Matera, 6-10 June 2016
- Course programme
- Fellowships and fees reduction
- Directions – accommodation
- Satisfactory survey
The open source spatio-temporal data analyses and processing summer school is an immersion 5 day experience opening new horizon on the use of the outstanding power of the Linux environment and the command line approach to process data.
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 on the self-learning perspective allowing participants to keep on progressing and updating their skills in a continuously evolving technological environment.
- Giuseppe Amatulli,Ph.D. (Yale University, USA, Spatial ecology ).
- Stefano Casalegno, Ph.D. (University of Exeter, UK; Spatial ecology ).
- Francesco Lovergine, Ph.D. (CNR Bari, Italy).
- Dr. Salvatore Manfreda (University of Basilicata Matera, Italy)
- Dr. Raffaele Albano (University of Basilicata Matera, Italy)
- Dr. Alba Minnini (University of Basilicata Matera, Italy)
The summer school is aimed at students who are currently at the master or doctoral level, as well as researchers and professionals with a common interest in spatio-temporal data analysis and modelling. Nonetheless, we also accept candidatures from undergraduate students. Participants should have basic computer skills and a strong desire to learn command line tools to process data. We expect students to have a special interest on geographical data analyses, and it will help them to already have experienced the use of Geographic Information Systems. Students need to bring their own laptop 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 skills of independent learning skills to be able to develop further in advanced data processing, which is a continuous journey of progress with the availability of more complex data and the ongoing technological revolution. 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 show their strengths, weaknesses and specificities for different objectives of data processing (ex.: modelling, data filtering, queries, GIS analyses, graphics or reporting) and data types. Specifically, we guide students to practice the use of softwares and tools with the focus of helping them to climb the steep learning curve, which is generally experienced while using a new way of analysing data with a programming command line approach. Broadly, we focus our trainings on helping students to develop independent learning skills 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 basics functioning of 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, so that students will follow the same procedure using their laptops. During tutorials students are guided by two trainers, one for the demonstrations and one to supervise the students’ work and to support with individual coding.
Hands on Exercise: In addition to tutorials and lectures, students are encouraged to embark on their independent projects during exercise sessions. Specific tasks will be set allowing to reinforce the newly learned data processing capacity presented in lectures and practically, learned during the tutorial sessions. Such exercise sessions equip students with the confidence and skills to become independent learners and to effectively engage with the demands of advanced spatial-data processing.
According to the number of participants and to their pre-existing knowledge in programming more or less topics can be addressed according to students’ needs. The exercises and examples 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 could be applied to different thematic applications and disciplines.
Our summer school will enable students to further develop and enhance their spatio-temporal data processing skills. Most importantly it will allow them to start using professionally a fully functional open source operating system including all required software toolkits. With continuous practise during the week students will get familiar with a command line approach and focus on developing specific areas, including:
- Developing a broad knowledge of existing tools and be able to judge the most appropriate one for their needs and which have more potential for their future learning.
- Building confidence with the use of several command line utilities for spatial data processing and with Linux operating system.
- Developing data processing skills and knowing more on data type, data modelling and data processing techniques.
- Encouraging independent learning, critical thinking and effective data processing.
Summer school certification
At the end of the summer school the attendees will receive a course certification upon successful completion of the course, although it is up to the participant’s university to recognize this as official course credit.
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: Spatio-temporal data processing 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
We offer two types of fellowships for this summer school:
Social media fellowships — CLOSED —
A grant of 100 Euro will be awarded to student posting multi-media content on facebook and twitter before, during and after the summer school. We are looking for someone with experience in the use of social media. This fellowship is open to all participants. Contact firstname.lastname@example.org for more details
University of Basilicata fellowships
Three students from the University of Basilicata will be able to attend the course free of the summer school fees. An awards committee from the U. of Basilicata and Spatial Ecology will select best candidates and we encourage students to apply ASAP. This call is closing 15.5.2016. During your registration please specify that you are a student from the University of Basilicata on the form to be filled and send us a short resume and motivation letter.
Discount for participants attending both weeks
Participants receive a 50% discount for the second week of training. So professionals pay 820GBP + 410GBP and students 410GBP + 205GBP for 2 weeks’ training.