The task of anomaly detection in geospatial data is to find data points in space and time that deviate so much from other observations as to arouse suspicion that it was generated by a different mechanism. Most of the existing work in geospatial data analysis relies on supervised machine learning, which involves training models on labeled datasets, allowing them to learn and make accurate predictions. Anomalies, by definition, are rare events that occur infrequently and unpredictably. As a result, labeled datasets for these events are either non-existent or extremely limited. Consequently, anomaly detection research calls for new strategies that can identify rare and unexpected events based on patterns and distributions within the data itself in an unsupervised fashion. These anomalies may manifest as unexpected changes in environmental conditions, natural disasters, unusual human activities, or irregular patterns in spatiotemporal distributions. To answer this call, we organized the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection (GeoAnomalies'24) at SIGSPATIAL'24. In this Newsletter Article, we report our first findings and map future research directions.
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).
Urban AI is an interdisciplinary field that integrates artificial intelligence, spatial computing, and urban science to tackle the multifaceted challenges of urbanization. The proliferation of urban data and the rapid digitization of city infrastructures have created unprecedented opportunities for leveraging data-driven machine learning approaches in urban science. Urban AI addresses innovative applications of AI techniques to urban problems, the development of AI-ready urban data infrastructures, and a wide array of practical urban applications. These applications span diverse areas, including urban planning, traffic forecasting, energy optimization, public safety, urban agriculture, and land use management.
Geospatial simulation is an effective tool to experience physical and/or cyber space (e.g., the metaverse). New experiences gained from geospatial simulations can bring significant benefits, including situational awareness, insight into environments, and entertainment. To take advantage of such simulations, it is crucial to advance the methodology of modeling and simulation, develop plausible models, and apply them to various domains, leveraging big data and evolving technologies.
The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems held a half-day workshop on Sustainable Urban Mobility. This was the second Workshop of this series, after a successful initial event at ACM SIGSPATIAL in 2023 in Hamburg [1]. The workshop explicitly considers urban mobility as an open, complex system, and hence points to a wicked problem: estimating the global impact of any intervention to a mobility system that is aiming for less emissions, less vehicles on the road and less single-person cars, or any other sustainability goal. Often, such interventions show unintended consequences. For example, efficiency gains in one mode of mobility may show rebound effects on that mode as well as feedback loops with other modes. While we typically ignore such interrelations as "too difficult", the workshop set out to explore what the spatial computing community can contribute.
The 21st century has seen major epidemics and pandemics caused by infectious diseases such as coronaviruses, influenza, and most recently, monkeypox. The spread of infectious diseases within human populations can be conceptualized as a complex system composed of individuals who interact and transmit viruses via spatiotemporal processes that manifest across and between scales. The complexity of this system ultimately makes it difficult to understand, predict, and effectively respond to infectious disease outbreaks. As spatial data becomes increasingly available at high spatial and temporal resolutions, and computing resources can more efficiently handle such data, there are new opportunities for data science and simulation-based solutions to improve public health.
Advances in artificial intelligence, hardware accelerators, and data processing architectures, continue to infiltrate the geospatial information sciences, with a transformative impact on many societal challenges. Recent breakthroughs in deep learning have brought forward an automated capability to learn representational features from massive and complex data, including text, images, and videos. In tandem, rapid innovations in sensing technologies enhance the collection of geospatial data in even higher resolution and throughput, supporting the observation, mapping, and analysis of different events and phenomena on the Earth's surface with unprecedented detail. Combined, these developments are offering the potential for breakthroughs in geographic knowledge discovery, impacting decision-making in areas such as humanitarian mapping, intelligent transport systems, urban expansion analysis, health data analysis and epidemiology, the study of climate change, handling natural disasters, the general monitoring of the Earth's surface, and achieving sustainability.
Since the onset of the COVID-19 pandemic, researchers in the SIGSPATIAL community have utilized computational solutions to better explain, predict, and respond to infectious disease outbreaks. Using spatial computing for pandemic preparedness has also been highlighted as a major application of mobility data science [16]. At the beginning of the COVID-19 pandemic, the SIGSPATIAL community rapidly published ideas to improve our understanding of the spread of the virus in two SIGSPATIAL Special Newsletter Issues in March and July 2020 [28, 29]. These efforts led to the 1st and 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology [5, 4] (formerly called Workshop on Modeling and Understanding the Spread of COVID-19 in 2020) which has provided authors of these newsletter articles a forum to present and discuss their solutions. Including both work published at the SIGSPATIAL Special Newsletter and regular peer-reviewed submissions, this workshop included topics such as the collection of large spatiotemporal datasets [20], leveraging data mining and spatial analysis techniques to analyze and visualize such data [2, 12, 25, 21, 11, 9, 8, 3], developing predictive spatial models and simulations [6, 1, 19, 13, 24, 10, 23, 14], and employing novel technologies towards contact tracing and surveillance [17, 26].
Researchers and practitioners working with spatial data often develop fundamental new techniques they would like to share with their community. These are not necessarily new research results, not yet in any textbook, but they are interesting, self-contained techniques for doing something useful in the domain of spatial data. We call these techniques "spatial gems".
The 15th International Workshop on Computational Transportation Science (IWCTS 2022) is particularly timely given the prominence of human mobility data, such as probe data from cell phones and connected automated vehicles, volunteered geographic information, and other sensing data. This unprecedented access to sensing data of mobility, and of integration of this analytics into smart cities management has led to innovations in intelligent transportation systems, building information management, and urban planning. Due to the scale of the data, these developments are deeply computational.
This report summarizes the focus of the 5 th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC 2022 - https://urbands.github.io/ARIC2022/) that was held in-person in Seattle, Washington on November 1, 2022. The attendance for the workshop was 20. The ARIC workshop started in 2017 with an aim to promote the interdisciplinary discussions among researchers, practitioners and developers to identify techniques and methods that would enable building next generation cities and infrastructures. This workshop has been continuing its mission to bring together researchers and practitioners to address the state-of-the-art techniques/methods, limitations and challenges of making cities that are both smart and resilient to extreme events.
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.
Today, many top conferences in computer science hold workshops and social events to promote women's role in their field. One of the events is Women in Machine Learning (WiML) Workshop, co-located with NeurIPS '19 [1]. Their mission is to "create opportunities for women to engage in substantive technical and professional conversations in a positive, supportive environment." Other related events include "Women in Artificial Intelligence and Machine Learning for Mental Health Applications" at ICML '22 [3] and "Women in Computer Vision" at CVPR '22 [4].
The field of Geospatial Humanities addresses the use of geographic information systems and other spatial technologies in humanities research, looking to address questions related to space and place. The field is constantly evolving, and currently it is posing very interesting challenges relating, for example, to the identification and analysis of real, vague, and imaginary space in humanities sources like archival manuscripts, maps, encyclopedias, newspapers, correspondence collections, and more. These kinds of documents pose new challenges for identifying and analyzing spatial information, and a strong emphasis is being put on new methodologies that leverage the standard tools from geographic information systems, as well as more advanced methods (e.g., based on machine learning) such as text- and image-based geographical analysis, this way combining GIS, NLP, Deep Mapping, Computer Vision, and Qualitative Spatial Representation, among others.
Geospatial simulation is an effective tool to experience physical and/or cyber space (e.g., metaverse). New experiences gained from geospatial simulations can bring significant benefits including situation awareness, insight into environments, and entertainment. To take advantage of such simulations, it is crucial to advance methodology of modeling and simulation, develop plausible models and apply them to domains, leveraging big data and evolving technologies.
The amount of publicly available geo-referenced data has seen a dramatic explosion over the past few years. Human activities generate data and traces that are now often transparently annotated with location and contextual information. At the same time, it has become easier than ever to collect and combine rich and diverse information about locations. For instance, in the context of geo-advertising, the use of geosocial data for targeted marketing is receiving significant interest from a wide spectrum of companies and organizations. With the advent of smartphones and online social networks, a multi-billion dollar industry that utilizes geosocial data for advertising and marketing has emerged. For example, people-specific geospatial data such as geotagged social media posts, GPS traces, data from cellular antennas and WiFi access points are used on a wide scale to directly access people for advertising, recommendations, marketing, and group purchases. Exploiting this torrent of geo-referenced data provides a tremendous potential to materially improve existing recommendation services and offer novel ones, with clear benefits in many domains, including social networks, marketing, and tourism.
The topic of knowledge graphs (KGs) has recently attracted extensive attention in both industry and academia. Knowledge graphs are a new paradigm for representing, retrieving, integrating, and reasoning data from highly heterogeneous and multimodal sources. For example, international conferences, such as the Knowledge Graph Conference, have emerged in the past years, not to mention the increasing number of specialized workshops on KGs co-located with major computer science conferences, including Knowledge Graph Workshop at KDD 2021, Workshop for Deep Learning in Knowledge Graphs at ISWC 2021, Workshop on Knowledge Graph Construction at ESWC 2021, to name but a few. Also, the number of KGs-related papers accepted at these major conferences, including SIGSPATIAL, is rapidly increasing. Meanwhile, we also witness the increasing popularity of knowledge graph technologies in geography, geoinformatics, and GIScience domains. There are growing numbers of knowledge graph-related manuscripts accepted to the top geospatial-related venues, such as the International Journal of Geographical Information Science, Transactions in GIS, the International Journal of Applied Earth Observation and Geoinformation, and so on. Transactions in GIS also held two special issues about knowledge graphs: 1) Symbolic and Subsymbolic GeoAI: Geospatial Knowledge Graphs and Spatially Explicit Machine Learning [4], and 2) Knowledge-based GIS (K-GIS): Theories, Techniques and Applications. In addition, government agencies, industries, and non-governmental organizations (NGOs) are also lifting resources on topics of exploring KGs to build up interdisciplinary science and applications. One example is NSF's Accelerate Convergence Program, which promotes the idea of building an Open Knowledge Network to harness the data revolution. Funded by this program, the KnowWhereGraph is presently among the largest geo-enabled knowledge graphs, which integrates 28 different data layers at the intersection between humans and their environment [2].
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.
This report presents the development and finalization of the 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022), which was held in Seattle, Washington, USA, November 1--4, 2022.
Emerging advances in artificial intelligence, hardware accelerators, and big data processing architectures continue to reach the geospatial information sciences, with a transformative impact on many societal challenges. Recent breakthroughs in deep learning have brought forward an automated capability to learn representative features from massive and complex data, including text, images, or videos. In tandem, rapid innovations in sensing technologies are supporting the collection of geospatial data in even higher resolution and throughput, supporting the observation, mapping, and analysis of different events/phenomena over the earth's surface and in socioeconomic environments with unprecedented detail. Combined, these developments are offering the potential for breakthroughs in geographic knowledge discovery, impacting decision-making in areas such as humanitarian mapping, intelligent transportation systems, urban expansion analysis, health data analysis and epidemiology, the study of climate change, handling natural disasters, and the general monitoring of the earth's surface.
