PopNet
PopNet uses a Convolutional Neural Network to predict the future spatial population distribution based on existing population projections.
The code in this repository is a result of a Master's thesis in Geoinformatics at Aalborg University Copenhagen, available for download from the AAU Project Library. The thesis contains a much more detailed description of what is going on here, so please refer to that if you get stuck along the way with the condensed documentation provided here.
Instructions/dependencies
For the data preparation part, the following dependencies need to be installed:
Besides PostGIS and the GDAL binaries, the Python dependencies can be set up in a separate Conda environment by running:
conda create --name popnet_prepare
conda activate popnet_prepare
conda install gdal
conda install psycopg2
The following datasets need to be downloaded:
- Corine Land Cover 1990 in SpatiaLite format
- Corine Land Cover 2012 in SpatiaLite format
- Copernicus slope (The Slope Full European Coverage file)
- European train stations in shape file format (
.shp) - Administrative boundaries at levels 0 and 2 in shape file format (
.shp) - Lakes in shape file format (
.shp). The database comes in SpatiaLite format; I had to export the lakes layer from it and retroject it to EPSG:4326 to make the code run - Population grid from JRC's Global Human Settlement Layer (each layer needs to be downloaded for 1975, 1990, 2000, and 2015 at 250m resolution)
- Global Roads Open Access Data Set in shape file format (
.shp)
Everything should be place in a folder called data, sitting in the data_scripts folder. Its content should look like this:
data
├── GADM
│ ├── gadm36_0.cpg
│ ├── gadm36_0.dbf
│ ├── gadm36_0.prj
│ ├── gadm36_0.shp
│ ├── gadm36_0.shx
│ ├── gadm36_1.cpg
│ ├── gadm36_1.dbf
│ ├── gadm36_1.prj
│ ├── gadm36_1.shp
│ ├── gadm36_1.shx
│ ├── gadm36_2.cpg
│ ├── gadm36_2.dbf
│ ├── gadm36_2.prj
│ ├── gadm36_2.shp
│ ├── gadm36_2.shx
│ ├── gadm36_3.cpg
│ ├── gadm36_3.dbf
│ ├── gadm36_3.prj
│ ├── gadm36_3.shp
│ ├── gadm36_3.shx
│ ├── gadm36_4.cpg
│ ├── gadm36_4.dbf
│ ├── gadm36_4.prj
│ ├── gadm36_4.shp
│ ├── gadm36_4.shx
│ ├── gadm36_5.cpg
│ ├── gadm36_5.dbf
│ ├── gadm36_5.prj
│ ├── gadm36_5.shp
│ └── gadm36_5.shx
├── GHS
│ ├── GHS_POP_GPW41975_GLOBE_R2015A_54009_250_v1_0
│ │ ├── GHSL_data_access_v1.3.pdf
│ │ ├── GHS_POP_GPW41975_GLOBE_R2015A_54009_250_v1_0.tif
│ │ ├── GHS_POP_GPW41975_GLOBE_R2015A_54009_250_v1_0.tif.ovr
│ │ └── GHS_POP_GPW41975_GLOBE_R2015A_54009_250_v1_0.tif.xml
│ ├── GHS_POP_GPW41990_GLOBE_R2015A_54009_250_v1_0
│ │ ├── GHSL_data_access_v1.3.pdf
│ │ ├── GHS_POP_GPW41990_GLOBE_R2015A_54009_250_v1_0.tif
│ │ ├── GHS_POP_GPW41990_GLOBE_R2015A_54009_250_v1_0.tif.aux.xml
│ │ ├── GHS_POP_GPW41990_GLOBE_R2015A_54009_250_v1_0.tif.ovr
│ │ └── GHS_POP_GPW41990_GLOBE_R2015A_54009_250_v1_0.tif.xml
│ ├── GHS_POP_GPW42000_GLOBE_R2015A_54009_250_v1_0
│ │ ├── GHSL_data_access_v1.3.pdf
│ │ ├── GHS_POP_GPW42000_GLOBE_R2015A_54009_250_v1_0.tif
│ │ ├── GHS_POP_GPW42000_GLOBE_R2015A_54009_250_v1_0.tif.aux.xml
│ │ ├── GHS_POP_GPW42000_GLOBE_R2015A_54009_250_v1_0.tif.ovr
│ │ └── GHS_POP_GPW42000_GLOBE_R2015A_54009_250_v1_0.tif.xml
│ └── GHS_POP_GPW42015_GLOBE_R2015A_54009_250_v1_0
│ ├── GHSL_data_access_v1.3.pdf
│ ├── GHS_POP_GPW42015_GLOBE_R2015A_54009_250_v1_0.tfw
│ ├── GHS_POP_GPW42015_GLOBE_R2015A_54009_250_v1_0.tif
│ ├── GHS_POP_GPW42015_GLOBE_R2015A_54009_250_v1_0.tif.aux.xml
│ ├── GHS_POP_GPW42015_GLOBE_R2015A_54009_250_v1_0.tif.ovr
│ └── GHS_POP_GPW42015_GLOBE_R2015A_54009_250_v1_0.tif.xml
└── ancillary
├── corine
│ ├── clc12_Version_18_5a_sqLite
│ │ ├── CLC_country_coverage_v18_5.pdf
│ │ ├── How\ use\ ESRI\ FGDB\ in\ QGIS.doc
│ │ ├── Legend
│ │ │ ├── CLC_legend.clr
│ │ │ ├── clc_legend.avl
│ │ │ ├── clc_legend.csv
│ │ │ ├── clc_legend.dbf
│ │ │ ├── clc_legend.lyr
│ │ │ ├── clc_legend.qml
│ │ │ ├── clc_legend.sld
│ │ │ ├── clc_legend.txt
│ │ │ ├── clc_legend.xls
│ │ │ └── clc_legend_qgis.txt
│ │ ├── clc12_Version_18_5.sqlite
│ │ ├── clc_12_18_5a_vector.xml
│ │ ├── readme_V18_5.txt
│ │ └── readme_V18_5_clc12.txt
│ └── clc90_Version_18_5_sqlite
│ ├── CLC_country_coverage_v18_5.pdf
│ ├── How\ use\ ESRI\ FGDB\ in\ QGIS.doc
│ ├── Legend
│ │ ├── CLC_legend.clr
│ │ ├── clc_legend.avl
│ │ ├── clc_legend.csv
│ │ ├── clc_legend.dbf
│ │ ├── clc_legend.lyr
│ │ ├── clc_legend.qml
│ │ ├── clc_legend.sld
│ │ ├── clc_legend.txt
│ │ ├── clc_legend.xls
│ │ └── clc_legend_qgis.txt
│ ├── clc90_Version_18_5.sqlite
│ ├── readme_V18_5.txt
│ └── readme_V18_5_clc90.txt
├── eu_lakes.cpg
├── eu_lakes.dbf
├── eu_lakes.prj
├── eu_lakes.qpj
├── eu_lakes.shp
├── eu_lakes.shx
├── european-train-stations
│ ├── european-train-stations.dbf
│ ├── european-train-stations.prj
│ ├── european-train-stations.shp
│ └── european-train-stations.shx
├── groads_europe
│ ├── gROADS-v1-europe.dbf
│ ├── gROADS-v1-europe.prj
│ ├── gROADS-v1-europe.sbn
│ ├── gROADS-v1-europe.sbx
│ ├── gROADS-v1-europe.shp
│ ├── gROADS-v1-europe.shp.xml
│ └── gROADS-v1-europe.shx
└── slope
└── eudem_slop_3035_europe.tif
When everything is in place, you are ready to run main.py to run the data preparation. Make sure to check and eventually change the configuration options at the top of that script.
Good luck...