import wget import rasterio from rasterio import mask import matplotlib.pyplot as plt import numpy as np import geopandas as gpd import pandas as pd import folium

url2020 = "ftp://ftp.worldpop.org.uk/GIS/Population/Global_2000_2020/2020/UGA/uga_ppp_2020.tif" #wget.download(url2020)

url2019 = "ftp://ftp.worldpop.org.uk/GIS/Population/Global_2000_2020/2019/UGA/uga_ppp_2019.tif" #wget.download(url2019)

raster_uga = rasterio.open("uga_ppp_2020.tif")

pop_uga_data = raster_uga.read(1)

pop_uga_data_pixel_sum = pop_uga_data[pop_uga_data > 0].sum()

plt.imshow(pop_uga_data);

# Plot using log scale(+1) plt.imshow(np.log1p(pop_uga_data));

def plot_raster(rast_data, title='', figsize=(10,10)): """ Plots population count in log scale(+1) """ plt.figure(figsize = figsize) im1 = plt.imshow(np.log1p(rast_data),) # vmin=0, vmax=2.1) plt.title("{}".format(title), fontdict = {'fontsize': 20}) plt.axis('off') plt.colorbar(im1, fraction=0.03)

title = 'Population Distribution (2020) in Uganda (Log Scaled) \n Est count: {}'.format(pop_uga_data_pixel_sum)

plot_raster(pop_uga_data, title)

uga_gdf = gpd.read_file("gadm36_UGA_2.zip") uga_gdf

uga_gdf['GID_1'].unique()

uga_gdf.plot() plt.title('Uganda: level 2 regions');

uga_gdf.crs

gtraster, bound = mask.mask(raster_uga, uga_gdf[uga_gdf.NAME_1 == 'Apac'].geometry, crop=True)

gtraster[0][gtraster[0]>0].sum()

uga_gdf = gpd.GeoDataFrame.from_file("gadm36_UGA_2.shp")

for year in range(2019, 2021): # Open and read raster file raster_uga = 'uga_ppp_{}.tif'.format(year) pop_raster_uga = rasterio.open(raster_uga) pop_uga_data = pop_raster_uga.read(1) # Loop through each polygon contained in the shapefile and use it as the mask to extract values _results = [] for i in uga_gdf['GID_1']: roi = uga_gdf[uga_gdf.GID_1 == i] # using the mask.mask module from Rasterio to specify the ROI gtraster, bound = mask.mask(pop_raster_uga, roi["geometry"], crop=True) # values greater than 0 represent the estimated population count for that pixel _results.append(gtraster[0][gtraster[0]>0].sum()) # Save the estimated counts for each year in a new column uga_gdf[str(year)] = _results

uga_gdf['growth_rate'] = uga_gdf[['2019', '2020']].pct_change(axis=1)['2020']*100

uga_gdf

fig, ax = plt.subplots(figsize=(12,8)) uga_gdf.plot(column="growth_rate", scheme='NaturalBreaks', k=5, cmap='coolwarm', legend=True, ax=ax, legend_kwds={'bbox_to_anchor':(0.25, 0.92)}) plt.title('Spatial distribution of population growth: Five natural breaks') plt.tight_layout() ax.axis("off") #plt.savefig('myMap.png',dpi=150, bbox_inches='tight') plt.show()