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This paper presents the results of a quantitative study in the Czech Republic to understand travellers' attitudes towards and motivation to use different means of transport. Two Czech cities, Olomouc and Ostrava, are compared from the point of view of factors influencing spatial and temporal patterns and citizen's selection of transport mode and transport behaviour (range and daily movements of the population, perception of the quality of public transport etc.). The data for the analysis were obtained from the survey with more than 500 respondents in each city. Spatial and temporal behaviour represented by the pattern of the movement in Olomouc and Ostrava city was identified by statistical and visual analytics methods. Based on a case study of two cities of a different size, we conclude that the size and shape of the city centre (spatial structure) influence not only the distances travelled but also the average speed of public transportation (slower for a smaller city). Distances and choice of transport mode also vary with the density of urban areas but can also be influenced by the spatial structure of the city. The walking distance to a public transport stop does not influence the most frequently used mode of transport. Temporal patterns in both cities are very similar and are not dependent on city size or city spatial structure. The spatial patterns of the car and public transport flows are similar in both cities. Different patterns can be observed for walking and shopping routes.

The fear of crime is an established research topic, not only in sociology, environmental psychology and criminology, but also in GIScience. Using spatial analysis to analyse patterns, explore hotspots and determine the significance of respective surveys is one reason for the increase in popularity of such research topics for geographers, cartographers and spatial data scientists. This paper presents the results of an intensive online map-based questionnaire with 1551 respondents from the city of Ostrava, Czech Republic. The respondents marked 3792 points associated with the fear of crime over a ten week period. The perception data were compared with recorded crime data acquired from police department records for the years 2015–2018. This paper explores the spatial autocorrelation from perceived hotspots and from recorded crime hotspots. Our findings fit into the literature confirming results about the locations that most frequently attract fear, but there is still room for more investigations regarding the links between recorded crime and the fear of crime.

We compare intra-urban localization patterns of advertising and IT companies in three large Czech cities. The main aim of our analysis is an empirically-based contribution to the question to what extent do knowledge bases affect the spatial distribution of various knowledge-intensive business industries. The central research question is: To what extent is the localization of these two industries influenced by different modes of innovation/knowledge bases (symbolic vs. synthetic) and to what extent by contextual factors, such as urban size, morphology, position in the urban hierarchy and economic profile of the given city. We found that the urban contexts shape the localization patterns of advertising and IT companies more than differences in knowledge bases-both industries cluster primarily in the inner cities and urban cores. Formation of more suburban IT "scientific neighborhoods" is limited. significantly to the economic transformation of formerly industrial cities (considering IT, Brno is now the most specialized city in Czechia and Ostrava is the third) [13][14][15].Advertising is a representative of KIBS with a purely symbolic knowledge base. These companies should require tight geographical proximity to their customers, suppliers or rivals [16,17], and cluster in urban cores and dense inner cities, close to the headquarters of large corporations and public institutions. By contrast, services with a predominantly synthetic knowledge base (such as IT) rely primarily on knowledge sourcing and innovation collaboration with partners inside value chains, which are not necessarily local [18,19]. IT companies should, thus, exhibit more dispersed spatial patterns (However, Zook [20] documented clustering of internet companies in the inner city, close to the financial institutions, Spencer [12] also noted this possible location pattern), although they may cluster at the neighborhood level as well [12].Méndez-Ortega and Arauzo-Carod [21] stated that " . . . in addition to industry-specific characteristics that determine some external requirements by these firms (such as accessibility to skilled labour or specialized IT suppliers), there are some city-specific characteristics (e.g., urban policies, spatial distribution of economic activity) that also matter, and which shape the location decisions taken by these firms." We aim to analyze exactly these city-specific characteristics and their effects on the spatial distribution of firms. Our central research question is: To what extent are localization patterns of selected KIBS influenced by their distinct modes of innovation (knowledge bases) and to what extent by the city-specific characteristics?Funding: This research wJan Žas funded by the Ministry of Education, Youth and Sports through a research grant "SMART technologies to improve the quality of life in cities and regions" (no.

One of the ways of improving the attractiveness of public transport is to bring it closer to its potential users. A long walking distance from a stop is often one of the critical factors limiting its more frequent and extensive use. Studies dealing with the accessibility of transport networks usually work only with the closest stop. This article analyses the actual walking distance from the place of residence to the preferred stop. The survey used a questionnaire method and was conducted in two cities in the Czech Republic—Ostrava and Olomouc. Based on the results of the study, the average walking distance was assessed and the impact of demographic characteristics (gender, age, education, number of members in the household, economic activity, the presence of a child in the household, and car ownership), transport behavior (preferred mode of transportation, car convenience and opinions on public transport), and urban characteristics (prevailing housing type) on the walking distance were analyzed. The main findings prove a significant impact on walking distance by a number of these factors, but the preferred use of a car for commuting or unemployment does not significantly affect walking distance.