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Spatial Data Science deals with the processing, manipulation, analysis and visualization of spatial data in a variety of forms. Spatial data are those which contain geographical coordinates enabling them to be used for spatial analysis and mapping and include, for example, images from remote sensing, coordinates collected using navigation technologies, or census information by area, among many others. Spatial data are fundamental to many geographical analyses and spatial data science draws strongly from key geographical concepts - such as Tobler’s classic 1970 law: "everything is related to everything else, but near things are more related than distant things".
This course provides a practical introduction to concepts and methods in data science for the analysis of spatial data. By completing the course, you will gain an understanding of the key concepts in spatial data and their collection, how to represent the environment and the world in spatial data, and the ability to apply basic spatial analysis techniques to geographic data using open source platforms such as R, QGIS, and Python. You will develop skills such as importing, manipulating, analyzing, and visualizing spatial data particularly using algorithms in R and Python. You will also develop an awareness of the current limitations and implications of geographic technology, its future development and data stewardship (particularly bi-cultural aspects of stewardship).
The overarching goal of this course is to foster the understanding of key concepts in spatial data science (SDS). The course offers a good balance between theory and practice by exploring SDS theory during the lectures and applying learned concepts in computer labs.After successfully completing this course, you will be able to:Represent and analyse real-world phenomena using computational models, such as raster and vector representation.Import, manipulate and visualize spatial data using the open-source R programming language.Perform basic spatial analysis using the spatial capabilities of the open-source R programming language.Critically reflect on the biases and limitations of different SDS analysis and visualization methods.Understand the current and future limitations and implications of SDS
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Critically competent in a core academic discipline of their award
Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.
Employable, innovative and enterprising
Students will develop key skills and attributes sought by employers that can be used in a range of applications.
Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.
Students must attend one activity from each section.
Students are expected to attend both lectures (A and B - 1h each) per week, attend one computer lab per week (2h), and invest on average 8h – 9h per week in self-study and assignment/project work during the course.Component Day Time LocationLecture A Mondays 16:00 – 17:00 Meremere 105 Lecture TheatreLecture B Tuesdays 10:00 – 11:00 Jack Erskine 443Computer Lab A Tuesdays 14:00 – 16:00 211A GIS Comp Lab Ernest Rutherford 211Computer Lab A Tuesdays 16:00 – 18:00 211A GIS Comp Lab Ernest Rutherford 211
Recommended textbook(s):1. Pebezma, E., & Bivand, R. (2021). Spatial Data Science with applications in R. https://keen-swartz-3146c4.netlify.app/ 2. Rowe, F., & Arribas-Bel D. (2021). Spatial Modelling for Data Scientists. https://gdsl-ul.github.io/san/ 3. Lovelace, R., Nowosad, J. & Muenchow, J. (2020). Geocomputation with R. Open source book available at https://geocompr.robinlovelace.net/ or from CRC Press. 4. Spatial Data Science with R: web materials at: https://rspatial.org/
Domestic fee $951.00
International fee $4,750.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
School of Earth and Environment