Analysis on area basis:

Data from OSM, GHS and GUF is clipped for each area and the following values are calculated for each dataset:

• area
• no of features
• mean center (x,y)
• standard distance

polygons (NUTS, HEX) feature loop

1. polygon -> get OSM buildings from dataset (sjoin)
2. polygon -> bounding box points
3. get raster names
4. load raster files and merge
5. calculate zonal statistics (for raster: rasterstats)

https://ghsl.jrc.ec.europa.eu/ghs_bu_s1_2019.php

• the tif filename can be fetched from a shp - fieldname: "location"
• values: 1 = building (built up area); 0 = empty
• year: 2018
• resolution: 20m

https://ghsl.jrc.ec.europa.eu/ghs_bu_s2_2018.php

• the tif filename can be fetched from a shp - fieldname: "location"
• values: probability from 0-100
• year: 2018
• resolution: 10m

https://ghsl.jrc.ec.europa.eu/download.php?ds=ESM

• the tif filename can be fetched from a shp - fieldname: "location"
• values: 0 = no data, 1 = land, 2 = water, 255 = built up
• year: 2015
• resolution: 2m

https://www.dlr.de/guf

filename:

GUF04_DLR_v02_
+ [e|w][aaa]_
+ [n|s][bb]_
+ [e|w][ccc]_
+ [n|s][dd]_
+ OGR04.tif

• aaa: 000-180 in steps of 5 degrees

• bb: 00-85 in steps of 5 degrees

• ccc: for w: aaa-5, for e: aaa+5

• dd: for n: bb-5, for s: bb+5

• values: 0 = building, 255 = empty

• resolution: 0.4 arcseconds (~12m at equator,

east and south is easy, always lower number then higher number:

GUF04_DLR_v02_e030_s05_e035_s10_OGR04

east and north:

GUF04_DLR_v02_e010_n55_e015_n50_OGR04

west and south:

GUF04_DLR_v02_w180_s30_w175_s35_OGR04

west and north:

GUF04_DLR_v02_w150_n65_w145_n60_OGR06

attention:

w000 is always e000:

GUF04_DLR_v02_w005_n05_e000_n00_OGR04

s00 is always n00:

GUF04_DLR_v02_e170_n00_e175_s05_OGR04