Our 9th year running!
Note: please read all details carefully!
On-line teaching: March, April & May 2023 (8 weeks)
- Start lecturing: 28 March, every Tuesday and Thursday at 3 pm – 5:45 pm UTC (CEST 5 pm, EDT 11 am, PDT 8 am)
- Catch-up session: Tuesday 25 April at 3 pm – 5:45 pm UTC (CEST 5 pm, EDT 11 am, PDT 8 am)
- Catch-up session: Thursday 27 April at 3 pm – 5:45 pm UTC (CEST 5 pm, EDT 11 am, PDT 8 am)
- Resume lecturing: 3 May, every Wednesday and Friday at 3 pm – 5:45 pm UTC (CEST 5 pm, EDT 11 am, PDT 8 am)
- End lecturing 26: May, at 3pm – 5:45 pm UTC (CEST 5 pm, EDT 11 am, PDT 8 am)
In-person workshop, Matera, Italy (if opting for the 1-week option, your workshop will begin 12 June)
- 5 – 9 June week 1
- 12 – 16 June week 2
For course reviews from last year’s course, please click here.
- Registration (for the on-line course and for the in-person week)
- Directions – Accommodation (for the on-site week in Matera)
On this course, students will be introduced to an array of powerful open-source geocomputation tools and machine learning methodologies under the Linux environment. Students who have never been exposed to programming under Linux will reach a level where they feel confident in using advanced open source data processing routines. Those with a programming background will find the course beneficial in enhancing their skills for modelling and coding. Our aim is to equip attendees with powerful tools and endow them with the ability to continue independent development. The skills acquired on this course will be beneficial, not only for GIS applications but for general data processing and applied statistical computing in a number of fields. These skills lay the foundation for career development as a geographic data scientist.
- Giuseppe Amatulli, Ph.D. (Yale University, USA, Spatial Ecology; Twitter @BigDataEcology )
- Antonio Henrique de Oliveira Fonseca (Yale University, USA; Spatial Ecology; Twitter @aho_fonseca )
The course 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. Course participants should have basic computer skills and a strong desire to learn command line tools to process data. A basic knowledge of python is requested and can it be achieved before the course by following a self-taught online course (e.g. https://geo-python-site.readthedocs.io/en/latest/ followed by https://autogis-site.readthedocs.io/en/latest/). Besides, we expect participants to have a specific interest in geographical data analyses, and prior experience in the use of Geographic Information Systems and basic statistic knowledge will be helpful. Basic concept of GIS, such as what is a raster/vector, overlays, buffering etc, and basic concept of statistic, such as mean standard, deviation, residuals will be assumed as given. Aside from the on-line lectures we estimate other 10-15 hours/week of homowork, material review, modeling implementation, coding, trouble shouting. Participants need to have their own laptops with a minimum of 8GB RAM and 40GB free disk space.
The proposed course intends to provide students with the opportunity to develop crucial skills required for advanced spatial data processing. Throughout the course 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 advancement. A diverse set of 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 curve of learning, command-line programming. 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 covers different teaching strategies, followed by a in-person workshop:
On-line live lectures: (15 min to 1 hour each) Students take part in a series of live 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.
Pre-recorded flipped classroom: (15 min to 1 hour each). With a flipped classroom, students will watch online lectures pre-recorded, and will meet the teachers to collaborate in online discussions, addressing doubts, replay to live questions, receiving from the teacher guidance and feedback, thus creating meaningful learning opportunities
On-line 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.
On-line 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. Exercises are designed to enhance participants’ programming skills and mathematical modelling understanding within the context of GIS and Remote Sensing. The exercises and examples provided are cross-disciplinary in nature. They may cover forestry, landscape planning, predictive modelling and species distribution, mapping, nature conservation, computational social science and other spatially related fields of studies. Furthermore, these case studies can be viewed as template procedures and easily adapted to be applied to different thematic challenges across disciplines.
Home assignments: For enhanced learning, we will give home assignments, which are solved in the following live online seminar. This allows everybody to benefit from question and answer sessions. Two different assignment will be given: suggested assignments and compulsory assignments. The suggested assignments are enhancing self learning attitude rather the compulsory assignments will be focusing on specific coding tasks emphasizing thus the problem solving and the critical thinking.
Recorded sessions: all the lectures will be recorded and they will be available for for asynchronous viewing, allowing therefore attending the entire course also for students in other time zones.
In-person workshop: This session will be offered in Matera, Italy, if at least 10 people are enrolled. To join this workshop, a deposit of EUR 100 is required at the point of registration. The deposit will be refunded in full when attendees arrive in Matera. The in-person week aims to achieve group collaboration and troubleshooting under the direct supervision of the instructors. During this week, there will be brief talks from the instructors, students, and the remaining time will be dedicated to student project development and assistance. The in-person week is not compulsory but strongly encouraged. Matera is a beautiful town and also offers a nice opportunity for sightseeing. Students will be responsible for purchasing flights and accommodation themselves.
Course administration and learning objectives:
This course 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 weeks, students will become familiar with command lines and cover numerous topics, including:
- Learning a large suite of existing tools and knowing which ones to employ for project-specific applications.
- Acquiring confidence in using several command line utilities for spatial data processing under 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.
Course requirements: Participants are expected to have intermediate skills in GIS/RS, and python, and a strong desire to learn GIS using open source tools. We assume that participants will have an interest in geographical data analyses as well as a basic, working knowledge of calculus and statistics.
Note-taking and organisation: We urge you to take notes extensively and regularly organise electronic data in order to gain the most from the classes. Curiosity in searching new research methods, commands and scripting procedures will be fundamental to your overall success.
PC and data Storage: A laptop with 40 GB of free disk space and 8GB of RAM.
Class materials: All class material will be presented on www.spatial-ecology.net, under https://spatial-ecology.net/docs/build/html/index.html.
At the end of the course, attendees will receive a course certificate, subject to successful completion of the course which requires 100% attendance, home work delivery, final project presentation (in-person at Matera, or online). For university students, course credit approval will be at the discretion of the concerned university.
- 3.00 – 3.20 PM materials recap and discussion (Q&A and homework solutions)
- 3.20 – 4.20 PM lecture
- 4.20 – 4.35 PM break
- 4.35 – 5.45 PM Lecture
- OSGeo-live operating system / Linux bash programming
- AWK – Gnuplot
- Gdal/OGR geospatial libraries, PKTOOLS, GRASS
- Basic statistical modeling, residuals analysis, error indices.
- Python for Machine Learning regression/classification in supervised framework.
- Students projects presentation.
The finalised course programme will be released in the coming months. Nonetheless, the preliminary course programme offers a helpful overview of the subject matter.