Where Can Pregnant Women Go? (WCPWG)

This all started after reading the NHS guidance on exercise in pregnancy.

Workflow

1. Download the GLOBE dataset (by running downloadGLOBE.py)
2. Convert the ZIP file of the GLOBE dataset to a BIN file (by running convertZIPtoBIN.py)
3. Compile the FORTRAN programs (by running Makefile)
4. Create the boolean mask (by running any of: createMask1, createMask2 or createMask3)
   • createMask1 applies the algorithm globally and loops until no more pixels are masked (or nmax is reached)
   • createMask2 applies the algorithm globally then on tiles and loops over both stages until no more pixels are masked (or nmax is reached)
   • createMask3 is the same as createMask2 but has extra output to make a pretty blog post
5. Compare the output between versions (by running join -t, createMask1.csv createMask2.csv > createMask.csv)
6. Compare the masks and summarise the study (by running compareMasks)
7. Convert all generated PBM images to PNG images (by running convertPBMtoPNG.sh)
8. Convert all generated PPM images to PNG images (by running convertPPMtoPNG.sh)
9. Convert the sequence of PNG images to a MP4 video (by running convertPNGtoMP4.py)
10. Convert the sequence of PNG images to a WEBP animation (by running convertPNGtoWEBP.py)
11. Make plots (by running makePlots.py)

Method

For each pixel, the FORTRAN programs check if the pixel is less than 2,500m ASL and if the pixel is next to a pixel that is accessible; if both checks are true then the pixel is marked as accessible. The FORTRAN programs continue looping over the entire world until no new pixels are marked as accessible. Below is an animation of how this looks.

Output

The output of compareMasks is:

93.802373% of the world is <= 2,500m ASL
93.800614% of the world is accessible

Note that the second number is smaller than the first, thus indicating that some of the pixels that are less than 2,500m ASL are not accessible by land/sea. Examples of places that are less than 2,500m ASL but which are not accessible can be found in Colorado (US), shown in orange below.

Dependencies

WCPWG requires the following Python modules to be installed and available in your PYTHONPATH.

• cartopy
• matplotlib
• numpy
• pyguymer3
• requests

Additionally, due to the dependency on my FORTRAN library, you will also require the following Python modules to be installed and available in your PYTHONPATH:

• matplotlib
• scipy

WCPWG uses some Global Self-Consistent Hierarchical High-Resolution Geography resources and some Natural Earth resources via the cartopy module. If they do not exist on your system then cartopy will download them for you in the background. Consequently, a working internet connection may be required the first time you run WCPWG.

Bugs

• The algorithm does not cross the meridian. Therefore, if a valley is only accessible by crossing the meridian then this program will incorrectly mark it as inaccessible.
• Due to what I assume is "a rendering error" or "an efficiency saving" in either cartopy or matplotlib, some of the plots produced by makePlots.py have green pixels touching orange pixels. The whole point of this project is to find the places in the world where green pixels and orange pixels are separated by red pixels. If a green pixel touches an orange pixel then the orange pixel should be green. I am convinced that this is not my fault and that my code is calculating the arrays correctly - it is purely a rendering error. Below are two screenshots: a) the first is a zoom in on a plot using Atom; and b) the second is a zoom in on a background image using Photoshop. Photoshop shows the green and orange separated by red, so I am happy that it is just a rendering error in either cartopy or matplotlib.