Jump start 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 competences to continue developing your skills independently. This self-learning perspective allows participants to keep on progressing and improving in a continuously evolving technological environment.
Trainers: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).
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 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
We offer two types of fellowships for this summer school:Social media 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 1.5.2017. During your registration please specify that you are a student from the University of Basilicata on the form to be filled.
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