Title page for ETD etd-06062012-112314

Type of Document Dissertation
Author Joseph, Myrtho
Author's Email Address mjose13@lsu.edu
URN etd-06062012-112314
Title Urban Population Density and Environmental Quality in Port-au-Prince, Haiti: A Geo-Statistical Analysis
Degree Doctor of Philosophy (Ph.D.)
Department Geography & Anthropology
Advisory Committee
Advisor Name Title
Wang, Fahui Committee Chair
Lam, Nina Committee Member
Leitner, Michael Committee Member
Wang, Lei Committee Member
Conrad, Max Z Dean's Representative
  • Population Density
  • Environmental Quality
  • Port-au-Prince
  • Urban density models
  • Urban structure
  • monocentric models
  • polycentric models
  • population estimation models
  • V-I-S
  • Geographically Weighted Regression
  • Vegetation-Impervious surface-Soil
  • GWR
  • Port-au-Prince Urban structure
  • Haiti
Date of Defense 2012-05-16
Availability restricted
This dissertation revolves around three issues on the urban area of Port-au-Prince, Haiti: the population distribution pattern, its estimation from remote sensing images, and its relationship with environmental quality. It follows a three-paper format. Paper 1 examines the population density pattern by the monocentric and polycentric models, based on the 2003 census data. The regression results show a poor fitting power of monocentric functions, and improved but less than satisfactory R2 by polycentric functions. A five-sector conceptual model is proposed to capture the urban structure shaped by the absence or lack of institutional enforcement of land use regulations and urban planning. Paper 2 proposes a population estimation model based on Landsat ETM+ images that are widely available. The subpixel vegetation-impervious surface-soil (VIS) fractions derived from the Landsat multispectral bands (the mean value of houses fraction image, the mean value of vegetation and the standard deviation of vegetation fraction image) are used as predictors for urban population density. The research indicates that the geographically weighted regression (GWR) model, which accounts for spatial non-stationarity, performs much better than its Ordinary Least Square counterpart. Paper 3 uses multiple factors to assess and map the urban environmental quality (UEQ). In addition to parameters typically considered in previous studies, this study includes natural hazards and other parameters unique to Port-au-Prince. Crowdedness, waste, lack of vegetation, presence of slums and water body pollutions are considered as the most critical factors (negatively) affecting the quality of the environment in Port-au-Prince. All are exacerbated by population pressure on the resources, i.e., population density. The scores for corresponding factors are integrated together by weights extracted from a panel of local experts. The overall UEQ results are validated by field surveys. Each paper discusses important implications of major findings for public policy and planning
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