Gottfried Tappeiner

Modelling vegetation patterns using natural and anthropogenic influence factors: preliminary experience with a GIS based model applied to an Alpine area

A modelling approach based on discriminant analysis and the GIS (geographical information system) is presented with the aim of identifying the influence of environmental parameters and human impact on the Alpine cultural landscape. For testing the model we used the vegetation patterns of an area of 5 km2 between 1300 and 2300 m above sea level in the Central Alps (Passeier Valley, Italy). The overall prediction for the highly heterogeneous vegetation (a total of 21 different vegetation units characterized by high biodiversity) is in the order of 78% (pixel to pixel correspondence), the probability of correct classification by chance is only 21%. Only two vegetation units were totally misclassified, whereas six were classified correctly at almost 100%, a further seven at 60–80% and the remaining six at 20–50%. The proportion of unexplained variability in the research area may to some degree be attributed to the snow distribution pattern and to the use of a more detailed scale of spatial grazing habits. The most important lack of information, however, concerns land use history. Simulation analysis shows that human impact and elevation exert a major influence on the vegetation, whereas hydrological parameters and radiation do not greatly affect biodiversity and vegetation patterns in the study area.

GIS-based modelling of spatial pattern of snow cover duration in an alpine area

Snow cover duration patterns of an alpine hillslope (approximately 2 km 2 ) were derived using daily terrestrial photographic remote sensing. We have developed a suite of quantitative models in order to investigate the relative controls of topographic factors, the degree of non-linearity, the effect of seasonal differences and a possible influence of further systematic influences. We have only used data that are relatively easily available to ensure applicability beyond the site. Elevation, slope angle and aspect, and potential irradiation for the winter period can be directly derived from a digital elevation model. The number of days with temperature 0°C was included using a regression with elevation. Furthermore, a coarse vegetation classification (forested/not forested) was included. To estimate the necessary degree of non-linearity for such modelling without forming exact assumption about the functional interrelations, results from a linear regression analysis are compared with an artificial neural network (ANN). The results show that a R 2 of 71% can be achieved by means of a linear approach, whereas a non-linear approach (ANN) leads to 81%. An indirect estimation demonstrates that a further 6% can be explained without considering data on annually specific weather conditions. The analysis of the residuals shows a clear spatial pattern. This indicates that additional spatial variables may allow a further improvement of the model.

Classifiers vs. input variables—The drivers in image classification for land cover mapping

The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification.

Performance contest between MLE and GMM for huge spatial autoregressive models

Abstract When using maximum likelihood estimation for spatial models, a well known problem is the computation of the logarithm of the determinant of the Jacobian, especially for problems with a huge number of observation units. In the recent literature there are various promising approaches to account for these numerical difficulties, relying on alternative decompositions or approximations. Recently, a general method of moments approach for estimating these models was developed. We compare all these different approaches with respect to their root mean-squared errors of the estimates and investigate the size and power of hypotheses tests with respect to the spatial correlation and the regression parameters.

A general qualitative technique for the comparison of economic structures

Quantitative methods normally do not fully exhaust nor sufficiently show the structural information contained in sets of data. Therefore the authors introduce a new general technique for analyzing structures which is based on qualitative methods. The proposed technique can be divided into several steps. First, the given structural information is prepared with the help of graph theoretical tools. Then the obtained results are condensed in several steps to manageable vectors (key values), the most important step being the construction of graph theoretical decay patterns. These strongly condensed data allow the use of statistical methods and offer the chance to compare even a large number of structures simultaneously. After having introduced the necessary theoretical tools, the authors then present the results of some empirical investigations which showed the usefulness of the proposed technique. Compared with the corresponding quantitative methods, the empirical investigations also showed that our technique is relatively robust with respect to short-comings in the primary material. This result opens up opportunities for obtaining more actual yet costsaving structural information.

Social Capital and Collective Memory: A Complex Relationship

The purpose of these analyses is to investigate collective memory, i.e. the shared historical experiences of a community, as driving force for contemporary social capital. Three societal characteristics are considered proxies for collective memory: the current institutional framework as indicator for present common experiences; the cultural attitudes as proxy for long‐term developments; and severe shocks in the history of the regions. The primary aim is thus to understand whether collective memory permits identification of not only the effects of recent (i.e. institutional) or distant (i.e. cultural) on‐going experiences, but also of the impact of such relevant shocks. For this purpose, a comprehensive case study is conducted within a cross‐border research area with special historical development, where it is possible to discriminate between these three indicators of collective memory. The findings suggest a significant impact of collective memory on social capital endowment. Particularly striking shocks are sustained in the collective memory of a community, influencing its behavior even long after the incident occurred. As a consequence, especially the levels of social trust and networking of the affected population are significantly influenced, such that the community develops protective measures in order to secure its norms, values and traditions. As a result, the social capital of a population is heavily influenced by events that occurred outside living memory.

Social Capital Formation and Intra Familial Correlation: A Social Panel Perspective

Social capital is widely regarded to constitute an important indicator for the economic performance of a society. This paper analyzes the impact of various socio-demographic characteristics on social capital. Proxy variables for social capital are obtained from a comprehensive principal components analysis exercise using survey data from the British Household Panel Survey (BHPS). The BHPS provides information on social and economic change at the individual and household levels in Britain and the UK with an annual survey of ca. 10,000 individuals from ca. 5,000 households. Based on the 13th wave of this database, we investigate the impact of exogenous qualities, individually acquired characteristics, and of the social environment using a spatial auto-regression framework. The results show that the formation of social capital can be modeled to a very high degree of statistical accuracy. The structural effect from the households contributes substantially to the social capital level of each household member. Thus, the social capital formation can be based equally on individual measures (such as education) and social contagion processes.

Statistical aspects of multilayer perceptrons under data limitations

Based on three case studies, the impact of sample size and sample randomness on the predictive accuracy of multilayer perceptrons (MLP) is investigated. The MLP prove to be useful for classification problems. Although they are dependent on the sample size and the non-linearity of the underlying problem, they achieve predictions superior to the classical methods. A so-called saturation curve describes the dependency of the network performance on the sample size. This function enables the user to evaluate the achieved network performance and the usefulness of additional data. For reliable and generalizable results, the calculation of prediction intervals for the network is essential. It is demonstrated that the network leads to narrower confidence intervals of the performance measures in comparison to classical methods even for small sample sizes. The experiments show the validity of the law, for even relatively small sample sizes, that the standard error of the hit ratio decreases by one over the square root of the sample size. Therefore, the suggestion is to estimate the standard error for a given sample size by randomly drawing smaller sample sizes, and then rescaling the standard error accordingly.

An Extension of Input–Output Employment Models

The usual input–output models of the labour market include some weaknesses which restrict their use with regard to analyses of concrete employment problems. The most important of these weaknesses are: (1) the assumption of an absolutely homogeneous labour force; (2) sectoral final demand is the only starting point for economic policy making; (3) if several types of labour are included, a non-substitutability between these types of labour is assumed; (4) there exists a one-sided direction of analysis from the economic policy parameters to the employment variables. In this article we propose some classes of models which try to overcome the above cited weaknesses. Furthermore these models allow us to take into account different useful economic constraints. To show the applicability of our models at the end of the article some empirical results for a small region are introduced.

Multivariate Stochastic Volatility via Wishart Processes: A Comment

This comment refers to an error in the methodology for estimating the parameters of the model developed by Philipov and Glickman for modeling multivariate stochastic volatility via Wishart processes. For estimation they used Bayesian techniques. The derived expressions for the full conditionals of the model parameters as well as the expression for the acceptance ratio of the covariance matrix are erroneous. In this erratum all necessary formulae are given to guarantee an appropriate implementation and application of the model.