Progress in Empirical Measurement Progress in Empirical Measurement of the Urban Environment:of the Urban Environment:

An exploration of the theoretical and empirical advantages of using An exploration of the theoretical and empirical advantages of using Nighttime Satellite Imagery in Urban StudiesNighttime Satellite Imagery in Urban Studies

Paul SuttonPaul Suttonpsutton@du.edupsutton@du.edu

Department of GeographyDepartment of GeographyUniversity of DenverUniversity of Denver

September, 2000September, 2000GIS/EM4GIS/EM4

Banff, Alberta, CanadaBanff, Alberta, Canada

OutlineOutline• Motivation: Motivation: Why Gather Information about the Urban Environment?Why Gather Information about the Urban Environment?

• Background: Background: What datasets exist and where do they come from?What datasets exist and where do they come from?

• Distinctions between Ground & Image based measuresDistinctions between Ground & Image based measures

• Potential of Satellite imagery for monitoring Urban EnvironmentPotential of Satellite imagery for monitoring Urban Environment

• New & Old Theories of Urban Form and StructureNew & Old Theories of Urban Form and Structure

• Models using Nighttime Imagery to estimate Population ParametersModels using Nighttime Imagery to estimate Population Parameters

• Theoretical and Empirical Advantages of these methodsTheoretical and Empirical Advantages of these methods

• Future Directions Future Directions

• Summary/ConclusionsSummary/Conclusions

MotivationMotivationWhy gather information about the Urban Environment?Why gather information about the Urban Environment?

• More than half the world’s population lives in urban areas and this fraction is growing

• Social, Economic, and Behavioral Demographic Data are the major gaps to be filled in globally integrated geo-information

• Existing Information is degrading due to increasing human mobility, and the fact that a growing proportion of the earth’s population live in developing countries which can’t afford to conduct accurate censuses

• Spatially referenced demographic information is a vital component of studies of: Hazard Planning and Response, Sustainability and Development Issues, and countless other cross-disciplinary investigations

BackgroundWhat datasets exist and where do they come from?

Global Population DatasetsGridded Population of the World (GPW): A Single Variable Model

Derived from National Statistical Offices of the world

LandScan: A Multiple Variable Model Derived from digital cartographic products and imagery

Integrated Global Environmental and Infrastructure DatasetsISCGM’s Global Map Global Spatial Data Infrastructure (GSDI) (Landcover, Transportation networks, Admin Boundaries, Lakes, Rivers, Elevation, Vegetation)

United Nations Geographic Database (does not exist yet)Social and/or Environmental Data? Potential for Symbiotic relationships with existing data

Distinctions between ground based Distinctions between ground based & &

map or imagery based datamap or imagery based data

Ground Based DataGround Based Data

Derived from Enumeration, Surveys Derived from Enumeration, Surveys and Administrative Recordsand Administrative Records

Primarily Social, Economic, and Primarily Social, Economic, and Behavioral Demographic Behavioral Demographic information information

Expensive to acquire, maintain, Expensive to acquire, maintain, update, and provideupdate, and provide

Coarse spatial and temporal Coarse spatial and temporal resolutionresolution

Usually obtained by governments and Usually obtained by governments and represents ‘official’ figuresrepresents ‘official’ figures

Map & Imagery Based DataMap & Imagery Based Data

Derived from digital cartographic Derived from digital cartographic products and remotely sensed imageryproducts and remotely sensed imagery

Primarily physical in nature e.g. Primarily physical in nature e.g. elevation, landcover, temperature, etc. elevation, landcover, temperature, etc.

Inexpensive to acquire, maintain, Inexpensive to acquire, maintain, update, and provideupdate, and provide

Variable spatial and temporal resolutionVariable spatial and temporal resolution

Obtained by various institutions and not Obtained by various institutions and not necessarily an ‘official’ measurenecessarily an ‘official’ measure

Potential contributions of remotely sensed Potential contributions of remotely sensed imagery to monitoring urban environmentimagery to monitoring urban environment

A diverse spectrum of remotely sensed information is available including: Nighttime Satellite Imagery, vegetation, Landcover, ‘greenness’, local and absolute elevation, etc.

Models that truly integrate diverse sources of this kind of information (e.g. use multiple sensors, spatial analysis, and cartographic info) to estimate urban and demographic information have not been fully explored.

The organizing framework that GIS implicitly provided allows for the development and validation of models with the potential to independently measure many variables including population density, income, economic activity, urban growth, etc. (fertility from space?)

Establishment of the validity and independence of these models will improve the spatial and temporal resolution of data coverage, and inform sampling strategies for ground based measurements in areas with limited resources

Classical Models of Urban FormStrengths

Describe Complex SpatialPatterns and Processes

WeaknessesDepend on subjective classificationDifficult to measure for a given city

Not Very General

Prospects of Image Based ModelsDerived from Easy to make Measurements thatCan be obtained objectively to produce general

Models of Urban Form

ChallengesDescribe Complex Spatial

Patterns and Processes

Classical Models of Urban StructureExamples: Clark’s Exponential Decay of Pop Den from CBD

Newling’s theories on evolution of Exp Decay Nordbeck’s Ln(area) vs. Ln(population) relationship

Utility: Measure parameters for areas with known data Identify general variations in those parameters Extend models to areas without known data

Example of Parameterization Process Example of Parameterization Process For Ln(area) vs. Ln(pop) for world citiesFor Ln(area) vs. Ln(pop) for world cities

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

A Nighttime Satellite Image of DenverA Nighttime Satellite Image of DenverWhat Urban Form and/or Structure can be seenWhat Urban Form and/or Structure can be seen

in this single band gray-scale image?in this single band gray-scale image?

Using Nighttime Satellite imagery to Using Nighttime Satellite imagery to estimate intra-urban population densityestimate intra-urban population density

• Allocate aggregate estimate of total city Allocate aggregate estimate of total city population to pixels within urban cluster population to pixels within urban cluster (utilizing (utilizing existing urban theory)existing urban theory)

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

(could be improved with non-linear models and/or additional bands of (could be improved with non-linear models and/or additional bands of remotely sensed data)remotely sensed data)

• Compare to residence and employment based Compare to residence and employment based measures of population densitymeasures of 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?

‘‘Ambient’ or Temporally Averaged Ambient’ or Temporally Averaged Population DensityPopulation 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?

Theoretical and Empirical Benefits of Image based Measures of the Urban Environment

• Theoretical Benefits:

Theory based on physical measures are more general

Image based and classical models mutually inform one another

• Empirical Benefits: Imagery is available globally at finer spatial & temporal resolution Image-based models naturally integrate into other environmental

models that interact with urban processes

Future Directions

• LandScan and GPW represent a bifurcation in methodology for obtaining social, economic, and behavioral demographic information that should be encouraged

• GSDI and United Nations Geographic Database potentially represent the same phenomena at a larger scale

• Independence between image based and ground based measurements should be fostered for many reasons including: symbiosis, validation, sampling strategy development, and Institutional considerations

• Resolve Scale Issues at Theoretical, Institutional, and Technical levels

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

This example only scratches the surface of possibilitiesThis example only scratches the surface of possibilitiesFor example: How well could a model perform if it included 3-D building For example: How well could a model perform if it included 3-D building

volume from DEMs, ‘Greenness’, and Transportation Infrastructure?volume from DEMs, ‘Greenness’, and Transportation Infrastructure?

Global Spatial Data Infrastructure and United Nations Global Spatial Data Infrastructure and United Nations Geographic Database development efforts should benefit Geographic Database development efforts should benefit from this potentialfrom this potential