CMP020N207 – Spatial and Temporal data Analysis

Aims

This course is designed as an introduction to how to work with spatial and temporal data. The aim is to provide students with an understanding and working knowledge of the programming concepts underlying construction and implementation of high quality web mapping applications and of statistical techniques for the empirical analysis and forecasting of time series. These are fundamental capabilities for any modern computer science in the current labour market.

u

Learning outcomes

On completion of this course the student should be able to:

• Critically assess the organizational benefits and challenges of developing Web Mapping applications;
• Design and implement an independent Web Mapping application.
• Demonstrate knowledge of, and a critical understanding of, the main concepts of time series analysis
• Evaluate current business opportunities

u

Indicative Content

1. Introduction
2. What are Spatial and Temporal data?
3. Set up Tools
4. Building Web Maps
5. Time Series Analysis
6. Elements of forecasting
7. Visualisation
8. Putting all together

u

Teaching and Learning Strategy

Total Hours: 75 ( Lecture Hours 20, Seminar/Tutorial Hours 20, Summative Assessment Hours 5, Directed Learning and Independent Learning Hours 30 )

u

Assessment

Your grade in this class will be determined on the basis of several different kinds of assessment:

• Tutorials & Exercises – 5 for a total of 50 points
• Final Project – 4 components for a total of 50 points
  • Proposal (15 points)
  • Technology & Data report (10 points)
  • Presentation (5 points)
  • Final report (20 points)

u

Bibliography

• Sergio J. Rey, Dani Arribas-Bel, Levi J. Wolf. Geographic Data Science with PySAL and the PyData Stack
• Geospatial Data
• Web Mapping with Python and Leaflet
• Hamilton (1994). Time Series Analysis. Princeton University Press.
• Tutorial: Time Series Analysis with Pandas