Hi,

I know this is not a fiona specific question but I figure since this kind of covers a variety of modules written by the same people (or at least understood) that I might get an answer here.

I am attempting to do a mask over a population image file using geometries but they are in different projections and different scales. Here are my modules below.

I am using this set of land boundaries: https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_boundary_lines_land.zip
and this population image file: https://ghsl.jrc.ec.europa.eu/download.php?ds=pop (with the 250 m resolution)

Here is how I read in the population file:

world_pop_image =

Here is how I do my reprojection:


Below are all my python packages

Python 3.6
with

Click	7
Fiona	1.8.11
Geometry	0.0.23
Pillow	6.2.1
PyContracts	1.8.12
PyGeometry	1.5.6
Rtree	0.8.3
Shapely	1.6.4.post2
affine	2.3.0
atomicwrites	1.3.0
attrs	19.3.0
certifi	2019.9.11
chardet	3.0.4
click-plugins	1.1.1
cligj	0.5.0
coverage	4.5.4
cupy	6.5.0
cycler	0.10.0
decorator	4.4.1
descartes	1.1.0
fastrlock	0.4
future	0.18.2
geopandas	0.6.1
h5py	2.10.0
idna	2.8
imageio	2.6.1
importlib-metadata	0.23
kiwisolver	1.1.0
matplotlib	3.1.1
more-itertools	7.2.0
munch	2.5.0
numpy	1.17.4
packaging	19.2
pandas	0.25.0
pip	19.3.1
pluggy	0.13.0
psutil	5.6.5
py	1.8.0
pyparsing	2.4.5
pyproj	2.4.1
pytest	5.2.1
pytest-cov	2.8.1
python-Levenshtein	0.12.0
python-dateutil	2.8.1
pytz	2019.3
rasterio	1.0.25
requests	2.22.0
scipy	1.3.1
setuptools	41.6.0
six	1.13.0
snuggs	1.4.7
urllib3	1.25.7
wcwidth	0.1.7
zipp	0.6.0

I did not finish my question. I have typed the rest.

Here is how I read in the population file:

world_pop_image = rasterio.open(path_to_image, nodata=0)

Here is how I read the boundaries file:

world_boundaries = gpd.read_file(path_to_boundaries)

Here is how I do my reprojection (the raster is in World Mollweide which is not explicitly supported so I found the below workaround):

world_boundaries.to_crs('+proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs')

Here is how I do my scaling:

from shapely.affinity import affine_transformation

population_image_affine = world_pop_image.transform

shapely_affine_repr = [population_image_affine.a, population_image_affine.b, population_image_affine.d, population_image_affine.e, population_image_affine.xoff, population_image_affine.yoff]

world_boundaries['geometry'] = world_boundaries['geometry].apply(lambda geometry: affine_transformation(geometry, shapely_affine_repr)

I do not know if this will be helpful at all but this is how I am doing my masks and population:


population_image_slice_arr, new_image_transform = rasterio.mask.mask(world_pop_image, [polygon_for_some_country], crop=True, nodata=0)

population_image_slice_arr[population_image_slice_arr < 0] = 0

total_population = population_image_slice_arr.sum()


I know this is incorrect because when i did manual scaling using the bounds of the image and shapely.ops.transform the population for say India would be 800 million when it should be 1.3 billion but then when I changed it to use the affine matrix the math came out to 0.0 population.

In case this helps. Here is how I did scaling it to get more accurate numbers originally before I switched but this was even flawed:

world_total_bounds = world_boundaries.total_bounds

population_image_total_bounds = world_pop_image.bounds

x_scale = (population_image_total_bounds.right - population_image_total_bounds.left) / (world_total_bounds[MAX_X] - world_total_bounds[MIN_X])
y_scale = (population_image_total_bounds.top - population_image_total_bounds.bottom) / (world_total_bounds[MAX_Y] - world_total_bounds[MIN_Y])

world_boundaries['geometry'] = world_boundaries['geometry'].apply(lambda geometry: shapely.ops.transform(lambda x, y, z=None: (x * x_scale, y * y_scale), geometry))

Update:

I saw the response to getting it in WGS84 and I will try that in a bit. But I think the major issue is that it actually does not include antarctic and cuts off greenland as shown in this representation (http://www.luminocity3d.org/WorldPopDen/#3/45.89/-7.73)