download nightlights data fromhttps://www.ngdc.noaa.gov/eog/viirs/download_dnb_composites.html. Use the 2015 annual composite in the 75N/060W tile and the 00N/060W tile. Choose the .tif file that has "vcm-orm-ntl" in the name. Save them to viirs2015.tif, where tile_descriptor is 75N/060W or 00N/060W.
get the LSMS survey data from the world bank. Download the 2016-2017 Malawi survey data, 2015-2016 Ethiopia data, and the 2015-2016 Nigeria data from https://microdata.worldbank.org/index.php/catalog/lsms. The World Bank wants to know how people use their data, so you will have to sign in and explain why you want their data. Make sure to download the CSV version. Unzip the downloaded data into countries//LSMS/. Country name should be either malawi_2016, ethiopia_2015, or nigeria_2015.
get an api key from either Planet or Google's Static Maps API service. Both of these should be free, but Planet may take some time to approve and require you to list a research project to be eligible for the free tier. Google's service should be free if you download under 100k per month. Save the api keys to planet_api_key.txt or google_api_key.txt in the root directory. I used Planet's API because then I could download images from 2015 and 2016, whereas Google's service only offers recent images over the last year. The code will show how to get the images from the API.
Combining satellite imagery and machine learning to predict poverty
Pycaffee https://github.com/nealjean/predicting-poverty
Pytorch https://github.com/jmather625/predicting-poverty-replication
To get the data, you need to do three things:
download nightlights data from https://www.ngdc.noaa.gov/eog/viirs/download_dnb_composites.html. Use the 2015 annual composite in the 75N/060W tile and the 00N/060W tile. Choose the .tif file that has "vcm-orm-ntl" in the name. Save them to viirs2015.tif, where tile_descriptor is 75N/060W or 00N/060W.
get the LSMS survey data from the world bank. Download the 2016-2017 Malawi survey data, 2015-2016 Ethiopia data, and the 2015-2016 Nigeria data from https://microdata.worldbank.org/index.php/catalog/lsms. The World Bank wants to know how people use their data, so you will have to sign in and explain why you want their data. Make sure to download the CSV version. Unzip the downloaded data into countries//LSMS/. Country name should be either malawi_2016, ethiopia_2015, or nigeria_2015.
get an api key from either Planet or Google's Static Maps API service. Both of these should be free, but Planet may take some time to approve and require you to list a research project to be eligible for the free tier. Google's service should be free if you download under 100k per month. Save the api keys to planet_api_key.txt or google_api_key.txt in the root directory. I used Planet's API because then I could download images from 2015 and 2016, whereas Google's service only offers recent images over the last year. The code will show how to get the images from the API.