bayeshack2016/sysj

Name: sysj

Owner: Bayes Hack 2016

Description: #DOC

Created: 2016-04-23 17:24:08.0

Updated: 2016-12-03 23:53:21.0

Pushed: 2016-12-08 03:22:06.0

Homepage: https://nightlights.terminal.com/

Size: 35950

Language: Jupyter Notebook

GitHub Committers

User	Most Recent Commit	# Commits

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README

Night Lights

Explore: https://nightlights.terminal.com/

Example: Take a look at the growth in Loving, Texas from 2014 to 2016 (this is the leading county in population growth and net domestic migration rate in the US).

Bayes Hack 2016

Team:

• Yang Hong (yanghong.ee @gmail.com)
• Sasha Targ (sasha.targ @ gmail.com)
• Steven Troxler (steven.troxler @ gmail.com)
• Jeff Wu (wuthefwasthat @ gmail.com)

Data sources

http://ngdc.noaa.gov/eog/viirs/download_monthly.html

Also see data/.

Setup

We assume you already have some basics, like git, python2.7, pip.

Then:

1. Git clone this repo

2. Download the python dependencies

   install -r requirements.txt
   

   NOTE: for the lxml module, you will need libxml e.g. on ubuntu:

    apt-get update -y
    apt-get install -y python-lxml
   

3. Download the data.

   Try

   on scripts/download_viirs_data.py -h
   

   to see how to use the script.

   As an example, to download only February data in North America:

   on scripts/download_viirs_data.py --months=2 --tiles=75N180W --outfolder=viirs_data --live
   

4. Install the usdata library

Run webserver

ite
on server.py

The future

• show NTL (night-time light) data and change over time (give us 1 more hour and we'll have these change in infrared light heatmaps done!)
• analyze whole world (using world bank PPP/CPI data), with focus on developing countries (NTL should be stronger signal there than in developed countries)
• show entire country view for state-to-state comparisons
• show entire state view for county-to-county comparisons
• automated pipeline that takes in raw VIIRS images, and generates both NTL statistics and powers the web app for data exploration