New Initiatives in ACM SIGSPATIAL 2025

This is the conference report of the 23 rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2015), held in Seattle, Washington, USA, November 3-6, 2015. This conference was the twenty-third edition in a series of symposia and workshops that began in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL) and 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, query processing, indexing and data mining.

These proceedings contain the papers from the 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2021), held online November 2-5, 2021. The conference started as a series of workshops and symposia back in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. It continues to provide 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, query processing, indexing and data mining. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).

The conference started as a series of workshops and symposia back in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. It continues to provide 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, query processing, indexing and data mining. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).

The topic of managing uncertain data has been explored in many ways. Different methodologies for data storage and query processing have been proposed. As the availability of management systems grows, the research on analytics of uncertain data is gaining in importance. Similar to the challenges faced in the field of data management, algorithms for uncertain data mining also have a high performance degradation compared to their certain algorithms. To overcome the problem of performance degradation, the MCDB approach was developed for uncertain data management based on the possible world scenario. As this methodology shows significant performance and scalability enhancement, we adopt this method for the field of mining on uncertain data. In this paper, we introduce a clustering methodology for uncertain data and illustrate current issues with this approach within the field of clustering uncertain data.

In the Internet of thing (IoT), with the geographic location of geospatial sensor data and the global positioning systems, location-based services (LBSs) can provide powerful location-aware IoT applications for mobile clients according to their current locations. For LBSs, a k-nearest neighbor (kNN) search can provide a mobile client with geospatial sensor data of k-nearest spatial points of interest (POIs) according to its current location. In this paper, we propose a spatial air index with neighbor information to organize IoT geospatial sensor data for processing kNN searches in the wireless broadcast systems. Since the answered POIs may be neighbors of each other, we add neighbor information to the index structure, which is interleaved with geospatial sensor data, to speed up the query processing. To avoid unnecessary examination of geospatial sensor data from the wireless channel, the proposed method provides the centroid of geospatial data and the corresponding longest distance between the centroid and geospatial data in the region. With this information, the query processing of a kNN search can quickly determine whether to skip examining this region, saving energy consumption of the mobile device. Performance evaluations have verified that the proposed method outperforms the distributed spatial index.

Geographic databases are becoming a popular subject for research projects. It is acknowledged that database requirements for such applications as Geographic Information Systems (GISs), computer cartography, remote-sensing image databases, and emergency routing/dispatching are distinct from those for traditional database applications; however, the specific database needs due to the properties of geographic data are frequently overlooked. Here, we describe a specific problem domain, a vehicle navigation system, and analyze the properties of data to be modeled appropriately and managed efficiently by database management systems. Vehicle navigation systems need continuous, fast access to very large databases. Through the combination of real-time access and geographic data, we identify particularly challenging database requirements of this application with respect to data models, query languages, and query processing.

Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer's accuracies, class-level precisions or user's accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development.

Abstract: Geospatial intelligence (GEOINT) is a multidisciplinary field that combines geographic information systems (GIS), remote sensing, and data analysis to provide critical insights for decision-making in various sectors. This research article explores the theoretical foundations, methodologies, and practical applications of geospatial intelligence. Through comprehensive analysis, the study identifies key techniques used in GEOINT, significant case studies, and the challenges faced by professionals in the field. The findings highlight the importance of geospatial technologies in enhancing situational awareness, disaster management, urban planning, and national security. Practical recommendations for geospatial practitioners, policymakers, and researchers are provided, along with suggestions for future research to further advance the field. Keywords: Geospatial Intelligence, Mapping the Future, Geospatial Technologies, Geographic Information Systems (GIS), Remote Sensing, Spatial Data Analysis, Geospatial Data, Earth Observation, Geospatial Analytics, Cartography, Spatial Information, Geospatial Applications, Location-Based Services, Spatial Intelligence, Geospatial Innovation, Geographic Data Processing, Spatial Decision Support, Geospatial Solutions, Environmental Monitoring, Urban Planning, Geospatial Big Data, Spatial Visualization, Geospatial Modeling, Geospatial Infrastructure and Future Geospatial Trends.

We are on the edge of a revolution in the way we visualize and query digital data about our environment. To date, computer displays of our environment in the third dimension have been limited to computer-aided design (CAD) packages and the query of related data limited to geographical information systems (GIS) packages in two dimensions. The current innovation wave across the spatial data information field is based on the development and dissemination of three-dimensional GIS (3-D GIS) which allows data to be visualized and queried on an x, y and z axis plane. A number of the key players in information visualization allow conventional two-dimensional data to be viewed and exported in a three-dimensional format, currently using the standard Virtual Reality Modeling Language 2.0 (VRML 2.0). However such methods of visualization and data query are limited in their practicality. The move towards 3-D GIS in standard packages has been rather hit and miss, with the third dimension often only used as a substitute for basic CAD-like visualization. We argue here that 3-D GIS will only become a reality when it is directly linked with CAD models; and that the Internet is the most appropriate medium through which this is most likely to occur. We illustrate these arguments in an overview of research into the virtual city in general and our own development of ‘Virtual London’ in particular. Further, we explore the rise of the global virtual city, a network of virtual cities that provide an insight into the future of digital space.

In data mining, the usefulness of a data pattern depends on the user of the database and does not solely depend on the statistical strength of the pattern. Based on the premise that heuristic search in combinatorial spaces built on computer and human cognitive theories is useful for effective knowledge discovery, this study investigates how the use of self-organizing maps as a tool of data visualization in data mining plays a significant role in human-computer interactive knowledge discovery. This article presents the conceptual foundations of the integration of data visualization and query processing for knowledge discovery, and proposes a set of query functions for the validation of self-organizing maps in data mining.

Relational database technology has enabled easy and efficient management of data in a variety of applications. This technology, however, has been used for computer simulations to a limited extent. In this context, users may need to analyze the results of multiple simulation runs, which typically capture interactions between various entities in the simulated world. To these users, storing such simulation data only in the form of relational tables may seem counter-intuitive or challenging, particularly if they are not familiar with database normalization theory. Furthermore, some complex queries, including those that examine the cumulative effect of an action (e.g., disease propagation after an initial outbreak), can neither be easily expressed nor efficiently executed on traditional database systems. We propose a new database approach that aims to achieve convenient and highly efficient storage and querying of simulation data by adopting a graph data model. We also discuss new challenges that arise in this research, with a focus on language design, coordination of simulations, data storage, and query processing.

ABSTRACTThe tight coupling between geospatial data and spatial analysis results in high costs in terms of efficiency when developing algorithms to accommodate different types of data, even when the analysis tasks are the same. Universal GIS (Geographic information system) algorithms, as alternatives to tightly coupled approaches, can reduce development costs. However, a unified representation of spatial data is necessary to support the development of universal GIS algorithms. To this end, this research proposes and implements a template-based approach using geometric algebra to create a unified representation of multidimensional data. The template is composed of parameters and operators for GIS representation and computation. The template approach can support general GIS analyses with parameter unfolding and operator integration methods. A case study of intersection analysis shows that developing programming scripts based on computation templates is much simpler than traditional methods. The results suggest that the template-based method is more efficient than traditional methods and more convenient for high-dimensional applications.

In a cloud environment, resources should be acquired quickly and automatically released at runtime. Traditional trajectory data partitions, indexing and query processing techniques are extended, so that they can take advantage of the cloud of large clusters of highly parallel processing capabilities. There are ways with trajectory data in the cloud database query processing. The advanced sensing techniques available today caused the existence of many types of trajectory datasets whose study can form a strong basis for decision making regarding a data context. Trajectory data generally includes trajectory classification, trajectory clustering, and trajectory associations and so on. All these tools need trajectory similarity measures to get comparisons in trajectory data. Some of the popular existing methods of trajectory similarity include Euclidean distance, semantic distance and there near variants. Each variant of these techniques exhibits notable differences in measurement of similarity and computational difficulty. This paper targeted to deal with this situation with the goal of lower computational efforts in trajectory similarity computations. A two‐phase trajectory similarity measure is defined. The first phase does point level analysis of trajectory points representing all trajectories and the following phase use the analysis to find the similarities. Using a hybrid similarity framework, new means for trajectory clustering and trajectory query processing are developed. From trajectory analysis followed by trajectory clustering, evaluation of cluster quality and processing of trajectory query are undertaken in this paper in different ways from the existing methods. All the proposed developments are tested with trajectory datasets with multiple attributes and the results are more favour to proposed framework than the existing ones.

Even just a decade ago having a chapter dedicated to Geographical Information Systems (GIS) in a book on methods in history would have been seen as unusual, but such is the development of digital mapping and spatial technologies over recent years that historians cannot now afford to be without them. This chapter provides an introduction to GIS and its applica- tion and potential for historical study. Given the scope of the chapter it can only be reasonably cursory and aims to show what can be done with GIS in historical research (and teaching), with some pointers for historians on how they might employ GIS and spatial technologies in their own work. It should become clear that GIS is no longer a highly specialised and technical approach to handling spatial and temporal information but an increasingly user-friendly and accessible basis for integrating, analysing and visualising historical and geographical data of a wide variety, both qualitative and quantitative in nature. With this in mind, the chapter sets out: first, what GIS is and how it works; second, why historians might wish to use GIS; thirdly, how GIS is useful for providing insights into the past; and lastly, what the future might be in using GIS for historical research, particularly its problems and challenges. The chapter also provides some directions for further study, including the locations of a growing number of digital online resources that use GIS to disseminate historical material.

This volume contains the papers selected for presentation at the 13th ACM International Symposium on Advances in Geographic Information Systems (ACM-GIS 2005) in conjunction with the 14th International Conference on Information and Knowledge Management (ACM-CIKM 2005) in Bremen, Germany on November 4 and 5, 2005.The mission of this 13th ACM-GIS is to bring together researchers, developers, users, and practitioners carrying out research and development in novel systems based on geo-spatial data and knowledge, and fostering interdisciplinary discussions and research in all aspects of geographic information systems. The symposium will provide a forum for original research contributions and practical system design, implementation, and applications of GIS.In response to the call for papers, the program committee received 85 submissions, out of which 31 were selected for presentation and included in these proceedings. Each paper was carefully reviewed by at least three members of the program committee and external reviewers. The selection of the ACM-GIS'05 papers was based primarily on their potential to influence future research. In addition, the program committee invited one keynote speaker for additional presentations in the symposium.