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Posts by Nick Bearman

Student working on a tablet computer making a map.

Student working on a tablet computer making a map.

A screenshot of a map created in R, showing Percentage population Ages 10 to 14 for Liverpool, UK.

A screenshot of a map created in R, showing Percentage population Ages 10 to 14 for Liverpool, UK.

Want to learn how to visualise and analyze spatial data? My #R #GIS courses are coming up in Apr-May 2026, starting next week:
Intro to using R as a GIS - No prior knowledge needed!
Advanced R as a GIS - For those ready to dive deeper
nickbearman.com/training-cou...

6 hours ago 1 1 0 0

Thanks! Check out the whole thing at github.com/nickbearman/..., compiled PDF to follow :-)

5 days ago 1 0 0 0
Glossary page titled 'Using R as a GIS' containing two columns of programming terms and GIS-related definitions, with terms in italics being glossary entries and terms with brackets being R functions.

Glossary page titled 'Using R as a GIS' containing two columns of programming terms and GIS-related definitions, with terms in italics being glossary entries and terms with brackets being R functions.

The same glossary page, shown in LaTeX source.

The same glossary page, shown in LaTeX source.

Updating my R GIS glossary with new commands from tmap v4 ready for my Intro to R as a GIS course, Intro to using R as a GIS. Coming up on 28 & 29 April with @ncrm.ac.uk nickbearman.com/training-cou...

5 days ago 1 2 1 0
Location markers.

Location markers.

Join @nickbearman.bsky.social online for Introduction to Spatial Data and using R as a GIS on 28-29 April 2026.

You will explore how to use R to import, manage and process spatial data, make choropleth maps and perform basic spatial analysis.

Sign up: www.ncrm.ac.uk/training/sho...

1 week ago 2 2 0 0
A screenshot of a map created in R, showing Percentage population Ages 10 to 14 for Liverpool, UK.

A screenshot of a map created in R, showing Percentage population Ages 10 to 14 for Liverpool, UK.

A screenshot of slides and video of Dr Nick Bearman's InStats course What is GIS?

A screenshot of slides and video of Dr Nick Bearman's InStats course What is GIS?

What is GIS? Check out my free seminar on @InStats What is GIS? instats.org/seminar/what... Hooked? Learn more in my #R #GIS courses coming up in Apr-May 2026:
Intro to using R as a GIS - No prior knowledge needed!
Advanced R as a GIS - For those ready to dive deeper
nickbearman.com/training-cou...

1 week ago 0 0 0 0
Generative AI Tools for Mapping – Nick Bearman

New blog post! Some thoughts on how generative AI (Claude) handles GIS and mapping for non-GIS users, nickbearman.com/blog/2026-04... from the excellant Generative AI Tools for Quantitative Research resource from David Bann and Liam Wright hosted by NCRM at repository.ncrm.ac.uk/resources/on...

2 weeks ago 0 0 0 0
A screenshot of a loop in R, creating three different maps.

A screenshot of a loop in R, creating three different maps.

Screenshot of a Zoom window with participants.

Screenshot of a Zoom window with participants.

Want to learn how to visualise and analyze spatial data? My #R #GIS courses are coming up in Apr-May 2026:
Intro to using R as a GIS - No prior knowledge needed!
Advanced R as a GIS - For those ready to dive deeper
nickbearman.com/training-cou...

2 weeks ago 0 0 0 0
Gerardo Ezequiel Martín Carreño will be presenting on "Shaping Cool Cities: Multi-source data fusion for urban heat mitigation and modelling across six European cities" at the 'London "Geo" Meetup #2' on 8th April, Wednesday at Geovation, 65 Goswell Road, London. Event starts at 6PM

Gerardo Ezequiel Martín Carreño will be presenting on "Shaping Cool Cities: Multi-source data fusion for urban heat mitigation and modelling across six European cities" at the 'London "Geo" Meetup #2' on 8th April, Wednesday at Geovation, 65 Goswell Road, London. Event starts at 6PM

For the upcoming 'London "Geo" Meetup #2', we have our final speaker Gerardo Ezequiel Martín Carreño,on "Shaping Cool Cities: Multi-source data fusion for urban heat mitigation and modelling across six European cities"

RSVP: lgm.jsonsingh.com

#OSM
#OSGeo
#Geospatial
#Opensource
#London
#meetup

2 weeks ago 6 3 0 0
Screenshot of Posit Cloud (formerly RStudio Cloud) interface for a project titled 'intro-r-spatial-analysis' by user Nick Bearman. The interface is divided into several sections: 1) Left side shows an R script editor with code visible on lines 231-250, including commands for calculating crime rates, joining population data to LSOA crimes, and creating a map using the tmap package with tm_shape() and tm_polygons() functions using a 'brewer.greens' color scale; 2) Upper right shows the Environment panel listing data objects including 'breaks', 'crimes', 'crimes_sf'; 3) Lower right displays a Plots panel showing a choropleth map with geographic boundaries filled in varying shades of green representing 'Rate of Crimes per 10,000 population' with a legend showing five categories ranging from 6-87 to 884-2,624; 4) Bottom shows Console/Terminal tabs with R console output. The interface includes standard RStudio menus (File, Edit, Code, View, Plots, Session, Build, Debug, Profile, Tools, Help) and toolbar buttons.

Screenshot of Posit Cloud (formerly RStudio Cloud) interface for a project titled 'intro-r-spatial-analysis' by user Nick Bearman. The interface is divided into several sections: 1) Left side shows an R script editor with code visible on lines 231-250, including commands for calculating crime rates, joining population data to LSOA crimes, and creating a map using the tmap package with tm_shape() and tm_polygons() functions using a 'brewer.greens' color scale; 2) Upper right shows the Environment panel listing data objects including 'breaks', 'crimes', 'crimes_sf'; 3) Lower right displays a Plots panel showing a choropleth map with geographic boundaries filled in varying shades of green representing 'Rate of Crimes per 10,000 population' with a legend showing five categories ranging from 6-87 to 884-2,624; 4) Bottom shows Console/Terminal tabs with R console output. The interface includes standard RStudio menus (File, Edit, Code, View, Plots, Session, Build, Debug, Profile, Tools, Help) and toolbar buttons.

Just testing out my material in Posit.cloud for my Introduction to Spatial Data and Using R as a GIS course coming up on 28 & 29 April nickbearman.com/training-cou... Posit.cloud is a great way of running R if you can't install it yourself! More details at nickbearman.com/installing-s...

2 weeks ago 1 0 0 0
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Figure 6: Example of autonomous GIS operation where the fine-tuned SLM translates natural language queries into executable functions (addWFS and SetStrokeColor), updating the map visualization in real-time. The screenshot shows the AWebGIS App interface with three main sections: 1) A left sidebar containing 'Analysis Tools' and 'Layer Manager' panels, with options to 'Add WMS', 'Add WFS', 'Import', and 'Add Base', plus layer options for 'states', 'wfs', 'OpenStreetMap', and 'base'; 2) A central map display showing a base map of North America with the United States outlined in red stroke, including state boundaries and the Great Lakes region; 3) A right sidebar with 'Chat Assistant' and 'Activity Log' tabs, showing a conversation thread with timestamps. The chat displays natural language commands such as 'Can you add 'states' layer from [http://localhost:8080/geoserver/cite/wfs](http://localhost:8080/geoserver/cite/wfs)?' (08:19 PM), followed by 'Executed: addWFS('states', '[http://localhost:8080/geoserver/cite/wfs](http://localhost:8080/geoserver/cite/wfs)', 'states')' (08:18 PM), then 'Set the color of the strokes to 'red' for 'states'' (08:19 PM), and 'Executed: SetStrokeColor('states', 'red')' (08:18 PM). At the bottom, the Active Model shows 'T5 Tiny-fine-tuned, Local' with a prompt to 'Ask me to perform GIS operations...' The interface includes top navigation buttons for 'GitHub', 'Print Map', and 'Full Screen'.

Figure 6: Example of autonomous GIS operation where the fine-tuned SLM translates natural language queries into executable functions (addWFS and SetStrokeColor), updating the map visualization in real-time. The screenshot shows the AWebGIS App interface with three main sections: 1) A left sidebar containing 'Analysis Tools' and 'Layer Manager' panels, with options to 'Add WMS', 'Add WFS', 'Import', and 'Add Base', plus layer options for 'states', 'wfs', 'OpenStreetMap', and 'base'; 2) A central map display showing a base map of North America with the United States outlined in red stroke, including state boundaries and the Great Lakes region; 3) A right sidebar with 'Chat Assistant' and 'Activity Log' tabs, showing a conversation thread with timestamps. The chat displays natural language commands such as 'Can you add 'states' layer from [http://localhost:8080/geoserver/cite/wfs](http://localhost:8080/geoserver/cite/wfs)?' (08:19 PM), followed by 'Executed: addWFS('states', '[http://localhost:8080/geoserver/cite/wfs](http://localhost:8080/geoserver/cite/wfs)', 'states')' (08:18 PM), then 'Set the color of the strokes to 'red' for 'states'' (08:19 PM), and 'Executed: SetStrokeColor('states', 'red')' (08:18 PM). At the bottom, the Active Model shows 'T5 Tiny-fine-tuned, Local' with a prompt to 'Ask me to perform GIS operations...' The interface includes top navigation buttons for 'GitHub', 'Print Map', and 'Full Screen'.

Figure 2: Conceptual workflow of the autonomous web-based geographical information systems (AWebGIS) approaches. The diagram shows a natural language query at the top splitting into two parallel processing paths. Approach I (Cloud-based LLMs), shown on the left in a dashed box, follows this sequence: 1) Natural-language query sent via HTTP to cloud API (OpenRouter to DeepSeek V3.1); 2) In the cloud, DeepSeek Chat V3.1 translates the query into a GIS function call using few-shot learning; 3) Predicted function call is returned as a text string to the web browser; 4) The JavaScript engine parses the function call and executes the corresponding GIS operation. Approach II (Browser-executable SLMs), shown on the right in a dashed box, follows this sequence: 1) Natural-language query processed locally through fine-tuned SLMs (e.g., T5-tiny); 2) In the user's web browser, an SLM translates the query into a GIS function call; 3) The JavaScript engine parses the function call and executes the corresponding GIS operation. Both approaches ultimately result in JavaScript execution of GIS operations, but differ in where the natural language processing occurs (cloud versus local browser).

Figure 2: Conceptual workflow of the autonomous web-based geographical information systems (AWebGIS) approaches. The diagram shows a natural language query at the top splitting into two parallel processing paths. Approach I (Cloud-based LLMs), shown on the left in a dashed box, follows this sequence: 1) Natural-language query sent via HTTP to cloud API (OpenRouter to DeepSeek V3.1); 2) In the cloud, DeepSeek Chat V3.1 translates the query into a GIS function call using few-shot learning; 3) Predicted function call is returned as a text string to the web browser; 4) The JavaScript engine parses the function call and executes the corresponding GIS operation. Approach II (Browser-executable SLMs), shown on the right in a dashed box, follows this sequence: 1) Natural-language query processed locally through fine-tuned SLMs (e.g., T5-tiny); 2) In the user's web browser, an SLM translates the query into a GIS function call; 3) The JavaScript engine parses the function call and executes the corresponding GIS operation. Both approaches ultimately result in JavaScript execution of GIS operations, but differ in where the natural language processing occurs (cloud versus local browser).

New article! Fantastic article from Mahdi Nazari Ashani and colleagues, showing how a SLM (Small Language Model) running a GIS can be run solely in a web browser tab with no data shared externally doi.org/10.1080/1523... #GISchat See their code at github.com/mahdin75/awe...

3 weeks ago 3 2 0 0
Figure 2: Results of the functional connectivity analysis of different age groups. Panel a) shows a circular chord diagram representing the functional connectivity matrix during Task 1, with brain regions labeled around the perimeter (PCC, PCA, MFG, UifG, MOG, PCUN, dlSFG, dSFG, PCUN, MFG) and small brain images positioned outside the circle. Connections between regions are shown as ribbons colored in pink (elderly greater than young, p < 0.01 and p < 0.005) and purple (elderly less than young, p < 0.01 and p < 0.005). Panel b) displays a similar chord diagram for Task 2, showing connections between regions including dlSFG_L, dlSFG_R, LinG, PCC, PCC, and MFG_R. Panel c) contains three box plots comparing elderly (pink) versus young (purple) groups: shortest path length for Task 1 and Task 2 (marked with ** and * for significance), normalized path length for Task 1 and Task 2 (marked with *), and global efficiency for Task 1 and Task 2 (marked with ** and *). Panel d) shows nodal metrics of network analysis, featuring two brain renderings (left and right hemispheres) with highlighted regions in purple (dlSFG_L and dlSFG_R for dorsolateral superior frontal gyrus). Four box plots surround the brain images comparing elderly versus young groups for nodal clustering coefficient and nodal local efficiency in both left and right dlSFG regions. Legend indicates: PCC=posterior cingulate cortex; LinG=lingual gyrus; MFG=middle frontal gyrus; MOG=middle occipital gyrus; PCUN=precuneus.

Figure 2: Results of the functional connectivity analysis of different age groups. Panel a) shows a circular chord diagram representing the functional connectivity matrix during Task 1, with brain regions labeled around the perimeter (PCC, PCA, MFG, UifG, MOG, PCUN, dlSFG, dSFG, PCUN, MFG) and small brain images positioned outside the circle. Connections between regions are shown as ribbons colored in pink (elderly greater than young, p < 0.01 and p < 0.005) and purple (elderly less than young, p < 0.01 and p < 0.005). Panel b) displays a similar chord diagram for Task 2, showing connections between regions including dlSFG_L, dlSFG_R, LinG, PCC, PCC, and MFG_R. Panel c) contains three box plots comparing elderly (pink) versus young (purple) groups: shortest path length for Task 1 and Task 2 (marked with ** and * for significance), normalized path length for Task 1 and Task 2 (marked with *), and global efficiency for Task 1 and Task 2 (marked with ** and *). Panel d) shows nodal metrics of network analysis, featuring two brain renderings (left and right hemispheres) with highlighted regions in purple (dlSFG_L and dlSFG_R for dorsolateral superior frontal gyrus). Four box plots surround the brain images comparing elderly versus young groups for nodal clustering coefficient and nodal local efficiency in both left and right dlSFG regions. Legend indicates: PCC=posterior cingulate cortex; LinG=lingual gyrus; MFG=middle frontal gyrus; MOG=middle occipital gyrus; PCUN=precuneus.

New article! Tianyang Bai, Weihua Dong and colleagues investigate Age-related deficits in reference frame switching of navigation ability, using fMRI doi.org/10.1080/1523... #GISchat

3 weeks ago 2 1 0 0
Figure 1: Overview of the POI conflation process applied to DSA use case. The flowchart begins with two parallel data collection processes: a) LBS POI Collection from Yelp, which involves search terms dictionary, location-based service query, and data wrangling and cleaning; and b) POI Geographic Attributes Sources from GEOFABRIK (indicated by a 'G' logo), which includes downloading OSM geographic attributes data and extracting both healthcare POIs and building polygon shapes with Census Block Group polygon shapes. These streams converge in the Conflation Process, which calculates similarity metrics and performs POIs matching. The workflow continues to Enrichment using Placekey API (indicated by Placekey logo), applying polygon overlay and geocoding polygons into Geohash. Next is Human in the Loop validation using Amazon Mechanical Turk (indicated by Amazon logo), involving search term and keyword relevance evaluation, data accuracy evolution, and filtering out irrelevant and inaccurate POIs. The process concludes with Quality Control using SafeGraph (indicated by SafeGraph logo) for cross-referencing with reference commercial dataset and quality control metrics. The final output is stored in an Enriched POI Database (shown as a cylinder shape).

Figure 1: Overview of the POI conflation process applied to DSA use case. The flowchart begins with two parallel data collection processes: a) LBS POI Collection from Yelp, which involves search terms dictionary, location-based service query, and data wrangling and cleaning; and b) POI Geographic Attributes Sources from GEOFABRIK (indicated by a 'G' logo), which includes downloading OSM geographic attributes data and extracting both healthcare POIs and building polygon shapes with Census Block Group polygon shapes. These streams converge in the Conflation Process, which calculates similarity metrics and performs POIs matching. The workflow continues to Enrichment using Placekey API (indicated by Placekey logo), applying polygon overlay and geocoding polygons into Geohash. Next is Human in the Loop validation using Amazon Mechanical Turk (indicated by Amazon logo), involving search term and keyword relevance evaluation, data accuracy evolution, and filtering out irrelevant and inaccurate POIs. The process concludes with Quality Control using SafeGraph (indicated by SafeGraph logo) for cross-referencing with reference commercial dataset and quality control metrics. The final output is stored in an Enriched POI Database (shown as a cylinder shape).

New article! Zeyad Kelani and colleagues collate Point of Interest data (POI) from a variety of open sources for use in Drug and Substance Abuse work and find that these are comparable with commercial POI data sets doi.org/10.1080/1523... #GISchat

3 weeks ago 3 1 0 0
Student working on a tablet computer making a map.

Student working on a tablet computer making a map.

A screenshot of a map created in R, showing Percentage population Ages 10 to 14 for Liverpool, UK.

A screenshot of a map created in R, showing Percentage population Ages 10 to 14 for Liverpool, UK.

Want to learn how to visualise and analyze spatial data in the social sciences? My #R #GIS courses are coming up in Apr-May 2026:
Intro to using R as a GIS - No prior knowledge needed!
Advanced R as a GIS - For those ready to dive deeper
nickbearman.com/training-cou...

3 weeks ago 2 3 0 0
Figure 13: Based on the proposed method, operators can dynamically view matching results from different positions and perspectives. Panel a) shows a long-distance perspective with a person in military-style clothing holding a tablet device outdoors on dusty terrain, with another person visible in the background. Panel b) displays a close-range perspective with hands holding a tablet under a concrete bridge structure, showing the device screen displaying augmented reality content aligned with the physical surroundings. Panel c) presents actual screenshots of geo-matching results featuring a 3D yellow wireframe model of bridge infrastructure overlaid on a photograph of gray concrete bridge support columns and beams, with text reading 'After geo-matching of bridge' at the bottom of the overlay.

Figure 13: Based on the proposed method, operators can dynamically view matching results from different positions and perspectives. Panel a) shows a long-distance perspective with a person in military-style clothing holding a tablet device outdoors on dusty terrain, with another person visible in the background. Panel b) displays a close-range perspective with hands holding a tablet under a concrete bridge structure, showing the device screen displaying augmented reality content aligned with the physical surroundings. Panel c) presents actual screenshots of geo-matching results featuring a 3D yellow wireframe model of bridge infrastructure overlaid on a photograph of gray concrete bridge support columns and beams, with text reading 'After geo-matching of bridge' at the bottom of the overlay.

Figure 15: Interactive spatial perception in outdoor mobile augmented reality (MAR). At approximately 40 meters from the building, users assess the alignment between virtual and real structures and query semantic attributes. Panel a) shows accurate alignment in position and orientation, displaying an aerial/top-down view of a white building with blue-tinted windows and a 3D model overlay. Panel b) shows GIS slicing revealing full indoor semantics, with a ground-level view of the building facade overlaid with numerous colored geometric icons (purple diamonds, orange hexagons, green squares, blue circles) representing different semantic attributes throughout all floors. Panel c) displays fourth-floor attributes including offices, restaurant, and restrooms, showing the same building view with fewer colored icons concentrated on specific floors. Panel d) presents first-floor attributes including meeting rooms, restrooms, and mother-and-baby room, with blue and green icons visible primarily on the lower portion of the building. Yellow flowers are visible in the foreground of panels b), c), and d), with palm trees and additional buildings visible in the background.

Figure 15: Interactive spatial perception in outdoor mobile augmented reality (MAR). At approximately 40 meters from the building, users assess the alignment between virtual and real structures and query semantic attributes. Panel a) shows accurate alignment in position and orientation, displaying an aerial/top-down view of a white building with blue-tinted windows and a 3D model overlay. Panel b) shows GIS slicing revealing full indoor semantics, with a ground-level view of the building facade overlaid with numerous colored geometric icons (purple diamonds, orange hexagons, green squares, blue circles) representing different semantic attributes throughout all floors. Panel c) displays fourth-floor attributes including offices, restaurant, and restrooms, showing the same building view with fewer colored icons concentrated on specific floors. Panel d) presents first-floor attributes including meeting rooms, restrooms, and mother-and-baby room, with blue and green icons visible primarily on the lower portion of the building. Yellow flowers are visible in the foreground of panels b), c), and d), with palm trees and additional buildings visible in the background.

New article! Kejia Huang, Niaz Muhammad and colleagues propose Geospatial Simultaneous Localization and Mapping for Outdoor Mobile Augmented Reality (GSOMAR), enabling real-time modeling and interaction in complex urban environments doi.org/10.1080/1523... #GISchat

3 weeks ago 3 2 0 0
In Figure 13 below: a) Best energy efficiency; b) best user preference; c) best visual quality; d) e) closest to ideal point; f) optimal solution. Figure 13 showing six different cartographic styling variations of the same urban area map. Each panel a) through f) displays an identical street network with yellow/orange/brown road lines overlaying land parcels in various colors including green spaces (parks), blue water bodies, and urban blocks in pink, purple, cyan, and other hues. Panel a) features a dark teal/navy background with bright contrasting colors. Panel b) shows an olive/brown background with softer tones. Panel c) has a light beige/cream background with pastel colors. Panels d), e), and f) all use black backgrounds with progressively different color palettes: d) uses yellow streets with bright multi-colored parcels, e) employs pink/magenta streets with rainbow-hued parcels, and f) features green streets with a cyan-green-red color scheme. The maps demonstrate different approaches to urban cartographic design and color theory while maintaining the same geographic information.

In Figure 13 below: a) Best energy efficiency; b) best user preference; c) best visual quality; d) e) closest to ideal point; f) optimal solution. Figure 13 showing six different cartographic styling variations of the same urban area map. Each panel a) through f) displays an identical street network with yellow/orange/brown road lines overlaying land parcels in various colors including green spaces (parks), blue water bodies, and urban blocks in pink, purple, cyan, and other hues. Panel a) features a dark teal/navy background with bright contrasting colors. Panel b) shows an olive/brown background with softer tones. Panel c) has a light beige/cream background with pastel colors. Panels d), e), and f) all use black backgrounds with progressively different color palettes: d) uses yellow streets with bright multi-colored parcels, e) employs pink/magenta streets with rainbow-hued parcels, and f) features green streets with a cyan-green-red color scheme. The maps demonstrate different approaches to urban cartographic design and color theory while maintaining the same geographic information.

New paper! Colorful map draining your phone battery? Hongyue Zhang and colleagues evaluate how to balance user preference, visual quality and energy efficiency in maps for mobile devices doi.org/10.1080/1523... #GISchat

4 weeks ago 5 2 0 0
Location markers.

Location markers.

Join @nickbearman.bsky.social online for Advanced R as a GIS: Spatial Analysis and Statistics, 19-20 May'26

Learn how to prepare/conduct spatial analysis on a variety of spatial data in R, including a range of spatial overlays and data processing techniques.
Sign up: www.ncrm.ac.uk/training/sho...

1 month ago 2 3 0 0
Student working on a tablet computer making a map.

Student working on a tablet computer making a map.

A screenshot of a map created in R, showing Percentage population Ages 10 to 14 for Liverpool, UK.

A screenshot of a map created in R, showing Percentage population Ages 10 to 14 for Liverpool, UK.

Want to learn how to visualise and analyze spatial data in the social sciences? My #R #GIS courses are coming up in Apr-May 2026:
Intro to using R as a GIS - No prior knowledge needed!
Advanced R as a GIS - For those ready to dive deeper
nickbearman.com/training-cou...

1 month ago 0 0 0 0
Figure 10: Dale of a surface watercourse, with note that no dale is computed for the culvert. The map displays a grayscale elevation raster with drainage network overlay. Red lines indicate existing drainage divides forming a complex network across the terrain. Orange lines show a proposed new drainage divide boundary. Blue lines represent existing surface watercourses, while a thick cyan line shows a new proposed surface watercourse. A thick green line indicates a culvert location. Critical points are marked with various symbols: inverted blue triangles for pits (local elevation minima), cyan diamonds for confluences (stream junctions), yellow circles for saddles (low points along ridges), green circles for transfluences (water flow across divides), and red triangles for pics (local elevation maxima). A scale bar shows distances in kilometers from 0.01 to 0.03. The legend on the right defines all symbols and line types.

Figure 10: Dale of a surface watercourse, with note that no dale is computed for the culvert. The map displays a grayscale elevation raster with drainage network overlay. Red lines indicate existing drainage divides forming a complex network across the terrain. Orange lines show a proposed new drainage divide boundary. Blue lines represent existing surface watercourses, while a thick cyan line shows a new proposed surface watercourse. A thick green line indicates a culvert location. Critical points are marked with various symbols: inverted blue triangles for pits (local elevation minima), cyan diamonds for confluences (stream junctions), yellow circles for saddles (low points along ridges), green circles for transfluences (water flow across divides), and red triangles for pics (local elevation maxima). A scale bar shows distances in kilometers from 0.01 to 0.03. The legend on the right defines all symbols and line types.

New article! Yassmine Zada, Eric Guilbert and Sylvain Jutras explore how to integrate culverts into drainage network mapping without altering the underlying digital terrain model (DTM) doi.org/10.1080/1523... #GISchat

1 month ago 4 2 0 0
Post image

New blog post! QGIS 4.0 is now available - but don’t upgrade….. yet!

QGIS 4.0 is an ‘Early Adopter’ version. I would also say for many basic users there isn’t much new that’s changed, so you are not missing out on much.

Check out my blog for more: nickbearman.com/blog/2026-03... #GISchat

1 month ago 1 0 0 0
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Front cover, for Cartography and Geographic Information Science, Volume 53, No 2, March 2026, The Journal of the Cartography and Geographic Information Society. Includes Figure 6 from Gołębiowska et al. Tested stimuli: a) 2D, b) 3D, c) and context in continuous (top row) and discrete (bottom row) RC. The images show visualizations of the subsurface temperature field and permafrost boundary inside the Zugspitze mountain in Germany. Data source: Noetzli et al. (2010), Gallemann et al. (2017), Deutscher Wetterdienst DWD, Geobasisdaten © Bayerische Vermessungsverwaltung 2011

Front cover, for Cartography and Geographic Information Science, Volume 53, No 2, March 2026, The Journal of the Cartography and Geographic Information Society. Includes Figure 6 from Gołębiowska et al. Tested stimuli: a) 2D, b) 3D, c) and context in continuous (top row) and discrete (bottom row) RC. The images show visualizations of the subsurface temperature field and permafrost boundary inside the Zugspitze mountain in Germany. Data source: Noetzli et al. (2010), Gallemann et al. (2017), Deutscher Wetterdienst DWD, Geobasisdaten © Bayerische Vermessungsverwaltung 2011

CaGIS Volume 52 Issue 2, our special issue on Smart cartography for sustainable development: International Cartographic Conference 2023 is now available online and the printed copy should be on its way to all our subscribers! www.tandfonline.com/toc/tcag20/5... #GISchat

1 month ago 5 2 1 0

Anyone got IMDB API access? I'm curious to see what's the biggest (most expensive/highest grossing) movie with an empty "Goofs" section.

1 month ago 0 1 0 0
Figure 2. Illustration of line densification when approximating a curve with a finite number of points. Panel (a) depicts the ground truth, showing the precise geometry of two curves, C1 and C2. Panel (b) illustrates finite approximations, A1 and A2, that use a sparse distribution of points along the respective curves. Topology is violated as A1 intersects itself and also A2. Panel (c) demonstrates line densification resulting in A0 1 and A0 2 preserving the true topology

Figure 2. Illustration of line densification when approximating a curve with a finite number of points. Panel (a) depicts the ground truth, showing the precise geometry of two curves, C1 and C2. Panel (b) illustrates finite approximations, A1 and A2, that use a sparse distribution of points along the respective curves. Topology is violated as A1 intersects itself and also A2. Panel (c) demonstrates line densification resulting in A0 1 and A0 2 preserving the true topology

New article! Fantastic work from Nihal Z. Miaji and colleagues on the cartogram creation process, and how we ensure topology is preserved and cartogram regions remain connected and not overlapping doi.org/10.1080/1523... #GISchat #OpenAccess

1 month ago 7 2 0 0
Location markers.

Location markers.

Join @nickbearman.bsky.social for Introduction to Spatial Data and using R as a GIS.

This online course, on 28-29 April 2026, will explore how to use R to import, manage and process spatial data, make choropleth maps and perform basic spatial analysis.

Sign up: www.ncrm.ac.uk/training/sho...

1 month ago 2 2 0 0
view from a railway bridge of a steam locomotive heading towards the camera. fields, clouds, railway lines, poles.

view from a railway bridge of a steam locomotive heading towards the camera. fields, clouds, railway lines, poles.

Sir Nigel Gresley steaming past @lancasteruni.bsky.social today!

1 month ago 5 1 0 0
Figure 7. Snapshot of the Web GIS dashboard (sewer networks are hidden due to confidentiality considerations. Screenshot of a Web GIS dashboard showing sampling results over a campus map. The central panel displays a map with colored building footprints and sampling locations marked with colored dots indicating results (negative, positive, suspicious, or other). A legend on the left explains the symbols for samplers and buildings. A sidebar allows neighborhood selection and zooming. On the right, panels show selectable basemaps, a summary indicator reporting **25 positive individual sites** in the current map extent, and a pie chart showing the composition of testing results (not collected, negative, positive, suspicious). The bottom section contains a bar chart showing the number of positive samplers over time and a line chart showing the time-series testing results for a selected sampler.

Figure 7. Snapshot of the Web GIS dashboard (sewer networks are hidden due to confidentiality considerations. Screenshot of a Web GIS dashboard showing sampling results over a campus map. The central panel displays a map with colored building footprints and sampling locations marked with colored dots indicating results (negative, positive, suspicious, or other). A legend on the left explains the symbols for samplers and buildings. A sidebar allows neighborhood selection and zooming. On the right, panels show selectable basemaps, a summary indicator reporting **25 positive individual sites** in the current map extent, and a pie chart showing the composition of testing results (not collected, negative, positive, suspicious). The bottom section contains a bar chart showing the number of positive samplers over time and a line chart showing the time-series testing results for a selected sampler.

New article! Wenwu Tang and colleagues present A web-based spatial decision support system of COVID-19 wastewater surveillance on a university campus doi.org/10.1080/1523... #GISchat

1 month ago 7 1 0 0
Figure 1: User attributes and navigation behavior. The diagram shows a central pedestrian figure with four attribute categories branching outward: 1) Gender - showing female (illustrated with person receiving directions 'Turn left before the red building') and male (illustrated with person at directional signpost receiving instruction 'Go west for 300 meters'); 2) Expertise - divided into Geography (showing topographic contour map with task 'Find the highest point in the terrain') and Non-geography (showing street map with task 'Find the hospital on Queen street'); 3) Spatial ability - showing high spatial ability (person solving maze saying 'Easy!') and low spatial ability (confused people asking 'Where is...?'); 4) Familiarity - showing familiar environment (person in park saying 'I have been there!') and unfamiliar environment (person consulting map with 'Map told me!'). Below these attributes are five navigation task types illustrated with icons: Self-localization (map with location pin asking 'Where am I?'), Object search (red house with 'Find the house with red roof'), Map target search (map showing 'Find the picnic point on the map'), Route memorization (map with dotted route to Bill's Wood), and Walking to the end (person walking in urban environment).

Figure 1: User attributes and navigation behavior. The diagram shows a central pedestrian figure with four attribute categories branching outward: 1) Gender - showing female (illustrated with person receiving directions 'Turn left before the red building') and male (illustrated with person at directional signpost receiving instruction 'Go west for 300 meters'); 2) Expertise - divided into Geography (showing topographic contour map with task 'Find the highest point in the terrain') and Non-geography (showing street map with task 'Find the hospital on Queen street'); 3) Spatial ability - showing high spatial ability (person solving maze saying 'Easy!') and low spatial ability (confused people asking 'Where is...?'); 4) Familiarity - showing familiar environment (person in park saying 'I have been there!') and unfamiliar environment (person consulting map with 'Map told me!'). Below these attributes are five navigation task types illustrated with icons: Self-localization (map with location pin asking 'Where am I?'), Object search (red house with 'Find the house with red roof'), Map target search (map showing 'Find the picnic point on the map'), Route memorization (map with dotted route to Bill's Wood), and Walking to the end (person walking in urban environment).

New article! Hua Liao and colleagues evaluate how much we can deduce from users visual behaviour in pedestrian navigation: quite a bit it seems, inc. gender, geographical expertise, spatial ability & familiarity of the environment #GISchat doi.org/10.1080/1523... Data at figshare.com/s/5b06a12bea...

1 month ago 5 2 0 0
Dr Stuart Grieve will be presenting on "Building partnerships between academia and industry" at the first ever 'London "Geo" Meetup' on 11th March, Wednesday at Geography Building, Queen Mary University of London. Event starts at 6PM

Dr Stuart Grieve will be presenting on "Building partnerships between academia and industry" at the first ever 'London "Geo" Meetup' on 11th March, Wednesday at Geography Building, Queen Mary University of London. Event starts at 6PM

Finally, we have our 3rd speaker Dr Stuart Grieve.

It's because of Stuart this event is taking place as he was able to secure the venue at Queen Mary.

If you are planning to hire for spatial roles soon, please come along.

RSVP: lgm.jsonsingh.com

#OSM
#OSGeo
#Geospatial
#London
#meetup

1 month ago 3 2 0 0
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Table 2: Mapping between tasks and candidate gestures. The table has two columns labeled 'Function' and 'Gesture 1'. Five rows show: 1) Pan - Open hand swipe gesture with illustration of flat hand moving horizontally; 2) Zoom In - Index finger and thumb moving from pinch to open position with spreading motion; 3) Zoom Out - Index finger and thumb moving from open to pinch position with closing motion; 4) Point Placement - Pinch fingers then open gesture, shown with two hand positions; 5) Switch Mode - Sweep open palm left and right, illustrated with palm moving in both directions. Each gesture is accompanied by simple black line drawings demonstrating the hand movements.

Table 2: Mapping between tasks and candidate gestures. The table has two columns labeled 'Function' and 'Gesture 1'. Five rows show: 1) Pan - Open hand swipe gesture with illustration of flat hand moving horizontally; 2) Zoom In - Index finger and thumb moving from pinch to open position with spreading motion; 3) Zoom Out - Index finger and thumb moving from open to pinch position with closing motion; 4) Point Placement - Pinch fingers then open gesture, shown with two hand positions; 5) Switch Mode - Sweep open palm left and right, illustrated with palm moving in both directions. Each gesture is accompanied by simple black line drawings demonstrating the hand movements.

New article! Ben Ma and colleagues evaluate how mid-air gestures could be used to interact with mobile maps, doi.org/10.1080/1523... #GISchat Data available at doi.org/10.6084/m9.f...

1 month ago 3 1 0 0
James Milner will be presenting on "Undo/Redo for Geospatial Drawing" at the first ever 'London "Geo" Meetup' on 11th March, Wednesday at Geography Building, Queen Mary Univeristy of London. Event starts at 6PM

James Milner will be presenting on "Undo/Redo for Geospatial Drawing" at the first ever 'London "Geo" Meetup' on 11th March, Wednesday at Geography Building, Queen Mary Univeristy of London. Event starts at 6PM

When I moved from India to London last year, James was kind enough to invite me for a lunch, and now I am inviting him to speak at the first London "Geo" Meetup.

James will be presenting on "Undo/Redo in Terradraw".

RSVP:: lgm.jsonsingh.com

#OSM #OSGeo #Geospatial #Opensource #London #meetup

1 month ago 2 2 0 1