Рассмотрены возможности оценки основных социально-экономических показателей регионов России на основе анализа спутниковых данных о ночной освещённости DMSP/OLS.Использовался архив за период с 1993 по 2013 г. и статистические данные о количестве городского и сельского населения, а также валовом региональном продукте субъектов Российской Федерации.Спутниковые данные предварительно были сглажены на основе специально разработанного ранее подхода.Установлено, что спутниковые данные DMSP/OLS о ночной освещённости коррелируют с показателями, описывающими социально-экономическое состояние регионов России, и могут быть использованы в качестве средства мониторинга общего состояния регионов.Самым надёжным показателем, характеризующим региональный валовой продукт, является ночная освещённость городских территорий.Средняя для

Forecasting urban expansion models are a very powerful tool in the hands of urban planners in order to anticipate and mitigate future urbanization pressures. In this paper, a linear regression forecasting urban expansion model is implemented based on the annual composite night lights time series available from National Oceanic and Atmospheric Administration (NOAA). The product known as 'stable lights' is used in particular, after it has been corrected with a standard intercalibration process to reduce artificial year-to-year fluctuations as much as possible. Forecasting is done for ten years after the end of the time series. Because the method is spatially explicit the predicted expansion trends are relatively accurately mapped. Two metrics are used to validate the process. The first one is the year-to-year Sum of Lights (SoL) variation. The second is the year-to-year image correlation coefficient. Overall it is evident that the method is able to provide an insight on future urbanization pressures in order to be taken into account in planning. The trends are quantified in a clear spatial manner.

Urban compactness is measured for a number of medium sized European cities based on metrics available in the literature. The information used is a combination of Urban Atlas and Urban Audit data sets. The former is a source of spatial data whereas the latter of population data. These datasets that have been made recently available providing for the first time the opportunity to perform comparative analysis of urban compactness across European countries. The results provide an interesting insight of variation amongst cities in different countries. The analysis is limited however due to the quality and generalization of the datasets.

The question of how many hidden layers and how many hidden nodes should there be always comes up in any classification task of remotely sensed data using neural networks. Until today there has been no exact solution. A method of shedding some light to this question is presented in this paper. A near‐optimal solution is discovered after searching with a genetic algorithm. A novel fitness function is introduced that concurrently seeks for the most accurate and compact solution. The proposed method is thoroughly compared to many other methods currently in use, including several heuristics and pruning algorithms. The results are encouraging, indicating that it is time to shift our focus from suboptimal practices to efficient search methods, to tune the parameters of neural networks.

The two recent population censuses in Greece clearly show that during the last two decades, Greece is shifting from a traditional emigration country to an immigration one. The considerable alteration of the migration profile of Greece led to a significant amount of literature referring to the social, economic and demographic dimensions of this phenomenon. However, in all these analyses the spatial dimension of the implications of immigration on the size and the structure of the population in a lower geographical level is lacking. The immigrant population is neither uniformly nor is it even proportionally distributed in comparison to the native one. In addition, it exhibits a significantly different demographic profile than the native population. Therefore, the examination of the impact of immigration in a low spatial scale might be useful in finding structures that is impossible to observe otherwise. This work, mainly based on the exploitation of the last census micro data concerning the individuals with foreign nationality (763.000 individual records) and the use of GIS, examines the impact of immigration on the population size and the demographic profile of the Greek municipalities (1034 units). In that, we use clustering techniques for defining homogenous groups of municipalities and highlighting spatial patterns according to the nationality composition of the immigrant population, as well as according to the severity of the impact of immigrants on the size, the age and sex distribution of the censused population by examining the simultaneous impact of immigrants on total population regarding their percentage of male population, mean age, proportion of female population of reproductive age and the percentage of immigrants births in total births.

The possibilities of the combined use of neural networks and fuzzy set theory in the form of constructing a so-called fuzzy neural network (FNN) or granular neural network (GNN) [1] for predicting crop yields in the Rostov oblast and Krasnodar and Stavropol krais are examined. The results of modeling plant growth on the basis of the CGMS simulation model as well as the values of the vegetation index NDVI, calculated from the SPOT VEGETATION satellite data, are the input parameters. As a result of training the neural network, the accuracy of predicting yields is on average about 75%.

The increased synergy between neural networks (NN) and fuzzy sets has led to the introduction of granular neural networks (GNNs) that operate on granules of information, rather than information itself. The fact that processing is done on a conceptual rather than on a numerical level, combined with the representation of granules using linguistic terms, results in increased interpretability. This is the actual benefit, and not increased accuracy, gained by GNNs. The constraints used to implement the GNN are such that accuracy degradation should not be surprising. Having said that, it is well known that simple structured NNs tend to be less prone to over‐fitting the training data set, maintaining the ability to generalize and more accurately classify previously unseen data. Standard NNs are frequently found to be accurate but difficult to explain, hence they are often associated with the black box syndrome. Because in GNNs the operation is carried out at a conceptual level, the components have unambiguous meaning, revealing how classification decisions are formed. In this paper, the interpretability of GNNs is exploited using a satellite image classification problem. We examine how land use classification using both spectral and non‐spectral information is expressed in GNN terms. One further contribution of this paper is the use of specific symbolization of the network components to easily establish causality relationships.