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Heatmap dendrograms visualizing the pairwise distance matrices and hierarchical clustering results for the dynamic time warping (DTW) distance.

Maps for the different cluster scenarios (2 to 7) based on the dynamic time warping (DTW) distance.

Fantastic new article from Lars De Sloover and colleagues from Department of Ggeography Ugent comparing impacts of Euclidean distance and dynamic time warping with spatiotemporal analysis of COVID-19 dynamics across Europe #GISchat doi.org/10.1080/1523...

Thrilled to share Exploropleth - our new #GIS #dataviz tool.

💡 @Mapmakers, you can simultaneously explore & compare 16+ data binning methods on a choropleth map, directly in your browser!

🎓 @Instructors, you can use the tool to teach how choropleth maps can mislead people!

exploropleth.github.io

Figure 5. Combine View lets users analyze a) a combination of one or more binning methods by visualizing b) the most consistent Bin, c) the frequency of the most consistent Bin, and d) both together for each U.S. county in separate choropleths. The new, resiliency binning method then utilizes this information to g) determine “resilient” bins (counts, intervals) that are also visualized in a choropleth. Hovering any county on the map shows a tooltip with relevant information about the county, as shown in e) and f).

New article! A fantastic new tool Exploropleth: exploratory analysis of data binning methods in choropleth maps, from @arpitnarechania.bsky.social Alex Endert & Clio Andris doi.org/10.1080/1523... #GISchat website exploropleth.github.io video of the tool in action: www.youtube.com/watch?v=lNV8...

MapLayNet can learn a concept hierarchy of map layout via an unsupervised data similarity measure. The resulting layout embedding can be further explored for more cartographic tasks.

A three-panel visualization showing bandwidth selection and spatial analysis for local intercept estimation. The top panel displays an AICc curve starting around 2000 at spatial unit 0, dipping to a minimum near bandwidth 220 (marked by a green vertical line), with an orange dashed line indicating another reference point, then rising steadily to about 5000 at spatial unit 3000. The middle panel shows a choropleth map of the contiguous United States with counties colored on a diverging red-to-blue scale, where darker red regions (values around -2) appear concentrated in the Southwest, particularly Texas and surrounding areas, lighter colors (near 0) cover much of the central and eastern US, and blue regions (positive values up to 4) appear in the Pacific Northwest and parts of the Northeast. The bottom panel presents a caterpillar plot showing coefficient estimates with confidence intervals across approximately 3000 spatial units, displayed as vertical bars in gray and colored segments (red for negative, blue for positive values), with most estimates clustering near zero but showing notable variation and wider intervals toward the right side of the plot.

Great new article! Victor Irekponor & Taylor M. Oshan
address reproducibility in spatially varying coefficient (SVC) models, and introduce svc-viz, a open source Python tool to-do this #GISchat doi.org/10.1080/1523... github.com/marquisvicto...

Figure 10 (left) showing ship channels for cargo, within the Great Lakes and Figure 11 (right) showing routes within those channels.

New article! Fascinating new method of reconstructing maritime route networks for nearshore waters, using step-by-step density-based clustering from Jiale Pan and colleagues doi.org/10.1080/1523...

Alternative options in the survey, including low or high narrative text, and the addition of maps and photos.

Great new article from Michala A. Garrison, Schyler A. Reis, Shiyu Zhang and Carolyn S. Fish, evaluating “narrative transportation” – how engrossed one becomes in a story when used in storytelling maps, and the impact of including maps and photos doi.org/10.1080/1523... #GISchat

A map showing the current footprint of Huaqiangbei (left) along with a breakdown of the categories of shops from 2018 to 2020.

New paper from Yunfei Ma, Yuan Zhang and colleagues, Where is Huaqiangbei? looking at how Huaqiangbei, one of Shenzhen’s largest business districts, has evolved over time doi.org/10.1080/1523...
#GISchat

An example of the spatial interaction model, with interactions between two regions (origin area and destination area) and within each area.

New paper! Marjan Ghanbari, Mohammad Karimi and colleagues analyse intra-city function regions using spatial analysis interaction, using a case study of Chicago, doi.org/10.1080/1523... #GISchat

This map shows spatiotemporal risk values for two villages (Xiaoguo village and Liujia Zuo village) as of January 6, 2021. The risk values are displayed using a color-coded legend ranging from 0 (white) to 6-24 (red), with intermediate categories of 0-2 (light gray), 2-4 (light blue), and 4-6 (orange). The map displays various geometric shapes and structures scattered across the area, with the highest risk concentrations (shown in red and orange) appearing to be concentrated around the central area between the two villages. Lower risk areas are shown in lighter colors.

New Paper! Yuanfang Chen and colleagues present a new way of contact-tracing, the PSTP-STRA method doi.org/10.1080/1523... #GISchat

A World of Tides The most recent issue of ArcUser, a magazine for Esri GIS software users spotlights cartographer Dave Taylor’s striking Oceanic Oscillations map.

A lovely ocean tides map using the Spilhaus projection for some Wednesday mappy goodness #MapoftheWeek mapoftheweek.substack.com/p/a-world-of...

A goat wearing a GPS collar

A map showing goat ranges, overlaid on a 250 buffer of mining sites

Fantastic paper from Yan Lin and colleagues, Practicing community-based research in GIScience, using GIS with an Indigenous community, presenting a case study on environmental health concerns related to mining legacies in the U.S., using goats wearing GPS collars #GISchat doi.org/10.1080/1523...

Today, we are thrilled to kick off our Annual International Conference, held in-person at @unibirmingham.bsky.social and online! 🌍

Watch the Conference’s Chair, Professor Patricia Noxolo, share what shaped this year’s theme, Creative geographies. #RGSIBG25

At the #RGSIBG25 conference and want to find out about GIS? Come along to our Best of GISRUK session in Arts LR5, 13:10 to hear the 3 Best papers from the GISRUK conference, inc. low traffic neighbourhoods, parks and GIS vs The City Council #GISchat @gisruk.bsky.social @rgsibg.bsky.social

Great to be at #RGSIBG2025 Come at chat to me about publishing in CaGIS @nickbearman.bsky.social

Figure 1 Research diagram illustrating map layout graph construction. Part a) shows a world map of seismic activity with 6 colored markers representing different elements on the map. Part b) transforms these into a graph theory representation where elements become vertices (v1-v6) connected by edges showing relationships.

Figure 10 Machine learning visualization showing how AI clusters similar map elements together. Each dot represents a different map and are grouped together in 2D space according to their layout. The algorithm successfully identifies layout patterns across different map types! #MachineLearning #Cartography

New paper from Jian Yang and colleagues, presenting MapLayNet, a way of understanding maps as a graph and classifying how they are laid out, with the aim of better map layout retrieval and design recommendation #GISchat #OpenAccess doi.org/10.1080/1523...

Screenshot from mixed reality wayfinding study showing a institutional hallway with AR overlays. Blue labels and wireframes highlight interface elements like 'Library' landmark labels, structural frames, and highlighting boxes that guide indoor navigation. Red 'Lab' text shows user-oriented wayfinding cues. Part of research on X-ray vision technology for seeing through walls to aid spatial learning.

New article from our ICC 2023 special issue, Shengkai Wang and colleagues evaluate the effectiveness of X-ray vision: Seeing through walls: indoor spatial learning with X-ray vision-enabled mixed reality #GISchat #OpenAccess doi.org/10.1080/1523...

If you are at the ICC in Vancouver, stop by the CaGIS booth and say hello!

New article from Musang Yoo & Hyeongmo Koo looking at spatial autocorrelation and it's impact on spatial cross-validation, doi.org/10.1080/1523... #GISchat

A flow chart showing the automated process of choosing locations for tag, using TIN construction. They compare a previous approach of placing the tag within the largest area available with their approach of incorporating the negative spatial auto-correlation into this.

New article from Zhiwei Wei & Nai Yang, who use a negative spatial auto-correlation index to improve tag map layout across multiple different scales, check out the paper at doi.org/10.1080/1523... and their project at github.com/TrentonWei/M... #GISchat

Screenshot of interactive tool, showing cities across Europe, with a pink disc around each one representing the proportion of artificial land use.

A novel way of comparing artificial land use in European cities, whilst adjusting for size. Check out a graphic below, the article at doi.org/10.1080/1523... and the interactive tool at land-use.shinyapps.io/UE_Land_use_.... Thanks Axel Pécheric, Rémi Lemoy and Marion Le Texier! #GISchat

Example of some of the different symbol shapes tested, including circle, square, triangle and star.

Fascinating new article! Silvia Klettner explores the The significance of the cartographic sign, evaluating influence of symbol shape on intuitive judgements, #GISchat, doi.org/10.1080/1523...

The five maps of Jerusalem used for our test and their deformative vector fields, with color showing the direction of deformation. See the article for more details.

Fantastic new article from @VaientiBeatrice and colleagues, using Deformation Analysis to identify copying processes in historical maps, doi.org/10.1080/1523... #GISchat #OpenAccess

An example of the class-based method and the new PageRank method for 4 cities in China.

New article! Tianshu Chu, Haowen Yan and colleagues explore a new way of generalizing road networks by making use of the PageRank algorithm to select the most 'important' roads, doi.org/10.1080/1523... #GISchat, An example of the class-based method and the new PageRank method for 4 cities in China.

An example of the class-based method and the new PageRank method for 4 cities in China.

New #article from Linfeng Jiang and colleagues, looking at a new way of visualising road connectivity: using CiteSpace, a bibliometric tool, with the literature replaced by the traffic trajectory and the keywords replaced by the roads, #gischat, doi.org/10.1080/1523...

Figure 3: A map of the world, showing the origin of publications included in the literature review. The countries with more than 10 publications are highlighted, and are USA 20, UK 14, Germany 17, Spain 18, Norway 10, Sweeden 10, Finland 27, Poland 21 and Australia 8.

New article: A systematic literature review from Mariana Vallejo-Velázquez & Ourania Kounadi on spatial perception and knowledge via structured digital sketch maps, doi.org/10.1080/1523..., #OpenAccess #GISchat

Issue 52-05 of CaGIS is now available! This time we have 8 papers including a systematic review of digital sketch maps, some great new spatial methods, a couple of really interesting applications and some new applications of spatial statistics. www.tandfonline.com/toc/tcag20/5... #GISchat

A spatial representation of Buchenwald, from Gilberto Salmoni’s holocaust testimony. This image shows a detailed diagram of the Buchenwald concentration camp layout and prisoner testimonies. The diagram includes: Top section: A map showing the location of Buchenwald in Germany, dated Early April - April 13, 1945. Middle section: Floor plans of different areas of the camp including: Entire Camp with entrance and various barracks Dark Room Transition Room 1st Barrack, 2nd Barrack, and 3rd Barrack Each barrack shows layout with bunkbeds, toilets, and dining tables Bottom section: Six panels containing prisoner testimony quotes and emotional response data. Each panel includes: Red squares indicating specific locations Quoted testimonies from prisoners describing conditions Colored grids showing emotional responses (using colors for emotions like Grief, Fear, Boredom, Distress, Shame, Disgust, Sadness, Sympathy, Anger, Contempt, Confusion, Surprise, Happiness, Relief, Pride, and Awe) Legend: Shows symbols for Family together, Family not together, Other people, and identifies individuals by relationship codes (M=Mother, F=Father, GF=Grandfather, etc.)

A fantastic article from Alberto Giordano, Tim Cole and Heather Swienton, presenting a novel approach to mapping place, using a Holocast survivor testimony doi.org/10.1080/1523... #GISchat #OpenAccess Buchenwald detail from Representational model of Gilberto Salmoni’s holocaust testimony

Screenshot of a map coloring system interface showing three main panels: 1) Conversation view on left with text dialogue for inputting design intents in natural language, including options to modify map colors and increase/decrease contrast and brightness; 2) Color design view in center showing data processing steps, color scheme options (Sequential, Diverging, Elegant), field information for population data, classification histograms, and color palette controls with LLM and Brewer color options; 3) Map view on right displaying a choropleth map of what appears to be East and Southeast Asia with green gradient shading representing population data, plus zoom controls and coordinate information.

Great new paper from Nai Yang and colleagues, presenting MapColorAI: designing contextually relevant choropleth map color schemes using a large language model, check out the screenshot below, video at figshare.com/articles/dat... and paper at doi.org/10.1080/1523... #GISchat

This figure shows visualization of MLR (Multi-Level Routing) recognition performance across three experimental areas (d, e, and f). Each panel displays a street network map with different colored line segments representing four categories of routing performance: Purple lines: MLR→MLR (successful MLR recognition) Yellow/orange lines: MLR→Non-MLR (MLR incorrectly classified as non-MLR) Blue lines: Non-MLR→MLR (non-MLR incorrectly classified as MLR) White lines: Non-MLR→Non-MLR (successful non-MLR recognition)

Great new article from Zhekun Huang &colleagues, using Topology Adaptive Graph Convolutional Networks to identify multi-lane highways from road data doi.org/10.1080/1523... #GISchat Below, which shows in purple correctly classified Multi Lane Roads, and in yellow or blue incorrectly classified MLR