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This paper is focused on mapping and monitoring of unemployment hot spots using registers of local authorities in the Ostrava City (Czech Republic) to identify socially excluded localities at the micro-scale. The register of unemployed persons and the population register provide data for quantification of specific indicators of a local labour situation. A share of the registered unemployed in reference to residents in productive age can substitute the rate of unemployment due to the high correlation of both indicators. The mapping of unemployment hot spots has been based on data from 2007, 2009, 2010 and 2011 using kernel density estimation. Various settings of bandwidths have been tested to identify socially excluded localities in the city in hopes of identifying the most accurate way to visualise the pattern. These localities have been identified in two expert studies and the most significant sign is considered to be increased unemployment. Appropriate settings of kernel density estimations of the unemployment indicator enable to identify the majority of experts' localised spots. The hot spots have been delimited by an isoline with a suitable boundary value obtained from an optimisation technique based on a coefficient of areal correspondence and an indicator of spatial extension. This method of isolines is recommended for mapping and monitoring of the development of localities in time. [ABSTRACT FROM AUTHOR]