5. Spread simulations
5.A Spread of pests and pathogens
Outline:- stochastic pest and pathogen spread simulation
- spatial models calibration: metrics and techniques
- open source software implementation
Lecture
Slides:Supplemental materials:
- Epidemiology analytics and simulations
- Mapping applications
- Simulation tools
- Non-spatial on-line epidemic calculator
- STEM for COVID-19 and read more about Spatiotemporal Epidemiological Modeler (STEM)(see headless mode for command line version)
- GLEAMviz: Global Epidemic and Mobility Model used in the past epidemics
- Pest or Pathogen Spread
- PoPS website
- PoPS (Pest or Pathogen Spread): GRASS GIS r.pops.spread and R version rpops
- Ross K. Meentemeyer, Nik J. Cunniffe, Alex R. Cook, Joao A. N. Filipe, Richard D. Hunter, David M. Rizzo, and Christopher A. Gilligan 2011. Epidemiological Modeling of Invasion in Heterogeneous Landscapes: Spread of Sudden Oak Death in California (1990-2030). Ecosphere 2:art17.
- Tonini, Francesco, Douglas Shoemaker, Anna Petrasova, Brendan Harmon, Vaclav Petras, Richard C. Cobb, Helena Mitasova, and Ross K. Meentemeyer, 2017. Tangible geospatial modeling for collaborative solutions to invasive species management. Environmental Modelling & Software 92: 176-188. DOI: 10.1016/j.envsoft.2017.02.020
- Petrasova, A., Gaydos, D.A., Petras, V., Jones, C.M., Mitasova, H. and Meentemeyer, R.K., 2020. Geospatial simulation steering for adaptive management. Environmental Modelling & Software 133: 104801. DOI: 10.1016/j.envsoft.2020.104801
- ABC calibration
- Minter, A., & Retkute, R. (2019). Approximate Bayesian Computation for infectious disease modelling. Epidemics, 29, 100368.
- Filippi, S., Barnes, C. P., Cornebise, J., & Stumpf, M. P. (2013). On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo. Statistical applications in genetics and molecular biology, 12(1), 87-107.
- Fire spread
- Fire spread modeling Tangible Modeling book, Chapter 13
- Jupyter notebooki by A. Coiman: Fire spread in Yosemite
Assignment part 5.A
Perform simulations of pathogen spread for various scenarios using r.pops.spread and calibrate the spread model using rpops:- r.pops.spread tutorial for Sudden Oak Death (SOD) disease, alternatively run the tutorial in a Jupyter Notebook using Binder
- Optional: Explore Spatiotemporal Epidemiological Modeler (STEM) for COVID-19. There are downloadable sample runs available for various epidemics.
5.B Urban growth modeling
Outline:- urban growth models: from cellular automata to patch-based stochastic simulations
- FUTURES modeling framework
- demand and development potential
- calibration
- development pressure and patch growing algorithm
- creating scenarios
Lecture
- Slides - Urban growth models: from cellular automata to growing patches with FUTURES
- Video 1 - Urban growth models and cellular automata
- Video 2 - Urban growth modeling with FUTURES
- FUTURES
- FUTURES website
- Meentemeyer, R. K., Tang, W., Dorning, M. A., Vogler, J. B., Cunniffe, N. J., Shoemaker, D. A. (2013). FUTURES: Multilevel Simulations of Emerging Urban-Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm. Annals of the Association of American Geographers, 103(4), 785-807.
- Petrasova, A., Petras, V., Van Berkel, D., Harmon, B. A., Mitasova, H., Meentemeyer, R. K. (2016). Open Source Approach to Urban Growth Simulation. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 953-959.
- NCGIS 2019 FUTURES talk
- Urban growth and land change modeling, cellular automata
- National Research Council. 2014.Advancing Land Change Modeling
- Li and Gong, 2016,
Urban growth models: progress and perspective
- Project Gigalopolis: SLEUTH urban and land use change model based on cellular automata, Python wrapper for SLEUTH
- Chaudhuri and Clarke, 2013,i The SLEUTH land use change model: A review
- Zhou, Y., Varquez, A.C.G., Kanda, M. 2019, High-resolution global urban growth projection based on multiple applications of the SLEUTH urban growth model. Sci Data 6, 34.
- UrbanSim
- CLUE (Conversion of Land Use and its Effects) model
- Origins of cellular automata: Game of Life and a related video
Assignment part 5.B
Perform urban growth simulation- FUTURES tutorial
- Alternative version of tutorial in Jupyter Lab in Binder or in WholeTale
- Instructions for creating animation for FUTURES
Optional: write your own cellular automata simulation, here is a Game of life code in Python, R and processing.js (for web) to start with
5.C Urban growth modeling on HPC
Outline:- Hazel HPC basics (login, storage, file editing, job submission)
- Run GRASS GIS on HPC (create location, import/export data, submit a serial job)
- Parallelize GRASS GIS computations on HPC
- Run parts of FUTURES model on HPC
Lecture
Go through Hazel Quick Start Tutorial (web based + videos) to learn how to login, work in the command line, and submit a job. The class' scratch directory is/share/gis714s23.
After you login for the first time, create your own directory and use that as your scratch space
for the tutorial and assignment:
mkdir /share/gis714s23/$USERSlides: Parallel processing and HPC
Supplemental materials:
Assignment part 5.C