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HISDAC-ES_data_production.py
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HISDAC-ES_data_production.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Jun 13 15:05:27 2021
@author: Johannes H. Uhl, University of Colorado Boulder, USA.
"""
import os,sys
import pandas as pd
import requests
from xml.etree import ElementTree as ET
import subprocess
from zipfile import ZipFile
import geopandas as gp
import numpy as np
import gdal
import scipy.stats
import time
###### input data ###########################
incsv='inspire_buildings_xml_links.csv'
gml_navarra='BuildingsNavarra.gml' #needs to be aquired from https://inspire.navarra.es/services/BU/wfs
gml_alava='alava.gml' #needs to be aquired from https://geo.araba.eus/WFS_Katastroa?SERVICE=WFS&VERSION=1.1.0&REQUEST=GetCapabilities
gml_gipuzkoa='Gipuzkoa.ES.GFA.BU.gml' #needs to be aquired from https://b5m.gipuzkoa.eus/web5000/es/utilidades/inspire/edificios/
path_bizkaia='./gml_bizkaia' #needs to contain gml files from https://web.bizkaia.eus/es/inspirebizkaia
## folders, to be created by user:
metadata_xml_folder='./xml'
data_gml_folder='./gml'
data_unzip_folder='./unzip'
shp_dir='./ES_buildings_shp'
shp_pt_dir='./ES_buildings_shp_pt'
shp_pt_dir_lu_harm='./ES_buildings_shp_pt_lu_harm'
rasterdir_utm='./ES_buildings_raster_utm'
rasterdir_laea='./ES_buildings_raster_laea'
rasterdir_can_regcan='./ES_buildings_raster_can_regcan'
bakdir='./backups'
## template raster layers. Rasters will be aligned to these grids.
template_raster_regcan = 'template_epsg4083_100m.tif'
template_raster_laea = 'template_epsg3035_100m.tif'
template_raster_utm = 'template_epsg25830_all_100m.tif'
lu_mapping_csv='landuse_mapping.csv'
## control variables for individual code blocks:
download=False ## downloads building footprint data per municipality in gml format for regions except Navarra and Basque Country.
convert_geopandas=False ## converts from GML to SHP for each municipality-level dataset.
harmonize_data=False ## takes the previously created shapefiles and the manually downloaded data for Basque country and Navarra, and harmonizes them.
mine_landuse=False ## analyzes the land use / building function types used in each data model
harmonize_landuse=False ##harmonizes the land use / building function classes used across data models.
rasterize_age=False ## create age-related gridded surfaces
rasterize_mutemp=False ## create evolutionary layers
rasterize_landuse_mutemp=False ##creates multitemporal land use layers
rasterize_physical_characteristics=False ##creates physical properties layers
## spatial and temporal dimensions:
resample_factor=100 #spatial resolution in meters
years=np.arange(1900,2026,5) #temporal resolution / coverage
## GDAL binaries:
gdal_edit = r'python C:\OSGeo4W\bin\gdal_edit.py' #path to gdal_edit.py
gdalwarp = r'C:\OSGeo4W\bin\gdalwarp.exe' #path to gdalwarp.exe
## some functions ##############################################################################################
def variety(x):
return np.unique(x).shape[0]
def mode(x):
vals,counts = np.unique(x, return_counts=True)
index = np.argmax(counts)
return vals[index]
def gdalNumpy2floatRaster_compressed(array,outname,template_georef_raster,x_pixels,y_pixels,px_type):
dst_filename = outname
driver = gdal.GetDriverByName('GTiff')
dataset = driver.Create(dst_filename,x_pixels, y_pixels, 1, px_type)
dataset.GetRasterBand(1).WriteArray(array)
mapraster = gdal.Open(template_georef_raster, gdal.GA_ReadOnly)
proj=mapraster.GetProjection() #you can get from a existing tif or import
dataset.SetProjection(proj)
dataset.FlushCache()
dataset=None
#set bounding coords
ulx, xres, xskew, uly, yskew, yres = mapraster.GetGeoTransform()
lrx = ulx + (mapraster.RasterXSize * xres)
lry = uly + (mapraster.RasterYSize * yres)
mapraster = None
gdal_cmd = gdal_edit+' -a_ullr %s %s %s %s "%s"' % (ulx,uly,lrx,lry,outname)
print(gdal_cmd)
response=subprocess.check_output(gdal_cmd, shell=True)
print(response)
outname_lzw=outname.replace('.tif','_lzw.tif')
gdal_translate = r'gdal_translate %s %s -co COMPRESS=LZW' %(outname,outname_lzw)
print(gdal_translate)
response=subprocess.check_output(gdal_translate, shell=True)
print(response)
os.remove(outname)
os.rename(outname_lzw,outname)
def getyear1(x):
try:
if '-' in x[:4]:
return 0
else:
return int(x[:4])
except:
return 0
xcoo_col,ycoo_col = 'x','y' ## don't change
################################################################################################
if download:
xmlurldf=pd.read_csv(incsv,header=None)
xmlurldf.columns=['url']
for xmlurl in xmlurldf.url.values:
province=xmlurl.split('_')[-1].replace('.xml','')
remotexmlfile = requests.get(xmlurl)
localxmlfile = metadata_xml_folder+os.sep+xmlurl.split('/')[-1]
open(localxmlfile, 'wb').write(remotexmlfile.content)
try:
root = ET.parse(localxmlfile).getroot()
except:
continue
for child in root:
for el in child:
if el.tag.split('}')[-1]=='id':
gmlzipurl = el.text
###download gml zip
remotefile = requests.get(gmlzipurl)
localfile = data_gml_folder+os.sep+gmlzipurl.split('/')[-1]
open(localfile, 'wb').write(remotefile.content)
print(province,gmlzipurl)
################################################################################################
if convert_geopandas:
count=0
for zipfile in os.listdir(data_gml_folder):
count+=1
try:
muni=zipfile.split('.')[-2]
province=muni[:2]
with ZipFile(data_gml_folder+os.sep+zipfile, 'r') as zipObj:
zipObj.extractall(data_unzip_folder)
ingml=data_unzip_folder+os.sep+zipfile.replace('.zip','.building.gml')
#### get_epsg:
inxml=data_unzip_folder+os.sep+zipfile.replace('.zip','.xml').replace('BU','BU.MD')
root = ET.parse(inxml).getroot()
for child in root:
for el in child:
if 'MD_ReferenceSystem' in el.tag:
for child2 in el:
if 'referenceSystemIdentifier' in child2.tag:
for child3 in child2:
if 'RS_Identifier' in child3.tag:
for child4 in child3:
if 'code' in child4.tag:
for child5 in child4:
if 'CharacterString' in child5.tag:
epsg=child5.text.split('/')[-1]
ingdf=gp.read_file(ingml)
ingdf.set_crs(epsg=int(epsg),inplace=True)
ingdf['begin_int']=ingdf.apply(lambda row : getyear1(row['beginning']),axis=1).map(int)
ingdf['end_int']=ingdf.apply(lambda row : getyear1(row['end']),axis=1).map(int)
ingdf.to_file(filename=shp_dir+os.sep+zipfile.replace('.zip','.shp'))
print('shp written',count,province,muni)
for file in os.listdir(data_unzip_folder):
os.remove(data_unzip_folder+os.sep+file)
except:
print('ERROR',count,province,muni)
print('error %s %s' %(province,muni), file=open('ES_buildings_errors.txt','a'))
#sys.exit(0)
################################################################################################
if harmonize_data:
yearcol_lookup={'ES_INSPIRE':'beginning',
'BIZKAIA':'end',
'NAVARRA':'BU_dateconsbegin',
'ALAVA':'dateOfConstruction',
'GIPUZKOA':'dateOfConstruction|DateOfEvent|end'}
usage_lookup={'ES_INSPIRE':'currentUse',
'BIZKAIA':'currentUse',
'NAVARRA':'BU_currentUseValue',
'ALAVA':'currentUse',
'GIPUZKOA':'currentUse'}
idcol_lookup={'ES_INSPIRE':'gml_id',
'BIZKAIA':'gml_id',
'NAVARRA':'gml_id',
'ALAVA':'inspireId',
'GIPUZKOA':'gml_id'}
numfloors_lookup={'ES_INSPIRE':'numberOfFl',
'BIZKAIA':'numberOfFloorsAboveGround',
'NAVARRA':'BU_numberOfFloorsAboveGround',
'ALAVA':'numberOfFloorsAboveGround',
'GIPUZKOA':'numberOfFloorsAboveGround'}
numdwel_lookup={'ES_INSPIRE':'numberOfDw',
'BIZKAIA':'numberOfDwellings',
'NAVARRA':'BU_numberOfDwellings',
'ALAVA':'',
'GIPUZKOA':'numberOfDwellings'}
numbunits_lookup={'ES_INSPIRE':'numberOfBu',
'BIZKAIA':'numberOfBuildingUnits',
'NAVARRA':'BU_numberOfBuildingUnits',
'ALAVA':'numberOfBuildingUnits',
'GIPUZKOA':''}
area_lookup= {'ES_INSPIRE':'value',
'BIZKAIA':'',
'NAVARRA':'BU_officialArea',
'ALAVA':'',
'GIPUZKOA':''}
areatype_lookup={'ES_INSPIRE':'officialAr',
'BIZKAIA':'',
'NAVARRA':'BU_officialAreaReference',
'ALAVA':'',
'GIPUZKOA':''}
total_files=[]
for file in os.listdir(shp_dir):
if file.split('.')[-1]=='shp':
total_files.append(['ES_INSPIRE',shp_dir+os.sep+file])
for file in os.listdir(path_bizkaia):
total_files.append(['BIZKAIA',path_bizkaia+os.sep+file+os.sep+'ES.BFA.BU.gml'])
total_files.append(['NAVARRA',gml_navarra])
total_files.append(['ALAVA',gml_alava])
total_files.append(['GIPUZKOA',gml_gipuzkoa])
total_files_df=pd.DataFrame(total_files,columns=['region','fullpath'])
year_stats=[]
for i,row in total_files_df.iterrows():
datadf=gp.read_file(row.fullpath)
yearcol=yearcol_lookup[row.region]
usagecol=usage_lookup[row.region]
idcol=idcol_lookup[row.region]
numfloors_col=numfloors_lookup[row.region]
numdwel_col=numdwel_lookup[row.region]
numbunits_col=numbunits_lookup[row.region]
area_col=area_lookup[row.region]
areatype_col=areatype_lookup[row.region]
datadf['yearbuilt']=datadf.apply(lambda row : getyear1(row[yearcol]),axis=1).map(int)
datadf['yearbuilt']= datadf['yearbuilt'].replace(0,np.nan)
datadf['area']=datadf.geometry.to_crs(epsg=3035).area # calculate footprint area in LAEA projection
datadf['idx']=datadf.index
datadf['source']=row.fullpath
datadf['region']=row.region
try:
datadf['usage']=datadf[usagecol]
except:
datadf['usage']=np.nan
datadf['identifier']=datadf[idcol]
datadf['num_floors']=datadf[numfloors_col]
if numdwel_col=='':
datadf['num_dwel']=0
else:
datadf['num_dwel']=datadf[numdwel_col]
if numbunits_col=='':
datadf['num_bunits']=0
else:
datadf['num_bunits']=datadf[numbunits_col]
if area_col=='':
datadf['offi_area']=0
else:
datadf['offi_area']=datadf[area_col]
if areatype_col=='':
datadf['areatype']=''
else:
datadf['areatype']=datadf[areatype_col]
datadf=datadf[['geometry','yearbuilt','area','idx','source','region','usage','identifier',
'num_floors','num_dwel','num_bunits','offi_area','areatype']]
datadf.geometry=datadf.geometry.centroid
if row.region=='GIPUZKOA':
datadf['x']=datadf.geometry.centroid.x
datadf['y']=datadf.geometry.centroid.y
datadf = gp.GeoDataFrame(datadf, geometry=gp.points_from_xy(datadf['y'], datadf['x']))
if row.region=='ALAVA' or row.region=='NAVARRA' or row.region=='GIPUZKOA':
datadf.set_crs(epsg=25830,inplace=True)
if row.region=='BIZKAIA':
datadf['x']=datadf.geometry.centroid.x
datadf['y']=datadf.geometry.centroid.y
datadf = gp.GeoDataFrame(datadf, geometry=gp.points_from_xy(datadf['y'], datadf['x']))
datadf=datadf.set_crs("+init=EPSG:4326",inplace=False)
datadf=datadf.to_crs(epsg=25830,inplace=False)
datadf['x']=datadf.geometry.centroid.x
datadf['y']=datadf.geometry.centroid.y
year_compl=100*((len(datadf.dropna(subset=['yearbuilt'])))/float(len(datadf)))
minyr,maxyr=min(datadf.yearbuilt.dropna().values),max(datadf.yearbuilt.dropna().values)
print(i,row.region,row.fullpath,year_compl,minyr,maxyr)
year_stats.append([row.region,row.fullpath,year_compl,minyr,maxyr])
outfile=shp_pt_dir+os.sep+'es_bldg_harm_pt_%s_%s.shp' %(row.region,i)
datadf.to_file(outfile)
################################################################################################
if rasterize_age:
params=[]
params.append([True,False,False])
params.append([False,True,False])
params.append([False,False,True])
for param in params:
do_can_regcan=param[0]
do_all_excpt_can_utm=param[1]
do_all_laea=param[2]
################################################# canaries regcan
if do_can_regcan:
template_raster = template_raster_regcan
crs_grid = 4083 #epsg of template_raster
surface_folder = rasterdir_can_regcan
do_canaries_only=True
################################################# iberic pen and canaries laea,
if do_all_laea:
template_raster = template_raster_laea
crs_grid = 3035 #epsg of template_raster
surface_folder = rasterdir_laea
do_canaries_only=False
################################################# iberic pen and bal, mel, ceu, UTM
if do_all_excpt_can_utm:
template_raster = template_raster_utm
crs_grid = 25830 #epsg of template_raster
surface_folder = rasterdir_utm
do_canaries_only=False
if do_canaries_only:
can_files=[]
for file in os.listdir(shp_dir):
if file.split('.')[-1]=='shp':
if '.BU.35' in file or '.BU.38' in file:
can_files.append(['CAN',shp_dir+os.sep+file])
else:
can_files.append(['IB',shp_dir+os.sep+file])
can_files_df=pd.DataFrame(can_files,columns=['region','fullpath'])
can_idx=can_files_df[can_files_df.region=='CAN'].index.values
raster = gdal.Open(template_raster)
cols = raster.RasterXSize
rows = raster.RasterYSize
geotransform = raster.GetGeoTransform()
topleftX = geotransform[0]
topleftY = geotransform[3]
pixelWidth = int(abs(geotransform[1]))
pixelHeight = int(abs(geotransform[5]))
rasterrange=[[topleftX,topleftX+pixelWidth*cols],[topleftY-pixelHeight*rows,topleftY]]
del raster
stats=[]
stats.append(['yearbuilt',np.nanmin,'min',gdal.GDT_Int16]) #gdal.GDT_Float32
stats.append(['yearbuilt',variety,'variety',gdal.GDT_Int16]) #gdal.GDT_Float32
stats.append(['yearbuilt',mode,'mode',gdal.GDT_Int16]) #gdal.GDT_Float32
stats.append(['yearbuilt',np.nanmax,'max',gdal.GDT_Int16]) #gdal.GDT_Float32
stats.append(['yearbuilt',np.nanmean,'mean',gdal.GDT_Float32]) #gdal.GDT_Float32
stats.append(['yearbuilt',np.nanmedian,'median',gdal.GDT_Float32]) #gdal.GDT_Float32
stats.append(['count',np.nansum,'sum',gdal.GDT_Int16]) #gdal.GDT_Float32
stats.append(['count_yearbuilt_missing',np.nansum,'sum',gdal.GDT_Int16]) #gdal.GDT_Float32
stats.append(['area',np.nansum,'sum',gdal.GDT_Float32]) #gdal.GDT_Float32
for stat in stats:
target_variable=stat[0]
statistic=stat[1]
statistic_str=stat[2]
bitdepth=stat[3]
#sys.exit(0)
out_surface =np.zeros((cols,rows)).astype(np.float32)
if statistic_str=='min':
out_surface = out_surface+2022
counter=0
for file in os.listdir(shp_pt_dir):
if not file.split('.')[-1]=='shp':
continue
if do_canaries_only:
curridx=file.replace('.shp','').split('_')[-1]
if not int(curridx) in can_idx:
continue
starttime=time.time()
#if 'BIZKAIA' in file:
# continue
indf = gp.read_file(shp_pt_dir+os.sep+file)
if len(indf)==0:
continue
if target_variable == 'count_yearbuilt_missing':
indf=indf[np.logical_not(indf.yearbuilt>0)]
if target_variable == 'count_area_missing':
indf=indf[np.logical_not(indf['area']>0)]
if len(indf)==0:
continue
indf.geometry = indf.geometry.to_crs(epsg=crs_grid)
indf[xcoo_col]=indf.geometry.x
indf[ycoo_col]=indf.geometry.y
if target_variable == 'count' or target_variable == 'count_yearbuilt_missing' or target_variable == 'count_area_missing':
indf[target_variable]=1
#################################################################
starttime=time.time()
counter+=1
indf = indf.dropna(subset=[target_variable])
indf=indf[[xcoo_col,ycoo_col,target_variable]]
statsvals = indf[target_variable].values.astype(np.int32)
curr_surface = scipy.stats.binned_statistic_2d(indf[xcoo_col].values,indf[ycoo_col].values,statsvals,statistic,bins=[cols,rows],range=rasterrange).statistic
if statistic_str=='min':
curr_surface = np.nan_to_num(curr_surface)
curr_surface[curr_surface==0]=2022
out_surface = np.minimum(out_surface,curr_surface)
else:
curr_surface = np.nan_to_num(curr_surface)
out_surface = np.maximum(out_surface,curr_surface)
print (counter,' of 7725',target_variable,statistic_str,file,time.time()-starttime)
#if counter==10:
# break
if statistic_str=='min':
out_surface[out_surface==2022]=0
gdalNumpy2floatRaster_compressed(np.rot90(out_surface),surface_folder+os.sep+'ES_buildings_%s_%s_%s.tif' %(target_variable,statistic_str,resample_factor),template_raster,cols,rows,bitdepth)
#sys.exit(0)
################################################################################################
if rasterize_mutemp:
params=[]
params.append([True,False,False])
params.append([False,True,False])
params.append([False,False,True])
for param in params:
do_can_regcan=param[0]
do_all_excpt_can_utm=param[1]
do_all_laea=param[2]
################################################# canaries regcan
if do_can_regcan:
template_raster = template_raster_regcan
crs_grid = 4083 #epsg of template_raster
surface_folder = rasterdir_can_regcan
do_canaries_only=True
################################################# iberic pen and canaries laea,
if do_all_laea:
template_raster = template_raster_laea
crs_grid = 3035 #epsg of template_raster
surface_folder = rasterdir_laea
do_canaries_only=False
################################################# iberic pen and bal, mel, ceu, UTM
if do_all_excpt_can_utm:
template_raster = template_raster_utm
crs_grid = 25830 #epsg of template_raster
surface_folder = rasterdir_utm
do_canaries_only=False
if do_canaries_only:
can_files=[]
for file in os.listdir(shp_dir):
if file.split('.')[-1]=='shp':
if '.BU.35' in file or '.BU.38' in file:
can_files.append(['CAN',shp_dir+os.sep+file])
else:
can_files.append(['IB',shp_dir+os.sep+file])
can_files_df=pd.DataFrame(can_files,columns=['region','fullpath'])
can_idx=can_files_df[can_files_df.region=='CAN'].index.values
raster = gdal.Open(template_raster)
cols = raster.RasterXSize
rows = raster.RasterYSize
geotransform = raster.GetGeoTransform()
topleftX = geotransform[0]
topleftY = geotransform[3]
pixelWidth = int(abs(geotransform[1]))
pixelHeight = int(abs(geotransform[5]))
rasterrange=[[topleftX,topleftX+pixelWidth*cols],[topleftY-pixelHeight*rows,topleftY]]
del raster
for year in years:
if year==1900:
lower_year=1
else:
lower_year=year-5
target_variable='count'
statistic=np.sum
statistic_str='sum_%s_%s' %(year,lower_year)
bitdepth=gdal.GDT_Int16
out_surface =np.zeros((cols,rows)).astype(np.float32)
out_surface_area =np.zeros((cols,rows)).astype(np.float32)
counter=0
for file in os.listdir(shp_pt_dir):
if not file.split('.')[-1]=='shp':
continue
if do_canaries_only:
curridx=file.replace('.shp','').split('_')[-1]
if not int(curridx) in can_idx:
continue
#if not 'GIPUZKOA' in file:
# continue
starttime=time.time()
indf = gp.read_file(shp_pt_dir+os.sep+file)
if len(indf)==0:
continue
indf = indf[np.logical_and(indf['yearbuilt']<=year,indf.yearbuilt>lower_year)]
if len(indf)==0:
continue
indf.geometry = indf.geometry.to_crs(epsg=crs_grid)
indf[xcoo_col]=indf.geometry.x
indf[ycoo_col]=indf.geometry.y
if target_variable == 'count':
indf[target_variable]=1
#################################################################
counter+=1
indf = indf.dropna(subset=[target_variable])
indf=indf[[xcoo_col,ycoo_col,target_variable,'area']]
statsvals = indf[target_variable].values.astype(np.int32)
curr_surface = scipy.stats.binned_statistic_2d(indf[xcoo_col].values,indf[ycoo_col].values,statsvals,statistic,bins=[cols,rows],range=rasterrange).statistic
curr_surface = np.nan_to_num(curr_surface)
out_surface = np.maximum(out_surface,curr_surface)
### bldg area:
statsvals = indf['area'].values.astype(np.int32)
curr_surface = scipy.stats.binned_statistic_2d(indf[xcoo_col].values,indf[ycoo_col].values,statsvals,statistic,bins=[cols,rows],range=rasterrange).statistic
curr_surface = np.nan_to_num(curr_surface)
out_surface_area = np.maximum(out_surface_area,curr_surface)
print (counter,' of 7725',target_variable,statistic_str,file,time.time()-starttime)
gdalNumpy2floatRaster_compressed(np.rot90(out_surface),surface_folder+os.sep+'ES_buildings_%s_%s_%s_increment.tif' %(target_variable,statistic_str,resample_factor),template_raster,cols,rows,bitdepth)
gdalNumpy2floatRaster_compressed(np.rot90(out_surface_area),surface_folder+os.sep+'ES_buildings_%s_%s_%s_increment.tif' %('area',statistic_str,resample_factor),template_raster,cols,rows,bitdepth)
##### now merge the increments to cumulative counts:
for year in years:
if year ==1900:
lower_year=1
else:
lower_year=year-5
target_variable='count'
statistic_str='sum_%s_%s' %(year,lower_year)
curr_count_incr = surface_folder+os.sep+'ES_buildings_%s_%s_%s_increment.tif' %(target_variable,statistic_str,resample_factor)
statistic_str='sum_%s' %(year)
curr_count_cum = surface_folder+os.sep+'ES_buildings_%s_%s_%s_cum.tif' %(target_variable,statistic_str,resample_factor)
target_variable='area'
statistic_str='sum_%s_%s' %(year,lower_year)
curr_area_incr = surface_folder+os.sep+'ES_buildings_%s_%s_%s_increment.tif' %('area',statistic_str,resample_factor)
statistic_str='sum_%s' %(year)
curr_area_cum = surface_folder+os.sep+'ES_buildings_%s_%s_%s_cum.tif' %('area',statistic_str,resample_factor)
statistic_str='%s' %(year)
curr_bua_cum = surface_folder+os.sep+'ES_buildings_%s_%s_%s_cum.tif' %('bua',statistic_str,resample_factor)
curr_count_incr_arr=gdal.Open(curr_count_incr).ReadAsArray()
curr_area_incr_arr=gdal.Open(curr_area_incr).ReadAsArray()
if year==1900:
total_count_surface=curr_count_incr_arr.copy()
total_area_surface=curr_area_incr_arr.copy()
else:
total_count_surface=total_count_surface+curr_count_incr_arr
total_area_surface=total_area_surface+curr_area_incr_arr
total_area_surface[total_area_surface<0]=0
#total_area_surface[total_area_surface>resample_factor*resample_factor]=resample_factor*resample_factor # max bufa should not exceed surface area (will be chopped off at 10k sqm). this was set to 0 in the first version (copy-paste mistake).
bua_surface = total_count_surface.copy()
bua_surface[bua_surface>1]=1
gdalNumpy2floatRaster_compressed(total_count_surface,curr_count_cum,template_raster,cols,rows,bitdepth)
gdalNumpy2floatRaster_compressed(total_area_surface,curr_area_cum,template_raster,cols,rows,bitdepth)
gdalNumpy2floatRaster_compressed(bua_surface,curr_bua_cum,template_raster,cols,rows,bitdepth)
print('cumulative %s' %year)
################################################################################################
if mine_landuse:
regions=[]
for file in os.listdir(shp_pt_dir):
regions.append(file.replace('es_bldg_harm_pt_','').replace(file.split('_')[-1],'')[:-1])
regions=list(set(regions))
for region in regions:
lutypes_reg=[]
sede_cad_count=0
for file in os.listdir(shp_pt_dir):
if not file.split('.')[-1]=='shp':
continue
if not region in file:
continue
if 'ES_INSPIRE' in file and sede_cad_count>30: ## we only need a small sample of sede cadastral data
continue
if 'ES_INSPIRE' in file:
sede_cad_count+=1
indf=gp.read_file(shp_pt_dir+os.sep+file)
unique_lus=indf.usage.unique()
if len(lutypes_reg)==0:
lutypes_reg=list(unique_lus)
else:
lutypes_reg==list(set(lutypes_reg+list(unique_lus)))
print(region,lutypes_reg)
################################################################################################
if harmonize_landuse:
regions=[]
for file in os.listdir(shp_pt_dir):
regions.append(file.replace('es_bldg_harm_pt_','').replace(file.split('_')[-1],'')[:-1])
regions=list(set(regions))
lu_mapping_df=pd.read_csv(lu_mapping_csv)
for region in regions:
no_lu=False
curr_lu_mapping_df=lu_mapping_df[lu_mapping_df.region==region]
if len(curr_lu_mapping_df)==0:
no_lu=True
else:
curr_lu_mapping_dict=dict(curr_lu_mapping_df[['source_lu','target_lu']].values)
for file in os.listdir(shp_pt_dir):
if not file.split('.')[-1]=='shp':
continue
if not region in file:
continue
indf=gp.read_file(shp_pt_dir+os.sep+file)
if no_lu:
indf['lu_harm']=' '
else:
indf['lu_harm']=indf.usage.map(curr_lu_mapping_dict)
indf.to_file(shp_pt_dir_lu_harm+os.sep+file)
print('lu harmonized',file)
################################################################################################
if rasterize_landuse_mutemp:
target_lus=['agriculture', 'publicservices', 'other', 'residential',
'industrial', 'office', 'commercial', ' ']
params=[]
params.append([True,False,False])
params.append([False,True,False])
params.append([False,False,True])
for param in params:
do_can_regcan=param[0]
do_all_excpt_can_utm=param[1]
do_all_laea=param[2]
################################################# canaries regcan
if do_can_regcan:
template_raster = template_raster_regcan
crs_grid = 4083 #epsg of template_raster
surface_folder = rasterdir_can_regcan
do_canaries_only=True
################################################# iberic pen and canaries laea,
if do_all_laea:
template_raster = template_raster_laea
crs_grid = 3035 #epsg of template_raster
surface_folder = rasterdir_laea
do_canaries_only=False
################################################# iberic pen and bal, mel, ceu, UTM
if do_all_excpt_can_utm:
template_raster = template_raster_utm
crs_grid = 25830 #epsg of template_raster
surface_folder = rasterdir_utm
do_canaries_only=False
if do_canaries_only:
can_files=[]
for file in os.listdir(shp_dir):
if file.split('.')[-1]=='shp':
if '.BU.35' in file or '.BU.38' in file:
can_files.append(['CAN',shp_dir+os.sep+file])
else:
can_files.append(['IB',shp_dir+os.sep+file])
can_files_df=pd.DataFrame(can_files,columns=['region','fullpath'])
can_idx=can_files_df[can_files_df.region=='CAN'].index.values
raster = gdal.Open(template_raster)
cols = raster.RasterXSize
rows = raster.RasterYSize
geotransform = raster.GetGeoTransform()
topleftX = geotransform[0]
topleftY = geotransform[3]
pixelWidth = int(abs(geotransform[1]))
pixelHeight = int(abs(geotransform[5]))
rasterrange=[[topleftX,topleftX+pixelWidth*cols],[topleftY-pixelHeight*rows,topleftY]]
del raster
for year in years:#####################
if year==1900:
lower_year=1
else:
lower_year=year-5
out_dfs=[]
for target_lu in target_lus:
out_df =pd.DataFrame()
out_dfs.append([out_df.copy(),target_lu])
counter=0
for file in os.listdir(shp_pt_dir_lu_harm):
if not file.split('.')[-1]=='shp':
continue
if do_canaries_only:
curridx=file.replace('.shp','').split('_')[-1]
if not int(curridx) in can_idx:
continue
counter+=1
indf = gp.read_file(shp_pt_dir_lu_harm+os.sep+file)
if len(indf)==0:
continue
indf = indf[np.logical_and(indf['yearbuilt']<=year,indf.yearbuilt>lower_year)]
if len(indf)==0:
continue
#indf = indf[['geometry','lu_harm']]
indf.lu_harm=indf.lu_harm.replace(np.nan,' ').replace('',' ').replace('None',' ')
for target_lu in target_lus:
starttime=time.time()
target_variable='lu_%s_count' %target_lu
currindf=indf[indf.lu_harm==target_lu]
currindf=gp.GeoDataFrame(currindf)
if not len(currindf)==0:
currindf['geometry'] = currindf.geometry.to_crs(epsg=crs_grid)
currindf[xcoo_col]=currindf.geometry.x
currindf[ycoo_col]=currindf.geometry.y
currindf[target_variable]=1
currindf = currindf.dropna(subset=[target_variable])
currindf=currindf[[xcoo_col,ycoo_col,target_variable]]
totaldf=out_dfs[target_lus.index(target_lu)][0]
totaldf=totaldf.append(currindf)
out_dfs[target_lus.index(target_lu)][0]=totaldf.copy()
print (year, counter,' of 7725',param,target_variable,file,time.time()-starttime)
for target_lu in target_lus:#####################
print('binning', target_lu, year)
starttime=time.time()
statistic=np.nansum
statistic_str=''
bitdepth=gdal.GDT_Int16
target_variable='lu_%s_count' %target_lu
curr_lu_df=out_dfs[target_lus.index(target_lu)][0]
if len(curr_lu_df)==0:
continue
statsvals = curr_lu_df[target_variable].values.astype(np.int32)
curr_surface = scipy.stats.binned_statistic_2d(curr_lu_df[xcoo_col].values,curr_lu_df[ycoo_col].values,statsvals,statistic,bins=[cols,rows],range=rasterrange).statistic
curr_surface = np.nan_to_num(curr_surface)
curr_lu=target_lu.replace(' ','')
gdalNumpy2floatRaster_compressed(np.rot90(curr_surface),surface_folder+os.sep+'ES_buildings_%s_%s_%s_%s_%s_%s.tif' %('landuse','count_mutemp',curr_lu,resample_factor,lower_year,year),template_raster,cols,rows,bitdepth)
print('done binning', target_lu, year, time.time()-starttime)
##### now merge the increments to cumulative counts:
for target_lu in target_lus:#####################
curr_lu=target_lu.replace(' ','')
for year in years:
if year ==1900:
lower_year=1
else:
lower_year=year-5
inraster = surface_folder+os.sep+'ES_buildings_%s_%s_%s_%s_%s_%s.tif' %('landuse','count_mutemp',curr_lu,resample_factor,lower_year,year)
try:
curr_incr_arr=gdal.Open(inraster).ReadAsArray()
except:
## if not exist, no records in current time slice. we add 0
curr_incr_arr=np.zeros(total_count_surface.shape)
if year==1900:
total_count_surface=curr_incr_arr.copy()
else:
total_count_surface=total_count_surface+curr_incr_arr
total_count_surface[total_count_surface<0]=0
curr_count_cum = surface_folder+os.sep+'ES_buildings_%s_%s_%s_%s_%s.tif' %('landuse','count_mutemp',curr_lu,resample_factor,year)
gdalNumpy2floatRaster_compressed(total_count_surface,curr_count_cum,template_raster,cols,rows,bitdepth)
print(target_lu, 'cumulative %s' %year)
################################################################################################
if rasterize_physical_characteristics:
target_vars=['area','offi_area','num_dwel','num_bunits']
stats=[np.nansum,np.nanmean]
stats_str=['sum','mean']
params=[]
params.append([True,False,False])
params.append([False,True,False])
params.append([False,False,True])
for param in params:
do_can_regcan=param[0]
do_all_excpt_can_utm=param[1]
do_all_laea=param[2]
################################################# canaries regcan
if do_can_regcan:
template_raster = template_raster_regcan
crs_grid = 4083 #epsg of template_raster
surface_folder = rasterdir_can_regcan
do_canaries_only=True
################################################# iberic pen and canaries laea,
if do_all_laea:
template_raster = template_raster_laea
crs_grid = 3035 #epsg of template_raster
surface_folder = rasterdir_laea
do_canaries_only=False
################################################# iberic pen and bal, mel, ceu, UTM
if do_all_excpt_can_utm:
template_raster = template_raster_utm
crs_grid = 25830 #epsg of template_raster
surface_folder = rasterdir_utm
do_canaries_only=False
if do_canaries_only:
can_files=[]
for file in os.listdir(shp_dir):
if file.split('.')[-1]=='shp':
if '.BU.35' in file or '.BU.38' in file:
can_files.append(['CAN',shp_dir+os.sep+file])
else:
can_files.append(['IB',shp_dir+os.sep+file])
can_files_df=pd.DataFrame(can_files,columns=['region','fullpath'])
can_idx=can_files_df[can_files_df.region=='CAN'].index.values
raster = gdal.Open(template_raster)
cols = raster.RasterXSize
rows = raster.RasterYSize
geotransform = raster.GetGeoTransform()
topleftX = geotransform[0]
topleftY = geotransform[3]
pixelWidth = int(abs(geotransform[1]))
pixelHeight = int(abs(geotransform[5]))
rasterrange=[[topleftX,topleftX+pixelWidth*cols],[topleftY-pixelHeight*rows,topleftY]]
del raster
target_var_count=-1
for target_var in target_vars:
target_var_count+=1
out_surfaces=[]
for stat_str in stats_str:
out_surface =np.zeros((cols,rows)).astype(np.float32)
out_surfaces.append([out_surface.copy(),'%s_%s' %(target_var,stat_str)])
counter=0
for file in os.listdir(shp_pt_dir_lu_harm):
if not file.split('.')[-1]=='shp':
continue
if do_canaries_only:
curridx=file.replace('.shp','').split('_')[-1]
if not int(curridx) in can_idx:
continue
counter+=1
indf = gp.read_file(shp_pt_dir_lu_harm+os.sep+file)
if len(indf)==0:
continue
indf[target_var]=indf[target_var].replace(np.nan,' ').replace('',' ').replace('None',' ').replace(0,' ').replace('0',' ')
for stat_str in stats_str:
starttime=time.time()
target_variable=target_var
statistic=stats[stats_str.index(stat_str)]
statistic_str=stat_str
bitdepth=gdal.GDT_Float32
curr__surface=out_surfaces[stats_str.index(stat_str)][0]
out_str=out_surfaces[stats_str.index(stat_str)][1]
if stat_str=='missing':
currindf=indf[indf[target_var]==' ']
target_variable='count'
currindf['count']=1
else:
currindf=indf[indf[target_var]!=' ']
try:
currindf=currindf[currindf[target_var]>=0]
except:
pass
currindf=gp.GeoDataFrame(currindf)
if not len(currindf)==0:
currindf['geometry'] = currindf.geometry.to_crs(epsg=crs_grid)
currindf[xcoo_col]=currindf.geometry.x
currindf[ycoo_col]=currindf.geometry.y
#currindf[target_variable]=1
try:
currindf[target_variable]=currindf[target_variable].str.replace(',','.')
currindf[target_variable]=currindf[target_variable].map(float)
except:
pass
currindf = currindf.dropna(subset=[target_variable])
currindf=currindf[[xcoo_col,ycoo_col,target_variable]]
statsvals = currindf[target_variable].values.astype(np.int32)
curr_surface = scipy.stats.binned_statistic_2d(currindf[xcoo_col].values,currindf[ycoo_col].values,statsvals,statistic,bins=[cols,rows],range=rasterrange).statistic
curr_surface = np.nan_to_num(curr_surface)
curr__surface = np.maximum(curr__surface,curr_surface)
out_surfaces[stats_str.index(stat_str)][0]=curr__surface.copy()
print (counter,' of 7725',param,out_str,file,time.time()-starttime)
#if counter==25:
# break