Python for Geospatial Data Analysis

Introduction

This includes short and minimalistic few sessions covering fundamentals of Python programing language for geospatial data analysis including vector and raster data.

Each chapter includes several Python Jupyter Notebooks with example codes. And data used in example codes are also included in chapter folders.

Libraries Used

• numpy
• gdal
• matplotlib
• geopandas

Content

Content of this tutorial is as follows,

• Chapter 0 - Installation

• Chapter 1 - Introduction of Python

  • Session 1.1 - Fundamentals of Python
  • Session 1.2 - Built-in Data Structures
  • Session 1.3 - Control Program Flow
  • Session 1.4 - Functions and Libraries

• Chapter 2 - Working with Raster Data in Python

  • Session 2.1 - Matrix (Images) in Python
  • Session 2.2 - Geo Referenced Images in Python
  • Session 2.3 - Plotting, Visualizations in Python
  • Session 2.4 - Analysis - Raster Operations (Case Studies)

• Chapter 3 - Working with Vector Data in Python

  • Session 3.1 - Read, Write and Visualize Shapefiles
  • Session 3.2 - Working with Attribute Table
  • Session 3.3 - Working with Geometries (Vector Operations)

Acknowledgements

Created by N. Lakmal Deshapriya for activites of Geoinformatics Center of Asian Institute of Technology, Thailand.

References for Sample Data Used

• Farr, T. G., et al. (2007), The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, doi:10.1029/2005RG000183.
• Hijmans, R.J., Guarino, L., Jarvis, A., O’Brien, R., Mathur, P., Bussink, C., Cruz, M., Barrantes, I. & Rojas, E. DIVA-GIS. Available at: www.diva-gis.org
• Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment.