The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2025 (ACM SIGSPATIAL 2025), the thirty-second edition, will be held in Minneapolis, MN, USA, from November 3 to November 6, 2025 (https://sigspatial2025.sigspatial.org/org/). The conference began as a series of symposia and workshops starting in 1993 with the aim of bringing together researchers, developers, users, and practitioners in relation to novel systems based on geospatial data and knowledge, and fostering interdisciplinary discussions and research in all aspects of geographic information systems. The conference provides a forum for original research contributions covering all conceptual, design, and implementation aspects of geospatial data ranging from applications, user interfaces, and visualization to data storage and query processing and indexing. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).

Analysis and management of big data are important areas of research for data researchers and scientists. Both the industry and governmental agencies have invested tremendous resources and effort in the area of big data analysis and management in the past decade. Within the realm of big data, spatial and spatio-temporal data are still one of the fastest-growing types of data. With advances in remote sensors, sensor networks, and the proliferation of location sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has continued to explode in recent years. In addition, significant progress in ground, air, and space-borne sensor technologies has led to unprecedented access to earth science data for scientists from different disciplines. For example, NASA recently collected its 10 millionth Landsat image [4] and the volume of satellite imagery being collected has reached the petabyte scale. Analysis of this large-scale data poses new challenges to researchers.

The study of animal and human movement has experienced a remarkable boom over the last decades, mainly due to the continuous development of location-aware sensors (e.g. bio-logging devices for animals; GPS trackers for humans) capable of capturing individual locations at a constantly increasing spatio-temporal resolution [7, 2]. Anthropogenic pressure plays a critical role in shaping animal movement and behavior. Similarly, human movement behavior can largely be influenced by disruptive environmental events causing people to shift their mobility patterns, which eventually may impact wildlife activity patterns in areas where wildlife intersects with human presence.

This report summarizes the 14 th edition of the International Workshop on Computational Transportation Science (IWCTS 2021).

This report describes the development and finalization of the 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2020), held virtually online, November 3-6, 2020. The attendance for the 2020 conference was 999, the highest in the history of ACM SIGSPATIAL.

As pandemic wide spread results in locking down vital facilities, digital contact tracing is deemed as a key for re-opening. However, current efforts in digital contact tracing, running as mobile apps on users' smartphones, fall short in being effective and present two major weaknesses related to accessibility and apparent privacy concern augmentation. Indeed, accessibility is affected by several factors such as smartphone penetration, age, or socio-economic conditions. The privacy concern on the other hand comes from the fear of having a piece of technology that is monitoring us all the time, everywhere, even when contact tracing is irrelevant. This paper lays out the vision and guidelines for the next era of digital contact tracing, where the contact tracing functionality is moved from being personal responsibility to be the responsibility of facilities that users visit daily. Our proposal tackles the two aforementioned shortcomings by disengaging users from using their own smartphones and requiring facilities to provide the technological devices needed for contact tracing. By doing so, we reassure users that their contacts are only considered in places where manual contact tracing is not effective, and cease being recorded as soon as they leave the facilities they visit. A privacy-preserving architecture is proposed, which can be mandated as a prerequisite for any facility to re-open during or after the pandemic. We finally outline research opportunities and challenges revolving around contact tracing system design and data management.

The Internet of Things (IoT) has recently received significant attention. An IoT device may possess an array of sensors that for example monitors the air temperature, carbon monoxide level, wifi signals, and sound intensity. IoT data is initially created on the device, then sent over to a central database system (e.g., the cloud) that organizes and prepares such data for the ongoing use by myriad applications, which include but are not limited to smart home, smart city, the industrial internet, connected cars, and connected health. Data generated by IoT devices is inherently spatial and temporal. For instance, an audio signal represents the variation of the sound intensity (retrieved by a sound sensor) over the time dimension. Furthermore, IoT devices are either installed in a static location (e.g., a building, a traffic intersection) or can be attached to moving objects such as a connected vehicle or a wearable device. In this article, we argue that existing IoT data systems do not properly consider the SpatioTemporal aspect of such data. Hence, the article represents a call for action to the SIGSPATIAL community in order to conduct research on building systems and applications that treat both the spatial and temporal dimensions of IoT data as first class citizens.

Novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a pandemic with 1,420,299 confirmed cases and 85,992 total deaths within the United States as of May 15th, 2020. As the number of cases continues to climb, detecting clusters of COVID-19 is critical to alleviate the strain on our public health system through improved resource allocation and decision-making. Here, we report on an analysis of daily case data at the county level using the prospective spatial-temporal scan statistic. In previous work, we performed the analysis for March 27th 2020 [1], and here we report updated results as of April 27th 2020, producing a new set of "active" and emerging clusters present. Our analysis resulted in sixteen significant space-time clusters of COVID-19 at the county level in the U.S. during the time span of March 22nd -- April 27th. The spacetime pattern of significant clusters mirrors active and emerging disease hot-spots at the end of our study period. The statistic can be rerun to support timely surveillance of COVID-19, as demonstrated here.

Web users are long-standing sources for rich renewable datasets that are exploited in a wide variety of applications. Such datasets include significant spatial and temporal challenges that shape today's techniques and future technologies in the spatial community. This article highlights microblogs as a renewable source of user-generated data with a great fortune of spatial and temporal information about users, locations, and events that are exploited in rich applications. The articles covers both data management and analysis, discussing some of the existing challenges and future directions.

Since the "Big Data Research and Development Initiative" launched by the White House in 2012, big data has received great attention from industry and federal agencies alike emerging as an important area of research for scientists worldwide. Within the realm of big data, spatial and spatiotemporal data continues to be among the fastest-growing types of data. With advances in remote sensors, sensor networks, and the proliferation of location sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has exploded in recent years. In addition, significant progress in the ground, air- and space-borne sensor technologies have led to unprecedented access to earth science data for scientists from different disciplines, interested in studying the complementary nature of different parameters. Today, analyzing this data poses a massive challenge to researchers.