Progress in Empirical Measurement of the Urban Environment: An exploration of the theoretical and empirical advantages of using Nighttime Satellite Imagery in Urban Studies Paul Sutton Department of Geography University of Denver September, 2000 GIS/EM4 Banff, Alberta, Canada

Outline Motivation: Why Gather Information about the Urban Environment?Motivation: Why Gather Information about the Urban Environment? Background: What datasets exist and where do they come from?Background: 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 DirectionsFuture Directions Summary/ConclusionsSummary/Conclusions

Motivation 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

Background What datasets exist and where do they come from? Global Population Datasets Gridded 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 Datasets ISCGM’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 & map or imagery based data Ground Based Data Derived from Enumeration, Surveys and Administrative Records Primarily Social, Economic, and Behavioral Demographic information Expensive to acquire, maintain, update, and provide Coarse spatial and temporal resolution Usually obtained by governments and represents ‘official’ figures Map & Imagery Based Data Derived from digital cartographic products and remotely sensed imagery Primarily physical in nature e.g. elevation, landcover, temperature, etc. Inexpensive to acquire, maintain, update, and provide Variable spatial and temporal resolution Obtained by various institutions and not necessarily an ‘official’ measure

Potential contributions of remotely sensed imagery 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 Form Strengths Describe Complex Spatial Patterns and Processes Weaknesses Depend on subjective classification Difficult to measure for a given city Not Very General Prospects of Image Based Models Derived from Easy to make Measurements that Can be obtained objectively to produce general Models of Urban Form Challenges Describe Complex Spatial Patterns and Processes

Classical Models of Urban Structure Examples: 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 For Ln(area) vs. Ln(pop) for world cities Scatterplot of All Cities/Urban Clusters of the World w/ Known Populations Scatterplot of All Cities/Urban Clusters of the World w/ Known Populations All Cities (N= 1,404): Ln(pop) =.850* Ln(Area) R 2 = 0.68 High Income Cities (N=471): Ln(pop) = 1.065*Ln(Area) R 2 = 0.77 Medium Income Cities (N=575): Ln(pop) = 1.011*Ln(Area) R 2 = 0.78 Low Income Cities (N=358): Ln(pop) = 0.989*Ln(Area) R 2 = 0.80 Venezuelan Cities (N=15):Venezuelan Cities (N=15): Ln(pop) = 1.164*Ln(pop) R 2 = 0.84

A Nighttime Satellite Image of Denver What Urban Form and/or Structure can be seen in this single band gray-scale image?

Using Nighttime Satellite imagery to estimate intra-urban population density Allocate aggregate estimate of total city population to pixels within urban cluster (utilizing existing urban theory)Allocate aggregate estimate of total city population to pixels within urban cluster (utilizing existing urban theory) Use linearly proportional relationship between light intensity and population densityUse linearly proportional relationship between light intensity and population density (could be improved with non-linear models and/or additional bands of remotely sensed data) (could be improved with non-linear models and/or additional bands of remotely sensed data) Compare to residence and employment based measures of population densityCompare to residence and employment based measures of population density

Formal & Graphical Representation of the Model

Actual, Modeled, and Smoothed Representations of Minneapolis

Some Results….

What do the Errors look like?

‘ Ambient’ or Temporally 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 km 2 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/km 2 )

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/Conclusions Nighttime Satellite imagery from DMSP OLS can be used to: 1) Estimate the population of urban agglomerations around the world 1) Estimate the population of urban agglomerations around the world 2) Estimate intra-urban temporally averaged measures of population density 2) Estimate intra-urban temporally averaged measures of population density This example only scratches the surface of possibilities For example: How well could a model perform if it included 3-D building volume from DEMs, ‘Greenness’, and Transportation Infrastructure? Global Spatial Data Infrastructure and United Nations Geographic Database development efforts should benefit from this potential