AcadGIS is presented, a free and open-source Python package that creates publication-oriented research maps from high-level commands under one namespace, import acadgis as agis, and provides place-name boundary access, automated study-area locator layouts, thematic cartography, raster and vector layers, curated Earth-observation products, terrain and hydrology context, and configurable PNG, PDF, and SVG export.
Abstract
Academic and project maps are often produced through a fragmented workflow: researchers locate boundaries, manage shapefiles, join tabular data, assemble locator insets, add cartographic decorations, and export figures through desktop GIS or multi-package Python scripts. This creates an accessibility barrier for non-GIS users and a reproducibility problem when data sources, styling choices, and manual edits are not captured in executable form. We present AcadGIS, a free and open-source Python package that creates publication-oriented research maps from high-level commands under one namespace, import acadgis as agis. AcadGIS provides place-name boundary access, automated study-area locator layouts, thematic cartography, raster and vector layers, curated Earth-observation products, terrain and hydrology context, and configurable PNG, PDF, and SVG export without requiring desktop GIS expertise or hand-managed shapefiles. Its design combines one-import access to the scientific-Python stack, publication-oriented defaults with progressive control, local caching, source attribution, and figure specifications based on code, named data, and a pinned package version. Through three representative use cases, we demonstrate how common paper, thesis, and project maps can be expressed as compact, inspectable scripts. Source code: https://github.com/riponcm/AcadGIS.
Abstract. GIS data is inherently multi-dimensional, involving space, time, and attributes, and interpreting it usually requires considerable time and expertise. Many users of web-mapping platforms struggle at the visualization stage, where they must interpret the data, choose appropriate visualization methods, and define map elements such as size, colour, symbols, transparency, and overall composition. They must also understand map rendering through Map Style JSON, which typically demands significant technical knowledge and design experience. To reduce these barriers, we propose a system that combines the Model Context Protocol (MCP) with open Large Language Models (LLMs) served locally through Ollama to automatically generate Map Style JSON conformant with the MapLibre Style Specification directly from vector-based spatial data. The system is built around purpose-built MCP tools that inspect the data, build and validate data-driven styling expressions, and are designed to embed cartographic design principles such as semantic colour selection and the perceptually grounded use of visual variables. In a preliminary evaluation on consumer hardware, using a 77-polygon GeoJSON dataset and a vector tile service, the system recoloured a map's fill from a plain-language request and returned a style that MapLibre rendered without manual correction. The evaluation also shows that the local open model acts mainly as a natural-language front-end, while correctness and renderer compatibility depend on the MCP tool layer. Relying on open models running locally, the approach aligns with the open geospatial ecosystem and integrates with other open-source tools.
Arissara Sompita· The International Archives o...· 0 citations
Abstract. The rapid growth in Earth observation (EO) and Digital Twin Earth data volumes creates a need for global, reproducible, and cloudnative spatial representations. Conventional latitude–longitude grids and projected tiling systems remain useful, but they introduce projection boundaries, non-uniform cell areas, and repeated reprojection costs when combining multi-source products. Grid4Earth addresses this problem through an open-source Python ecosystem built around an ellipsoidal HEALPix Discrete Global Grid System (DGGS) representation and Zarr-based data handling. The ecosystem consists of four composable packages: healpix-geo for WGS84-aware indexing and coverage queries, healpix-resample for CPU/GPU-capable regridding, healpix-plot for visualisation, and healpix-analyse for diagnostics and analysis. We situate Grid4Earth in relation to previous DGGS comparisons, XDGGS, and OGC API – DGGS work, focusing on the implementation layer required for EO workflows. When WGS84 geodetic latitude is passed directly to spherical HEALPix, local cell areas vary by up to approximately 0.9% because of Earth’s non-spherical shape. Grid4Earth preserves the HEALPix equal-area property on WGS84 through an authalic-latitude mapping. The ecosystem has been exercised in HEALPix/Zarr workflows for Sentinel-2, Sentinel-3, ERA5, CAMS, and DestinE Climate Digital Twin outputs. This paper describes the ellipsoidal geometry, the CPU/GPU-capable resampling architecture, and the implementation of metadata fields defined by CF 1.13 and version 1 of the Pilot Zarr DGGS convention, establishing ellipsoidal HEALPix/Zarr as a common representation that connects climate and Digital Twin Earth datasets with ellipsoidal EO datasets.
Tina Odaka, Jean-Marc Delouis, Justus Magin et al.· The International Archives o...· 0 citations
Abstract. Interpreting multi-storey property rights is difficult when information is scattered across 2D plans and text or locked inside desktop projects. We present a web-based pathway that communicates strata lots and common property consistently across levels in a standard browser. Aligned with the 3D Cadastral Survey Data Model and Exchange (3D CSDM) of Australia, we propose an open-source, web-first approach. The method couples a lightweight browser viewer (level/tenure filters, plan overlay, search, readable legend) with an explicit conversion step that standardises common GIS inputs into a fixed core JSON profile, with limited official CSDM-aligned JSON-LD hooks applied only to selected keys that have exact matches in the published vocabularies, while all remaining source attributes are preserved as standard JSON fields. Using a New South Wales case study, we evaluated the viewer against ISO 9241-11 criteria (effectiveness, efficiency). Across repeated trials (cache disabled/enabled), mean page-open times were 0.60 s (Chrome) and 1.48 s (Edge); interaction averaged 50–60 FPS; level filters applied in 40–55 ms; all five tasks succeeded. Practically, this delivers fast, consistent 3D communication of lots and common property without installs, lowering access barriers for agencies and owners while aligning with 3D CSDM’s web-first direction. Next, we will finalise viewer parity between Upload-and-View and the Reference Viewer and add a light in-viewer validation panel.
Pavan Sai Goud Goddu, S. Zlatanova, M. Kalantari· ISPRS Annals of the Photogra...· 0 citations
The African Bird Atlas Project (ABAP) represents one of the largest citizen-science biodiversity monitoring initiatives, generating large-scale bird occurrence and survey datasets across the African continent. However, utilizing this pentad-scale map data for high-level analysis or publication often requires complex workflows involving manual data downloads, cleaning, and GIS processing. To resolve this technical barrier, the
AfriAtlas
, an open-source R package is designed to streamline the retrieval and visualization of ABAP data. The package interfaces directly with the BirdMap API to fetch spatial occurrence and survey effort data.
AfriAtlas
leverages the
sf, rnaturalearth, ggplot2
packages to handle dynamic administrative boundaries and render standardized, publication-ready maps. Key features include automated effort-adjusted choropleth maps, species distribution plots incorporating a Widespreadness Index developed within
AfriAtlas
to quantify the spatial extent and distribution patterns of species, and fault-tolerance mechanisms for batch processing. By automating these workflows,
AfriAtlas
promotes reproducibility and lowers the entry barrier for ornithological and conservation research in Africa.
Bello A. Danmallam, Michael Brooks, U. Ottosson· Frontiers in Bird Science· 0 citations
Background Automated record linkage is often defined as applying a single threshold to classify candidate record pairs as matches or non-matches, in contrast to the Fellegi–Sunter two-threshold approach that relies on manual review for uncertain pairs. However, eliminating manual review alone does not constitute fully automated linkage, which also requires automation of data preprocessing, parameter selection, linkage quality evaluation, and report generation—steps that remain largely manual in practice and contribute substantially to the time and cost of data linkage projects. Objective To develop an open-source, fully automated, record linkage framework that supports scalable, reproducible, and transparent workflows. Implementation We developed three R packages, publicly available at https://github.com/CHIMB, that together form a fully automated linkage pipeline: datastan, autolink, and linkrep. The datastan package automates data standardization and preprocessing by transforming each data source into a consistent format prior to linkage, including variable naming, date formatting, variable categorization, standardization of name spelling variations, and handling of missing data. Autolink enables users to define and execute complex linkage algorithms, supporting deterministic, probabilistic, fuzzy, and phonetic matching. Linkrep automatically generates comprehensive linkage quality reports. Both the data standardization rules and linkage algorithms are easily editable through a graphical user interface and can be defined once, saved, and set to execute automatically for subsequent linkages (e.g., when data updates become available), without further user intervention. Implications By automating the full linkage lifecycle, this framework reduces turnaround times while improving reproducibility and transparency—facilitating more efficient, equitable, and sustainable linkages for routinely updated datasets.
Barret A. Monchka, Cole Chuchmach, E. Stoughton et al.· International Journal of Pop...· 0 citations
Bioinformatics workflows rely heavily on visual representations. Quality-control plots, cell embeddings, heatmaps, genome-browser tracks, and interactive dashboards are not merely illustrations, but instruments for making analytical decisions. For blind and low-vision researchers who use screen readers, braille displays, or audio-based interfaces, these create a barrier: the evidence used to justify an analysis is often encoded in visual form, while the underlying decision remains undocumented. We argue that non-visual accessibility and computational reproducibility are closely aligned, as they both require analyses to be transparent and to record why decisions were made. We present ten simple rules for non-visual bioinformatics, covering plots as decision records, cautious use of AI-generated figure descriptions, accessible computing environments, text-first literate programming, structured data and metadata, compact object summaries, accessible publication formats, collaboration practices, shared community infrastructure, and accessibility as part of FAIR research. The intended audience is computational biologists and developers. Using single-cell RNA-seq as a running example, we show that the accessible equivalent of a plot is a structured decision record. That is, a plot companion that goes beyond storing the underlying data by also stating the purpose of the analysis and the resulting quantitative evidence and uncertainty. We argue that treating accessibility in this way makes bioinformatics more inclusive and also more transparent and auditable.
Jacqueline G. Kientsch, S. Neuhauss, I. Mallona· 0 citations