An Overview of Methods for An Overview of Methods for Estimating Urban Populations Estimating Urban Populations

Using Nighttime Satellite ImageryUsing Nighttime Satellite Imagery

Paul SuttonPaul Sutton

psutton@du.edupsutton@du.edu

Department of GeographyDepartment of Geography

University of DenverUniversity of Denver

May, 2000May, 2000

OutlineOutline

• ‘‘Known’ Population Data how good/bad is it?Known’ Population Data how good/bad is it?

• Data: brief description of DMSP OLS imageryData: brief description of DMSP OLS imagery

• Estimating the Population of cities/urban clustersEstimating the Population of cities/urban clusters

• Estimating intra-urban population densityEstimating intra-urban population density

• ‘‘Temporally-Averaged’ Population DensityTemporally-Averaged’ Population Density

• Questions of spatial and temporal scaleQuestions of spatial and temporal scale

• Summary/ConclusionsSummary/Conclusions

How Good are the numbers and who cares?How Good are the numbers and who cares?When did the world population reach 6 billion?When did the world population reach 6 billion?

Absolute population of CitiesAbsolute population of Cities Mexico CityMexico City

– U.S. Census Bureau 28 millionU.S. Census Bureau 28 million– United Nations 16 millionUnited Nations 16 million

Sao PauloSao Paulo

- U.S Census Bureau 25 million- U.S Census Bureau 25 million

- United Nations 16 million- United Nations 16 million

ShanghaiShanghai - U.S. Census Bureau 8 million- U.S. Census Bureau 8 million - United Nations 15 million- United Nations 15 million

IstanbulIstanbul - Nat. Geog. Atlas* (1999)- Nat. Geog. Atlas* (1999) 2,938,000 [3,258,000] 2,938,000 [3,258,000] - Nat. Geog. Atlas* (1995)- Nat. Geog. Atlas* (1995) 6,620,200 [7,309,200] 6,620,200 [7,309,200]

* Cited PRB and U.S. Census Bureau* Cited PRB and U.S. Census Bureau

Percent of Population UrbanPercent of Population Urban

Models described here produce national Models described here produce national population estimates very sensitive to population estimates very sensitive to these numbers. Errors inflate with these numbers. Errors inflate with increasing rural fraction of populationincreasing rural fraction of population

Spatial AccuracySpatial Accuracy The 1994 Guatemala census included The 1994 Guatemala census included

hundreds of populated places never hundreds of populated places never previously enumerated. Nevertheless, the previously enumerated. Nevertheless, the spatial characteristics of these data were spatial characteristics of these data were rudimentary. The “maps” supplied to rudimentary. The “maps” supplied to enumerators in some frontier districts were enumerators in some frontier districts were generally hand drawn and based on anecdotal generally hand drawn and based on anecdotal information. As a consequence, we have information. As a consequence, we have better information than ever before regarding better information than ever before regarding the size and character of the Guatemalan the size and character of the Guatemalan population, we still lack a clear sense of population, we still lack a clear sense of where these people are.where these people are.

Nighttime Satellite ImageryNighttime Satellite Imagery (DMSP OLS) ‘Percent Observation’(DMSP OLS) ‘Percent Observation’

This hyper-temporal imagery used to This hyper-temporal imagery used to measure urban areal extentmeasure urban areal extent

Aggregate Estimation of Total City PopulationsAggregate Estimation of Total City Populations

Method I: Conterminous U.S.Method I: Conterminous U.S.

Imagery: DMSP OLS “Percent Observation” Imagery: DMSP OLS “Percent Observation”

‘‘Truth’: Wall to wall grid of Pop. Den. From 1990 Census Block GroupsTruth’: Wall to wall grid of Pop. Den. From 1990 Census Block Groups

Method: Cluster adjacent pixels & Count them to measure Areal Extent of Method: Cluster adjacent pixels & Count them to measure Areal Extent of Cluster, overlay to obtain corresponding Population Cluster, overlay to obtain corresponding Population

Method II: All Nations of the WorldMethod II: All Nations of the World

Imagery: DMSP OLS “Percent Observation” Imagery: DMSP OLS “Percent Observation”

‘‘Truth’: Point Dataset of over 3000 cities with known populationTruth’: Point Dataset of over 3000 cities with known population

Method: Threshold, Cluster, & Count pixels for Area, Geo-reference & Overlay to Method: Threshold, Cluster, & Count pixels for Area, Geo-reference & Overlay to obtain nationally specific slope & intercept parameters for the Ln(Area) vs. obtain nationally specific slope & intercept parameters for the Ln(Area) vs. Ln(Popualtion) relationship from known cities, apply to all clustersLn(Popualtion) relationship from known cities, apply to all clusters

Method 1: Proof of Concept with U.S. DataMethod 1: Proof of Concept with U.S. Data(Note: This also worked well with Mexico Data)(Note: This also worked well with Mexico Data)

Method II: Going GlobalMethod II: Going GlobalUse 1,383 Known Urban Populations to Estimate Populations Use 1,383 Known Urban Populations to Estimate Populations

of the 22,920 urban clusters found in DMSP OLS imageryof the 22,920 urban clusters found in DMSP OLS imagery

• ThresholdingThresholding: : Trade-off between too much conurbation Trade-off between too much conurbation and ability to see small settlementsand ability to see small settlements

• Geo-LocationGeo-Location: Provide each identified urban cluster : Provide each identified urban cluster with Country ID and related national Statswith Country ID and related national Stats

• RegressionRegression:: Using Ln(Area) vs. Ln(Population) Using Ln(Area) vs. Ln(Population) relationship to identify nationally specific slope and relationship to identify nationally specific slope and intercept parameters for every nationintercept parameters for every nation

• EstimationEstimation:: Estimate population of all 22,920 cluster Estimate population of all 22,920 cluster with parameters and use % urban statistic to get total with parameters and use % urban statistic to get total national population estimatenational population estimate

Thresholding:As thresholding increases intercomparisons of parameters become increasingly difficult

Medium & High GDP/Capita – 80 Low GDP/Capita - 40

RegressionRegression

Scatterplot of All Scatterplot of All Cities/Urban Clusters of Cities/Urban Clusters of

the World w/ Known the World w/ Known PopulationsPopulations

• All Cities (N= 1,404): Ln(pop) = .850* Ln(Area) + 9.107 R2 = 0.68 • High Income Cities (N=471):

Ln(pop) = 1.065*Ln(Area) + 7.0647.064 R2 = 0.77• Medium Income Cities (N=575):

Ln(pop) = 1.011*Ln(Area) + 8.1748.174 R2 = 0.78• Low Income Cities (N=358):

Ln(pop) = 0.989*Ln(Area) + 8.8898.889 R2 = 0.80• Venezuelan Cities (N=15): Venezuelan Cities (N=15):

Ln(pop) = 1.164*Ln(pop) + Ln(pop) = 1.164*Ln(pop) + 6.4756.475 R R22 = 0.84 = 0.84

Example of estimating nationally specific Example of estimating nationally specific regression parameters for Venezuelaregression parameters for Venezuela

Some ResultsThe Big Ugly Table that you can’t read….

Estimated and actual populations, regression parameters etc.

Some more results….Some more results….A smaller table you might be able to readA smaller table you might be able to read

How did it go with the Biggest Cities?How did it go with the Biggest Cities?

Disaggregate or ‘Intra-Urban’ Disaggregate or ‘Intra-Urban’ estimates of Population Densityestimates of Population Density

• Allocate aggregate estimate of total city Allocate aggregate estimate of total city population to pixels within urban clusterpopulation to pixels within urban cluster

• Use linearly proportional relationship Use linearly proportional relationship between light intensity and population between light intensity and population densitydensity

• Compare to residence and employment Compare to residence and employment based measures of population densitybased measures of population density

Radiance Calibrated DMSP OLS images of DenverRadiance Calibrated DMSP OLS images of Denveraka ‘Low-Gain’ or Light Intensity aka ‘Low-Gain’ or Light Intensity

This imagery used to model intra-urban population densityThis imagery used to model intra-urban population density

Formal & Graphical Formal & Graphical Representation of the ModelRepresentation of the Model

Actual, Modeled, and Smoothed Representations of MinneapolisActual, Modeled, and Smoothed Representations of Minneapolis

Some Results….

What do the Errors look like?

Temporally Averaged Population DensityTemporally Averaged Population Density

• Census data is typically a residence based measure of population density

• People, work, shop, go to school, and entertain & transport themselves outside of the home

• Is a temporally averaged measure of population density useful?

• (e.g. for a given 1 km2 area with 600 people in it for 8 hrs, 300 in in the next 8 hours, and 0 people in it the last 8 hours it has a temporally averaged population density of 300 persons/km2)

Are DMSP OLS based estimates of population density a temporally averaged measure of population density?

Questions of Spatial & Temporal ScaleQuestions of Spatial & Temporal Scale

• Is a population density dataset at a 1 kmIs a population density dataset at a 1 km22 spatial spatial resolution useful forresolution useful for– Vulnerability studies?Vulnerability studies?– Land-use Land-cover change studies?Land-use Land-cover change studies?– Environmental Modeling?Environmental Modeling?

• What kind of temporal resolution of population What kind of temporal resolution of population density representations are useful and needed?density representations are useful and needed?

• What measures other than simple density are What measures other than simple density are needed and what means are there to acquire them?needed and what means are there to acquire them?

• When are errors of population numbers and/or When are errors of population numbers and/or spatial location unacceptably large?spatial location unacceptably large?

What’s Going on in 1 km2?

Summary/ConclusionsSummary/ConclusionsNighttime Satellite imagery from DMSP OLS can be used to:Nighttime Satellite imagery from DMSP OLS can be used to:

1) Estimate the population of urban agglomerations around the world1) Estimate the population of urban agglomerations around the world

2) Estimate intra-urban temporally averaged measures of population density2) Estimate intra-urban temporally averaged measures of population density

Continuing research will shed light on improved means of delineating areal Continuing research will shed light on improved means of delineating areal extent of cities using the radiance calibrated datasets, better extent of cities using the radiance calibrated datasets, better explanations of the national variations in the slope and intercept explanations of the national variations in the slope and intercept parameters, and a greater understanding of the spatio-temporal nature of parameters, and a greater understanding of the spatio-temporal nature of the population density estimates produced by these methodsthe population density estimates produced by these methods

Future research should be informed by the potential users of these datasets as Future research should be informed by the potential users of these datasets as to the spatial and temporal scale required, and the numerical and spatial to the spatial and temporal scale required, and the numerical and spatial accuracy requiredaccuracy required

There is potential for inclusion of these methods into the suite of tools used There is potential for inclusion of these methods into the suite of tools used for conducting national censuses throughout the world for conducting national censuses throughout the world