GIS Mapping Software

Exploring House Transactions in London Check out the interactive visualization here: https://lnkd.in/g-S3APXf I explored a dataset published by University College London (UCL) containing over 22 million house transactions across England and Wales. For this exploration, I focused on London, given its familiarity and popularity. The dataset contains individual house transactions, including prices paid and property characteristics, with postcode-level information serving as the spatial reference. This makes it well suited for analyzing spatial and temporal patterns in the housing market. From a visualization perspective, however, the scale of the data presents a clear challenge. The core design question: How can millions of spatio-temporal data points be explored without overwhelming users or sacrificing orientation? - Plotting all points at once results in dense, unreadable clusters. - Raster-based aggregations often obscure the underlying map, making  spatial context harder to maintain. The design approach: a lens metaphor. Instead of showing everything at once, users interactively place a lens over the map to explore a local region. Within this focus area, the visualization reveals: - Average price per square meter - Number of transactions - Temporal trends over the years 2009, 2012, 2015, 2018, 2021 and 2024 This interaction encourages exploration driven by familiarity and curiosity, allowing users to relate known places to newly discovered patterns. For more details on the dataset please refer to: https://lnkd.in/gRj5PKft Related publication: Chi, B., Dennett, A., Oléron-Evans, T., & Morphet, R. (2021). A new attribute-linked residential property price dataset for England and Wales, 2011–2019. UCLOE, 2. DOI: 10.14324/111.444/ucloe.000019 #RealEstate #London #DataVisualization #Geospatial #InteractiveVisualization #UrbanData

𝗛𝗼𝘄 𝗚𝗼𝗼𝗴𝗹𝗲 𝗠𝗮𝗽 𝗪𝗼𝗿𝗸𝘀 1. 𝗨𝘀𝗲𝗿𝘀    Access Google Maps through various devices, initiating requests for navigation, location searches, or routing. 2. 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗿    Distributes incoming user requests across multiple servers to ensure efficiency and avoid overload, providing consistent response times. 3. 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗶𝗻𝗱𝗲𝗿    Pinpoints users’ locations accurately, serving as the foundation for further search or navigation requests. 4. 𝗥𝗼𝘂𝘁𝗲 𝗙𝗶𝗻𝗱𝗲𝗿    Determines the best possible routes, leveraging data from various sources to optimize for time, distance, or user preferences. 5. 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗼𝗿    Guides users in real time, providing turn-by-turn directions and adjusting dynamically based on real-world conditions. 6. 𝗣𝘂𝗯-𝗦𝘂𝗯 𝗦𝘆𝘀𝘁𝗲𝗺 (𝗞𝗮𝗳𝗸𝗮)    A messaging system that facilitates communication between services. It ensures smooth data flow for area searches, route updates, and more. 7. 𝗔𝗿𝗲𝗮 𝗦𝗲𝗮𝗿𝗰𝗵 𝗦𝗲𝗿𝘃𝗶𝗰𝗲    Searches for locations, addresses, or points of interest in the vicinity, using real-time data to help users discover places near them. 8. 𝗚𝗿𝗮𝗽𝗵 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗦𝗲𝗿𝘃𝗶𝗰𝗲      Analyzes and processes the complex network of roads, connections, and traffic data, allowing for quick and efficient route calculations. 9. 𝗚𝗿𝗮𝗽𝗵 𝗗𝗕       Stores the road network in a graph format, making it easier to run algorithms for shortest path and efficient route computation. 10. 𝗚𝗿𝗮𝗽𝗵 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 & 𝗧𝗵𝗶𝗿𝗱-𝗣𝗮𝗿𝘁𝘆 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻       Builds and updates the road network, incorporating data from third-party sources to stay current with road conditions, construction, and traffic patterns. 11. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗦𝗲𝗮𝗿𝗰𝗵 𝗦𝘆𝘀𝘁𝗲𝗺       Supports large-scale search operations across a distributed network, enabling high-speed, accurate location searches even during peak times. Each component plays a vital role in making Google Maps a reliable, user-friendly tool for navigation and location-based searches. From data flow management with Kafka to graph-based route optimization, this system design shows the complexity behind a tool we often take for granted.

🌊🔍Exploring Flood Impact Analysis and Visualization with ArcGIS Pro🌍✨   Flooding is one of the most devastating natural disasters, exacerbated by climate change, impacting communities, economies, and environments. With the power of ArcGIS Pro, we can conduct comprehensive flood impact analyses and create stunning visualizations that help us understand and mitigate these risks. "... visible danger is the best argument for prevention - this also applies in digital worlds ..." 🌡️ Climate Change and Flooding: Climate change is leading to increased rainfall (in some areas to decreased as well), rising sea levels, and more frequent extreme weather events, resulting in heightened flood risks. Understanding these changes is crucial for effective planning and response. 📈 Key Benefits of Using ArcGIS Pro for Flood Analysis: 1️⃣ Data Integration: Combine various datasets, including elevation, land use, climate models, and historical flood events, to create a robust analysis. 2️⃣ 3D Visualization: Utilize 3D capabilities to visualize flood extents and impacts on infrastructure and communities, considering future climate scenarios. 3️⃣ Scenario Modeling: Simulate different flood scenarios under varying climate conditions to assess potential impacts and plan effective responses. 4️⃣ Hydrological Analysis Tools: Use tools like the Hydrology toolset to analyze watershed dynamics and flood risk. 5️⃣ Remote Sensing: Leverage satellite imagery and remote sensing data to monitor changes in land cover and water bodies due to climate change. 6️⃣ Community Engagement: Share interactive maps and visualizations with stakeholders to raise awareness and drive action. By leveraging these tools, we can enhance our preparedness and response strategies, ultimately saving lives and reducing economic losses. 💪🌈 🤝 Let's spark a conversation! How are you leveraging ArcGIS for flood Analysis? Share your insights, challenges, and success stories below. Let's amplify our collective GIS capabilities! 💬💡 💡 🌟 #FloodAnalysis #ClimateChange #DataVisualization #Resilience #FloodManagement #ArcGISPro #RiskMitigation #Esri #GIS #SpatialAnalysis #ArcGIS #flood #climatechange #FloodManagement #DisasterResponse #UrbanPlanning #Sustainability #ClimateChangeAdaption #EsriDeutschland #ArcGISPro #esrivoices🔍 🚀 🌱

I'm excited to share our recent research on autonomous GIS with the development of a "GIS Copilot" that allows users to perform spatial analysis in QGIS using natural language for both vector and raster data. The Copilot was tested with more than 100 spatial analysis tasks spanning various complexity levels. The result shows that the GIS Copilot has strong potential in automating foundational GIS operations, with a high success rate for basic and intermediate tasks, while challenges remain in achieving full autonomy for more complex tasks without explicit guidance. This work represents a promising step toward the development of autonomous GIS, aiming to make spatial analysis more accessible to all. To learn more, please check out our preprint article at https://lnkd.in/euPQphf3 The GIS Copilot can be downloaded from the official QGIS plugin page at https://lnkd.in/eGjBzS-w Video demonstrations: https://lnkd.in/ejt7tZ94 The 110 test cases/analysis examples can be found at https://lnkd.in/eKUXWA8h https://lnkd.in/eAKu8xR8 The system is open sourced at https://lnkd.in/eFrdmjBk Temitope Ezekiel Akinboyewa, Zhenlong Li, Huan Ning, M. Naser Lessani #artificialintelligence #gis #giscience #geoai #spatialanalysis #QGIS #GDAL #autonomous #largelanguagemodels #chatgpt #generativeAI #agent

Are you still wasting time collecting site data from 5 different portals? Building footprints from one source. Wind patterns from another. Topography? Probably buried in a PDF somewhere. It’s 2025, and with AI tools around, we shouldn’t be spending hours stitching datasets together just to start a design. I use Aino to cut through the noise and get clean, reliable data fast. Here’s what makes it work so well for site studies: 👉 Building footprints and building use mapped in seconds 👉 Adjustable building heights visualised in a gradient 👉 Real-time wind movement overlays 👉 Street network identified and simplified 👉 Topography with contour clarity 👉 Open spaces sorted into categories My favourite features: ✅ Traffic Heatmaps ↳ See where bottlenecks occur and plan circulation with confidence. ✅ Clip and Export ↳ Crop any area and export in PNG, SVG, PDF, or DXF for design workflows. With Aino, you spend less time on data chaos and more time designing with clarity. Want to see how it works in real projects? I’ve added a short tutorial video below.

⸻ Excavation Safety Management (Summary) 1. Excavation Permit Before any excavation work begins, a Permit to Dig must be obtained. This ensures: • Identification of underground hazards such as gas, electricity, and telecom lines. • Planning of risk controls and safe work procedures. • Coordination between HSE, site engineers, and relevant authorities. • Proper documentation of all planned activities. Reference: OSHA 29 CFR 1926 Subpart P, ADOSH CoP 11.0 – Permit to Work Systems, Dubai Municipality Safety Code. 2. Site Safety and Risk Control Measures The following measures must be implemented at all excavation sites: • Conduct a site survey using GPR or certified utility maps. • Provide shoring or shielding for excavations deeper than 1.2 m or in unstable soil. • Place excavated material at least 60 cm from the edge. • Provide ladders or ramps every 7.5 m for safe access and egress. • Install barricades and warning signs along all excavation edges. • Ensure adequate ventilation in confined or deep excavations to prevent gas accumulation. • Conduct daily inspections by competent personnel, especially after rain or vibration. • Implement water management systems such as sump pumps or drainage. Reference: OSHA 1926.651 & 1926.652, ADOSH CoP 18.0 – Trenching and Excavation Safety, Trakhees EHS Standards. 3. Heavy Equipment Control When using excavators, hoists, loaders, or similar machinery: • Carry out daily and periodic inspections of all equipment. • Ensure only trained, licensed, and authorized operators are engaged. • Define clear movement zones and avoid operating too close to excavation edges. • Use warning devices including reverse alarms, flashing beacons, and clear signage. • Follow proper shutdown procedures: park safely, turn off engine, engage brakes. • Maintain effective communication between operators and ground crew using radios or hand signals. Reference: OSHA 1926.602, ISO 45001 Clause 8.1, ADOSH CoP 10.0 – Safe Use of Machinery. ⸻ General HSE Compliance Notes • Always use Permit to Work (PTW) systems. • Apply Hierarchy of Controls: Elimination > Substitution > Engineering > Admin > PPE. • Provide Toolbox Talks (TBT) daily before excavation. • Ensure Emergency Response Plan is in place. ⸻ #ExcavationSafety #WorkplaceSafety #ConstructionSafety #HSE #SafetyFirst #OSHAStandards #OSHAD

🚗 One kilometer north or a two-kilometer drive? Which is more relevant to your spatial analysis? Most distance analyses assume as the crow flies travel, but real mobility happens on streets, rail, bike lanes, and transit networks. Turning those into OD (origin–destination) matrices is one of the more compute intensive tasks in geospatial, in most cases you need to calculate the routes for all the points in your analysis (i.e. 100 X 100 = 10,000 routes to calculate) The latest city2graph release makes this far more accessible. city2graph converts raw geospatial data into graph structures ready for advanced spatial analytics. It plugs into GeoPandas, NetworkX, and PyTorch Geometric, so you can move from OSM, GTFS, POIs, or land use to network aware analysis without heavy routing engines. New in v0.1.6: 🛣️ "Metapaths" for heterogeneous graphs to connect amenities → streets → amenities 🗺️ Contiguity graphs to analyze adjacency & neighborhood relationships 🔄 OD matrix support to generate and analyze true network-based flows with less friction 👏 Huge thanks to Yuta Sato and contributors for pushing this forward. 🌎 I'm Matt and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 9k+ others learning from my newsletter → forrest.nyc

🎓 My master's thesis on #BIM and #GIS Integration is now published in the Hochschule für Technik Stuttgart repository! 🔍 The research includes a structured literature review covering: • BIM Fundamentals: ISO 19650, classification systems, dimensions, and LOD. • GIS Fundamentals: Data representation, coordinate systems, geo-referencing, CityGML, and LOD. • BIM-GIS Integration: Challenges, opportunities, and technical requirements. 🛠️ In the practical part, I developed an integration model in #ArcGISPro using real geospatial datasets (#DTM, #CityGML, #ALKIS) processed with #GDAL. I then tested multiple BIM (prepared in #Revit) import methods, including: • Direct Import from Local Files • Integration via Autodesk Construction Cloud • Conversion to Geodatabase • Creation of Building Scene Layers The integrated model was used to simulate flood scenarios and published to #ArcGISOnline. I also compared ArcGIS workflow with an alternative Cesium-based approach. 📄 All workflows are fully documented and serve as a step-by-step guide for anyone interested in implementing BIM-GIS integration. You can download the full thesis here: https://lnkd.in/eq_HCXMW Esri, Autodesk, die STEG Stadtentwicklung GmbH, Master Smart City Solutions, #SmartCities #DigitalTwins #UrbanPlanning #3DVisualization #Geospatial #HFTStuttgart #MasterThesis

How do you create this dashboard? Where the power cable break triggers the visual alert. Here is the technical blueprint for this tiny DT. ⏩The Data Source Layer You can't visualize what you don't measure. For the electricity and water metrics shown use: - Sensors/PLCs: Smart meters for floor-wise consumption and leak detectors for pipes. - Protocols: Most commercial buildings use BACnet or Modbus. For a modern twin, these are typically converted to MQTT to make them "cloud-ready." ⏩The Ingestion Layer The "Power Cable Break" alert needs to travel from the transformer to the dashboard in sub-seconds. - Message Broker: MQTT (Mosquitto/EMQX) is the standard here. - Data Pipeline: Tools like Node-RED or Apache Kafka handle the logic. Here's a simple Logic Example: If Voltage < Threshold, then Publish Alert to the Alerts topic. ⏩The Data "Brain" The video shows historical data (Day/Month/Year toggles). You need two types of databases: - Time-Series Database (TSDB): Use InfluxDB to store the electricity and water consumption spikes. This allows for the "Floorwise Consumption" graphing. - Graph Database: Use Neo4j or AWS Neptune to store the relationships, e.g. Transformer A is connected to Building B. This is how the system knows exactly which icon to turn red on the map when a specific asset fails. ⏩The Visualization Layer This is the PropX interface seen in the video. - 3D Engine: Lots of twins use Three.js or Babylon.js for web-based rendering. For more photorealistic cityscapes, developers use Unreal. - GIS Integration: To place the buildings accurately, use CesiumJS to overlay the 3D model onto real-world coordinates. - Frontend Framework: React or Vue.js to handle the data-heavy sidebars. ⏩The Unified Namespace The reason most DT fail is that data is siloed. A Unified Namespace acts as a single source of truth where every asset has its unique specific address: Site / Building / Floor / Room / Asset / Sensor When the "Transformer Break" happens at Location: FIN 21/2702, the dashboard knows exactly where to "ping" the map because the asset's ID matches the 3D model's metadata. Just consider for a second the implications of this on a city-wide level🖐️ ----------------- Follow me for #digitaltwins Wishing all of my followers a warm and Merry Christmas!

𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Machine learning is no longer just an analytical tool - it’s becoming the backbone of geospatial intelligence. From wildfire prediction and groundwater mapping to disease forecasting, carbon estimation, and urban sprawl detection, these papers show how spatial data + ML are reshaping environmental risk assessment, urban analytics, agriculture, and climate science. If you're working at the intersection of GIS, remote sensing, and AI - this collection of recent research papers is worth bookmarking. My tutorials: https://lnkd.in/dXpjUz3K ⬇️ Full list in the end ⬇️ 1. Exploration of Geo-Spatial Data and Machine Learning Algorithms for Robust Wildfire Occurrence Prediction https://lnkd.in/dtTW_iau 2. Enhancement of Groundwater Resources Quality Prediction Using an Improved DRASTIC Method and Machine Learning https://lnkd.in/dNhTsieN 3. Remote Sensing-Based Forest Cover Classification Using Machine Learning https://lnkd.in/dZfAUZs4 4. Forest Age Estimation Based on a Machine Learning Pipeline Using Sentinel-2 and Auxiliary Data https://lnkd.in/dr3c79-P 5. Factors of Acute Respiratory Infection Among Under-Five Children Using Machine Learning Approaches https://lnkd.in/d6DUxbAh 6. SAR Image Integration for Multi-Temporal Wetland Dynamics Analysis Using Machine Learning https://lnkd.in/dY4--gep 7. Effects of Non-Landslide Sampling Strategies in Landslide Susceptibility Mapping https://lnkd.in/d7mRkFWv 8. Enhancing Co-Seismic Landslide Susceptibility and Risk Analysis Through Machine Learning https://lnkd.in/dtsigmG8 9. 10-m Scale Chemical Industrial Parks Map Along the Yangtze River Based on Machine Learning https://lnkd.in/dS9qGi68 10. Geospatial Distribution and Machine Learning Algorithms for Assessing Surface Water Quality in Morocco https://lnkd.in/dwxSamAt ... 20. Wheat Crop Genotype Identification Using Multispectral Radiometer Data and Machine Learning 21. Geospatial Data for Peer-to-Peer Communication Among Autonomous Vehicles Using Optimized ML Algorithms ... 𝐅𝐮𝐥𝐥 𝐥𝐢𝐬𝐭: https://lnkd.in/dNC9VA7E