Category: geospatial
-
Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code
Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code Understand air quality: access the available data, interpret data types, and execute starter codes The post Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code appeared first on Towards Data Science. Prithviraj…
-
RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar
RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar The high-resolution physics turning microwave echoes into real-time flood intelligence The post RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar appeared first on Towards Data Science. Aakash Goswami Go to original source
-
Building a Geospatial Lakehouse with Open Source and Databricks
Building a Geospatial Lakehouse with Open Source and Databricks An example workflow for vector geospatial data science The post Building a Geospatial Lakehouse with Open Source and Databricks appeared first on Towards Data Science. Robert Constable Go to original source
-
Where Hurricanes Hit Hardest: A County-Level Analysis with Python
Where Hurricanes Hit Hardest: A County-Level Analysis with Python Use Python, GeoPandas, Tropycal, and Plotly Express to map the number of hurricane encounters per county over the past 50 years. The post Where Hurricanes Hit Hardest: A County-Level Analysis with Python appeared first on Towards Data Science. Lee Vaughan Go to original source
-
The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated
The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated The saying goes that 80% of data collected, stored and maintained by governments can be associated with geographical locations. Although never empirically proven, it illustrates the importance of location within data. Ever growing data volumes put constraints on systems that handle geospatial data. Common Big…
-
USGS DEM Files: How to Load, Merge, and Crop with Python
USGS DEM Files: How to Load, Merge, and Crop with Python A quick guide to prepping digital elevation data Continue reading on Towards Data Science » Lee Vaughan Go to original source
-
GPS Interpolation Using Maps and Kinematics
GPS Interpolation Using Maps and Kinematics How do you apply dead reckoning to your geospatial dataset? The picture above illustrates the GPS interpolation process. The red dots represent the known and repeated GPS locations, with more than one location per dot, while the blue dots represent the inferred locations of the repeated points along the…