shedding light upon the dark continent
TRANSCRIPT
Shedding Light Upon The Dark Continent
Estimating population, GDP, and CO2 emissions of African countries using DMSP night light data
Benjamin IgnacDigital Image ProcessingDr. Kirsten de BeursUniversity of Oklahoma
The distribution, presence, and
absence of night light in Africa can tell us a story about social and
environmental phenomena
Correlation between night light and POPULATION
TEMPORAL change of night light in
Africa
Correlation between night light and GDP
Correlation between night light and CO2
EMISSIONS
DMSP offers free Stable Light data on an annual basis from
1992 to 2013Satellites: Defense Meteorological Satellite Program (DMSP)
Sensor: Operational Linescan System (OLS)
Swath: 3000km
Spatial resolution: 2.8km (smoothed)
Temporal resolution: sunsynchronous – between 20:30 and 21:30 each night
Spectral resolution: two broadband sensors, one in the visible/near-infrared (VNIR, 0.4 – 1.1μm) and thermal infrared (10.5 – 12.6μm)
Composite: 30 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude
DN: Frequency of light detected per pixel (0-63)
Subset: 10,000x10,000 pixels (Africa)
CompositesF101992F101993F101994F121995F121996F121997F121998F121999F142000F142001F142002F142003F152004F152005F152006F152007F162008F162009F182010F182011F182012F182013
Ancillary data
World Bank: Population and GDP midyear estimates by country (1981-2015)European Commission: CO2 time series by country (1970-2014)
http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html
http://data.worldbank.org/indicator/SP.POP.TOTL http://data.worldbank.org/indicator/NY.GDP.MKTP.CD http://edgar.jrc.ec.europa.eu/overview.php?v=CO2ts1990-2014&sort=des9
F18 2013 Nighttime Lights Composite
Methods
1. Download DMSP data2. Create GIF using TIFFs from all 21 years of data (1992-2013)3. Create Africa subsets in ENVI for 1992 and 20134. Open subsets ArcGIS
Spatial Analyst>Zonal>Zonal Statistics As Table5. Join zonal statistics to African country shape file6. Attribute GDP, population and CO2 data7. Create maps and scatterplots, calculate correlations
Compared to other continents Africa is less lit at night and has been developing slower over time
+92% total night lights
+33% per capita
Negative change in lights per capita (2013-1992):
NigeriaCentral African RepublicLibyaGabonZimbabwe
1000 10000 100000 1000000300
3000
30000
300000f(x) = 0.904879986968124 x^0.858701197791254R² = 0.881925667264371
Relationship between night light and GDP
Total night light (DN_SUM)
GDP
(in
mill
ion
USD
)
1000 10000 100000 100000079999.9999999999
799999.999999999
7999999.99999999
79999999.9999999f(x) = 13711.1580538128 x^0.585944330468512R² = 0.479974208791063
Relationship between night light and total population
Total night light (DN_SUM)
tota
l pop
ulati
on
1000 10000 100000 100000050
500
5000
50000
f(x) = 0.124672423579276 x^0.930187401998754R² = 0.884521991502267
Relationship between night light and CO2 emissions
Total night light (DN_SUM)
Tota
l CO
2 em
issi
ons (
Kilo
tons
)
Night-time lights in Africa are an adequate estimator for GDP and CO2 Emissions but less
accurate for population estimates due to Africa’s high rural population
Conclusions
R² = 0.48 (60% of Africa is rural)
R² = 0.8819
R² = 0.8845
+92% total night lights+33% per capita (1992-2013)
sources
Data:• http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html• http://data.worldbank.org/indicator/SP.POP.TOTL • http://data.worldbank.org/indicator/NY.GDP.MKTP.CD • http://edgar.jrc.ec.europa.eu/overview.php?v=CO2ts1990-2014&sort=des9 • https://giphy.com/gifs/l3V0lh7j34jKrshos
Literature:• Huang, Q., Yang, X., Gao, B., Yang, Y., & Zhao, Y. (2014). Application of DMSP/OLS Nighttime
Light Images: A Meta-Analysis and a Systematic Literature Review. Remote Sensing, 6(8), 6844-6866.
• Doll, C. N. (2008). CIESIN Thematic Guide to Night-time Light Remote Sensing and its Applications. CIESIN Thematic Guide. Retrieved from: http://sedac.ciesin.columbia.edu/binaries/web/sedac/thematic-guides/ciesin_nl_tg.pdf