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.

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.

The article gives a spotlight on a three universities (Moscow State University of Geodesy and Cartography, Siberian State Academy of Geodesy, Wuhan University) and ISPRS’ cooperation in organizing and holding summer student seminars for undergraduate, master, and PhD students of the universities specializing in photogrammetry, remote sensing, GIS, aerial and space cartography. Principles of topics of the seminar selection, lecture courses delivering and practical training as well as international joint team work on projects and their defence are described. The description of the basic stages of 3S implementation, and the goals of this sort of the international event is given.

There are basically two levels of calibrations and validation of digitally acquired spectral and other information via sensors carried on space-borne or airborne platforms. The basic level is carried out by the data producers executed by comparison made of results taken over test fields for example. The second level, more a part of a supervised classification effort are carried by the data users and value added spatial information users or providers to edge users. The latter is quite typical for supervised classification protocols. This is either for establishing libraries of spectral signatures for each relevant class-type or for ad-hoc classification where no previous information or specific knowledge wee kept. Such methods indicate and support even strongly the need of the basic Cal/Val step of the sensors made by the original data providers. The paper is reviewing the method of database-driven concept that allows for automatic recognition of detected features within the digital spatial 2-D (yet) realm to its identification within the digital 2.5D spatial vector information within existing large Big-data national core spatial data bases to be updated. These Large data bases are Big enough to operate the resourceful Munchhausen method of self-pulling information out of the huge abandon of data resources.