Essential Information
Never has so much data been readily available to the public. Most of this data is geographic, this is especially true for environmental data. The ability to access, analyse and understand these massive data sources is a crucial skill for environmental engineers.
You will learn how to leverage scripting languages such as R or Python to process, analyse and visualize geospatial data. Using these open-source languages provides you with a scalable way to analyse complex data, opening the way to big data and machine learning.
The focus is on geospatial data questions and solutions, but the tools used are applicable to a wide field of challenges, making it an invaluable resource for environmental engineers seeking a comprehensive skill set in navigating complex problems across various domains.
Module Contents
Setting up your coding environment:
- Installation and configuration of a development environment
- Overview of common tools for (geo-) computation
- Familiarization with key functionalities of integrated development environments
Importing Data:
- Techniques for importing data, with a special focus on geospatial data
- Handling diverse data formats and sources (databases, geoJSON)
- Quality checks and validation during the data import process
Visualizing and presenting Data:
- Exploring visualization methods and techniques for geospatial data
- Customizing plots and maps for effective representation
- Incorporating interactivity for enhanced data exploration
Manipulating Data:
- Generic data manipulation methods for geospatial datasets
- Transformation and cleaning procedures for spatial data
- Introduction to basic scripting for automating data manipulation tasks
Study Information
The language of instruction is English.