Estimation of GDP at Sub-National Scales using Nighttime Satellite Imagery Paul Sutton Department of Geography University of Denver April, 2005 Presentation.

Slides:

Advertisements

Similar presentations

VIIRS Data Evaluation Over Thailand and Japan February 13, 2012 Chair, Chris Elvidge NOAA-NGDC 1.

Advertisements

Methods for investigating zoning effects Mark Tranmer CCSR.

DEPARTMENT OF GEOMATIC ENGINEERING Mapping Anthropogenic Activities from Earth Observation Data Christopher Doll, Jan-Peter Muller Workshop on Gridding.

Assessment of black carbon in the Arctic: new emission inventory of Russia, model evaluation and implications Kan Huang 1, Joshua S. Fu 1,2, Xinyi Dong.

Parameter Estimation, Dummies, & Model Fit We know mechanically how to “run a regression”…but how are the parameters actually estimated? How can we handle.

Global Mapping of Impervious Surface Area Benjamin Tuttle* - CIRES, University of Colorado, Boulder Christopher D Elvidge, PhD - NOAA-National Geophysical.

Evaluating district-level income distribution for India using nighttime satellite imagery and other datasets Tilottama Ghosh, CIRES, University of Colorado,

An Exploration of using Nighttime Satellite Imagery from the DMSP OLS for Mapping Population and Wealth in Guatemala Paul C.Sutton Department.

Relationships between Nighttime Imagery and Population Density for Hong Kong Qing Liu Paul C. Sutton Christopher D. Elvidge Asia Pacific Advanced Network.

Improving intraurban land use characterization with nighttime imagery Sharolyn Anderson, Assistant Professor, University of Denver Paul C. Sutton, Associate.

Spatial Statistics in Ecology: Case Studies Lecture Five.

19 th Advanced Summer School in Regional Science Overview of advanced techniques in ArcGIS data manipulation.

Urbanization, climate change, and changes to ecosystem services in low lying coastal areas in the next 50 years Dr. Paul C. Sutton Department of Geography.

Using Impervious Surface as a spatially explicit Proxy Measure of CO 2 Emssions Dr. Paul C. Sutton Dr. Sharolyn Anderson Dr. Sharolyn Anderson Department.

Vegetation Change in North Africa through the analysis of satellite data, Professor Stephen Young Department of Geography, Salem State College.

Paving the Planet: Mapping and Monetizing Human Impact on the Earth Presentation Footprint Forum June 11, 2010 Colle di Val d’Elsa Paul C. Sutton Department.

Mapping the Dollar value of natural production and the Dollar cost of human consumption globally Paul C. Sutton & Benjamin Tuttle Department of Geography.

Using Impervious Surface as a spatially explicit Proxy Measure of CO 2 Emssions Dr. Paul C. Sutton Department of Geography University of Denver AAG presentation.

Progress in Empirical Measurement of the Urban Environment: An exploration of the theoretical and empirical advantages of using Nighttime Satellite Imagery.

An Empirical Environmental Sustainability Index derived solely from Nighttime Satellite Imagery and Ecosystem Service Valuation Paul Sutton

Welcome to AP Human Geography

An Overview of Methods for Estimating Urban Populations Using Nighttime Satellite Imagery Paul Sutton Department of Geography University.

Estimating Gross Domestic Product, Informal Economy and Remittances of Mexico using Nighttime Satellite Imagery Tilottama Ghosh Dr. Paul C. Sutton Dr.

Preliminary Estimation of Annual Gas Flaring Volumes in South and East Asia Chris Elvidge NOAA – National Geophysical Data Center 325 Broadway, E/GC2 Boulder,

High-resolution global CO 2 emissions from fossil fuel inventories for 1992 to 2010 using integrated in-situ and remotely sensed data in a fossil fuel.

Estimation of poverty rates based on satellite observed nighttime lights Chris Elvidge, NOAA National Geophysical Data Center (NGDC) Boulder, Colorado.

Ms. Costa By PresenterMedia.comPresenterMedia.com.

Maps Geography Culture Government Economy

September 18-19, 2006 – Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development Using Geographic Information Systems (GIS) as.

Can Poverty Levels Be Estimated Using Satellite Data? Chris Elvidge NOAA – National Geophysical Data Center 325 Broadway, E/GC2 Boulder, Colorado 80305,

Collaborative Tool for Collecting Reference Data on the Density of Constructed Surfaces Worldwide Chris Elvidge NOAA-NESDIS National Geophysical Data Center.

Scale Effect of Vegetation Index Based Thermal Sharpening: A Simulation Study Based on ASTER Data X.H. Chen a, Y. Yamaguchi a, J. Chen b, Y.S. Shi a a.

Uneven Intraurban Growth in Chinese Cities: A Study of Nanjing Yehua Dennis Wei Department of Geography and Institute of Public and International Affairs.

VIIRS Global DNB Product Status Chris Elvidge Earth Observation Group NOAA National Geophysical Data Center Kim Baugh, Feng-Chi Hsu, Mikhail Zhizhin CIRES,

Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery James A. Shine 1 and Daniel B. Carr 2 34 th Symposium on.

The Spatial GINI Coefficient

With the rapid development of the Chinese economy in the last two decades, more and more people have relocated to cities and adjacent areas from rural.

Development and evaluation of Passive Microwave SWE retrieval equations for mountainous area Naoki Mizukami.

Gridded Rainfall Estimation for Distributed Modeling in Western Mountainous Areas 1. Introduction Estimation of precipitation in mountainous areas continues.

Welcome to AP Human Geography Find a seat - set up your territory :-) Start the Icebreaker activity- try to meet at least five people as you fill in the.

Using Productivity Modeling to Assess Regional Advantage ST&E Policy Lab Research Methods Seminar April 2, 2009 Joshua Drucker University of Illinois at.

U.S. Department of the Interior U.S. Geological Survey The use of remotely sensed and GIS-derived variables to characterize urbanization in the National.

September 18-19, 2006 – Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development Conducting and interpreting multivariate analyses.

Global Programme on Evidence for Health Policy Mapping Poverty: Predicting Income using the LandScan Database Workshop on Gridding Population Data CIESIN,

AP Human Geography What is it? Welcome to AP Human Geography Find a seat - set up your territory :-) Start the Icebreaker activity- try to meet at least.

Chapter 9: Correlation and Regression Analysis. Correlation Correlation is a numerical way to measure the strength and direction of a linear association.

 Find the Least Squares Regression Line and interpret its slope, y-intercept, and the coefficients of correlation and determination  Justify the regression.

AP STATISTICS Section 3.2 Least Squares Regression.

AP Human Geography Course Outline Geography: Its Nature and Perspectives 5-10% Population: 13-17% Cultural Patterns and Processes: 13-17% Political Organization.

Psychology 202a Advanced Psychological Statistics October 22, 2015.

Lecture 5 Ending Extreme Poverty The Age of Sustainable Development.

AP Human Geography “HuG” It Out! Course Outline  Geography: Its Nature and Perspectives 5-10%  Population 13-17%  Cultural Patterns and Processes.

Analysis of satellite imagery to map burned areas in Sub-Saharan Africa CARBOAFRICA conference “Africa and Carbon Cycle: the CarboAfrica project” Accra.

COOL MAPS SPLASH 2013 Daniel Noble.

Impervious Surface Area of the Conterminous United States Christopher D. Elvidge John B. Dietz Paul S. Sutton.

Landsat Satellite Data. 1 LSOS (1-ha) 9 Intensive Study Areas (1km x 1km) 3 Meso-cell Study Areas (25km x 25km) 1 Small Regional Study Area (1.5 o x 2.5.

INTRODUCTION Despite recent advances in spatial analysis in transport, such as the accounting for spatial correlation in accident analysis, important research.

Michael Xie, Neal Jean, Stefano Ermon

Adam Storeygard, Tufts University

Studying Geography Social Studies.

The SEEA indicator initiatives A preliminary note

Outline. A 2010 Mapping of the Constructed Surface Area Density for China Preliminary Results.

Igor Appel Alexander Kokhanovsky

Course Outline Geography: Its Nature and Perspectives 5-10%

Paul C. Sutton & Benjamin Tuttle Department of Geography

Department of Geography

Paul C. Sutton & Benjamin Tuttle Department of Geography

Mathematics Unit 5: Continent Area

Method vs Perspective Geographic Method: using geographic information to describe the earth Geographic Perspective: a geographic grid through which information.

Latin America Natural Resource Use

Presentation transcript:

Estimation of GDP at Sub-National Scales using Nighttime Satellite Imagery Paul Sutton Department of Geography University of Denver April, 2005 Presentation for Association of American Geographers Conference Denver, Colorado

Outline Nationally Integrated Nighttime Imagery has some correlation with National GDP figuresNationally Integrated Nighttime Imagery has some correlation with National GDP figures Linear allocation of GDP to Light Intensity on a Pixel by Pixel basis produces an un-validatable map of GDP at 1 km resolution….Linear allocation of GDP to Light Intensity on a Pixel by Pixel basis produces an un-validatable map of GDP at 1 km resolution…. Sub-national GDP figures Compared with Sub-National integration of Nighttime ImagerySub-national GDP figures Compared with Sub-National integration of Nighttime Imagery “Regional Parameters”“Regional Parameters” A Cheeze-Ball Statistical Technique? A Cheeze-Ball Statistical Technique? Future Research… (aka Back to the Drawing Board )Future Research… (aka Back to the Drawing Board )

Scatterplot of Night Light Energy & PPP of GDP for 208 nations

Global map of Marketed Economic Activity as measured by Nighttime Satellite Image Proxy

Motivation Question: Why estimate Known GDP values with Nighttime Satellite Imagery? Answer #1: Because it would be interesting if it is possible to do. Answer #2: If these methods are developed they may provide estimates of GDP in nations with large informal economies (e.g. Mexico?) Answer #3: Also, they may provide independent estimates of economic activity in places where ‘official’ numbers are suspect

Data and The “Cheeze-Ball” Statistics Trick: “Regional Parameters” Applied to China, Turkey, U.S., and India (China example below) Dummy Code the “Region” parameter derived from Residuals (definitely cheating); However, then apply the derived regression parameters do a different TIME.

Apply Non-Linear Areal Extent vs. Population rule for cities to GDP estimates… Light ‘Blobs’ are Population ‘Blobs’ are Money (GDP) ‘Blobs’ Why not model them that way?

Regression Model Development for China (1990 data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Regression Model Application for China (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Urban Pop Model Application for China (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Regression Model Development for Turkey (1990 data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Regression Model Application for Turkey (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Urban Pop Model Application for Turkey (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Regression Model Development for India (1990 data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Regression Model Application for India (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Urban Pop Model Application for India (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Regression Model Development for United States (1990 data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Regression Model Application for United States (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Urban Pop Model Application for United States (2000 Data) Note: OVER-estimates are in Green; UNDER-estimates in RED

Aggregate Estimates of National GDP by Summation of State/Canton estimates Note: GDP numbers all in different currencies

Discussion These methods do not work well enough for much of anything yet…. “Learn by Losing”…….. In Turkey Istanbul was underestimated and Ankara was over-estimated (Primate & Capitol City effects?) Ankara 41% Too High (capitol city effect?) Istanbul 53% Too Low (primate city effect?)

Conclusions… Future Avenues of Investigation…. Interpret the residuals and use that information. Convert all GDP data to gold standard currency like perhaps the EURO. Increase the “Dummy Coding” (Is this legit?) Want to work on this? Send me an , I’ll send you the data:

Acknowledgements Without Chris Elvidge at NOAA’s NGDC there might not be any DMSP OLS derived data products. I am working with Chris Elvidge and others to develop better estimators of GDP using this imagery and other relevant information. Sharolyn Anderson and others with Grid command of doom: Zoldor = int(exp((float(floatnat30int) / 1000) + (float(nat30slope) / 1000) * (float(lnclustarea30) / 1000) / float(clustarea30))