NCSU GIS 714: Geospatial Computation and Simulations

1. Introduction to Geocomputation

1.A Foundations

Outline:

geocomputation definitions
motivations for geosimulation
models for geosimulation and their properties
applications in science and practice

Lecture

Slides:

Supplemental materials:

Geocomputation and geosimulation:
- Geocomputation with R
- Geocomputation with Python
- Geocomputation: A Practical Primer
- Geosimulation with focus on urban environments, includes crowd simulations
- Castle C, Crooks A T, de Smith M J, Goodchild M F and Longley P A (2018) Geocomputational methods and modeling, Chapter 8 in de Smith M J, Goodchild M F, and Longley P A (2018) Geospatial Analysis, 6th ed, The Winchelsea Press, UK
Open source software development and GRASS GIS
- Petras V., Mitasova H., Petrasova, A. 2021. Open Source Software Development. In: The Geographic Information Science & Technology Body of Knowledge (2nd Quarter 2021 Edition), John P. Wilson (ed.).
- GRASS GIS as open source innovation platform, talk at AutoCarto 2016

Assignment

Learn how to perform basic and more complex geospatial processing, modeling and visualization in GRASS GIS:

GRASS GIS desktop: Getting started with GRASS GIS desktop (GIS582 Geospatial Modeling course),
Unlock the Power of GRASS GIS Jupyter Notebooks for FOSS4G NA 2023 workshop (run part 1 and part 2 only). You can also run them on your computer, see GRASS in Jupyter notebooks on Windows .

1.B Dynamic spatial phenomena

Outline:

observation time series and multitemporal data
fundamental physical, biological, and socio-economic processes
static and dynamic simulations
managing geospatial multitemporal data
dynamic visualization

Lecture

Slides:

Dynamic spatial phenomena

Supplemental materials:

Assignment

Process, analyze and visualize multitemporal data and time series in GRASS GIS:

GRASS Temporal Analysis workshop, run in binder: processing climate data, visualization of dynamic topography, visualization of modeling output, FOSS4G 2014 workshop shows GUI, CLI and python approach.
OPTIONAL:Analyzing space-time satellite data for disease ecology applications with GRASS GIS and R by Veronica Andreo with associated Presentation

Homework 1. A and B

Submit notebbok(s) associated with the assignment and a proposal for your dissertation-related project. In your proposal, highlight the computational components as well as the related spatial and temporal scales of your data, analyses or modeling.