Change detection is amongst the most effective critical examination methods used in remote sensing technology. In this research, new methods are proposed for building and vegetation change detection using only LiDAR data without using any other remotely sensed data. Two LiDAR datasets from 2009 and 2013 will be used in this research. These datasets are provided by the City of Surrey. A Parcel map which shows parcels in the study area will be also used in this research because the objective of this research is detecting changes based on parcels. Different methods are applied to detect changes in buildings and vegetation respectively. Three attributes of object –slope, building volume, and building height are derived and used in this study. Changes in buildings are not only detected but also categorized based on their attributes. In addition, vegetation change detection is performed based on parcels. The output shows parcels with a change of vegetation. Accuracy assessment is done by using measures of completeness, correctness, and quality of extracted regions. Accuracy assessments suggest that building change detection is performed with better results.

Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census block level. The maximum total estimation accuracy that can be attained by the models is 94.21%. Accuracy assessments suggest that the GWR models outperformed linear regression models due to their better handling of spatial heterogeneity. Models generated from building volume and area gave better results. The models have lower accuracy in both densely populated census blocks and sparsely populated census blocks, which could be partly attributed to the lower accuracy of the LiDAR data used.

The purpose of this study was to determine the effects of attendance at a senior center on the well being and quality of life of grandparents that were rearing grandchildren. Using convenience sampling, grandparents (N=130) who were rearing grandchildren were given a self administered demographic data survey along with an attendance at a senior center questionnaire, the Quality of Life Scale, the Well Being Scale by Liang, the UCLA Loneliness Scale, the Caregiver Burden Scale, and the Role Satisfaction Scale. An initial MANOVA (F 7, 69 = 2.72, p < .01) suggesting that senior center attendance affect the measures as a set was conducted and then a series of one way ANOVAs were carried out to test the hypothesis that attending a senior center has an effect on the dependent variables: well being, quality of life, role satisfaction, caregiver burden, loneliness, current health, and heath one year ago. Subsequently, a hierarchical regression analysis was conducted to find out whether frequency and quality of attendance of a senior center predicted quality of life, caregiver burden, well being, loneliness, and role satisfaction, controlling for the demographic data. The results of the MANOVA showed that the dependent variables: quality of life, caregiver burden, …

Buruli ulcer (BU) is an environmental bacterium caused by Mycobacterium ulcerans. Modes of transmission and hosts of the disease remain unknown. The purposes of this study are to explore the environmental factors that are possibly explain the spatial distribution of BU, to predict BU cases by using the environmental factors, and to investigate the impact of land use and land cover change on the BU distribution. The study area covers the southwest portion of Ghana, 74 districts in 6 regions. The results show that the highest endemic areas occur in the center and expand to the southern portion of the study area. Statistically, the incidence rates of BU are positively correlated to the percentage of forest cover and inversely correlated to the percentages of grassland, soil, and urban areas in the study area. That is, forest is the most important environmental risk factor in this study. Model from zero-inflated Poisson regression is used in this paper to explain the impact of each land use and land cover type on the spatial distribution of BU. The results confirm that the changes of land use and land cover affect the spatial distribution of BU in the study area.