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 genomic epidemiology in nextstrain
- 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
- Jones, C., Jones, S. Petrasova, A., Petras, V., Gaydos, D.A., Skrip, M.M, Takeuchi, Y., Bigsby, K., and Meentemeyer, R.K. 2021. Iteratively forecasting biological invasions with PoPS and a little help from our friends, Frontiers in Ecology and the Environment, 19(7).
- 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 notebook by A. Coiman: Fire spread in Yosemite
Assignment part 5.A
Perform simulations of pathogen spread for various scenarios using r.pops.spread:- r.pops.spread tutorial for Sudden Oak Death (SOD) disease, alternatively run the tutorial
- in a Jupyter Notebook locally or online in a Binder or in google colab
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.
- Sanchez, G.M., A. Petrasova, A., M.M. Skrip, E.L. Collins, M.A. Lawrimore, J.B. Vogler, A. Terando, J. Vukomanovic, H. Mitasova, and R.K. Meentemeyer. 2023. Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk. Sci Rep 13, 18869.
- 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, 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.
- Liu, Y.; Wu, C.; Wu, J.; Zhang, Y.; Bi, X.; Wang, M.; Yan, E.; Song, C.; Li, J. Projected Spatiotemporal Evolution of Urban Form Using the SLEUTH Model with Urban Master Plan Scenarios. Remote Sens. 2025, 17, 270. https://doi.org/10.3390/rs17020270
- UrbanSim
- CLUE (Conversion of Land Use and its Effects) model
- LANDIS-II
- Origins of cellular automata: Game of Life and a related video
Assignment part 5.B
Perform urban growth simulation- in Jupyter notebook locally or in Jupyter Lab in Binder
- FUTURES tutorial
- 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/$USER - Lecture Slides:
Supplemental materials:
Assignment part 5.C