Berkeley, CA, USA, August 20th – 24th 2018
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 will guide newbies who have never used a command line terminal to a stage where they will be able to understand and use very advanced open source data processing routines. Our focus is to give you the tools and competencies to continue developing your skills independently. This approach towards becoming self-taught allows participants to continue progressing and improving in an ever-evolving technology environment.
- Giuseppe Amatulli, Ph.D. (Yale University, USA; Spatial Ecology).
- Mr. Jacob Bukoski (University of California, Berkeley)
The Summer School is aimed at students who are currently at a masters or doctoral level, as well as researchers and professionals with an interest in spatio-temporal data analysis and modelling. We also accept undergraduate student candidates. 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 4GB RAM and 30GB free disk space.
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 to provide an overview of the existing arena of open source softwares available for spatial data processing. We demostrate their strengths, weaknesses and specificities for different objectives of data processing (e.g.: modelling, data filtering, queries, GIS analyses, graphics or reporting) and data types. Specifically, we guide students to practice using software and tools, and aid them in climbing the steep learning curve that is generally experienced in new approaches to data analysis via a programming command line approach. Broadly, we focus our training on helping students to develop independent learning skills, as well as 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: (15min to 1h each) Students will take part in a series of lectures introducing basic tools, theoretical aspects or background information needed for a better understanding of concepts to be successively applied in data processing.
Hands-on Tutorials: Students will be guided during hands-on sessions where trainers will perform data analyses on real case study datasets, with students simultaneously following identical procedures on their own laptops. During the tutorials, students will be guided by two trainers, one for the demonstrations and another to supervise the students’ work and support with individual coding.
Hands-on Exercises: In addition to tutorials and lectures, students will be encouraged to embark on their independent projects during exercise sessions. Specific tasks will be set to reinforce the newly learned data processing skills acquired from the lectures and tutorial sessions. These exercises will equip students with the confidence and skills to become independent learners and effectively engage with the demands of advanced spatial-data processing.
More or fewer topics can be addressed according to each student’s needs, their existing skill level and overall demand from the participants. The exercises and examples are cross-disciplinary, covering 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.
Our Summer School will enable students to develop and enhance their spatio-temporal data processing skills. Most importantly, it will make them proficient in the use of a fully-functional open-source operating system along with the requisite software toolkits. With continuous practice during the week students will become familiar with the command line approach and turn their focus on specific areas, including:
- Developing an extensive knowledge of tools, and the skills to apply the optimal ones for the application at hand.
- Building confidence with the use of several command line utilities for spatial data processing and the Linux operating system.
- Acquiring data processing skills and a deeper knowledge of data types and data modelling.
- Independent learning, critical thinking and effective 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
- 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