Exposure spatial disaggregation

Overview

The purpose of this code is to distribute a coarse exposure model to a finer resolution based on additional datasets (e.g. WorldPop).

Dependencies

To install all dependencies using pip, run the following command:

pip install -r requirements.txt

The dependencies are as follows:

• numpy
• pandas
• fiona
• geopandas
• rasterio
• shapely^
• osgeo
• GDAL^^

^There is currently a known issue where importing from shapely returned an AssertionError when loading the GEOS library. This can be resolved by installing shapely before fiona, rasterio, and GDAL. See this link or this link for more details. If that doesn't work, try using the command pip install shapely --no-binary shapely

^^GDAL requires installation prior to pip installation. This can be done using brew. Windows users might consider installing GDAL using OSGeo4W. macOS users might consider using the KyngChaos installer. Additionally, if the pip installation fails, be sure to check that the versions between brew and pip correspond to one another.

Getting started

First, ensure you are in the appropriate working directory (i.e. the main directory)

Downloading WorldPop data

We will need raster data from WorldPop to successfully execute the demo. This file was not included with the repository due to the large file size.

WorldPop data can be downloaded using the function download_worldpop.py.. This downloads the dataset to the expected location by main_script.py. You can call it as follows:

python download_worldpop.py <country>

For example, to download the WorldPop data for Austria, you can call:

python download_worldpop.py AUT

In this case, the ISO 3166-1 alpha-3 must be provided for the <country>.

By default, the UN-adjusted 1km population dataset is used. However, you can also download the higher resolution UN-adjusted 100m population dataset as follows:

python download_worldpop.py AUT 100m

Disaggregating exposure data

With WorldPop data downloaded, you can execute the core script as follows:

python main_script.py <country> <group>

Demo files are included for the case of Austria (AUT) and the commercial (COM) and residential (RES) groups. Therefore, you can test the code first by using either commands:

python main_script.py AUT COM
python main_script.py Austria RES

Note that for <country> either the ISO 3166-1 alpha-3 or the country name can be used, and the input files are expected to have consistent names.

Similarly, the <group> is expected to be included on the exposure input file, and will also be included in the exposure output file. This is for convenience where there are multiple groups (e.g. occupancies) within one country.

If successful, you should something similar to the following print statements:

AUT.1_1
AUT.2_1
AUT.3_1
AUT.4_1
AUT.5_1
AUT.6_1
AUT.7_1
AUT.8_1
AUT.9_1
There are 94262 buildings in the exposure model
Code took 5.1628 seconds

The computation time is related to the number of administrative boundaries in the input data.

If the entire spatial-disaggregation repository was cloned, then the code should execute successfully provided the listed dependencies are installed.

To run for a different country or other use, you would need to manually enter some information to the _config.py file (such as the country name and ISO 3 of interest). Additionally, you may need to pre-download certain datasets at this stage. The code will be developed such that advance download and arrangement of external data is not necessary (provided you have a reliable internet connection), but at a later stage.