Arthur A.’s Post

🎥 New Video Alert: Mapping Greenland & Antarctica's Bathymetry with Python! 🌎❄️ I’m excited to share my latest tutorial, where I show you how to map the bedrock and ice surface bathymetry of Greenland and Antarctica using the ETOPO 2022 dataset. You’ll learn: ✅ How to read and plot NetCDF data. ✅ How to stream data via OPeNDAP. ✅ Writing data to NumPy arrays and Pandas DataFrames. ✅ Plotting maps for bedrock and ice surface bathymetry and ice thickness. ✅ Combining data from multiple files on a THREDDS server. This tutorial is helpful for researchers, data scientists, and anyone interested in using Python to explore FAIR geospatial datasets. 🌐 🔗 Watch the full tutorial here: https://lnkd.in/dBgzDrCU 🗒️ Code and explanations: https://lnkd.in/d89gDwc8 📊 The data: https://lnkd.in/dSjUKVYy 💡 Let me know what you think or share what you’d like to see in future videos! #Python #FAIRData #Greenland #Antarctica #Bathymetry Arctic PASSION

🌍 Drought Analysis Using Google Earth Engine and Folium 🌍 Excited to share my recent work on visualizing drought-affected areas using MODIS and CHIRPS data on Google Earth Engine! This project combines several indices, including NDVI, LST, VCI, and SPI, to detect drought conditions in the Donga Mantung region, Cameroon. Using the power of Python, Folium, and Earth Engine, I was able to create a detailed, interactive map showing potential drought areas with easy-to-interpret color legends. 💡 Key features of the code: • Integration of MODIS and CHIRPS datasets. • Drought detection based on NDVI, LST, VCI, and SPI thresholds. • Interactive map visualization with Folium. • A user-friendly legend for interpreting results. 🔗 You can explore the full implementation here: Link to your Colab A big thanks to Qiusheng Wu Wu for his insightful tutorial on drought analysis using Earth Engine! His guidance made this project much easier to accomplish. If you’re interested in learning more, be sure to check out his tutorial here: Qiusheng Wu Wu’s Drought Analysis Tutorial. #DroughtAnalysis #GoogleEarthEngine #MODIS #CHIRPS #GeospatialAnalysis #RemoteSensing #Python #Folium #GIS #ClimateChange #SustainableDevelopment Please correct me if iam doing any wrong

This is exactly what I've been thinking for future geospatial society development dashboard

🌍 #30DayMapChallenge | Day 1 - Point 🌍 A bit late, but it's never too late 😁 For the first challenge, I created an interactive map using Python to visualize global earthquake data. The map displays earthquake locations and magnitudes sourced from the USGS. 🔹 Technologies Used: Python with Pandas & Folium 🔹 Data Source: USGS Earthquake Data Check out the map here ⬇ [https://lnkd.in/gD2VBFNe] #GIS #DataVisualization #Python #Folium #Pandas #Mapping #Earthquake #DataScience #Geospatial #Point

🚀 Check out our Webinar on 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗔𝗹𝗴𝗮𝗹 𝗕𝗹𝗼𝗼𝗺𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗦𝗮𝗻𝗱𝗯𝗼𝘅 using #Dunia. 🌿 Johannes Schmid from GeoVille Information Systems and Data Processing GmbH will dive deeper into the usage of #Sentinel3 for marine chlorophyll analysis. 👇 Click on the Post of Space in Africa below to find the registration link.

Day 11 of the #30DayMapChallenge - Arctic For Day 11 of the #30DayMapChallenge, I created a map showing 2023 cruise ship landings in Svalbard. The map highlights key landing sites across the Arctic, color-coded by the number of landings. 🚢 Key Landing Sites: Adambreen Adolfbukta Adriabukta Advent City Agardhbukta This map was made using Python, with tools like GeoPandas for handling the GeoJSON data, Cartopy for the map projection, and Matplotlib for creating the visual layout and color gradients. The dataset comes from the Norwegian Polar Institute and offers great insights into cruise tourism in the Arctic. 🔗 Dataset: Norwegian Polar Institute (DOI: 10.21334/npolar.2008.926d599e) #Mapping #GIS #Python #DataVisualization #Svalbard #Arctic #CruiseTourism #30DayMapChallenge

#30𝐃𝐚𝐲𝐌𝐚𝐩𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 - 𝐃𝐚𝐲 11 - 𝐀𝐧𝐭𝐚𝐫𝐜𝐭𝐢𝐜𝐚 While Day 11 is officially about the Arctic, I really wanted to get another go at Antarctica instead. Last year I already found a really cool high-resolution SAR image of the whole continent provided by the National Snow and Ice Data Center showing the surface morphology of the icecap. Now, I went after how to visualize it in 3D using Python! Python tutorial on Substack coming soon! #datascience #networkscience #connectingthedots #GIS #spatialanalytics #geospatialdata #geospatial #datascience #datavisualization 

🌍 Local Climate Zone Classification for Colombo Municipal Council Area 🗺️ I am thrilled to share a map I have created using Python and QGIS software, which classifies the Local Climate Zones (LCZ) for the Colombo Municipal Council Area. This map is a result of extensive data processing and spatial analysis, leveraging the power of pandas and geopandas libraries. 📊 Methodology: • Utilized Python for data manipulation and classification. • Used QGIS for spatial visualization and mapping. • Classified zones based on Building Height (BH) and Building Coverage Ratio (BCR) using a custom function to determine the appropriate LCZ. This project has been a fantastic learning experience, and I am grateful for the guidance provided by my lecturer, Nayomi Kankanamge, whose expertise and support were instrumental in completing this project successfully. #UrbanPlanning #GeospatialAnalysis #Python #QGIS #DataScience #LCZ #Colombo

🔥 Analyzing Wildfire Activities in Australia 🔥 I created a visualization to showcase the distribution of estimated fire brightness across different regions in Australia. The stacked histogram provides a clear comparison of fire activities in NSW, NT, QL, SA, TA, VI, and WA. If you're interested in the code and the detailed analysis, check out my GitHub: https://lnkd.in/emkESxKD. #DataScience #DataVisualization #WildfireAnalysis #Australia #Python #Seaborn #Folium #DataAnalytics

Day 21 | Conflict | #30DayMapChallenge 🌏 For this challenge, I created an interactive map that visualizes the global conflict landscape. The map reveals territories affected by conflict, the frequency and intensity of these conflicts, and a comprehensive list of all conflicts, including the parties involved and their alliances. 📒 Explore the notebook here: https://lnkd.in/d53qRW7Q #esri #maps #GIS #conflicts #world #python