One driver of today’s information society is geospatial data. Recent years have seen an increase in volume and diversity of geospatial data. In this course, you will use algorithmic thinking and programming skills to find, retrieve, store, and explore various geospatial datasets. In most scientific research, significant time and effort go into acquiring, understanding, and cleaning the data before the actual analysis begins. Maps and diagrams are not only used to present the final results but also to verify and explore the data during the whole data processing phase. After this course, you will have a basic overview of the acquisition and exploration of geospatial data principles and methods.
You will acquire both theoretical knowledge and hands-on experience on methods of acquisition and exploration of a broad range of geospatial data, ranging from publicly available databases, to remotely sensed and even crowdsourced data. This might be the course for you if you want to develop your skills on data analysis, organization and acquisition methods and techniques, but also if you want to learn the basic data analysis methods to be able to use geospatial data for specific application questions.
- Geodata curation, manipulation and transformation
- Handling data in files and databases
- Principles of geodata curation
- Expressing relevant questions over the geodata and learning from the results
- Stepwise query development for advanced geodata queries
- Spatial operations and predicates
- Advanced spatial and spatiotemporal consistency
- Model-driven architecture for transformational design of geospatial information systems
- UML for data store design
- Simple spatial features
- Object classes
- Data acquisition
- From space, airborne, terrestrial and in situ sensor systems
- Through human sensors and from existing repositories (crowdsourced data)
- Strategies (field survey planning, sampling, collaborative mapping, use of mobile applications)
- Basics of calculus, probability and statistics for data exploration
- Basic rules of probability
- Distributions of random variables
- Graphical tools for descriptive statistics
- Inferential statistics
- Confidence intervals
- Hypothesis testing
- Least squares and linear regression
- Non-linear regression and non-linear adjustment
- Spatial data quality. Elements of spatial data quality in relation to sensors and other data in a workflow context.
- Cartographic Design Principles
- Maps, cartography
- Thematic maps, Base maps
- Requirement and data analysis
- Map and cartographic symbol design
Upon completion of this course, you will be able to:
- Apply file-based and database data handling methods for consistent and reliable geospatial data retrieval, storage and maintenance, and is capable of designing a simple spatial information system following model-driven architecture principles.
- Make informed decisions on: the best sensor, settings and methods for data acquisition, including data acquisition through field surveys, SDI’s (spatial data infrastructures), crowdsourcing and web-scraping.
- Analyze geospatial data to describe their spatial, temporal, and attribute quality by making use of statistics and calculus concepts.
- Apply cartographic design principles to create proper map displays on different devices for particular use cases to explore and present the data.
This course will give you a brief overview of the steps required to retrieve, systematically organise, manipulate, analyse, test and visualise geospatial data. Through tutorials, but mostly hands-on training in class and through assignments, you will experience the full data acquisition and exploration process and learn how to navigate through these steps. The multi-disciplinary background of this course ranging from database management, systems architecture, earth observation, crowdsourced data, to statistics and data visualization and cartography, will expose you to this broad range of topics.
Upon successful completion of this course, you will receive a Certificate which will include the name of the course.
Along with your Certificate you will receive a Course Record providing the name, and if applicable, all the subjects studied as part of the course. It states: the course code, subject, EC credits, exam date, location and the mark awarded.
This certificate course is part of the accredited Master’s Geo-information Science and earth Observation at ITC. If you decide to take the full Master’s Geo-information Science and Earth Observation at ITC, the Examination Board will give you in principle exemption from the course you followed successfully as a certificate course.
Academic level and background
Applicants for the Certificate programme should have a Bachelor degree or equivalent from a recognized university in a discipline related to the course, preferably combined with working experience in a relevant field.
Some courses in the Certificate programme or separate modules require knowledge of, and skills in, working with GIS and/or digital image processing of remotely sensed data.
Skills in taught or related subjects are a prerequisite for some courses in the Certificate programme or separate modules. Even if the applicant satisfies the overall admission requirements, acceptance is not automatic.
The faculty accepts transcripts, degrees and diplomas in the following languages: Dutch, English, German, French and Spanish. It is at the discretion of the faculty to require additional English translations of all documents in other languages as well.
As all courses are given in English, proficiency in the English language is a prerequisite.
If you are a national of one of the countries in this list (PDF), you are exempted from an English language test.
Please note: the requirements when applying for fellowships may vary according to the regulations of the fellowship provider.
English language tests: minimum requirements
Only internationally recognised test results are accepted.
TOEFL Paper-based Test (PBT)
TOEFL Internet-based Test
British Council / IELTS
C2 Proficiency / C1 Advanced
Applicants lacking computer experience are strongly advised to follow basic courses in their home countries.