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Geospatial Analytics

In this course, you learn the essentials of Geospatial Analytics.

About This Course

In this course, participants learn the essentials of Geospatial Analytics. We start with an introduction to Geospatial Analytics and its core concepts. We discuss the key components of Coordinate Reference Systems (CRS), the types of geospatial data and the use of external data sources. Next, we show how to work with spatial data in R. We begin with how to import spatial data in R and we zoom in on map projections and geocoding. Then, we learn how to build static and interactive maps and conclude with basic spatial operation and manipulations. This course ends with 3 real-life cases that apply the concepts from the first two chapters of the course.

The course features a little over 1 hour of video lectures, multiple choice questions, and various references to interesting resources. A certificate signed by the instructors is provided upon successful completion.

We can also come and teach this course on-site in classroom format. If interested, please mail us at: Bart@BlueCourses.com.

Price

The enrollment fee for this course is EUR 250 (VAT excl.) per participant. Payments are securely handled by PayPal. If you are a company in the European Union, then we can apply VAT reverse charge. For this, please mail your VAT number to Bart@BlueCourses.com. Part of our course revenue is used towards funding organizations involvement in protecting and cleaning our oceans. See our about page to learn more about our mission statement.

After enrollment, participants will get 1 year unlimited access to all course material (videos, R/Python scripts, quizzes and certificate).

Requirements

Before subscribing to this course, you should have a basic understanding of descriptive statistics (e.g., mean, median, standard deviation, histograms, scatter plots, etc.) and inference (e.g., confidence intervals, hypothesis testing).

Course Outline

  • Introduction
    • Instructor
    • Course Outline
    • Introduction to R
    • Disclaimer
  • Introduction to Geospatial Analytics
    • What is Geospatial Analytics?
    • Coordinate reference systems
      • Geographic coordinates
      • Geodetic datum
      • Map Projections
    • Geospatial dataformats
    • External data sources
    • Quiz
  • Spatial Operations in R
    • Importing spatial data in R
    • Spatial projections in R
    • Geocoding in R
    • Static and interactive maps in R
    • Basic spatial operations and manipulations in R
  • Overview of Cases
    • Product launch optimization
    • Bad payer prediction
    • Mode of transport prediction
  • Product launch optimization in R
  • Bad payer prediction in R
  • Mode of transport prediction in R
  • Pointers and further reading

Course Staff

 Dr. Ing. Florian Vandecasteele

Dr. Ing. Florian Vandecasteele

Florian Vandecasteele received his MSc degree in industrial engineering focusing on information and communication technology from the University of Ghent in 2015. Following his studies, Florian joined the IDLAB of Ghent University-imec as a researcher and he enrolled as a doctoral candidate. Under the supervision of Prof. Bart Merci and Prof. Steven Verstockt he investigated the multimodal data fusion for spatio-temporal fire behavior analysis. The research explored the usage of information techniques, in particular BIM tools, video information and thermal image footage analysis to support fire forecasting and fire behavior analysis. In 2019 Florian successfully defended his PhD .

Florian is author or co-author of several national and international publications. Furthermore, Florian was also involved in educational activities (master thesis guidance, lab sessions). Finally, Florian was concerned with the research activities related Spatio-temporal Enrichment and Analysis (project management, feasibility study, stakeholder management and proposal writing).

Since 2019 Florian started as a data science consultant for AE - Architects for Business and ICT. As a data consultant he helps clients with projects related to Computer vision, Machine Learning, Spatial and open data visualization and prediction.

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