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What’s new in #ArcGIS GeoAnalytics Engine 2.0 tinyurl.com/y4vx2svd

#bigdata #raster #imagery #spatial #analysis #GIS #esri #GISchat #geospatial #TheScienceOfWhere #geosky

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GIS for good? 🤔 Where have I heard that before? 😆 #GISchat #Geo4good #G4G #geospatial

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GIS for Good: Esri’s Commitment to People, Planet, Prosperity, and Peace This collection of programs and resources supports organizations that seek to create a more sustainable future.

#GISchat: GIS for Good: Esri’s Commitment to People, Planet, Prosperity, and Peace

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Has anyone had an issue in #qgis with the align rasters tool. When I try and configure a layer there's a message stating to select a single layer (which I've done!)

#gischat

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#GeoSky #GISchat

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What’s new in #ArcGIS Solutions (March 2026) tinyurl.com/mrybmn83

#apps #govtech #GIS #esri #dataviz #mapping #GISchat #geospatial #ArcGISApps #TheScienceOfWhere #geosky

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Auch 2026: Frohe (Geo-) Ostern! / Happy (geo) Easter in 2026 too! geoobserver.de/2026/04/02/a... a perfect #easteregg via @Ventuskycom #gistribe #gischat #fossgis #foss4g #OSGeo #spatial #geospatial #gis #geo #geoObserver pls RT

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#gischat can you interact with 3D Tiles or are they more static than that? can you click on it and get a value back?

obviously just dipping my toe in the water here

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Map showing Africa's terrain— from the lowlands till on top of Mount Kilimajaro .

📊 Data: Gebco bathymetry

Made with qgis and blender

#gischat #Africa #GIS #Cartography

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Map showing Africa's terrain— from the lowlands till on top of Mount Kilimajaro .

📊 Data: Gebco bathymetry

Made with qgis and blender

#gischat #Africa #GIS #Cartography

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Wrote an algorithm to hydrologically split calculation cells for hydrodynamic modelling.

#gischat

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#gischat #climatechange

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#infographic #gischat

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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...

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Osterspaziergang & Streuobst erfassen? / Easter walk & recording orchard trees? geoobserver.de/2026/04/01/o... #gistribe #gischat #fossgis #foss4g #OSGeo #spatial #geospatial #opensource #mapping #opendata #osm #openstreetmap #gis #geo #geoObserver pls RT

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How fast does road transport get electrified in Europe ?

To answer this question, #Eurostat just published a new digital #map on the #accessibility of electrical vehicle (EV) charging points in Europe for 2025 and 2023.

➡️ ec.europa.eu/assets/estat...

#gischat #mapping #cartography #gis #transport

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#gischat

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I'm going to be spending more time with our map collection as a couple of scanning projects get underway and I'm curious...
What would you like to know about academic library map collections? What kinds of social media content around them would interest you?
#gischat #geosky

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#GeoSky #GISchat #EnergySky

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A Joy Plot Map of elevation and population for Ohio. Each line shows the rising and falling value of either population or surface elevation.

A Joy Plot Map of elevation and population for Ohio. Each line shows the rising and falling value of either population or surface elevation.

Next Joy Plot Map is Ohio.

Lots of people, very flat.

#gis #gischat #cartography

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♻️ openstreetmap: Government data can be wrong. You should be able to fix it.

🏳️‍⚧️ Happy Trans Day of Visibility! 🏳️‍⚧️

#tdov #TransDayOfVisibility #OpenStreetMap #OSM #gischat

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Learn how #NCTCOG uses #ArcGISMonitor to help manage their enterprise #GIS tinyurl.com/2f84k5fs

#ArcGISEnterprise #observability #ArcGISAdmin #esri #arcgis #TX #govtech #GISchat #geospatial #bestpractices #geosky

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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

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Government data can be wrong. You should be able to fix it.

🏳️‍⚧️ Happy Trans Day of Visibility! 🏳️‍⚧️

#tdov #TransDayOfVisibility #OpenStreetMap #OSM #gischat

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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

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The geobounds package by Diego Hernangómez gives you direct access to the open global geoBoundaries dataset. 🗺️

URL: dieghernan.github.io/202602_geobo...

#RStats #RSpatial #GISchat

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The geobounds package by Diego Hernangómez gives you direct access to the open global geoBoundaries dataset. 🗺️

URL: https://dieghernan.github.io/202602_geobounds/

#RStats #RSpatial #GISchat

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#ArcGIS Instant #Apps: It all starts with the web #map tinyurl.com/596cmvfa

#mapping #dataviz #WebGIS #GIS #esri #GISchat #geospatial #ArcGISApps #TheScienceOfWhere #geosky

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THX FOSSGIS 2026 & GeoBasis_Loader: 44.444 Downloads geoobserver.de/2026/03/30/t... #fossgis #fossgis2026 qgis #plugin #switch2qgis #gistribe #gischat #foss4g #OSGeo #spatial #geospatial #opensource #opendata #osm #openstreetmap #gis #geo #geoObserver pls RT

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A limitation of #GIS which rarely gets discussed is that it
1) models maps data
2) in only 2 dimensions
3) from old data

For planning or zoning that might be okay, but not for complex systems like wildfire or utility models. Using a database or AI doesn’t fix that problem.

Thoughts? #GISChat

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