Skip to content

Latest commit

 

History

History
81 lines (58 loc) · 3.7 KB

README.md

File metadata and controls

81 lines (58 loc) · 3.7 KB

India Well-Being

How to setup the environment

  • Create an .env file and do not commit it. Here is an example :
AZURE_URL = <path_to_omdena_azure_blob_storage>
BLOB_CONTAINER = "tif-images"
LOCAL_BLOB_PATH = <local_path_to_download_blob_storage>
DATA_DIR = <local_path_to_data_dir>
OSM_DIR = <path_to_dir_to_download_osm_data>
NTL_HDF5_DIR = <path_to_dir_to_download_hdf5_files>
NTL_TIF_DIR = <path_to_store_hdf5_converted_to_tiff>
LADS_TOKEN = <lads token as obtained from LADS site>

please make sure never to commit this file into Git.

In your DATA_DIR store the shape files related to india in gadm36_shp folder.

  • Download the India related shapefiles from here
  • Make sure pip is installed in your OS and then install pipenv pip install pipenv
  • After cloning any of the following branches dssg/<specific-name> do the following
pipenv shell
pipenv install

Download Nighttime Lights from NASA Site

How to create LADS Token for downloading Night Time Lights Files

Add dssg directory to PYTHONPATH

  • To be able to work with all modules add the dssg directory to python path:
    • In Linux to your .bashrc add export PYTHONPATH=<path_to_WRI_India_ext/dssg/>:$PYTHONPATH
    • For Windows follow the instructions here

How to use the python script

  • Run the script download-nightlights.py from command line using the following steps
pipenv shell
python dssg/apps/download-nightlights.py <district_name> <start_date> <end_date>

an example : python dssg/apps/download-nightlights.py 'Araria' '2015-01-01' '2015-01-05'.

  • Before running the script make sure to set the following variables in the .env file:

    • NTL_HDF5_DIR where all the h5 files will be downloaded,
    • NTL_TIF_DIR where all the geo tiff files converted from h5 files are stored.
    • LADS_TOKEN that you created.

  • After the script finishes you will find the following file e.g., araria-2015-01-01-2015-01-05.json created in the NTL_HDF5_DIR. This will contain the list of all files associated with a district in the date range. e.g.,

"district_id": 61, 
"start_time": "2015-01-01", 
"end_time": "2015-01-05", 
"file_list": ["VNP46A2.A2015001.h26v06.001.2020220053423.h5", "VNP46A2.A2015002.h26v06.001.2020220091927.h5", "VNP46A2.A2015003.h26v06.001.2020220134728.h5", "VNP46A2.A2015004.h26v06.001.2020220174848.h5", "VNP46A2.A2015005.h26v06.001.2020220232713.h5"]

Computing weighted voronoi tessellation

The weighted voronoi tessellation is computed in a separate algorithm and the resulting shapefile can be used directly in the notebook araria_voronoi.ipynb . The full process of computing the weighted voronoi of the DHS data for a country has been implemented separately here