Python for Geographic Information System (GIS) Training Course

Course Code

pygis

Duration

21 hours (usually 3 days including breaks)

Requirements

A prior experience with Python for Machine Learning and with the libraries like pandas, matplotlib is highly recommended.

Overview

A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics.

The use of Python with GIS has substantially increased over the last two decades, particularly with the introduction of Python 2.0 series in 2000, which included many new programming features that made the language much easier to deploy. Since that time, Python has not only been utilized within commercial GIS such as products by Esri but also open source platforms, including as part of QGIS and GRASS. In fact, Python today is by far the most widely used language by GIS users and programmers.

This program covers the usage of Python and its advance libraries like geopandas, pysal, bokeh and osmnx to implement your own GIS features. The program also covers introductory modules around ArcGIS API, and QGIS toolboox.

Course Outline

  • Introduction to GIS

  • Installation and Required Packages

  • Introduction to Shapely for Geometric Objects

  • Intro to Pandas and GeoPandas

  • Managing maps and Projections

  • Geocoding and ArcGIS API

  • Geocoding Point in Polygons with GeoPandas

  • Spatial join

  • Data Classification; pysal map classifier

  • Overlay Analysis

  • Aggregating spatial data

  • Geometries simplifications

  • Visualization with Bokeh

    • Static and Interactive Maps

  • Using GIS Applications

    • ArcGIS API usage and processing toolbox

    • Python in QGIS; Processing toolbox & graphical modeller

    • Creating own processing toolbox;

  • Network Analysis and OpenStreetMap

    • Downloading and working with OpenStreetMap data; Osmnx

    • Network analysis in Python; Networkx; Osmnx;

  • Conclusion

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