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Christopher Doll JSPS-UNU Postdoctoral Fellow United Nations - - PowerPoint PPT Presentation
Christopher Doll JSPS-UNU Postdoctoral Fellow United Nations - - PowerPoint PPT Presentation
Population detection profiles of DMSP- OLS night-time imagery by regions of the world Christopher Doll JSPS-UNU Postdoctoral Fellow United Nations University Institute of Advanced Studies & University of Tokyo Asia-Pacific Advanced
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Night-time lights and electricity access
Energy access is often
regarded as ‘the missing Millennium Development Goal’
Electricity in particular is
seen as crucial to development, not least because electrical lighting, lengthens the productive time available in the day
Around 1.6 billion people
do not have access to electricity
Can night-time lights
help in monitoring access to electricity
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Overview of the study
The properties of the DMSP-OLS sensor
prompted the evaluation of rural electrification rates
– Overglow effects in urban areas deemed it unsuitable for assessing the urban component of electricity access Access to electricity was assessed by evaluating
population present in unlit areas of the world and comparing it to the total rural population
Regional and national electrification rates were
calculated
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Population density in areas without electricity access (no light)
From Doll & Pachauri, 2010
Areas with the lowest levels of electricity access
are also some of the least densely populated
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Sub-Saharan Africa has
93% of rural areas without access to electricity
The population density in
(these) unlit areas is 26 people/km2
Compared to India where
the unlit population density is over 200people/km2
The geographical
dimension of access to electricity will require differentiated solutions in
- rder to be economically
attractive
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Obstacles to using night-time lights for estimating access to electricity
Where population density can be detected,
usage is not dense enough to be detected
Population density is not high enough to be
detected
– What is the fundamental population density that DMSP- OLS can observe?
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Population detection rates vary by region
This graph shows how the
proportion of unlit pixels varies with population density
We see that in the developed
world population is consistently detected from around 25persons/sqkm
But for developing
countries, lower levels of access to electricity mean this only higher population densities are detectable
At 250 persons/km2:
– Africa 80% undetected – Asia 50% undetected – Latin America 25% undetected
Source: Doll & Pachauri, 2010 Energy Policy
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The study..
Using this method of evaluating % unlit cells
by population density class, this paper investigates further the variation in detection rates of DMSP-OLS with regard to two spatially explicit population datasets
– CIESIN’s GRUMP (census based population rendering) – ORNL’s Landscan (modelled ambient population dataset) Detection profiles of population density with
respect to DN are then evaluated by assessing the weighted average of population density per DN value
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Regional groupings used
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OECD regions are 90% detected between 50- 100persons/km2
Population Density (persons.km‐2) Range 0‐500 Fraction of unlit pixels
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Developing regions show more variation
Population Density (persons.km‐2) Range 0‐2,000 Fraction of unlit pixels
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Former Soviet Union, Centrally Planned Asia and the Middle East
Population Density (persons.km‐2) Range 0‐2,000 Fraction of unlit pixels
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Over the long range detection settles around 10-15% globally
Population Density (persons.km‐2) Range 0‐15,000 Fraction of unlit pixels
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Summary
Detection rates vary widely over levels of
development
Interestingly, ambient population is
consistently less detectable than residential in all regions
Globally around 90% of population is detected
at 10,000persons/km2
What about the population density within lit
areas?
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GRUMP DN-Population density profiles
Population Density (persons.km‐2) Range 0‐8,000 DN Value
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Landscan DN-Population density profiles
Population Density (persons.km‐2) Range 0‐8,000 DN Value
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Summary table of typical population densities by region
DN 10 25 40 50 60 63 average desnity NAM GRUMP 4 29 82 159 251 522 1620 53 WEU GRUMP 17 107 327 557 762 1449 3355 114 PAO GRUMP 4 184 467 702 961 1639 4879 126 MEA GRUMP 16 280 575 910 1027 1297 2771 54 LAM GRUMP 11 254 502 760 1022 1658 4115 41 CPA GRUMP 77 796 1422 1762 2200 3639 6800 155 FSU GRUMP 9 129 467 869 1444 1849 3222 31 EEU GRUMP 39 137 476 942 1160 2192 3876 109 AFR GRUMP 28 693 979 1405 1954 2547 3260 40 SAS GRUMP 153 665 1530 2113 2249 3954 9547 277 PAS GRUMP 49 527 914 1288 1794 3441 7439 126
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Summary table of typical population densities by region
DN 10 25 40 50 60 63 Average desnity (regional) NAM GRUMP 4 29 82 159 251 522 1620 53 WEU GRUMP 17 107 327 557 762 1449 3355 114 PAO GRUMP 4 184 467 702 961 1639 4879 126 MEA GRUMP 16 280 575 910 1027 1297 2771 54 LAM GRUMP 11 254 502 760 1022 1658 4115 41 CPA GRUMP 77 796 1422 1762 2200 3639 6800 155 FSU GRUMP 9 129 467 869 1444 1849 3222 31 EEU GRUMP 39 137 476 942 1160 2192 3876 109 AFR GRUMP 28 693 979 1405 1954 2547 3260 40 SAS GRUMP 153 665 1530 2113 2249 3954 9547 277 PAS GRUMP 49 527 914 1288 1794 3441 7439 126
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Summary
South Asia has the most linear profile of DN to
population density
Two regions of similar population density but
different development levels can correspond to a factor of two population density difference for a given DN value
North America stands out even compared to
- ther developed nations and the ratio is even
higher between other regions
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