Geospatial data analysis with python
What you'll learn
- Reading and writing of geospatial data
- Visualization of geospatial data using python
- Benefits of python over GIS software
- Resampling, reprojection, reclassification of data
- Most essential geospatial libraries
- Essential things for geopandas, fiona, shapely, rasterio etc
- Basic programming knowledge
- Basic understanding about the geospatial data
Geospatial data is also known as spatial data. It contains the locational information of the things or objects. In this course, we are going to read the data from various sources (like from spatial database) and formats (like shapefile, geojson, geo package, GeoTIFF etc), perform the spatial analysis and try to find insights for spatial data. In this course, we lay the foundation for a career in the Geospatial community.
Here is the list of topics that I covered in this course,
Installation of required geospatial libraries (GDAL, GeoPandas, rasterio, fiona, shapely, pandas, numpy etc)
Reading and Writing the spatial data from various sources/formats
Visualization of geospatial data using python
Working with the attribute table and geometries
Resampling, Reprojection, and Reclassification of satellite data
Mathematical operation with Raster
NDVI calculation using NIR and RED band
Here are the introductions to the main topics that are covered in this course:
GeoPandas: It is the open-source python package for reading, writing and analyzing the vector dataset. It extends the datatypes used by pandas to allow spatial operations on geometric types. It further depends on fiona for file access and matplotlib for visualization of data.
Rasterio: It is a GDAL and Numpy-based Python library designed to make your work with geospatial raster data more productive, and fast. Rasterio reads and writes raster file formats and provides a Python API based on Numpy N-dimensional arrays and GeoJSON.
Shapely: It is the open-source python package for dealing with the vector dataset.
Fiona: It can read and writes geographic data files and thereby helps Python programmers integrate geographic information systems with other computer systems. Fiona contains extension modules that link the Geospatial Data Abstraction Library (GDAL).
Each section contains a summary and a walkthrough with code examples that will help you learn more effectively. After completing this course, you will be confident to do the spatial analysis by python. You can automate the processing of your geospatial data without GIS software (eg. ArcGIS, QGIS etc).
Who this course is for:
- Students who want to became a geospatial software developer
- Python users who are interested to work with geospatial data
- GIS users who are new to python and want to automate the geospatial analysis
- Students who want to became a data scientist by show-case these projects on his/her resume
- Anyone who is interested to work on spatial data
I am a geomatics engineer graduated from the Institute of Engineering, Pashchimanchal Campus, Pokhara, Nepal. Current I am working as a research associate at Geoinformatics Center, Asian Institute of Technology (AIT), Thailand. I am mainly doing research on GIS, Remote Sensing (passionated in disaster management), and spatial data analysis fields. I am highly interested in Free and Open-Source Geospatial Software development. As an interest, I have developed several open-source packages like geoserver-rest, pySLD, leaflet-geoserver-request etc. I am always curious to learn and share knowledge about the latest technologies and solutions in the geospatial industry.