Matera 2020 Programme: International Summer School in “Geo-computation using free & open source software”

Matera: 1 – 5 June 2020 (Potentially rescheduled – final decision on 30th of April, 2020)

Course programme

The International Summer School on Geocomputation using free and open source software is an immersive 5-day experience opening new horizons in the use of the outstanding power of Linux and the command line approach for processing geospatial data.

Jumpstart with R, Grass, Python, Gdal/Ogr library and Linux operating system.

We guide newcomers who have never used a command line terminal to a stage where they are able to understand and use very advanced open source data processing routines. Our focus is to provide attendees the tools and competencies to continue developing their skills independently. This heuristic approach allows participants to continue progressing and improving in an ever-evolving technology environment.


Course requirements:

The Summer School is aimed at masters or doctoral degree candidates, and researchers and professionals with an interest in spatio-temporal data analysis and modelling. We also accept undergraduate students. Summer School participants should have basic computer skills and a strong desire to learn command line tools to process data. We expect participants to have a specific interest in geographical data analyses, and prior experience in the use of Geographic Information Systems will be helpful. Participants need to bring their own laptops with a minimum of 8GB RAM and 30GB free disk space.

Academic program:

The Summer School provides students with the opportunity to develop crucial skills required for advanced spatial data processing. Throughout the week students will focus on developing fundamental and independent-learning skills in advanced data processing – a field that is continuously evolving with the availability of increasingly complex data and ongoing technological advances. Many different, complementary and sometimes overlapping tools will be presented for an overview of the universe of open source softwares available for spatial data processing. We demonstrate their strengths, weaknesses and key features for various data processing objectives (ex.: modelling, data filtering, queries, GIS analyses, graphics or reporting) and data types. Specifically, we guide students in using these tools and software and assist them along the steep learning curve that is typical of data analysis under a command-line programming approach. We focus our training on helping students to develop independent learning skills and to find online help, solutions and strategies, in order to fix bugs, and independently progress with complex data processing problems.

The Academic Programme is divided into 3 main areas of study:

Lectures: (15 min to 1 hour each) Students take part in a series of lectures introducing the basic functioning of tools, theoretical aspects or background information needed for a better understanding of concepts that are subsequently applied in data processing.

Tutorials: Students are guided during hands-on sessions where trainers perform data analyses on real case study datasets, allowing the former to replicate the procedures on their own laptops. During tutorials students are taught by two trainers, one for demonstrations and the other to offer one-on-one supervision and assistance with coding.

Exercises: In addition to tutorials and lectures, students are encouraged to embark on their respective projects of interest during exercise sessions. Specific tasks are set to help reinforce the newly learned data processing skills. Such exercise sessions equip students with the confidence and resources to become independent learners and to effectively address the demands of advanced spatial-data processing.

Topics are addressed according to the number of participants and their programming knowledge-level and needs. The exercises and examples provided are cross-disciplinary: forestry, landscape planning, predictive modelling and species distribution, mapping, nature conservation, computational social science and other spatially related fields of study. Furthermore, these case studies are template procedures and can  be applied to different thematic applications and disciplines.

Learning objectives

Our Summer School will enable students to further develop and enhance their spatio-temporal data processing skills. Most importantly, it will endow them with proficiency in a fully-functional open source operating system with all the requisite software tools. With continuous practice through the programme week, students will become familiar with command lines and cover numerous topics, including:

  • A wide-ranging suite of existing tools and knowing which ones to employ for project-specific applications.
  • Acquiring confidence in the use of several command line utilities for spatial data processing and with the Linux operating system.
  • Developing data processing skills; and understanding data types, data modelling and data processing techniques.
  • Independent learning, critical thinking and efficient data processing.

Summer School certification

At the end of the Summer School, attendees will receive a course certificate, subject to successful completion of the course. For university students, course credit approval will be at the discretion of the concerned university.

Time table: (7h teaching/day)

  • 9:00 – 10:45   morning session 1         1h45
  • 10:45 – 11:05  coffee break
  • 11:05 – 12:50  morning session 2        1h45        
  • 12:50 – 14:00  Lunch
  • 14:00 – 15:45  afternoon session 1        1h45
  • 15:45 – 16:00  break
  • 16:00 – 17:45  afternoon session 2        1h45

Course programme

  • Day 1: OSGeo-live operating system /  Linux bash programming
  • Day 2: AWK – Gnuplot – Gdal/OGR geospatial libraries
  • Day 3:  Geocomputation and modelling. R environment for statistics and graphics. QGIS and GRASS Geographic Information Systems.
  • Day 4: Hands on Spatial Ecology applications: Hydrological modelling;  species distributions models; remote sensing images analyses; spatio-temporal statistics in forestry with SpatiaLite.
  • Day 5: Spatial data processing with Python; Working on students needs and requests

Preliminary course programme

University of Basilicata fellowships

Three students from the University of Basilicata are invited to attend the course free of charge. An awards committee from the University will select the candidates, and we encourage these students to apply ASAP. This invitation closes on 31 March 2020. The selected University of Basilicata applicants should specify their affiliation when contacting us or completing the online registration form.

Refund policy

A written request for registration cancellation must be e-mailed to See the registration page for full details on refunds.