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Geospatial 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. This course provides a practical introduction to concepts and methods in Geospatial Data Science for the analysis of spatial data. In this course you will gain an understanding of key concepts in Geospatial Data Science, learn how to represent the environment and the world using spatial data, and apply basic spatial analysis techniques to geographic data using programming languages. You will develop skills such as importing, manipulating, analyzing, and visualizing spatial data particularly using algorithms in R and/or Python. You will also develop an awareness of the current limitations and implications of geographic technology.
The primary aim of this course is to provide an applied introduction to key technical concepts and methods for spatial data science, using Python and JupyterHub. After completing this course, we expect you to have gained the following: An understanding of the key concepts in spatial data and spatial data collection Learn how to represent the environment and the world in spatial data Skills to import, manipulate and present spatial data, particularly using algorithms in PythonThe ability to apply basic spatial analysis techniques to geographic data using spatial capabilities of open-source Python programming language An awareness of the current limitations and implications of geographic technology, its future development, and data sovereignty, particularly multi-cultural aspects of sovereignty.
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.
Biculturally competent and confident
Students will be aware of and understand the nature of biculturalism in Aotearoa New Zealand, and its relevance to their area of study and/or their degree.
Globally aware
Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.
Entry subject to the approval of the Programme Director.
GISC101
Students must attend one activity from each section.
The field trip will take place over two days, on the 22nd and 23rd of February 2025, with an overnight stay at Cass field station.Catering for the field trip costs $25. There will be information on the Learn page on how to pay.
Vanessa Bastos
Recommended textbook(s):1. Dorman, M., Graser, A., Nowosad, J., Lovelace, R. (2025). Geocomputation with Python. https://py.geocompx.org/ 2. Tenkanen, H., Heikinheimo, V. & Whipp, D. (2022). Introduction to Python for Geographic Data Analysis. https://pythongis.org/ 3. McClain, B. P. (2022). Python for Geospatial Data Analysis. https://ebookcentral.proquest.com/lib/canterbury/detail.action?docID=30190351# 4. Rey, S., Arribas-Bel, D., & Wolf, L.J. (2020). Geographic Data Science with Python. https://geographicdata.science/book/intro.html# 5. Severance, C.R. (2016). Python for Everybody: Exploring Data in Python 3. https://www.py4e.com/html3/ 6. Parker, J. R. (2016). Python: An Introduction to Programming. https://ebookcentral.proquest.com/lib/canterbury/detail.action?docID=6522952
Course Outline
Prerequisites: NARestrictions: GISC101Recommended preparation: NA
Domestic fee $1,213.00
International Postgraduate fees
* 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.
This course will not be offered if fewer than 3 people apply to enrol.
For further information see School of Earth and Environment .