Laveesh Bhandari, Koel Roychowdhury Indicus Analytics Private Limited New Delhi, India ,
� Introduction � Research objectives � Research objectives � Datasets used � Methods ◦ Satellite Image Processing ◦ GDP data processing � Results � Results ◦ Correlations ◦ Models � Maps � Maps ◦ Total GDP ◦ Sectoral GP ◦ Regions of Development R i f D l t � Summary 2
» DMSP-OLS night-time images primary source of data for project for project » DMSP-OLS used for a variety of applications (e g » DMSP-OLS used for a variety of applications (e.g. environmental sustainability, urban mapping and light pollution etc) » Problem of unavailability of data on economic activities particularly for small administrative regions activities, particularly for small administrative regions » Propose an approach to produce GDP data at the sub- Propose an approach to produce GDP data at the sub national level using night-time satellite images 3
What What is is the the utility utility of of DMSP OLS DMSP-OLS image image for for accurately predicting Gross Domestic Product (Total and Sectoral) at a sub-national level? and Sectoral) at a sub national level? Achieve this by: � Investigating statistical relationships between GDP and information derived from DMSP-OLS images g � Development of prediction models � Validation of models � Application of models to derive GDP maps at sub national level � Application of models to derive GDP maps at sub national level for India 4
� Satellite Images � Satellite Images ◦ DMSP-OLS � Average Digital Number (DN) data (2008) � Average Digital Number (DN) data (2008) � GDP Datasets GDP D t t ◦ Indicus Analytics Pvt. Limited (2008) 5
� Differences in average DN between satellites � Differences in average DN between satellites � Reference Image: captured by satellite F12 in 1999 over Sicily � Second order regression equation: � Calculation of SUM of stable lights Details can be found at: Elvidge, C, Ziskin, D, Baugh, K, Tuttle, B, Ghosh, T, Pack, D, Erwin, E & Zhizhin, M 2009, 'A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data', Energies, vol. 2, no. 3, pp. 595-622. 6
� Distributing the state level GDP of the sector into � Distributing the state level GDP of the sector into each district. � Creating two indices using principle inputs and � Creating two indices using principle inputs and outputs. � Y(K, L, M) ◦ where Y is the output, K is the capital, L is labour and M is a general variable related to land and natural resources. � The second index is an additive index based on � The second index is an additive index based on normalized sectoral data. � The natural log of the GDP at the district level was � The natural log of the GDP at the district level was calculated and was used in this study. 7
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Ln (Y) = α + β 1 Ln(X 1 ) + β 2 Ln(X 2 ) + β 3 Ln(X 3 ) + β 4 X 4 + β 5 X 5 + β 6 X 6 + β 7 X 7 + β 8 X 8 + β 9 X 9 GDP from GDP from GDP from Primary Secondary Tertiary P Predictor Variables di V i bl T Total GDP l GDP S Sector Sector S Sector S (Constant) , α 0.86 ‐ 2.42 1.25 ‐ 0.97 Log Normal of Sum of Lights, β 1 0.36 0.31 0.53 0.33 Log Normal of Area , β 2 ‐ 0.11 0.04 ‐ 0.14 ‐ 0.14 Log Normal of Total Population, β 3 0.47 0.57 0.24 0.58 Dummy for Metropolitan Districts, β 4 1.49 ‐ 0.62 1.45 ‐ 1.73 Dummy for Suburbs of Metro cities, β 5 0.90 ‐ 0.41 1.46 1.00 Dummy for Large Towns, β 6 0.52 ‐ 0.09 0.79 0.63 Dummy for Capital Districts, β 7 0.62 ‐ 0.57 0.86 0.85 Dummy for Mountainous Districts, β 8 0.04 * 0.17 * ‐ 0.01 * 0.12 * Dummy for Snow ‐ Covered Districts, β 9 0.10 ‐ 0.38 0.44 0.19 Adjusted R 2 0.88 0.73 0.73 0.87 * Significant at less than 50% of the cases 9
Predicted Total GDP for % Error in the predicted India India results results 10
Predicted GDP for % Error in the predicted Primary sector for India Primary sector for India results results 11
% Error in the predicted Predicted GDP for results results Secondary sector for India Secondary sector for India 12
Predicted GDP for % Error in the predicted Tertiary sector for India Tertiary sector for India results results 13
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� Strong correlations exist between sum of stable lights as g g obtained from DMSP-OLS night time images and GDP at the district level for India. Non-linear correlations were noted at this level. � Total GDP was predicted for the districts of the country as a whole from the models. The model was proposed with an adjusted r 2 value 0.87. Sum of lights exhibited the highest impact on log of GDP than other predictor variables in the impact on log of GDP than other predictor variables in the model. � Models for sectoral GDP were also predicted. This included the major sectors of the economy such as primary the major sectors of the economy such as primary, secondary and tertiary sectors. GDP in primary sector was mainly predicted on the impact of sum of lights and total population. Sum of lights also demonstrated to have the p p g highest impact on the models predicting GDP from secondary and tertiary sources. RMIT University 15
� The urban centres were found to be more developed with the highest contribution to GDP developed with the highest contribution to GDP in the secondary and tertiary sectors. � High, medium and less developed zones of the g , p country were identified, the latter with very low contribution to GDP by the tertiary and secondary sectors. d t This paper conclusively shows that the information obtained Thi l i l h h h i f i b i d from the night time DMSP-OLS images can be successfully used to predict GDP at the district level and map areas on used to predict GDP at the district level and map areas on the basis of their economic development. RMIT University 16
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