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Geo-Computation and Environmental Analysis Workshop

basic and intermediate levels

New Haven, USA, Yale University, 15th-19th May 2017

Questionnaire Yale

Description of the course

The open source spatio-temporal data analyses and processing workshop is an immersion 5 day experience opening new horizons on the use of the vast potentials of the Linux environment and the command line approach to process geographical data. We will guide newbies who have never used a command line terminal to a stage which will allow them to understand and use very advanced open source data processing routines. Our focus is to enhance the approach of self-learning allowing participants to keep on progressing and updating their skills in a continuously evolving technological environment.


Dr. Giuseppe Amatulli (Yale University, USA, Mr. Steve Weston (Yale University, USA)

Course requirements:

The workshop is aimed at students who are currently at the master or doctoral level, as well as researchers with a common interest in spatio-temporal data analysis and modelling. Nonetheless, we accept undergraduate students as well. 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, previous experience in Geographic Information Systems would be helpful. Students need to bring their own laptops with a minimum of 4GB RAM and 30GB free disk space.

Academic programme

The workshop provides students with the opportunity to develop key skills required for advanced spatial data processing. Throughout the training students will focus on developing independent learning skills which will be fundamental for a continuous learning process of advanced data processing. This is a progressing journey of development with the availability of more complex data and the ongoing technological revolution. Within the course many different, complementary and sometimes overlapping tools will be presented to provide an overview of the existing open source software available for spatial data processing. We will discuss their strengths, weaknesses and specificity for different data processing objectives (eg.: modelling, data filtering, query, GIS analyses, graphics or reporting) and data types. In particular, we will guide students to practice the use of different types of software and tools with the objective to assist in gaining a steep learning curve, which is generally experienced while using the new approach of analysing data within a programming command line environment. Broadly, we focus our training on helping students to develop independent learning skills to find online help, solutions and strategies to fix bugs and independently progress with complex data processing problems.

The Academic Programme is divided into the following areas of study and interactions:

Lectures: (15min to 30min each) Students will take part in a series of lectures introducing basic functions of tools, theoretical aspects and background information, which is needed for a better understanding of the deeper 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, while the students fill follow those example procedure using their laptops. During tutorials sessions students are supported by two trainers, one for the demonstrations and one to supervise the students' work as well as helping with individual guidance on coding.

Hands on Exercises: In addition to tutorials and lectures, students are encouraged to take up their own independent study during the 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 effectively engaged with the demands of advanced spatial-data processing. Depending on the number of participants and their previous knowledge in programming, the more or the less topics can be addressed in accordance to the students' needs. The exercises and examples will be cross-disciplinary: forestry, landscape planning, predictive modelling and species distribution, mapping, nature conservation, computational social science and other spatially related fields of studies. Nevertheless these case studies are template procedures and could be applied to any thematic applications and disciplines.

Round table discussions: these sessions are mainly focused on exchanging experiences, needs and point of views. We aim at clarify specific student’s needs and challenges, focus on how to help and how to find solutions while problem solving.

Learning objectives:

Our workshop 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 with software. With continuous practise during the week students will get more and more familiar with the command line and will focus on developing specific areas, including:

Developing a broad knowledge of existing tools and be able to judge the most appropriate for their needs. Building confidence with the use of several command line utilities for spatial data processing and Linux operating system. Developing data processing skills and increasing knowledge on data types, data modelling and data processing techniques. Encouraging independent learning, critical thinking and effective data processing.

wiki/yaleworkshop2016.txt · Last modified: 2017/12/05 22:53 (external edit)