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The implementation of smart projects can contribute to solving the current development problems of municipalities and cities of varied sizes. Although the concept of smart development is a vague term in the literature, in practice it refers to projects based on the use of modern technologies, to improve the quality of life considering economic, social, and environmental dimensions. However, not all local governments in the Czech Republic implement smart projects, even though the smart city concept is currently receiving considerable attention from national governments and from the European Union. This paper characterizes the perception of barriers to the implementation of smart projects from the perspective of fifteen representatives and officials of local governments located in the Brno Metropolitan Area in the Czech Republic. The research was conducted using semi-structured interviews with these fifteen territorial actors. It was found that the barriers to the implementation of smart projects are related to internal factors in the municipalities, such as the lack of interest of municipal leaders and officials or potential technical complications accompanying the implementation of projects. However, external factors such as the Czech government’s vague grasp of the smart cities concept or cyber threats also play a role. Perceived barriers were categorized according to their type and schematized.

Visibility analyses in geographical information systems (GIS) are used to quantify the visible and non-visible parts of the landscape. This study aims to evaluate the changes in viewshed outputs after the unmanned aerial vehicle (UAV) data refinement for the near surroundings of the observer. This research accounts for the influence of data age, mainly due to vegetation growth, and the impact of the input data quality on the final study output. The raw data that were used for UAV refinement were publicly available data (one dataset at the global level, two datasets at the national level of the Czech Republic) and airborne laser scanning (ALS) data. Three localities were selected in order to compare the viewshed evaluation that was processed over ten raster elevation models. The comparison was performed using the kappa coefficient, which considers not only the matching visible pixels, but also false visibility and invisibility. Over the span of five years (2013–2018), the visible area at two sites has decreased by more than 7%. Although with some variations (kappa coefficient varied from 0.02 to 0.92), all the study sites showed a decreasing trend of the visible area with the data aging, which was caused by the vegetation growth or landscape changes. The results showed the effect of data aging in forested areas on the resulting visibility within a couple of years. At all the sites, major changes in visibility were observed after three years (2021 vs. 2018) due to vegetation growth, forest management, and natural phenomena, such as windfalls. This study concludes that UAV data will increase the accuracy of visibility analysis, even when using freely available low-resolution data, and may also help us to update obsolete input data. The results of this research can be used to refine visibility analysis when current digital surface model (DSM) data is not available.

Background Health inequities exist within and between societies at different hierarchical levels. Despite overall improvements in health status in European Union countries, disparities persist among socially, economically, and societally disadvantaged individuals. This study aims to develop a holistic model of health determinants, examining the complex relationship between various determinants of health inequalities and their association with health conditions. Methods Health inequalities and conditions were assessed at the territorial level of Local Administrative Units (LAU1) in the Czech Republic. A dataset of 57 indicators was created, categorized into seven determinants of health and one health condition category. The necessary data were obtained from publicly available databases. Comparisons were made between 2001–2003 and 2016–2019. Various methods were employed, including composite indicator creation, correlation analysis, the Wilcox Test, aggregate index calculation, cluster analysis, and data visualization using the LISA method. Results The correlation matrix revealed strong relationships between health inequality categories in both periods. The most significant associations were observed between Economic status and social protection and Education in the first period. However, dependencies weakened in the later period, approaching values of approximately 0.50. The Wilcox Test confirmed variations in determinant values over time, except for three specific determinants. Data visualization identified persistently adverse or worsening health inequalities in specific LAU1, focusing on categories such as Economic status, Education, Demographic situation, Environmental status, Individual living status, and Road safety and crime. The health condition indices showed no significant change over time, while the aggregate index of health inequalities improved with widened differences. Conclusion Spatial inequalities in health persist in the Czech Republic, influenced by economic, social, demographic, and environmental factors, as well as local healthcare accessibility. Both inner and outer peripheries exhibit poor health outcomes, challenging the assumption that urban areas fare better. The combination of poverty and vulnerabilities exacerbates these inequalities. Despite the low rates of social exclusion and poverty, regional health inequalities persist in the long term. Effectively addressing health inequalities requires interdisciplinary collaboration and evidence-based policy interventions. Efforts should focus on creating supportive social and physical environments, strengthening the healthcare system, and fostering cooperation with non-medical disciplines.

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