- 1 Outline Thresholding: As thresholding increases intercomparisons of parameters become increasingly difficult Medium & High GDP/Capita 80 Low GDP/Capita – 40 Regression Some Results The Big Ugly Table that you cant read . Estimated in addition to actual populations, regression parameters etc. Some more results . A smaller table you might be able to read
- 2 Williams, Geoff Contributing Writer
- 3 Journal Ratings by Coker College
Outline Thresholding: As thresholding increases intercomparisons of parameters become increasingly difficult Medium & High GDP/Capita 80 Low GDP/Capita – 40 Regression Some Results The Big Ugly Table that you cant read . Estimated in addition to actual populations, regression parameters etc. Some more results . A smaller table you might be able to read
Williams, Geoff, Contributing Writer has reference to this Academic Journal, PHwiki organized this Journal An Overview of Methods as long as Estimating Urban Populations Using Nighttime Satellite Imagery Paul Sutton firstname.lastname@example.org Department of Geography University of Denver May, 2000 Outline Known Population Data how good/bad is it Data: brief description of DMSP OLS imagery Estimating the Population of cities/urban clusters Estimating intra-urban population density Temporally-Averaged Population Density Questions of spatial in addition to temporal scale Summary/Conclusions How Good are the numbers in addition to who cares When did the world population reach 6 billion Absolute population of Cities Mexico City U.S. Census Bureau 28 million United Nations 16 million Sao Paulo – U.S Census Bureau 25 million – United Nations 16 million Shanghai – U.S. Census Bureau 8 million – United Nations 15 million Istanbul – Nat. Geog. Atlas (1999) 2,938,000 [3,258,000] – Nat. Geog. Atlas (1995) 6,620,200 [7,309,200] Cited PRB in addition to U.S. Census Bureau Percent of Population Urban Models described here produce national population estimates very sensitive to these numbers. Errors inflate with increasing rural fraction of population Spatial Accuracy The 1994 Guatemala census included hundreds of populated places never previously enumerated. Nevertheless, the spatial characteristics of these data were rudimentary. The maps supplied to enumerators in some frontier districts were generally h in addition to drawn in addition to based on anecdotal in as long as mation. As a consequence, we have better in as long as mation than ever be as long as e regarding the size in addition to character of the Guatemalan population, we still lack a clear sense of where these people are.
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Nighttime Satellite Imagery (DMSP OLS) Percent Observation This hyper-temporal imagery used to measure urban areal extent Aggregate Estimation of Total City Populations Method I: Conterminous U.S. Imagery: DMSP OLS Percent Observation Truth: Wall to wall grid of Pop. Den. From 1990 Census Block Groups Method: Cluster adjacent pixels & Count them to measure Areal Extent of Cluster, overlay to obtain corresponding Population Method II: All Nations of the World Imagery: DMSP OLS Percent Observation Truth: Point Dataset of over 3000 cities with known population Method: Threshold, Cluster, & Count pixels as long as Area, Geo-reference & Overlay to obtain nationally specific slope & intercept parameters as long as the Ln(Area) vs. Ln(Popualtion) relationship from known cities, apply to all clusters Method 1: Proof of Concept with U.S. Data (Note: This also worked well with Mexico Data)
Method II: Going Global Use 1,383 Known Urban Populations to Estimate Populations of the 22,920 urban clusters found in DMSP OLS imagery Thresholding: Trade-off between too much conurbation in addition to ability to see small settlements Geo-Location: Provide each identified urban cluster with Country ID in addition to related national Stats Regression: Using Ln(Area) vs. Ln(Population) relationship to identify nationally specific slope in addition to intercept parameters as long as every nation Estimation: Estimate population of all 22,920 cluster with parameters in addition to use % urban statistic to get total national population estimate Thresholding: As thresholding increases intercomparisons of parameters become increasingly difficult Medium & High GDP/Capita 80 Low GDP/Capita – 40 Regression Scatterplot of All Cities/Urban Clusters of the World w/ Known Populations All Cities (N= 1,404): Ln(pop) = .850 Ln(Area) + 9.107 R2 = 0.68 High Income Cities (N=471): Ln(pop) = 1.065Ln(Area) + 7.064 R2 = 0.77 Medium Income Cities (N=575): Ln(pop) = 1.011Ln(Area) + 8.174 R2 = 0.78 Low Income Cities (N=358): Ln(pop) = 0.989Ln(Area) + 8.889 R2 = 0.80 Venezuelan Cities (N=15): Ln(pop) = 1.164Ln(pop) + 6.475 R2 = 0.84
Example of estimating nationally specific regression parameters as long as Venezuela Some Results The Big Ugly Table that you cant read . Estimated in addition to actual populations, regression parameters etc. Some more results . A smaller table you might be able to read
How did it go with the Biggest Cities Disaggregate or Intra-Urban estimates of Population Density Allocate aggregate estimate of total city population to pixels within urban cluster Use linearly proportional relationship between light intensity in addition to population density Compare to residence in addition to employment based measures of population density Radiance Calibrated DMSP OLS images of Denver aka Low-Gain or Light Intensity This imagery used to model intra-urban population density
Formal & Graphical Representation of the Model Actual, Modeled, in addition to Smoothed Representations of Minneapolis Some Results .
What do the Errors look like Temporally Averaged Population Density Census data is typically a residence based measure of population density People, work, shop, go to school, in addition to entertain & transport themselves outside of the home Is a temporally averaged measure of population density useful (e.g. as long as a given 1 km2 area with 600 people in it as long as 8 hrs, 300 in in the next 8 hours, in addition to 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 Scale Is a population density dataset at a 1 km2 spatial resolution useful as long as Vulnerability studies L in addition to -use L in addition to -cover change studies Environmental Modeling What kind of temporal resolution of population density representations are useful in addition to needed What measures other than simple density are needed in addition to what means are there to acquire them When are errors of population numbers in addition to /or spatial location unacceptably large Whats Going on in 1 km2 Summary/Conclusions Nighttime Satellite imagery from DMSP OLS can be used to: 1) Estimate the population of urban agglomerations around the world 2) Estimate intra-urban temporally averaged measures of population density Continuing research will shed light on improved means of delineating areal extent of cities using the radiance calibrated datasets, better explanations of the national variations in the slope in addition to intercept parameters, in addition to a greater underst in addition to ing of the spatio-temporal nature of the population density estimates produced by these methods Future research should be in as long as med by the potential users of these datasets as to the spatial in addition to temporal scale required, in addition to the numerical in addition to spatial accuracy required There is potential as long as inclusion of these methods into the suite of tools used as long as conducting national censuses throughout the world
Williams, Geoff Contributing Writer
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Journal Ratings by Coker College
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