Radiance Calibrated Nighttime Lights Tutorial

Satellite imagery of nighttime lights provides a unique perspective on the spread of electric lighting across the globe. While these data are often used to track development, the nighttime lights available before 2012 - those using DMSP-OLS sensors - are typically bounded in the level of brightness they can record. This means that city centers and other areas with extensive lighting appear saturated in the standard DMSP-OLS data and display no variation within these saturation bounds. To counter this problem, a smaller subset of recorded data were collected by adjusting the settings of these sensors to enable more sensitive detection of bright lights at the cost of detecting low light areas. For those studying urban development and hoping to use nighttime light variation within city boundaries, this Radiance Calibrated Nighttime Lights dataset can be invaluable, providing images and light intensity values from 1996 through 2010 that can be used to evaluate the development of lighting within population centers.
This Geo4Dev tutorial provides a complete introduction to these radiance calibrated nighttime lights data for users interested in applying them to any research or policy questions. Users will learn to collect the data from Google Earth Engine, filter for areas of interest, extract light intensity statistics, and visualize the lighting over populated areas for further analysis. As an example of these data in use, the tutorial also replicates selected results from “Subways and Urban Growth: Evidence from Earth” (2018) by Marco Gonzalez-Navarro and Matthew Turner. The authors consider the effect of subway systems on city centralization, using nighttime lights and the light gradient radiating outward from the center as measures of centralization. For any individuals interested in the development of cities, this tutorial will provide a straightforward introduction to using radiance calibrated nighttime lights to answer a wide-ranging set of research questions.

Data and Resources

Radiance Calibrated Nighttime Lights Tutorial

HTML

Full tutorial on the use of DMSP-OLS Radiance Calibrated Nighttime Lights.

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Subways and Urban Growth: Evidence from Earth

PDF

Paper replicated in usage example

Authors

Marco Gonzalez-Navarro and Matthew Turner

Publish Date

2018

Journal

Journal of Urban Economics

Abstract

We investigate the relationship between the extent of a city’s subway network, its population and its spatial configuration. For the 632 largest cities in the world we construct panel data describing population, measures of centralization calculated from lights at night data, and the extent of each of the 138 subway systems in these cities. These data indicate that large cities are more likely to have subways but that subways have an economically insignificant effect on urban population growth. Our data also indicate that subways cause cities to decentralize, although the effect is smaller than previously documented effects of highways on decentralization. For a subset of subway cities we observe panel data describing subway and bus ridership. For those cities we find that a 10% increase in subway extent causes about a 6% increase in subway ridership and has no effect on bus ridership.

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Github Repository with Source Code for Radiance Calibrated Nighttime Lights Tutorial

ipynb

Repository containing Jupyter Notebook with full replication code for tutorial.

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Field	Value
Author(s)	Natalie Ayers
Last Updated	April 6, 2022, 23:59 (UTC)
Created	September 10, 2021, 19:01 (UTC)
Stable Link	https://learn.geo4.dev/Radiance%20Calibrated%20Nighttime%20Lights.html
Date	2021-09-10
Content Type	Training Materials
Primary Category	Land Use & Land Cover
Sub Category	LULC
Country Name	Global, Turkey
Location-Region/City	Istanbul
Associated Datasets	Global Radiance Calibrated Nighttime Lights (https://ngdc.noaa.gov/eog/dmsp/download_radcal.html)
Publishing Organization	Center for Effective Global Action

Dataset

Radiance Calibrated Nighttime Lights Tutorial

Data and Resources