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Geographic Information Science (GIScience) 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 GIScience for the analysis of spatial data. In this course you will gain an understanding of key concepts in GIScience, 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 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 Python programming language.Perform basic spatial analysis using the spatial capabilities of the open-source Python 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.
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 is a 3-day field trip and will run from 12th - 14th February 2024.
Vanessa Bastos
McClain, Bonny P; Python for Geospatial Data Analysis : theory, tools, and practice for location intelligence ; O'Reilly Media, Inc., 2022 (https://ebookcentral.proquest.com/lib/canterbury/detail.action?docID=30190351#).
Parker, James R; Python : an introduction to programming ; Mercury Learning & Information, 2017 (https://ebookcentral.proquest.com/lib/canterbury/detail.action?docID=6522952).
Rey, S., Arribas-Bel, D., & Wolf, L.J; Geographic Data Science with Python ; 2020 (https://pythongis.org).
Severance, C. R; Python for Everybody: Exploring Data in Python 3 ; 2016 (https://www.py4e.com/html3).
Tenkanen, H., Heikinheimo, V. & Whipp, D; Introduction to Python for Geographic Data Analysis ; 2022 (https://pythongis.org).
Domestic fee $1,145.00
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 .