A Comparison of Public and Private School Teachers\u27 Job Satisfaction When Controlling for Policy Perspectives, Individual, and Workplace Characteristics

The purpose of this study is to contribute to the empirical literature concerning the factors that contribute to the job satisfaction levels of public and private school teachers. Furthermore, an emphasis is placed on how policy, procedures, and workplace characteristics affect levels of job satisfaction. This study focused on other contributing factors of job satisfaction such as personal attributes, human capital, occupational characteristics, and school characteristics. Inferential statistics concluded that there was a statistically significant difference between the job satisfaction levels of public and private school teachers

Review of British Sociological Association Conference, University of Sussex, April 1-4, 1978

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Spatiotemporal dimensionality and Time-Space characterization of multitemporal imagery

Spatiotemporal dimensionality refers to the continuum of spatial and temporal patterns in an image time series. Time-Space characterization refers to a way of representing this continuum of patterns as combinations of spatial and temporal constituents — with a minimum of assumptions about the forms of the patterns. Patterns can be related to processes through modeling. By combining characterization and modeling, two complementary analytical tools can be used together so that each resolves a key limitation of the other. This study describes a straightforward extension of Principal Component Analysis and Spectral Mixture Analysis to multitemporal imagery and illustrates how characterization of the dimensionality and eigenstructure of the data can inform modeling of the processes represented in the data. The relationships among spatiotemporal processes can be represented as combinations of temporal endmembers in a temporal feature space where the dimensions represent different components of the temporal patterns present in the data. The topology of the feature space and the processes being modeled together inform the selection of temporal endmembers and the structure of the model chosen to represent the processes. The dimensionality revealed by the characterization can also provide a partial solution to the problem of endmember variability. The characterization and modeling process is illustrated with the vegetation phenology of the Ganges–Brahmaputra delta using a MODIS vegetation index time series. Additional applications and limitations of Time-Space characterization and mixture modeling are further illustrated by comparing the eigenstructures and temporal feature spaces of Landsat vegetation fraction and DMSP-OLS night light time series

Mapping Decadal Change in Anthropogenic Night Light

AbstractThe Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) sensors have imaged emitted light from Earth's surface since the 1970's. Temporal overlap in the missions of 5 OLS sensors allows for intercalibration of the annual composites over the past 19 years [1]. The resulting image time series captures a spatiotemporal signature of human settlement growth and evolution. We use temporal Empirical Orthogonal Function (EOF) analysis to characterize and quantify patterns of temporal change in stable night light brightness and spatial extent since 1992. Temporal EOF analysis provides a statistical basis for representing spatially abundant temporal patterns in the image time series as uncorrelated vectors of brightness as a function of time from 1992 to 2009. The variance partition of the eigenvalue spectrum combined with temporal structure of the EOFs provides a basis for distinguishing between deterministic temporal trends and stochastic year to year variance. The low order EOFs and Principal Components (PC) space together discriminate both earlier (1990s) and later (2000s) increases and decreases in brightness. Inverse transformation of these low order dimensions reduces stochastic variance sufficiently so that tri-temporal composites depict deterministic decadal trends. The most pronounced changes occur in Asia. Throughout Asia a variety of different patterns of brightness increase are visible in tri-temporal brightness composites – as well as some conspicuous areas of apparently decreasing background luminance and, in many places, intermittent light suggesting development of infrastructure rather than persistently lighted development. Vicarious validation using higher resolution imagery reveals multiple phases of urban growth in several cities, numerous instances of highway construction, extensive terracing networks and hydroelectric dam construction [3]. Lights also allow us to quantify the size distribution and connectedness of different intensities of development. Over a wide range of brightnesses, size distributions of spatially contiguous lighted area are well-fit by power laws with exponents near -1 as predicted by Zipf's Law. However, the larger lighted segments are much larger than individual cities; they correspond to vast spatial networks of contiguous development.[2

Correlation scales of digital elevation models in developed coastal environments

Accuracy of digital elevation models (DEMs) often depends on how features of different spatial scales are represented. Scale dependence is particularly important in low gradient coastal environments where small vertical errors can affect large areas and where representation of fine scale topographic features can influence how DEMs are used for modeling inundation. It is commonly observed that different types of DEMs represent larger, coarse-scale topographic features similarly but differ in how they represent smaller, finer-scale features. The spatial-scale dependence of DEM accuracy can be quantified in terms of the correlation scale (λC); the spatial wavelength above which models agree with spectral coherency > 0.5 and below which they differ. We compare cross spectral analyses of the GDEM2 and SRTM global DEMs with 14,572 LiDAR-derived elevations along transects in diverse coastal environments of New York City. Both global DEMs have positive bias relative to LiDAR ground elevations, but bias (μ) and uncertainty (σ) of GDEM2 (μ: 8.1 m; σ: 7.6 m) are significantly greater than those of SRTM (μ: 1.9 m; σ: 3.6 m). Cross-spectral coherency between GDEM2 and the LiDAR DEM begins to roll-off at scales of λ < ~ 3 km, while coherency between SRTM and the LiDAR DEM begins to roll-off at scales of λ < ~ 1 km. The correlation scale below which coherency with LiDAR attains a signal to noise ratio of 1 is ~ 1 km for GDEM2 and ~ 0.5 km for SRTM; closely matching the divergence scales where the surface roughness of the land cover exceeds the roughness of the underlying terrain

Observations of cyclone-induced storm surge in coastal Bangladesh

Water level measurements from 15 tide gauges in the coastal zone of Bangladesh are analyzed in conjunction with cyclone tracks and wind speed data for 54 cyclones between 1977 and 2010. Storm surge magnitude is inferred from residual water levels computed by subtracting modeled astronomical tides from observed water levels at each station. Observed residual water levels are generally smaller than reported storm surge levels for cyclones where both are available, and many cyclones produce no obvious residual at all. Both maximum and minimum residual water levels are higher for west-landing cyclones producing onshore winds and generally diminish for cyclones making landfall on the Bangladesh coast or eastward producing offshore winds. Water levels observed during cyclones are generally more strongly influenced by tidal phase and amplitude than by storm surge alone. In only 7 of the 15 stations does the highest plausible observed water level coincide with a cyclone. While cyclone-coincident residual water level maxima occur at a wide range of tidal phases, very few coincide with high spring tides. Comparisons of cyclone-related casualties with maximum wind speed, hour of landfall, population density and residual water level (inferred storm surge) show no significant correlations for any single characteristic. Cyclones with high casualties are often extreme in one or more of these characteristics but there appears to be no single extreme characteristic shared by all high casualty cyclones.Comment: 23 pages, 7 figure