fire_prediction

Using MODIS active fire data and NOAA GFS data to predict the spread of forest fires, particularly in Alaska.

The project is organized as such:

• carpentry: converting raw data into more usable formats
  • get_modis_data: Convert MODIS active fire dataset from local TSVs to a pandas DataFrame (tutorial in tutorial folder). A copy of the raw data exists on /extra/zbutler0/data/mcd14ml.tar.gz
  • get_gfs_data: Pull GFS data from NOAA servers and save only the relevant layers for relevant locations into a dictionary (tutorial in tutorial folder). Some, but not all, of the raw data exists in /extra/zbutler0/data/gfs/
  • make_cluster_df: Make a cluster feature DataFrame from a MODIS DataFrame and GFS dict
  • read_station_data: Read data from a weather station CSV from NOAA NCDC. An example raw CSV exists at /extra/zbutler0/data/fairbanks.csv, and its corresponding DataFrame at /extra/zbutler0/data/fairbanks.pkl. Note that there's some hackery in here: in this particular file, we found that accurate precipitation data existed on the last timestamp of a day but not elsewhere--this might not hold true elsewhere!
  • get_burn_data: Get MODIS burned area product. I haven't done anything with this data yet--all this function does is pull the data from the server.
• data: Includes wrapper functions for loading data. You'll need to add your own paths though
• exploratory: Exploratory iPython notebooks. The older ones use older data standards and are thus not necessarily reproducible.
• geometry: Contains fire_clustering which adds a "cluster" column to a MODIS active fire DataFrame and grid_conversion, which contains several helper functions for dealing with an XY grid instead of lat/lon
• plotting: functions/iPython notebooks to produce various plots.
• prediction: contains model code. Currently our only style of model is ClusterRegression
• testing: Unit tests
• tutorial: iPython notebooks to acquaint user with this package
• util: various utilites

Project Organization

• Makefile

• README.md

• data

  • raw <- Original, immutable data
  • interim <- Intermediate data that has gone through some transformation
  • processed <- Terminal form of the data; ready for modeling
  • exploratory <- Transformed data that is not intended for modeling

• docs <- Sphinx project for generating documentation

• models <- Trained models, predictions, or other model outputs

• notebooks <- Jupyter notebooks. Format is "<versioning #>--description"

  • exploratory <- Notebooks for exploring data and results
  • reports <- More polished notebooks that can be exported to reports
  • tutorial <- Notebooks that are instructive for learning about the project

• references <- Manuals and reference material

• reports <- Generated analysis (e.g. HTML, PDF, LaTeX)

  • figures <- Graphics and figures for reports

• requirements.txt

• src <- Source code

  • init.py
  • data <- Downloading and generating data
  • features <- Transforming and processing data
  • models <- Training and testing models
  • visualization <- Creating exploratory and report visualizations/figures

• tox.ini

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Project structure based on the cookiecutter data science project template.