A geographic information system (GIS) is a conceptualized framework that provides the ability to capture and
analyze spatial and geographic data. GIS applications (or GIS apps) are computer-based tools that allow the
user to create interactive queries (user-created searches), store and edit spatial and non-spatial data,
analyze spatial information output, and visually share the results of these operations by presenting them as
maps.
An interactive Jupyter/IPython widget for accessign data from any public ERDDAP server
Notebooks used or made in development of prediksicovidjatim
These Notebooks are exclusively for Coursera Capstone
Docker image for GDAL and jupyter-notebook
Docker image with datascience-notebook's jupyter and some geo packages
Just a simple collection of JupyterLab notebooks for exploring GeoPandas library, using the builtin data sets.
Intergrate functions of ArcGIS Desktop and ENVI/IDL via python script packages/APIs. Use Jupyter Notebook for codeblock management.
Contains python notebooks used for clipping of satellite data using Geojson AOI, calculating LST from Landsat 8 Images and Correlation analysis between CO and LST
🌏 Master Python Geocoding: From addresses to coordinates! Interactive notebooks covering geocoding essentials, API integration, and real-world applications with Tokyo locations. Perfect for developers and GIS enthusiasts. 📍✨
Jupyter Notebook with flow calculation of zonal statistics for selected polygons using geopandas, google earth engine api, rasterio, rasterstats and folium.
ODI Starter: Remote Sensing in 3 Days. Zero-install Earth Engine + Colab notebooks to build an NDVI map, cloud-masked NDVI + histogram, and a seasonal NDVI chart. Beginner-friendly, portfolio-ready.
This project demonstrates how to extract geospatial features (e.g., fire hydrants) from OpenStreetMap (OSM) and convert them to ArcGIS-compatible formats (ESRI Shapefiles). It includes an automated Jupyter Notebook workflow for reading OSM data, filtering objects, and exporting to .shp.
Explore a diverse collection of freely available geospatial datasets in this repository. Whether you're working with Shapefiles, GeoJSON, KML, or other formats, these datasets are ready to enrich your geospatial analyses. Dive into Python notebooks from other repositories to see how you can integrate and leverage these datasets seamlessly