Journal of Spatial Information Science

Spatial data science languages: commonalities and needs

Sat, 27 Dec 2025 00:00:00 +0000

Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modeling communities, and statistical models. The following set of recommendations have been formulated:
(i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies;
(ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those;
(iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of simple features,
(iv) managing communities and fostering diversity is a necessary, on-going effort, and
(v) tools for cross-language development need more attention and support.

Preserving privacy of spatial distances using randomized geometric surface calculations

Jonas Klingwort, Sarah Redlich — Sat, 27 Dec 2025 00:00:00 +0000

This paper introduces and evaluates a novel method for privacy-preserving distance computations. The method is based on randomized geometric surface calculations and replaces coordinates with contextual variables representing information about the coordinates or the distances between coordinates. The method is presented with an accompanying step-by-step workflow. Its applicability is demonstrated with real-world spatial data sets from Germany and the Netherlands that contain information about hospital and school locations. Open data was used to enable reproducibility. The method's utility is evaluated in detail using correlations, the relative root mean squared error (RRMSE), a Monte Carlo simulation, and the Wasserstein distance. The results show that the method yields high correlations, provides reasonably accurate results as an RRMSE of about 20% is achieved, converges fast, and preserves the spatial distribution of the true coordinates.

Implementing checkpoint-based movement data analysis using cordon networks: A framework validated with transportation case studies

Yaguang Tao, Qian Sun, Matt Duckham — Sat, 27 Dec 2025 00:00:00 +0000

Checkpoint data are generated by movement past fixed "checkpoints," such as smart-card readers. The heterogeneity of checkpoint data sources, data structures, data models, and granularities means existing research on checkpoint-based movement analytics relies on bespoke or ad-hoc analytics frameworks. This work addresses this gap by developing a consistent framework for analyzing network-constrained checkpoint movement data based on the "cordon network." The cordon network is a simple, graph-based computational structure that captures the underlying spatial structure and heterogeneous granularity of movement through checkpoints. This paper explores the design, development, and testing of an analytics toolkit founded on the cordon network. The approach and its ability to handle heterogeneous checkpoint data added within transportation networks is validated using three diverse transportation case studies. While this study focuses on transportation-related checkpoint data, the discussion and conclusions outline key factors for extending the framework to other domains, providing guidelines for checkpoint movement analysis across different contexts.

Benchmarking large language models for geolocating colonial Virginia land grants

Ryan Mioduski — Sat, 27 Dec 2025 00:00:00 +0000

Virginia's seventeenth- and eighteenth-century land patents survive primarily as narrative metes-and-bounds descriptions, limiting spatial analysis. This study systematically evaluates current-generation large language models (LLMs) in converting these prose abstracts into research-grade latitude/longitude coordinates. A digitized corpus of 5,471 Virginia patent abstracts (1695–1732) is released, with 43 rigorously verified test cases for benchmarking. Six OpenAI models across three architectures—o-series, GPT-4-class, and GPT-3.5—were tested under two paradigms: direct-to-coordinate and tool-augmented chain-of-thought invoking external geocoding APIs. Results were compared against a professional GIS workflow, Stanford NER geoparser, Mordecai-3 neural geoparser, and a county-centroid heuristic.

The top single-call model, o3-2025-04-16, achieved a mean error of 23 km (median 14 km), a 67% improvement over professional GIS methods and 70% better than Stanford NER. A five-call ensemble further reduced errors to 19 km (median 12 km) at minimal additional cost (~USD 0.20 per grant). Paired Wilcoxon tests confirm ensemble superiority (W=629, p=0.03 vs. single-shot). A patentee-name redaction ablation slightly increased error (~9%), showing reliance on metes-and-bounds reasoning rather than memorization. The cost-effective gpt-4o-2024-08-06 model maintained a 28 km mean error at USD 1.09 per 1,000 grants, establishing a strong cost-accuracy benchmark. External geocoding tools offer no measurable benefit for this task.

These findings demonstrate that LLMs can georeference early-modern records as accurately and significantly faster and cheaper than traditional GIS workflows, enabling scalable spatial analysis of colonial archives.

Airy criterion and the ideal mapping of an ellipsoid of revolution onto a sphere

Elena Novikova — Sat, 27 Dec 2025 00:00:00 +0000

This paper addresses the problem of constructing an ideal projection of an ellipsoid of revolution onto a sphere based on the Airy criterion. General equations required to solve the problem are derived. In particular, the Euler–Urmaev system is obtained, allowing a clear illustration of Gauss's theorem that a distortion-free projection between these surfaces cannot exist. The Euler–Ostrogradsky system is also derived to find the projection that minimizes distortion according to the Airy criterion. Natural boundary conditions for the ideal projection are analyzed. It is shown that on the boundary of the mapping region, Tissot’s indicatrices are aligned either along the normals or tangents to the boundary, and one of the extremal linear scale factors is equal to unity. Since the value of the Airy criterion depends not only on the projection’s mapping functions but also on the radius of the sphere, an additional integral condition is introduced alongside the Euler–Urmaev system, the Euler–Ostrogradsky system, and the natural boundary conditions. According to this condition, the integral of the area distortion over the entire mapping region of the ideal projection must be equal to zero. Two specific cases are examined in detail: projection of the entire ellipsoid and of a region bounded by a parallel. For comparison, conformal projections optimized according to the Airy criterion were also constructed for the same mapping regions. The resulting ideal projections can be used in geodesy for solving direct and inverse geodetic problems, and in cartography for constructing double projections.

Editorial

Judith A. Verstegen — Sat, 27 Dec 2025 00:00:00 +0000