GIS-DRIVEN ANALYSES OF REMOTELY SENSED DATA FOR QUALITY ASSESSMENT OF EXISTING LAND COVER CLASSIFICATION

Automatization of processes for revision and updating existing GIS information is essential for the modern maintenance of spatial databases. The integration of remotely sensed multi-spectral data into the process of database revision is affected here by the implementation of GIS-driven analyses. The adoption of the GIS-driven principles, provide also an accurate geographical basis for a future supervised classification of the spectral data. The goal of the present research was to define and develop an automatic quality assessment method for the Land Cover classification layer of the Israeli National GIS database. During the experiments on multi-spectral remotely sensed data, effort was carried out in attempt to define "typical " spectral ranges as statistical maximum-likelihood criteria for the classification of each of the land cover phenomenon. These ranges were envisaged to characterize each of the land cover classification groups and to provide quantitative criteria for the definition of various groups of land cover type-classes. The definition of a typical-spectral-variance was executed on the basis of visual, multi-spectral and index bands of remotely sensed data. The decision whether existing GIS classification match the new image reality was made by statistical criteria of maximum likelihood for each investigated land cover type, according to the results of each and every spectral band. The study was based on multi-spectral data of the CASI airborn

Geo-Based Statistical Models for Vulnerability Prediction of Highway Network Segments

This study describes four statistical models—Poisson; Negative Binomial; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial—which were devised in order to examine traffic accidents and estimate the best probability estimating model in terms of future risk assessment at interurban road sections. The study was conducted on four sets of fixed-length sections of the road network: 500, 750, 1000, and 1500 m. The contribution of transportation and spatial parameters as predictors of road accident rates was evaluated for all four data sets separately. In addition, the Empirical Bayes method was applied. This method uses historical accidents information, allowing regression to the mean phenomenon so as to improve model results. The study was performed using Geographic Information System (GIS) software. Other analyses, such as statistical analyses combined with spatial parameters, interactions, and examination of other geographical areas, were also performed. The results showed that the short road sections data sets of 500 and 750 m yielded the most stable models. This allows focused treatment on short sections of the road network as a way to save resources (enforcement; education and information; finance) and potentially gain maximum benefit at minimum investment. It was found that the significant parameters affecting accident rates are: curvature of the road section; the region and traffic volume. An interaction between the region and traffic volume was also found

Exploratory Spatial Data Analysis of Congenital Malformations (CM) in Israel, 2000–2006

Congenital Malformations (CM) impose a heavy burden on families and society. Identification of spatial patterns of CM is useful for understanding the epidemiology of this public health issue. In Israel, about 1,000,000 births and 25,000 CM cases at 37 groups were geocoded during 2000–2006. These were geo-analyzed using global-Moran’s-I statistics. Eight groups demonstrated geospatial heterogeneity and were further analyzed at both the census tract (Local Indicator of Spatial Association (LISA) and hot spot analyses) and street levels (spatial scan statistics with two population threshold sizes). The positional definition of results is further discussed in relevance to possible exposure to teratogenic sources in the region. Limitations of data and methods used are presented as well

Green spaces and adverse pregnancy outcomes

Objective: The objective of this study was to evaluate the associations between proximity to green spaces and surrounding greenness and pregnancy outcomes, such as birth weight, low birth weight (LBW), very LBW (VLBW), gestational age, preterm deliveries (PTD) and very PTD (VPTD). Methods: This study was based on 39 132 singleton live births from a registry birth cohort in Tel Aviv, Israel, during 2000–2006. Surrounding greenness was defined as the average of satellite-based Normalised Difference Vegetation Index (NDVI) in 250 m buffers and proximity to major green spaces was defined as residence within a buffer of 300 m from boundaries of a major green space (5000 m2), based on data constructed from OpenStreetMap. Linear regression (for birth weight and gestational age) and logistic regressions models (for LBW, VLBW, PTD and VPTD) were used with adjustment for relevant covariates. Results: An increase in 1 interquartile range greenness was associated with a statistically significant increase in birth weight (19.2 g 95% CI 13.3 to 25.1) and decreased risk of LBW (OR 0.84, 95% CI 0.78 to 0.90). Results for VLBW were in the same direction but were not statistically significant. In general, no associations were found for gestational age, PTD and VPTD. The findings were consistent with different buffer and green space sizes and stronger associations were observed among those of lower socioeconomic status. Conclusions: This study confirms the results of a few previous studies demonstrating an association between maternal proximity to green spaces and birth weight. Further investigation is needed into the associations with VLBW and VPTD, which has never been studied before