Course code InfT3032

Credit points 3

Automating of GIS processes

Total Hours in Course81

Number of hours for lectures16

Number of hours for laboratory classes16

Independent study hours49

Date of course confirmation18.10.2022

Responsible UnitInstitute of Computer Systems and Data Science

Course developer

author Datoru sistēmu un datu zinātnes institūts

Laima Bērziņa


Prior knowledge

DatZ1009, Introduction to Programming I

DatZ1010, Introduction to Programming II

DatZ2004, Database Technologies I

DatZ2005, Database Technologies II

Course abstract

The aim of the course is to provide a theoretical understanding of geographic information systems (GIS) and their application. Students acquire practical knowledge for solving GIS tasks and the basics of Python scripting, as well as acquire skills to use Python scripts in the processes of digital map creation and geospatial data management. The task of the course is to provide knowledge about automation solutions for diverse GIS tasks and the importance of IT in their implementation.

Learning outcomes and their assessment

Knowledge – is able to show understanding of the role of GIS technologies and main GIS processes (theory test). Skills – is able to independently use the theoretical knowledge to implement solutions in GIS, by making scripts for automation of processes that need to be repeated often for geospatial analysis (laboratory works). Competence – is able independently access, analyse and use programming as a tool for maintenance and operation of GIS, as well as ability of making scripts for GIS processes automation using Phyton scripting basics (independent work).

Course Content(Calendar)

1. GIS, Geocomputation, and GIS Data (2 h).
2. Data Models for GIS. Vector and raster data management (2h).
3. GIS data analysis methods and modelling. The need for GIS automation (2 h).
4. GIS Software. ESRI platform (2 h).
5. Introduction to GIS modelling and Python (2h).
6. Creating Python functions and classes (2 h).
7. Automation of tasks for visualization of geospatial data (2 h).
8. Foundations of modelling and using ModelBuilder to automate geoprocessing tasks (4 h).
9. GIS data access and manipulation with Python (4 h).
10. Automation of vector data analysis (4 h).
11. Automation model for raster data analysis (4 h).
12. Creating custom tools for automating user-specific tasks (2 h).

Requirements for awarding credit points

Requirements for the cumulative exam:
performed all laboratory works;
completed independent practical work;
written test of the theoretical part of studies.

Description of the organization and tasks of students’ independent work

During the semester, the student must complete one independently done practical work that provides the solution for automating a specific task of GIS. Description of the work must be written (at least 8 pages) and submitted electronically on the e-learning site. The results must be presented at a closing seminar.

Criteria for Evaluating Learning Outcomes

All laboratory works are completed (max. 5 points).
Independently done practical work is submitted and represented at the final seminar (maximum 2 points).
Theoretical test (maximum 3 points).
The sum of the scores is the cumulative score of the semester.

Compulsory reading

1.Crampton J. Mapping: A Critical Introduction to Cartography and GIS. John Wiley & Sons, 2011. 232 p.
2.Toms S., Parker B. Python for ArcGIS Pro: Automate cartography and data analysis using ArcPy, ArcGIS API for Python, Notebooks, and pandas. Packt Publishing, 2022. 586 p.
3.Allen D. W. GIS Automation with ModelBuilder: for ArcGIS Pro. GIS Guidebooks Publishing, 2022. 84 p.

Further reading

1.Carreira P. Geospatial Development By Example with Python. Packt Publishing, 2016. 340 p.
2.Garrard C. Geoprocessing with Python. Manning Publications, 2016. 360 p.
3.Learning Geospatial Analysis with Python: Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7, 3rd Edition. ‎ Packt Publishing, 2019. 456 p.

Periodicals and other sources

ESRI mājas lapa [tiešsaiste]. Pieejams:
Introduction to Python for Geographic Data Analysis [tiešsaiste]. Pieejams:
Automating GIS-processes 2021 [tiešsaiste]. Pieejams:


Professional study programme Information Technologies for Sustainable Development