Introduction

Introduction#

This is a repository for spatio-temporal big data analytics (772225) module

This module provides an overview of concepts and methods for visualizing, modelling, and analysing spatial and temporal big data. Students will explore data mining and machine learning skills and techniques tailored for spatio-temporal analysis in addressing real-world challenges.

Module Information

Weeks

Lectures (1 hour)

Lab tutorials (2 hours)

Week 1

L1 (Introduction to spatial and temporal big data analytics)

T1 Anaconda set up, Geopandas – geodata type, projection

Week 2

L2 (Spatial data operation and processing)

T2 Geopandas – spatial join, overlay, buffer analysis

Week 3

L3 (Visualisation for geospatial big data)

T3 Map visualisation – static and interactive mapping

Week 4

L4 (Spatial pattern analysis)

T4 Spatial pattern analysis, point pattern analysis

Week 5

L5 (Geospatial statistical modelling)

T5 Different geospatial regression models

Week 6

L6 (Temporal data processing and modelling)

T6 Temporal data processing, time series models: ARIMA to SARIMA

Week 7

L7 (Clustering for spatio and temporal big data)

T7 GMM, DBSCAN and its variants, clustering metrics

Week 8

L8 (Machine learning for spatio-temporal prediction 1)

T8 Spatial Cross-validation, SVM, RF, spRF and compare to statistical model

Week 9

L9 (Machine learning for spatio-temporal prediction 2)

T9 XGBOOST, LightGBM, CatBoost for spatial and temporal big data

Week 10

L10 (Deep learning for spatio-temporal analysis)

T10 Applied deep learning approach: ANN, CNN, RNN and LSTM

Week 11 and 12

We will use these if needed.

N/A