Introduction

Data collected in time and/or space exhibit unique properties that require attention to draw proper conclusions from statistical analyses. In this workshop, students are introduced to statistical concepts that are particularly useful for analyzing spatial-temporal data.

Who: This course is targeted primarily at graduate students and researchers who are interested in applied spatial-temporal data analysis

Requirements: Participants must bring a laptop with a few specific software packages installed (see Pre-Workshop Instructions).

Prerequisites: Previous experience with R or Python and knowledge of basic statistical concepts (at least one statistics course or experience statistically analyzing data).

Contact: Please mail gregleebritten@gmail.com or ymohajer@uci.edu for more information.

Tentative Schedule

Syllabus

1. Introduction/Preparation
   • Jupyter notebooks
   • R packages: ‘gstat’, ‘sp’; Python interface via package ‘rpy2’
2. Introduction to Spatial-Temporal Statistics
   • Autocorrelation function and uncertainty quantification
   • Periodicity (seasonality, daily cycles, etc.)
3. Applied Time Series Analysis
   • Trend analysis
   • Harmonic regression
   • Time series decomposition
   • Model selection
4. Applied Spatial Analysis
   • Spatial autocorrelation, variogram functions
   • Spatial interpolation (Kriging)
   • Spatial regression
   • Model selection

Pre-Workshop Instructions

See the course’s GitHub page for instructions: https://github.com/UCIDataScienceInitiative/SpaceTime We’ll expect you to have the Anaconda Python distribution installed with version 2.7 activated: https://www.continuum.io/downloads

Registration