Abstract: Dynamic measures of human populations are critical for global health management but are often overlooked, largely because they are difficult to quantify. Measuring human population dynamics can be prohibitively expensive in under-resourced communities. Satellite imagery can provide measurements of human populations, past and present, to complement public health analyses and interventions. We used anthropogenic illumination from publicly accessible, serial satellite nighttime images as a quantifiable proxy for seasonal population variation in five urban areas in Niger and Nigeria. We identified population fluxes as the mechanistic driver of regional seasonal measles outbreaks. Our data showed 1) urban illumination fluctuated seasonally, 2) corresponding population fluctuations were sufficient to drive seasonal measles outbreaks, and 3) overlooking these fluctuations during vaccination activities resulted in below-target coverage levels, incapable of halting transmission of the virus. We designed immunization solutions capable of achieving above-target coverage of both resident and mobile populations. Here, we provide detailed data on brightness from 2000-2005 for 5 cities in Niger and Nigeria and detailed methodology for application to other populations.
Files for article ���Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria��� authored by Bharti, Tatem, 2018.
In this work: 1 readme file, 3 data files, 1 animation, 1 R script file:
- data file: ntl.cal155.brt.csv (Data Citation 1) Calibrated but not smoothed mean brightness values across 155 days for each of 925 pixels.
A: X: unique pixel id - unique to each pixel in this file (same as column A from ntl.calsmth365.brt.csv)
B: pixel_ID: unique pixel id within each city but there are duplicate ids between cities (same as column C from ntl.calsmth365.brt.csv)
C: Xcoord: x-coordinate
D: Ycoord: y-coordinate
E to FC: the calibrated (not smoothed) pixel value for each pixel of each city
- data file: ntl.calsmth365.brt.csv (Data Citation 2) Smoothed daily brightness values for 365 days, 925 pixels across five cities: four in Niger, one in Nigeria. Used to create Brightness curves for 5 cities.tif and Figure 3 animation.mp4.
A: X: unique pixel id - unique to each pixel in this file, same as column A from ntl.cal155.brt.csv
B: cit.ind: city names, there are five different cities, pixels are clustered by cities
C: pixel_ID: unique pixel id within each city but there are duplicate ids between cities
D: Xcoord: x-coordinate
E: Ycoord: y-coordinate
F to NF: 1:365 - daily smoothed brightness values for one year from day 1, Jan 1, to day 365, Dec 31.
- data file: ntl.niameypixcomm.csv (Data Citation 3) Niamey is divided into three communes. This file lists each pixel ID and commune designation.
A: Xcoord: x-coordinate
B: Ycoord: y-coordinate
C: pixelID, same as C pixelID for Niamey pixels from ntl.calsmth365.brt.csv
E: X: unique pixel id - unique to each pixel in this file, same as column A from ntl.cal155.brt.csv
- Animation file: ntl.animation.mp4 (Data Citation 4)
Nighttime lights animation: Annual seasonal fluctuations of brightness across each of five cities in Niger and Nigeria. The top plot for each city in Niger shows the estimated measles transmission curves for each city in Niger on a scale of 0 (center) to 2 (perimeter) by month (January to December) from measles case reports from 1995-2005 (citations 4,18); data unavailable for Katsina, Nigeria. Each pixel in the animation reflects the local maximum of the quantified brightness value from DMSP-OLS satellite images at the time indicated by transmission plot. This nighttime lights animation, was created in D3.js (https://d3js.org/). Basemap: Map tiles by Carto (https://carto.com/), under CC BY 3.0, data by OpenStreetMap, under ODbL.
- R script file: code for ntl.code.r (Data Citation 5)
R code to work with *.csv data files and plot the brightness curves of the cities and the communes using the *.csv files mentioned above. Allows user to recreate figure 2, which shows annual brightness curves of each of five cities included in this study, as well as additional plots.
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