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Term 2 - Online
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Spatial Analytics is the study of geospatial digital data, information, knowledge and models to understand trends, complexities and inform decision process. The subject explores approaches at the intersection of spatial information, statistics and spatial analytics to further students’ understanding of the built environment.
The new science of cities is driven by the deluge of data that enables the mapping of new geographies that can be explored, analysed and synthesized. Studies of urban settlements require a deeper knowledge of digital data and how to access, interrogate and synthesis such data.
A range of research methods will be taught in combination with case studies to provide fundamental skills in Python, spatial analysis and sharpen critical spatial and analytical thinking. At the end you will have a set of Jupyter Notebooks in Python that can be deployed in future cases such as health, urban planning and real estate for evidence-based decision making.
Intended learning outcomes
On completion of this subject, students should be able to:
- Understand the rationale for using spatial information
- Understand and deploy common approaches to interrogate spatial information
- Infer Spatial relation in and between datasets
- Understand scale and spatial relation
- Identify Spatial autocorrelation
- Model building for evaluation and prediction
- Understanding the limits of models and data
- Synthesize fine scale data to support decision making on multiple scales
- Conduct spatial-statistical analysis of data
Students will be provided with the opportunity to practice and reinforce:
- High level written communication skills
- Advanced information and interpretation skills
- Advanced analytic, integration and problem-solving skills
- Demonstrate competence in critical and theoretical thinking through report writing and online discussions
Last updated: 11 February 2021