The presented paper deals with the regionalization of the electoral support of the Czech Pirate Party (Pirates) in regional elections using methods and techniques of spatial data analysis. The aim is to answer the question whether the territorial distribution of Pirate electoral support allows this party to participate in governance at the regional level and thus influence the form of regional policy in individual regions. The results of the analysis show that the spatial distribution of Pirates’ electoral support in regional elections differed quite significantly not only from the pattern found in the elections to the Chamber of Deputies of the Czech Parliament and elections to the European Parliament, but also between individual regional elections. This suggests the current lack of anchorage of Pirates’ electoral support in regional politics, but at the same time, it may have its origins in the second-order character of regional elections and the candidacy of many local and regional entities in regional elections. On the other hand, the results of the regional elections in 2020 meant that the Pirates received seats in all regional councils, but especially in nine of the thirteen regions they joined the regional government (similarly to two years earlier when they joined government of capital city of Prague), gaining the opportunity to influence, with regard to its priorities, the form of regional governance in most Czech regions.
In the past, the social and economic impacts of industrial revolutions have been clearly identified. The current Fourth Industrial Revolution (Industry 4.0) is characterized by robotization, digitization, and automation. This will transform the production processes, but also the services or financial markets. Specific groups of people and activities may be replaced by new information technologies. Changes represent an extreme risk of economic instability and social change. The authors described available published sources and selected a group of indicators related to Industry 4.0. The indicators were divided into five groups and summarized by negative or positive impact. The indicators were analyzed by precedence analysis. Extremes in the geographical dislocation of factor values were found. Furthermore, spatial dependencies in the distribution of these extremes were found by calculating multiple (long) precedencies. European countries were classified according to individual groups of indicators. The results were compared with the real values of the indicators. The indicated extremes and their distribution will allow to predict changes in the behavior of the population given by changes in the socio-economic environment. The behavior of the population can be described by the behavior of autonomous systems on selected infrastructure. The paper presents research related to the creation of a multiagent model for the prediction of spatial changes in population distribution induced by Industry 4.0.
The replacement of specialized, highly sophisticated human work with systems using artificial intelligence is one of the features of the 4th Industrial Revolution known as Industry 4.0. The upcoming innovations and transformations of production processes, the digitized of information and the automation will bring about changes at the social level. These are mainly changes in the company’s behavior. There is a significant risk for specific groups of people, especially those that can be replaced by new information technologies. In the context of the current sixth wave of globalization, new forms of migration of people and capital can be expected related to transnational nature of productive activities, the global form of communications and information. In the context of socio-economic structures, an individual is confronted with a set of factors. The aim of an individual’s behavior is usually to change his localization with respect to the values of the preferred socioeconomic variables, such as availability of work, safety, air quality, etc. On the other hand, the position of an individual will influence the values of socio-economic variables. Behavior can be simulated using multiagent systems. The paper informs about the first phase of the research. Local maxima of factor values were identified.
Anotace Key words regional development, competitiveness, municipality with extended powers JEL classification: R23 ÚvodČlánek byl tvořen v rámci projektu, kdy jedním z dílčích cílů je zmapování podnikatelského prostředí Karvinského regionu. Na základě komparace podnikatelských aktivit realizovaných na úrovni obcí s rozšířenou působností (dále již jen obcí ORP) na území Moravskoslezského kraje (dále jen MSK) bude vymezeno postavení Karvinska z pohledu malého a středního podnikání (dále jen MSP). Zhodnocení vychází z dat Českého statistického úřadu, regionálních strategických dokumentů, Rozboru udržitelného rozvoje regionu, aj. Na základě analýzy bude zkoumána korelace mezi MSP a vzdělanostní strukturou. Přestože existuje řada zpracovaných studií a probíhá řada výzkumů na toto téma, kontinuální výzkum umožní vytvořit přístupy k řešení nedostatečného rozvoje, kdy např. podle Tvrdoně (Tvrdoň, 2013) MSK a tedy i jeho jednotlivé územní celky v posledních letech
The research question is if an increase in pandemics corresponds with significant changes in mobility (supported by the public stay-at-home orders and willing decrease of movements) by the spheres of economic activities (parks (leisure time spending), grocery stores, workplaces, pharmacies, transportation stations, retail, recreation, and home) in the Czech Republic. The additional research question is if this pattern correlates with a high decrease in salaries and employment. This paper aims to answer these research questions. This research applies the graphical analysis and fixed-effects regression methods for high-frequency data for answering these questions. The main result is that an increase in the number of infected people significantly decreases human mobility and increases their visits to pharmacies and staying at homes. At the same time, the government support measures can be effective, because there is no huge drop in salaries and employment in the Czech Regions. This pattern contradicts the expectations based on the US patterns. The output of the regression analysis is that 2-5 thousand new infections a day can paralyze mobility in the entire region.
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