Geocoding solutions is using commercial map services (e.g., ...

The ability to collect large numbers of trajectory data through GPS-enabled devices have enabled a myriad of very important applications that are widely used on a daily basis. This includes urban computing, transportation, and map APIs for routing and ...

Deep learning models are modern tools for spatio-temporal graph (STG) forecasting. Though successful, we argue that data scarcity is a key factor limiting their recent improvements. Meanwhile, contrastive learning has been an effective method for ...

We design a novel unsupervised approach to delineate building footprints on large-scale LiDAR point clouds. By computing an α-shape on low-height points, we delineate the building bottoms on the ground. We then use the terrain ruggedness index and vector ...

Airborne LiDAR point clouds record three-dimensional structures of ground surfaces with high precision, and have been widely used to identify geospatial objects, facilitating the understanding of the distribution and changing dynamics of the environment. ...