10 años de Spatial Ecology!
Curso on-line, con explicaciones en español y con material en inglés: Noviembre y Diciembre 2024 (5 semanas)
- Start lecturing: 19 November, every Tuesday, Thursday at 3 pm – 5:45 pm UTC (UTC-6 9am; UTC-5 10am; UTC-4 11am; UTC+1 4pm)
- End lecturing: 19 December, 3pm – 5:45pm UTC (UTC-6 9am; UTC-5 10am; UTC-4 11am; UTC+1 4pm)
Course programme
On this course, students will be introduced to an array of powerful open-source geocomputation tools (GDAL & GRASS) under Linux environment. Students who have never been exposed to programming under Linux are expected to reach a stage where they feel confident in using very advanced open source data processing routines. Students with a programming background will find the course beneficial in enhancing their programming skills for better modelling and coding proficiency. Our aim is to equip attendees with powerful tools and hone their ability for independent study afterwards. The acquired skills will be beneficial, not only for GIS related applications, but also for general data processing and applied statistical computing in a number of fields. We aim to provide a sound foundation for career development as a geographic data scientist.
Course requirements:
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 attendees should be motivated by a desire to learn command line tools to process data.
A strong knowledge of geo-spatial science is expected, Additionally, we expect participants to have a specific interest in geographical data analyses. Therefore, experience in the use of Geographic Information Systems and basic statistical knowledge is essential. Basic concepts of GIS, such as rasters/vectors, overlays, and buffering, as well as fundamentals of statistics, such as mean standard, deviation, and residuals are prerequisites for this course.
Overall, 10-15 hours per week are required outside class time for homework, material review, model implementation, coding, and trouble shooting.
Registration:
Instructor:
- Giuseppe Amatulli, Ph.D. (Yale University, USA, Spatial Ecology; Twitter @BigDataEcology )
Teaching assistant
- Juana Mercedes Perlaza Rodriguez, Ph.D. (Freelance: https://novamanto.com/)
Enrollment and Registration Fee Administrator:
- Juana Mercedes Perlaza Rodriguez, Ph.D. (Freelance: https://novamanto.com/ ; e-mail jperlaza35@gmail.com)
Programme structure:
This course enables students to enhance their spatio-temporal data processing skills. Most importantly, it endows them with proficiency in a fully-functional open source operating system with all the requisite software tools. 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 in an overview of open source software available for spatial data processing. We demonstrate their strengths, weaknesses and key features for various data processing objectives (e.g. 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 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 an in-person workshop:
Online 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 watch online lectures pre-recorded, and 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
Online tutorials: Students are guided during hands-on sessions where instructors perform data analyses on real case study datasets, allowing the former to replicate the procedures on their own laptops.
Online 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. These exercises equip students with the confidence and resources to become independent learners, and to effectively address the demands of advanced spatial-data processing. The cross-disciplinary exercises are designed to enhance programming skills and the understanding of mathematical modelling within the context of GIS and Remote Sensing. 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 considered template procedures and are easily adapted to multi-disciplinary challenges.
Home assignments: We assign homework, which is discussed in the following live online seminar. This allows everybody to benefit from question and answer sessions. Assignments are of two different kinds: suggested assignments and compulsory assignments. The suggested assignments encourage heuristics whereas the compulsory assignments address a specific coding challenge, and are designed for problem solving and critical thinking.
Recorded sessions: all the lectures are recorded and made available for asynchronous viewing, which is particularly helpful to students working across time zones.
Course administration:
With continuous practice throughout the duration of the programme, students 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 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 formulating new research methods, commands and scripting procedures will be fundamental to your overall success.
PC and data Storage: A laptop with at least 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.
Course certification
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, and final project presentation. For university students, course credit approval will be at the discretion of the concerned university.
Syllabus overview
| Subject | n. lectures |
| LINUX: | 1 |
| BASH/AWK: | 2 |
| GDAL: | 2 |
| GRASS: | 3 |
- OSGeo-live operating system
- Linux bash programming AWK
- Gdal/OGR geospatial libraries,
- GRASS
Time table:
- 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
All classes will be recorded