This paper describes the formation of the natural repose angle of non-cohesive bulk materials. The repose angle of particulates α is the angle between the tangent plane to the surface of the slope and the solid base. The paper presents a thermodynamic theory for the formation of the most energy-efficient natural slope α≈ 30 ^∘ that can be found very often both in nature and industry. The theoretical foundation is based on Janssen theory of the statistical distribution of vertical and horizontal stress and adds its own consideration about dissipative work during the movement of material as a slope is being formed. The presented model is expanded to include an experimental part describing four methods of creation of a natural repose angle on three sand samples. The experiments performed demonstrated the validity of the submitted theory and the thermodynamic model with certain deviations derived from the essence of the experiments performed. These experiments explain a frequent occurrence of natural slopes with a repose angle of around 30 ^∘ .

Public transport faced various challenges during the COVID-19 period due to a large ebb in passengers during the pandemic waves, COVID-related restrictions, em-ployee sickness, and economic pressure to balance transport supply and demand. The study assesses the impact of the timetable changes on the accessibility between 2019 and 2021 in the hinterlands of two Czech cities – Hradec Králové and Ostrava. The research question is if there are considerable changes in public transport accessibility during this period which were influenced by the pandemic. Municipal accessibility is determined by the share of inaccessible municipalities, average travel time, population weighted average travel time, and average number of transfers under conditions suitable for seniors. Optimal trips to all municipalities are established with a local OpenTripPlanner server using all public urban and regional timetables including peak and off-peak hours, workdays, and non-working days. Unlike Ostrava, the pattern of accessibility of the Hradec Králové hinterland is influenced by the railway networks and bus transport corridors. Mean accessibility in 2021 slightly improved in the Hradec Králové region and slightly worsened in the Os-trava region. Some municipalities, however, showed grave decline. The most sensitive in-dicator is the share of inaccessible municipalities adapted for seniors' needs. The study confirms the importance of choosing a time of departure/arrival for the results of accessi-bility assessment. The most significant differences in the accessibility of municipalities are visible only for one of the selected departure times. Only a few municipalities show differences for both departure times, indicating variable effects on accessibility depending on the time of day-situation. The results did not confirm the anticipated general deterioration of public transport accessibility in the hinterlands of regional capitals during the pandemic period. URL: https://www.gcass.science.upjs.sk/

The paper investigates if and where registered crime and the fear of crime intersect in the four locations. This information is important to reduce crime and increase the sense of security of the population. In the past, research was applied to small parts of a city or a whole city, but not in as much detail as this paper. Our research is detailed, and at the same time is applied to four entire locations - three Czech cities (Ostrava, Olomouc, and Kolín) and one Prague district (Prague 12). We placed registered crime and fear of crime on one map to show locations where people feel afraid, where crime happens, and where both events occur together. We drew these phenomena using Moran’s I in a bivariate map. The outputs will be applied by Municipal governments and police departments.

For landslide surface monitoring, the Global Navigation Satellite System (GNSS) has been widely used in landslides due to its real-time, all-weather, high-precision, simple operation and a high degree of automation. However, these data are not intuitive and visual data will be more interesting for users without professional knowledge. At the same time, the conventional data representation method is in the form of curves or tables for three-dimensional data of landslide surface deformation collected by GNSS. To make the data more intuitive, clear and valuable, it is easier for people to understand the process of landslide deformation and finally realize the visualization of decision. Here we show that a polar coordinate system rather than a Cartesian coordinate system is adopted to visualize the horizontal data, which not only shows the horizontal deformation of the landslide, but also easily knows the direction of the landslide deformation. The vertical data is in the form of slices rather than curves, which not only shows the deformation of the landslide surface, but also shows the process of the vertical change of the landslide in terms of the time series. Single GNSS monitoring station is composed of a GNSS receiver, GNSS antenna with random, a solar power unit, and a network transmission module. The system can be powered by the solar energy system, which can realize 24-hour unmanned operation, 7 days a week. The system can receive satellite signals in real-time process and analyze deformation data, then it automatically broadcast early warning information. Our results demonstrate that it is a better choice that the thematic map of Geographic Information System (GIS) is a technical system for collecting, storing, managing, calculating, analyzing and displaying geographic data supported by computer hardware and software systems. Here we show that the multi-dimensional properties of deformation monitoring and multiple expressions of the attribute values are displayed synchronously in order to obtain more useful information from the visual graphics.

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.

Abstract 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.

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.

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On the basis of individual crime data, spatial analysis of crime development in Ostrava was conducted followed by analysis of potential urban factors influencing the discovered development. The analysis was preceded by a complex semi-automatic process of harmonisation and data geocoding. A combination of automated and expert techniques were utilised using the description of objects, addresses and even selected situations, continuously improving the location quality. With the most precisely located crime incidents, the spatial distribution of crimes was analysed utilising the method of kernel density estimation. Data from 2009 to 2011 were processed and the development and changes of crime distribution over this period were analysed. In Ostrava, 86 located crime hot spots areas were identified and divided into six categories according to annual changes in the areas. Potential urban factors influencing discovered deve-lopment were considered (i.e. number of flats, use of buildings, slot machines, bars). Mainly the number of flats and use of buildings proved to have a significant influence on the prevailing development of crime hot spots.

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 article gives a new approach to the assessment of objects in terms of various criteria which by its nature belong to the issue of multi-criteria decision making and analysis. The proposed variant of multi-criteria decision-making is based on a comparison of the real considered object model that is created according to user’s requirements with the reference value. The geographic object means a real object in this case the object is stored in digital geo-database in the geographic information system (GIS). The reference value represents optimal geographic object which is the most suitable for user’s purposes. The comparison of the values of individual criteria is based on the theory of tolerance and metric spaces. Supplementary GIS-based application to calculate the weights of the criteria, which have served to comparison was used. The proposed procedure for the evaluation of various criteria has been validated on a pilot project “SMART Regions” in the city Brno district of Nový Lískovec neighbourhood in the Czech Republic. Around the city district known by its typical of prefabricated blocks of flats it is necessary to compare the different options for renewal urban housing development. Urbanization city prefabricated housing estates using GIS opportunities will be ready to quick respond to the call of various changes in the field of energy sustainability. Therefore, it is important to utilize the available environmental resources for energy sustainability. D. Bartoněk (&) Faculty of Civil Engineering, Institute of Geodesy, Brno University of Technology, Veveří 330/95, 602 00 Brno, Czech Republic e-mail: bartonek.d@fce.vutbr.cz D. Bartoněk European Polytechnic Institute, Osvobození 899, 686 04 Kunovice, Czech Republic S. Dermeková J. Škurla Faculty of Civil Engineering, AdMaS Center, Brno University of Technology, Purkyňova 139, 602 00 Brno, Czech Republic e-mail: dermekova.s@fce.vutbr.cz J. Škurla e-mail: skurla.j@fce.vutbr.cz © Springer International Publishing AG 2018 I. Ivan et al. (eds.), Dynamics in GIscience, Lecture Notes in Geoinformation and Cartography, DOI 10.1007/978-3-319-61297-3_1 1

Drawing on the concept of knowledge neighbourhoods we aim to explain spatial distribution of firms in creative industries in two medium-sized Czech cities - Brno and Ostrava. Hubs of creative industries are identified by the kernel density, firm-level data refer to the year 2010. Despite significant differences in the morphology (monocentric Brno, polycentric Ostrava), creative hubs in both cities are excessively concentrated to the urban cores and adjacent inner cities. Nevertheless, firms in creative industries exhibit higher rate of spatial concentration into the historic cores of Brno and Ostrava, which is probably a result of their smallerpopulation/economic size and lower density.

Public transport conditions are analysed using simulated commuting to important employers and recording data about significant features of all simulated trips. Two forms of overall public transport accessibility evaluation are compared—rule based and multivariate based classifications. Rule based classification was developed in several variants integrating two or four indicators, average and non-aggregated values. More valuable results were obtained using extended set of indicators for non-aggregated trips. The multivariate classification utilizes a novel approach to K-means cluster analysis using decile values. The comparison of both classifications shows a primary role of expert based classification. K-means cluster analysis based on deciles or median values are suitable for establishing more common typology but not for a local accessibility evaluation.

The authors examine the patterns and determinants of spatial distribution of selected knowledge-intensive business services in Czechia, a small post-communist country whose capital city holds a strong position and where a significant share of manufacturing and business R&D employment is located in non-metropolitan regions. The central research question asks to what extent the localization of knowledge-intensive business services can be explained by the position of cities in urban hierarchy. Correspondingly, the authors analyse the role of local factors such as regional economic specialization, regional firm size distribution or concentration of (high-tech) manufacturing or business R&D centres. The authors specifically concentrate on the role of large industrial centres in non-metropolitan regions and on the hypothesis of a spatial mismatch between knowledge-intensive business services and manufacturing, dispersed and overrepresented in smaller cities. Empirical results clearly confirmed the former hypothesis. Although the evidence on the latter hypothesis is more complex, it does not hold for the most of knowledge-intensive business services in Czechia.

Sparse data sets may be considered as a one of the issues of big data generating extremely uneven frequency distribution. To deal with this issue, special methods must be applied. The study is focused on the Czech graffiti crimes and selected factors (property offences, buildings, flats, garages, educational facilities, and gambling clubs) which may influence the graffiti crimes occurrence. For regression analysis decision trees with the exhaustive CHAID growing method were applied. Grid models with 100, 500 and 1000 m cells were tested. The model of 1 km grid was evaluated as the best. The most influencing factors are the occurrence of secondary schools and gambling devices enhanced for several territorial units. The results of the decision tree for 1 km grid are validated using alternative models of data aggregation -aggregation around the randomly selected building and randomly distributed points.

Among various socio-pathological events, the crime is perceived as the most serious issue. Understanding of factors influencing the intensity, structure and dynamic of crime is essential for appropriate targeting of preventive efforts. Due to the complexity of crime, it is necessary to integrate various sources of data which are usually complicated by differences in spatial referencing, scale, temporal referencing, and data semantic. The study demonstrates data integration based on multidimensional modelling using Online Analytical Processing (OLAP) for analysis. Data from selected sources (e.g. population, crime, dwelling) were aggregated into 1 km grid with an appropriate temporal interval, transformed into relative indicators describing the local demographic and environmental features, and intensities of selected types of crime. The analysis includes evaluation of pairwise correlation and regression analysis (using backward method). The results in the Ostrava pilot area show the highest explanation of variability in the case of thefts (R2 0.60; important descriptors are several other types of crime but also higher share of retired population and unemployed with basic education), burglaries (R2 0.54, share of young population, long-term unemployed), violent crime and property offences (both R2 0.5). To the opposite, the overall index of crime can be explained by the set of independent factors from only 11%. It supports the idea of the selective influence of explored factors which differently affect particular types of crime. It was confirmed that the spatial distribution of selected types of crime intensity depends on the demographic features. The findings emphasize the role of social factors in crime preventive efforts and the necessity to utilize operative registers of public administration for data integration and monitoring of the current state of social conditions.