Metody agregace a adjustace geodat pro sledování prostorové segregace na příkladu Ostravy

Abstract

The aim of this work is to evaluate the possibility of using different methods of aggregation and adjustation of geodata to monitor spatial segregation in urban environment. Several methods were tested e.g. mapping the relative risk, aggregation of data within variousadministrative unit, regular square network and aggregation using the kernel density estimation. As an area of interest the city of Ostrava was selected, which passed through significant development in the labor market during last several years. Unemployment in the second half of 90 years led to the development of socially problematic and excluded areas. It is fact, that Ostrava has special prerequisites for the development of these localities due to its specific nature, strongly influenced by industrial development. Residential areas are interspersed with a number of vacant, industrial areas and social and business blocks. This situation may facilitate the development of spatial segregation. The introduction presents the problem of spatial segregation and its relation to unemployment and a short description of the development of Ostrava in the last two decades. The next part specifies the aims of the thesis and the following chapter defines terms such as unemployment, labor market, social exclusion and especialy spatial segregation. Chapter 5 describes the socially excluded localities in Ostrava, as they were defined in expert studies. Also the data sources and unemployment indicators used to identify the excluded areas are described. The major chapter of the methodology consists of three main parts: the localization of data, adjustation of data, and data aggregation. The results of this chapter is followed by the chapter about spatial autocorrelation, which contains the theoretical basis of this issueas well as the practical impacts. Chapter 10 describes the boundaries of excluded localities based on the data obtained from the Labour Office in Ostrava. Chapter 11 is focused on the calculation of the rates of segregation at all levels of aggregation, including evaluation of the localization coefficient and the Gini coefficient. The next chapter describes the physical and social barriers around localities which enhance their seggregation. Chapter 13 is focused on monitoring the development of localities and their boundaries in time. For this monitorig the kernel density maps and selected isolines were used. Development was observed on the space, value and structural level. The aim was to assess whether the individual characteristics are changing and whether such identifacation of localities based on the kernel density estimation is stable and too variable in time. The final chapter evaluates and summarizes all results and benefits of this thesis.