I doubt anybody bothered to read the
peer reviewed
scientific acadamic study I posted earlier. But I will give a summary here. It involved using hundreds of satellite images over period of time to build a composite that offset any deviations or anomalies that might arise. Then that data was offset and tabulated against population density. Obviously in a desert there will be only a few people thus light emission will be less. In a densely packed city the reverse is true so the researchers built complex mathematical models to offset this and other variables. Then once a complex model was devoloped they tested out the result on sample areas where they had very good records or statistics. By doig this they refined the model. This and the complex methodology and the maths is explained behind this in the paper. One of the senior researchers is I believe Indian.
Sustainability 2013, 5(12), 4988-5019; doi:10.3390/su5124988
Article
Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being
Tilottama Ghosh 1,*, Sharolyn J. Anderson 2, Christopher D. Elvidge 3 and Paul C. Sutton 2,4
1 Cooperative Institute for Research in the Environmental Sciences, University of Colorado, Boulder, CO 80309, USA
2 School of Natural and Built Environments and The Barbara Hardy Institute, University of South Australia, Adelaide 5001, Australia
3 NOAA National Geophysical Data Center, Boulder, CO 80305, USA
4 Department of Geography, University of Denver, Denver, CO 80208, USA
* Author to whom correspondence should be addressed; Tel.: +91-8826860007.
Received: 23 September 2013; in revised form: 8 October 2013 / Accepted: 4 November 2013 / Published: 26 November 2013
Abstract
Improving human well-being is increasingly recognized as essential for movement toward a sustainable and desirable future. Estimates of different aspects of human well-being, such as Gross Domestic Product, or percentage of population with access to electric power, or measuring the distribution of income in society are often fraught with problems. There are few standardized methods of data collection; in addition, the required data is not obtained in a reliable manner and on a repetitive basis in many parts of the world. Consequently, inter-comparability of the data that does exist becomes problematic. Data derived from nighttime satellite imagery has helped develop various globally consistent proxy measures of human well-being at the gridded, sub-national, and national level. We review several ways in which nighttime satellite imagery has been used to measure the human well-being within nations.
Link >
http://www.mdpi.com/2071-1050/5/12/4988/htm
The results are for the entire globe however I have extracted the map data for South Asia but if people want to see the entirety of the data please refer to the full article. I provided the link above. Here is the map data for South Asia produced by researchers who are mostly American academics from various US universities as listed above.
As you can see Pakistan is mostly
green. So is South India but Ganga India is loaded and dominated by
orange. We already know that this part of India has over 500 million people. This shows the scale of real poverty India. States like Bihar, Uttar Pradesh, Jharkand, Odisha, West Bengal are afflicted by vast poverty which will not come as any surprise. South India, Indian Punjab, Gujrat do well again which not a surprise. All this is consistent with what we know about poverty and states in India not doing well. However this demonstrates the scale of poverty in India and Bangladesh.
You can stick your Mars, Venuses and Milky Way down the Ganga. Pakistan as whole comes out far, far
better than India. Perhaps the reason why half of India has to poop outside is not because they are too tight with their money but maybe because they are too poor to eat let alone think of the post eating problems like building toilets.
Full Scale Map dataset for world here >
http://www.mdpi.com/sustainability/.../html/images/sustainability-05-04988-g004.png
@Pluralist @Kambojaric @protest @SOUTHie
@django @WhyCry @Joe Shearer @nair @Talwar e Pakistan etc
