Using Landsat Imagery and Census Data for Urban Population Density Modeling in Port-au-Prince, Haiti

Joseph, M. M.; Wang, Lei; Wang, Fahui

doi:10.2747/1548-1603.49.2.228

Cited by 27 publications

(26 citation statements)

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“…This study defines greenness as the vegetation fraction based on the VegetationImperviousness-Soil (VIS) model from the Landsat Enhanced Thematic Mapper Plus (ETMþ) image (Joseph, Wang, & Wang, 2012).…”

Section: Defining Physical Environment Factorsmentioning

confidence: 99%

GIS-based assessment of urban environmental quality in Port-au-Prince, Haiti

Joseph

Wang

2014

Habitat International

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Section: Defining Physical Environment Factorsmentioning

confidence: 99%

GIS-based assessment of urban environmental quality in Port-au-Prince, Haiti

Joseph

Wang

2014

Habitat International

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“…As a result of the development in the sensor technologies and image classification algorithms, different approaches of population estimation using remotely sensed satellite data have been applied (Hostert, 2007). Spatial distribution of population has been estimated from coarse-spatial-resolution data (Bagan & Yamagata, 2015;Bhaduri et al, 2007;Dobson, Bright, Coleman, & Bhaduri, 2003;Lo & Welch, 1977;Tobler, 1969), medium-spatial-resolution data (Alahmadi, Atkinson, & Martin, 2013Dong, Ramesh, & Nepali, 2010;Joseph, Wang, & Wang, 2012;Li & Lu, 2016;Lo, 2008;Patel et al, 2015), fine-spatial-resolution data (Alahmadi, Atkinson, & Martin, 2015;Lu, Im, Quackenbush, & Halligan, 2010;Silvan-Cardenas et al, 2010;Ural, Hussain, & Shan, 2011) and Light Detection And Ranging (Dong et al, 2010;Silvan-Cardenas et al, 2010;Tomás, Fonseca, Almeida, Leonardi, & Pereira, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Lu et al (2006) concluded that the residential impervious surface model was the best model to estimate the population density. Joseph et al (2012) applied a multivariate regression model using three urban remote-sensing variables: mean of impervious surface, mean of vegetation and standard deviation of vegetation.…”

Section: Introductionmentioning

confidence: 99%

Spatial non-stationarity analysis to estimate dwelling units in Riyadh, Saudi Arabia

Alahmadi

2018

European Journal of Remote Sensing

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Dwelling unit (population) data at finer zones are important for different applications. The census data are not available every year and published at coarse zones. While the remotely sensed data have addressed this limitation, analysis of spatial non-stationarity in the relationship between dwelling and detailed urban remote-sensing covariates for dwelling estimates is almost novel. Riyadh, Saudi Arabia, is chosen as a case study. The remote-sensing variables have been derived from QuickBird data using an object-based image analysis. To analyse the spatial non-stationarity, ordinary least squares (OLS) and geographically weighted regression (GWR) approaches are applied. The OLS models suggest that utilizing three-residential classes provides more accurate results than built area and residential built area. The GWR models are more accurate than the OLS model. This research proves that the GWR approach cannot completely handle the spatial non-stationarity problem without efficient explanatory variables. For example, the GWR model that uses three-residential classes is the best model to estimate dwelling compared with the GWR model that uses residential built area. This article confirms that the application of the OLS approach with efficient explanatory variables can successfully account the spatial non-stationarity and the results are relatively comparable with the GWR model.

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“…Further experiments dealing with innovative methods have included the use of interpolation (Xie 2006;Reibel and Agrawal 2007) or refined dasymetric maps (Langford 2006;Briggs et al 2007;Azar et al 2010;Kim and Yao 2010), and more advanced techniques, such as residual kriging (Liu, Kyriakidis, and Goodchild 2008), geographically weighted regression (GWR; Lo 2008), sub-pixel analysis (Lu, Weng, and Li 2006;Wu and Murray 2007;Azar et al 2010), GWR coupled with sub-pixel analysis (Joseph, Wang, and Wang 2012), geographical information system (GIS) data for spatially refined analyses (Qiu, Woller, and Briggs 2003;Reibel 2007;Deng, Wu, and Wang 2010), and cellular automata (Zhan, Silva, and Santillana 2010), to mention only a few examples. More recent works have also started regularly to introduce very-high-resolution (VHR) images for assessing population spatial distribution and density.…”

Section: Introductionmentioning

confidence: 99%

Urban population estimation based on residential buildings volume using IKONOS-2 images and lidar data

Tomás

Fonseca

Almeida

et al. 2015

International Journal of Remote Sensing

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This paper presents a methodological approach to estimation of urban population using the volume of single houses and high-rise residential buildings obtained from an IKONOS-2 ortho-image and light detection and raging (lidar) data. The estimates are directly executed at the finest scale level (i.e. the housing unit) and are then aggregated at the census district level for further validation with the aid of official data supplied by the local and federal governments. Unlike prior works, this study executes a thorough assessment of horizontal and elevation accuracy for the IKONOS-2 and lidar data used in the experiment. The methodological stages are threefold: the construction of a 3D city model, the accuracy assessment of the ortho-image and digital surface models (DSMs), and the quantification of urban population. The validation was accomplished by means of linear regression and associated hypothesis tests, considering the estimated population and the reference data. The results showed that there was a systematic underestimation of population. On average, the conducted estimates assessed 31 fewer inhabitants per district and lie 1.35% below the expected values given by the reference data. In spite of the observed underestimation, the estimated population can be regarded as equivalent to the population provided by the reference data at a 1% level of significance. ARTICLE HISTORY

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Using Landsat Imagery and Census Data for Urban Population Density Modeling in Port-au-Prince, Haiti

Cited by 27 publications

References 50 publications

GIS-based assessment of urban environmental quality in Port-au-Prince, Haiti

GIS-based assessment of urban environmental quality in Port-au-Prince, Haiti

Spatial non-stationarity analysis to estimate dwelling units in Riyadh, Saudi Arabia

Urban population estimation based on residential buildings volume using IKONOS-2 images and lidar data

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