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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 31 January 2026 | Viewed by 7714

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Special Issue Editors

Dr. Andriani Skopeliti

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Guest Editor

School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15780 Zographos, Greece
Interests: cartography; VGI; web mapping; geographic information science
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ioannis Giannopoulos

E-Mail Website
Guest Editor

Department of Geodesy and Geoinformation, Technische Universitat Wien, Vienna, Austria
Interests: GIScience; GeoAI; human-computer interaction; eye tracking; location-based services; mobility; augmented reality
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Dr. Vassilios Krassanakis

E-Mail Website
Guest Editor

Department of Surveying and Geoinformatics Engineering, School of Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: cartography; eye tracking; geovisualization; GIS; visual perception
Special Issues, Collections and Topics in MDPI journals

Dr. Guillaume Touya

E-Mail Website
Guest Editor

ENSG-IGN, Université Gustave Eiffel, IGN, 77455 Marne-la-Vallee, France
Interests: map generalisation; multi-scale visualisation; spatial cognition, AI; data matching; road network; OpenStreetMap; crowdsourced data quality; crowdsourced data evolution

Special Issue Information

Dear Colleagues,

In the past few years, cartography and visual analytics have supported the challenge of understanding and communicating spatial phenomena in the era of big data. Since spatial information has become increasingly vital to policymaking, governance, disaster management, urban planning, and environmental monitoring, effective and novel methods and tools for cartography and visual analytics have become essential.

Cartography is the mother science that states the theory and practice of spatial data representation. Visual analytics builds on cartography by combining interactive visuals, data analysis, and focus on users' needs. By combining analytical reasoning, cognitive science, and multiple data visualizations, we can explore and understand large and varied geospatial datasets. This teamwork between cartography and visual analytics enhances our ability to handle uncertainty, identify trends, and make informed decisions.

This Special Issue aims at collecting new theoretical foundations and design principles, novel tools and technologies, and cases of successful applications of cartography and visual analytics. We welcome submissions that cover, but are not limited to, the following topics:

Cognitive aspects of map-based visual analytics;
Ethics and bias in cartographic visualizations;
Evaluation methods for geovisual analytic tools;
Interactive and dynamic map interfaces;
Visual analytics for spatial-temporal data;
Machine learning and AI in visual cartography;
Immersive technologies (AR/VR) in geovisualization;
Web-based visual analytic platforms for spatial data;
Big geospatial data and scalable visualization techniques;
Visual analytics in urban planning and smart cities;
Public health and epidemiological mapping;
Geovisual analytics for disaster management;
User-centered approaches in geovisualization;
Uncertainty visualization in geospatial analysis;
Collaborative and participatory mapping analytics;
Multiscale and multiresolution cartographic visualization;
Eye tracking and visual analytics.

Dr. Andriani Skopeliti
Prof. Dr. Ioannis Giannopoulos
Dr. Vassilios Krassanakis
Dr. Guillaume Touya
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

cartography
visual analytics
interactive geographic visualization
analytical reasoning
visual interfaces
user centered design
AR/VR
AI
eye tracking

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Published Papers (6 papers)

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Research

25 pages, 8743 KB

Open AccessArticle

Irregular Area Cartograms for Local-Level Presentation of Selected SDGs Indicators Based on Earth Observation Data

by Anna Markowska and Dariusz Dukaczewski

ISPRS Int. J. Geo-Inf. 2025, 14(12), 500; https://doi.org/10.3390/ijgi14120500 - 18 Dec 2025

Abstract

The objective of this study is to explore the applicability of irregular area cartograms for the visualization of sustainable development indicator components, utilizing earth observation (EO) data. The analysis focuses on selected Sustainable Development Goals (SDG 11 ‘Make cities and human settlements inclusive, safe, resilient and sustainable’ and SDG 13 ‘Take urgent action to combat climate change and its impacts’) and specific targets and indicators related to green urban areas and air quality (targets: 13.2, 11.6, and 11.7; indicators: 11.6.2., 11.7.1., 13.2.2.). A comprehensive review of the relevant literature indicates that irregular area cartograms are employed only sporadically in the context of SDG monitoring, particularly at lower levels of territorial division (i.e., communes and counties). To address this gap, a series of thematic maps, including choropleth maps and irregular area cartograms, was developed. These visualizations are based on EO-derived datasets and supplemented with statistical information obtained from the Local Data Bank of the Statistics Poland. The analysis demonstrates that irregular area cartograms provide an effective means of visualizing spatial disparities in variables such as urban green space availability and air pollution at the commune and county levels. These visualizations enhance the interpretability of complex indicator structures and support more nuanced assessments of progress toward selected Sustainable Development Goals, especially in spatially detailed analytical frameworks. Preliminary usability testing among potential users revealed that irregular area cartograms are perceived as an interesting visualization technique that enhances data interpretation. Full article

(This article belongs to the Special Issue Cartography and Geovisual Analytics)

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30 pages, 8433 KB

Open AccessArticle

Creating Choropleth Maps by Artificial Intelligence—Case Study on ChatGPT-4

by Parinda Pannoon and Rostislav Netek

ISPRS Int. J. Geo-Inf. 2025, 14(12), 486; https://doi.org/10.3390/ijgi14120486 - 9 Dec 2025

Viewed by 370

Abstract

This study explores the potential of ChatGPT-4, an AI-powered large language model, to generate thematic maps and compare its outputs to the traditional method in which maps are produced manually by humans using GIS software. Prompt engineering is a crucial methodology of large language models that can enhance output quality. The main objective of this study is to assess the capability of AI-generated maps and to compare the quality with a traditional method. The study evaluates two prompt patterns: basic (zero-shot prompts) and advanced (Cognitive Verifier and Question Refinement). The performance of AI-generated maps is assessed based on attempts, errors, incorrect results, and map completeness. The final stage involved evaluating AI-generated maps against cartographic rules to assess their suitability. ChatGPT-4 performs well in generating suitable choropleth maps but faced challenges in understanding the prompts and potential errors in the generated code. Advanced prompts reduced errors and improved the quality of outputs, particularly for complex map elements. This paper enhances the understanding of AI’s role in cartography and further research in automated cartography. The study assesses cartographic aspects, offering insights into the strengths and limitations of AI in cartography, illustrating how large language models can process geospatial data and adhere to cartographic principles. The study also paves the way for future innovations in automated geovisualization. Full article

(This article belongs to the Special Issue Cartography and Geovisual Analytics)

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28 pages, 4616 KB

Open AccessFeature PaperArticle

Analysis of Semi-Global Factors Influencing the Prediction of Crash Severity

by Johannes Frank, Cédric Roussel and Klaus Böhm

ISPRS Int. J. Geo-Inf. 2025, 14(11), 454; https://doi.org/10.3390/ijgi14110454 - 19 Nov 2025

Viewed by 470

Abstract

As road users and means of transport in Germany become more diverse, we must better understand the causes and influencing factors of serious crashes. The aim of this work is to develop an AI-supported analysis approach that identifies and clearly visualizes the causes of crashes and their impact on crash severity in the urban area of the city of Mainz. The machine learning models predict crash severity and use Shapley values as explainability methods to make the underlying patterns understandable for urban planners, safety personnel, and other stakeholders. A particular challenge lies in presenting these complex relationships in a user-friendly way through visualizations and interactive maps. Full article

(This article belongs to the Special Issue Cartography and Geovisual Analytics)

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22 pages, 3516 KB

Open AccessArticle

Hurricane Precipitation Intensity as a Function of Geometric Shape: The Evolution of Dvorak Geometries

by Ivan Gonzalez Garcia, Alfonso Gutierrez-Lopez, Ana Marcela Herrera Navarro and Hugo Jimenez-Hernandez

ISPRS Int. J. Geo-Inf. 2025, 14(11), 443; https://doi.org/10.3390/ijgi14110443 - 8 Nov 2025

Viewed by 502

Abstract

The Dvorak technique has represented a fundamental tool for understanding the power of tropical cyclones based on their shape and geometric evolution. However, it should be noted that the Dvorak technique is purely morphological in nature and was developed for wind, not precipitation. The role of shape methods in precipitation prediction remains uncertain, particularly in the context of modern multi-sensor capabilities. This uncertainty forms the motivation for the present study. In an attempt to enrich Dvorak’s technique, this study proposes a novel hypothesis. This study tests the hypothesis that higher precipitation intensity is associated with more organized cloud-system morphology, as captured by simple geometric descriptors and indicative of dynamically coherent convection. A total of 3419 cloud-system objects (after size filter) were utilized to establish geometric relationships in each of them. For the case study of Hurricane Patricia over the Mexican coast in 2015, 3858 geometric shapes were processed. The cloud-system morphology was derived from geostationary imagery (GOES-13) and collocated with satellite precipitation estimates in order to isolate intense-rainfall objects (>50 mm/h). For each object, simple geometric descriptors were computed, and shape variability was summarised via Principal Component Analysis (PCA). The present study sought to evaluate the associations with rain-rate metrics (mean, mode, maximum) using rank correlations and k-means clustering. Furthermore, sensitivity analyses were conducted on the rain threshold and minimum object size. A Shape Descriptor: ratio between perimeter and diameter was identified as a promising tool to enhance early prediction models of extreme rainfall, contributing to enhanced meteorological risk management. The study indicates that cloud shape can serve as a valuable indicator in the classification and forecasting of intense cloud systems. Full article

(This article belongs to the Special Issue Cartography and Geovisual Analytics)

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12 pages, 3911 KB

Open AccessArticle

Study Area Map Generator: A Web-Based Shiny Application for Generating Country-Level Study Area Maps for Scientific Publications

by Cesar Ivan Alvarez, Juan Gabriel Mollocana-Lara, Izar Sinde-González and Ana Claudia Teodoro

ISPRS Int. J. Geo-Inf. 2025, 14(10), 387; https://doi.org/10.3390/ijgi14100387 - 3 Oct 2025

Viewed by 2119

Abstract

The increasing demand for high-quality geospatial visualizations in scientific publications has highlighted the need for accessible and standardized tools that support reproducible research. Researchers from various disciplines—often without expertise in Geographic Information Systems (GIS)—frequently require a map figure to locate their study area. This paper presents the Study Area Map Generator, a web-based application developed using Shiny for Python, designed to automate the creation of country- and city-level study area maps. The tool integrates geospatial data processing, cartographic rendering, and user-friendly customization features within a browser-based interface. It enables users—regardless of GIS proficiency—to generate publication-ready maps with customizable titles, basemaps, and inset views. A usability survey involving 92 participants from diverse professional and geographic-based backgrounds revealed high levels of satisfaction, ease of use, and perceived usefulness, with no significant differences across GIS experience levels. The application has already been adopted in academic and policy contexts, particularly in low-resource settings, demonstrating its potential to democratize access to cartographic tools. By aligning with open science principles and supporting reproducible workflows, the Study Area Map Generator contributes to more equitable and efficient scientific communication. The application is freely available online. Future developments include support for subnational units, thematic overlays, multilingual interfaces, and enhanced export options. Full article

(This article belongs to the Special Issue Cartography and Geovisual Analytics)

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16 pages, 1329 KB

Open AccessEditor’s ChoiceArticle

Vector Data Rendering Performance Analysis of Open-Source Web Mapping Libraries

by Dániel Balla and Mátyás Gede

ISPRS Int. J. Geo-Inf. 2025, 14(9), 336; https://doi.org/10.3390/ijgi14090336 - 30 Aug 2025

Cited by 2 | Viewed by 3179

Abstract

Nowadays, various technologies exist with differing rendering performance for interactive web maps. These maps are consumed on devices with varying capabilities; therefore, choosing the best-performing library for a dataset is emphasized. Unlike existing research, this study presents a comparative analysis on libraries’ native performance for rendering large amounts of GeoJSON vector data, partially extracted from OpenStreetMap (OSM). Four libraries were analyzed. Results showed that regardless of feature types, Leaflet and OpenLayers excelled for features up to 10,000. Up to 5000 points, these two were the fastest, above which the libraries’ performance converged. For 50,000 or more, Mapbox GL JS rendered them the quickest, followed by OpenLayers, MapLibre GL JS and Leaflet. For up to 50,000 lines and 10,000 polygons, Leaflet and OpenLayers were the fastest in all scenarios. For 100,000 lines, OpenLayers was almost twice as fast as the others, while Mapbox rendered 50,000 polygons the quickest. The performance of Leaflet and OpenLayers scales with the increasing feature quantities, yet for Mapbox and MapLibre, any performance impact is offset to 1000 features and beyond. Slow initalization of map elements makes Mapbox and MapLibre less suitable for rapid rendering of small feature quantities. Other behavioural differences affecting user experience are also explored. Full article

(This article belongs to the Special Issue Cartography and Geovisual Analytics)

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