The diploma thesis is focused on the application of the Geographically Weighted Regression (GWR) in forestry models. This is a prospective method for coping with spatially heterogeneous data. In forestry, this method has been used previously in small areas with good results, but in this diploma thesis it is applied to a bigger area in the Region of Murcia, Spain. Main goal of the thesis is to evaluate GWR for developing of large scale height-diameter model based on data of National Forest Inventory of Spain. Final model is compared with local height-diameter model on validation plots. The obtained results are very different according to the level of input data and GWR calibration type. The best result is obtained with individual tree data and with fixed kernel calibration. In order to improve quality of GWR calibration several suggestion were made, such as change weighted functions. GWR method is highly promising because in the case of need a particular model for some large area, there is possible to make sufficiently precise model at any point of the area of interest without need of any additional measurements in particular forest stand.

The topic of this thesis is to solve spatial heterogenity in forestry models by means of utilizing linear mixed-effects models (LMM) and geographically weighted regression (GWR) to model a height-diameter curve. Both of these methods were previously tested, and they have a high potential to reduce the minimal necessary amount of data needed, and at the same time, increase precision. The data come from VŠLP Křtiny, LÚ Borky, a complex of forests utilized for educational purposes by Mendel’s university in Brno. We choosed beech as the model species. We split the data into training and validation sets for fitting, and consequent prediction assessment. Resulting models were compared with OLS fitted global model. Local OLS models were unreliable, as only a very few measured trees were available for each plot. Results were different for GWR and LMM. GWR models failed at prediction, but had good results on training plots, especially considering the reduction of autocorrelation of model residuals. LMM provided the best results for both training and validation plots

This master thesis deals with the analysis of housing prices in Prague. The main goal is to identify and explain the factors which have an influence on the prices of flats at the macro and micro level. Two spatial statistic methods, namely multiple linear regressions and geographically weighted regressions (GWR), are used in the first part of the thesis, which deals with the prices in Prague in general. The influence on the values of flats in Prague basic settlement units caused by several factors such as the distance from the Old Town Square, age of dwellings, the presence of migrants or air pollution was investigated using these two methods. The price map of the association of real estate agencies, the Czech Statistical Office and the Prague Institute of Planning and Development provided the data used in the presented research. Price profiles from the centre of Prague to the suburbs in various directions were also created and analyzed. Factors with an influence on housing prices at the micro level in a case study of the Prague cadastral territory of Modřany are described in the second part of the thesis. The analysis of new developer projects and older flats in panel houses investigates the influence on the housing prices caused by factors such as noise, physical condition of apartments and the quality of...

This thesis deals with the modelling of applying techniques of ordinary least squares method and geographically weighted regression for all administrative divisions of the Czech Republic using ArcGIS software. In general this thesis introduces a GWR method which partially solves the problems associated with the analysis of spatial data. The research compares benefits of using geographically weighted regression with spatial data compared with linear regression in thesis conclusion.

The thesis introduces several methods of real estate price modelling suitable either for prediction of the housing prices or for exploring the relationships between the price and its determinants. We compared the conventional linear regression approach to the tree-based methods of machine learning. The comparison analysis on the dataset of 28 019 apartments in Prague suggests that regression trees (especially the Random forest) yield a higher accuracy in the price prediction. Another objective was to examine the effects of location attributes (especially its accessibility and environmental quality) on the prices of nearby apartments. To address the spatial interactions in the geographical data, we employed three spatially conscious models to achieve more reliable results. The local analysis performed with the geographically weighted regression confirmed the presence of spatial heterogeneity and described the price effects relative to the location. In some areas, an increase of 100 meters in distance from the nearest metro station and the nearest park are associated with a decrease in the apartment prices by 644 CZK/m2 and 916 CZK/m2 , respectively. These findings are especially important for the apartments near the stations of the new metro line, which is currently in construction.

The competitiveness of public transport on example of the Pardubice Region Abstract This diploma thesis is devoted to the competitiveness of public transport in the municipalities of the Pardubice Region. Competitiveness is understood mainly in terms of the price difference between travel time and cost of public and individual car transport, and also in terms of the real demand in the municipalities, thus the share of commuting by public transport. Other parameters of mode choice, that is understood as the main indicator of competitiveness, is the number of public transport lines and automobilization. The main objective of this work is to determine how public transportation depends on the other transport characteristics of municipalities. To select significant variables, multiple linear regression analysis was used. After that, geographically weighted regression was applied in order to explain the share of commuting from municipalities. Most data originate in public databases (The Register of vehicles of Department of Transport, population census, digital geographic databases ArcČR and CEDA) and web portals (OREDO, IDOS), part of the data was obtained within questionnaire survey in selected municipalities. An expected negative relationship between the degree of automobilization and the number of public...

Impact of demographic changes on municipal technical infrastructure in the Czech Republic - case study of waste management Abstract The aim of this study is to quantify the impact of demographic development on the current household production of municipal waste in the Czech Republic and at the same time to compare their influence with the impact of other factors. Based on a literature review and statistical data available for the Czech Republic, a set of 22 explanatory variables concerning structure of population by age, gender, highest education and employment, as well as the size of households, unemployment, purchasing power, population density and basic characteristics of housing was identified in order to explain the production of municipal solid waste, mixed municipal waste and separately collected waste components (glass and plastics). It has turned out that demographic variables, especially the average household size, gender, age, level of education or sector of employment have a statistically significant but rather weak impact on the production of these waste streams. Given that the detailed analysis of the variables indicated that there is a significant spatial variability of these characteristics and that they exhibit spatial non- stationarity, the next step was to analyze the relationship of the...