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The active involvement of citizens in decision-making processes via geoparticipatory spatial tools is becoming a popular research field among geographers, GIScientists, environmental psychologists, political scientists and many others. This paper presents the idea of the Index of geoparticipation – an indicator-based index divided into three dimensions (communication, participation, transparency) that helps to evaluate the state of geoparticipation among Czech municipalities. It describes the current state of geoparticipation at the municipality level in Czechia. It aims to fill the research gap in finding which components of geoparticipation at the municipality level are being used, and how their use is affected by the size of municipalities and their membership of Local Agenda 21 networks. The paper builds upon an extensive dataset collected by the authors from all Czech municipalities (n=6 258) and presents various “shades” of geoparticipation in Czechia. Highlights for public administration, management and planning: • The highest average values of the Index of geoparticipation were obtained from the regions’ capital cities, followed by statutory cities, municipalities with extended powers, municipalities which are members of the Healthy Cities of the Czech Republic (HCCZ) network and cities. • HCCZ member municipalities use geoparticipatory tools significantly more than the rest of the Czech municipalities • The communication dimension is the most widely implemented pillar of the Index of geoparticipation • There is no significant relationship between social exclusion and geoparticipation at the nationwide level

The article is focused on a detailed micro-study describing changes in the behaviour of the authors in three months before and during the COVID-19 pandemic. The study is based on data from Google Location Service. Despite the fact it evaluates only three people and the study cannot be sufficiently representative, it is a unique example of possible data processing at such a level of accuracy. The most significant changes in the behaviour of authors before and during the COVID-19 quarantine are described and interpreted in detail. Another purpose of the article is to point out the possibilities of analytical processing of Google Location while being aware of personal data protection issues. The authors recognize that by visualizing the real motion data, one partially discloses their privacy, but one considers it very valuable to show how detailed data Google collects about the population and how such data can be used effectively.

Due to the current situation, and preventive measures taken to tackle COVID-19, it is crucial to keep society well-informed. Besides media and official news, that often include tabular data, it has also become a new standard for information sources to incorporate a map application or geovisualization. This paper offers a comprehensive and systematic overview describing the most prominent and useful map applications and map visualizations. News outlets should place the same importance on data analysis and interpretation as they place on data visualization. This paper emphasizes the role of geospatial data and analysis during the COVID-19 pandemic and aims to provide insights into the topic in order to better understand the consequences caused by the disease. Specifically, the paper deals with the COVID-19 Community Mobility Reports dataset, offering unique information about changes in human activity due to the pandemic. We show how this dataset can be utilized in terms of geovisual analytics and clustering in order to reveal the spatial pattern of such changes in human behavior.

In this data description, we introduce a unique (geo)dataset with publicly available information about the municipalities focused on (geo)participatory aspects of local administration. The dataset comprises 6258 Czech municipalities linked with their respective administrative boundaries. In total, 55 attributes were prepared for each municipality. We also describe the process of data collection, processing, verification, and publication as open data. The uniqueness of the dataset is that such a complex dataset regarding geographical coverage with a high level of detail (municipalities) has never been collected in Czechia before. Besides, it could be applied in various research agendas in public participation and local administration and used thematically using selected indicators from various participation domains. The dataset is available freely in the Esri geodatabase, geospatial services using API (REST, GeoJSON), and other common non-spatial formats (MS Excel and CSV).

The main goal of this contribution is to evaluate the development of CO2 emissions and selected economic indicators of EU28 countries in the period from 2005 to 2015, and to capture geographical pattern and spatial distribution of countries emitting pollution. This will be performed within the context of the EU Emissions Trading System (ETS), which has represented a major scheme in Europe to cope with CO2 emissions since 2005. The actual situation in the field of the EU ETS is described and key scientific studies focusing on the EU ETS are presented. Based on a broad set of indicators we examine and evaluate possible geographical pattern in the development of selected indicators within the EU and provide detailed spatial analysis of economic and environmental data of EU28 countries, with the use of (geo)visual analysis of spatial data and spatial statistics (grouping analysis). The preliminary results of the (geo)visual analysis show that CO2 emissions within EU countries were decreasing in the selected period 2005–2015, with some exceptions (e.g. Iceland and Latvia). As the development of CO2 emissions in all EU countries is not similar, the other economic and environmental indicators were included (e.g. GDP, Investments) into the analysis in order to reveal a common (geographical) pattern and explain the current situation. Based on grouping/cluster analysis, it is possible to form territorial groups of EU states with similar development, which are almost perfectly in the line with current EU member states strategies of CO2 emissions trade. The current auction markets are well in tune with geographical and economic characteristics of particular EU countries. Results of grouping analysis of all indicators in 2015 using six K-nearest neighbours underline current separate auction markets for Germany, the United Kingdom and Poland.It indicates that the system of emission auctions has logical background and the markets represent natural platforms for emission trading, corresponding to both economic and spatial characteristics of particular countries/polluters. Presented approach thus brings valuable information for policymakers both on the national and international level for the next phases of EU ETS scheme planning.

The differences in welfare amongst European countries are especially evident in border regions, and this affects cross-border cooperation and relationships. Due to the historical development of Central and Eastern European countries over the last century, the affected countries are unique “laboratories” for geographical research. This study assesses disparities in socio-economic indicators representing socio-economic phenomena in the Czech-Polish border region, through the analysis of cross-border (spatial) continuity, using quantitative methods (multivariate statistics and socio-economic profiling), GIS analysis and cartographic visualisation. It is demonstrated how such a combination of methods is useful for the comparison and evaluation of the complex socio-economic situations in neighbouring countries. This research project identifies the most suitable common indicators for a proper evaluation of cross-border (spatial) continuity, and it reveals the spatial patterns as reflected by a cluster analysis. The greatest cross-border (spatial) continuity is apparent in the easternmost part of the borderlands, while significant differences on both sides of the border are evident in the very central part of the areas under study. The paper also describes methodological aspects of the research in order to provide a quantitative approach to borderland studies.

There have been many changes in the spatial composition and formal delimitation of administrative boundaries of Czech municipalities over the past 30 years. Many municipalities have changed their official status; they separated into ones that were more independent or were merged with existing ones, or formally redrew their boundaries due to advances in mapping technology. Such changes have made it almost impossible to analyze and visualize the temporal development of selected socioeconomic indicators, in order to deliver spatially coherent and time-comparable results. In this data description, we present an evolution of a unique (geo) dataset comprising of the administrative borders of the Czech municipalities. The uniqueness lies in time and topologically justified spatial data resulting in a common division of the administrative units at the LAU2 level, valid from 1995 to 2019. Besides the topologically correct spatial representations of municipalities in Czechia, we also provide correspondence tables for each year in the mentioned period, which allows joining tabular statistics to spatial data. The dataset is available as a base layer for further temporal and spatial analyses and visualization of various socioeconomic statistical data.

There are several reasons for environmental taxation implementation. Besides its environmental impact, the main reason for such taxation is its fiscal impact, particularly in generating revenues of public budgets. The main goal of this paper is to observe possible spatial patterns in fiscal impacts of environmental taxation in the EU countries, and to depict the groups of countries with the same (or similar) fiscal impact of these instruments on public budget revenues, including environmental and economic characteristics. Two methods of cluster analysis are used, Ward linkage and K-nearest neighbors (spatial) cluster analysis to observe potential geographical links or implication of fiscal impact. The study is performed for the years 2008 and 2017. Based on the results, we can say that in the year 2008, the EU countries were divided into &ldquo, the west&rdquo, and &ldquo, the east&rdquo, with some exceptions. The western countries were characterized by high environmental tax revenues, the eastern countries by low environmental tax revenues. For 2017, the situation is different. The border between old and new EU member states is not so abrupt and clear. The results show higher diversification between EU countries concerning the fiscal impacts of environmental taxation.

The availability of advanced technologies, the time of information society, and also the development of geographic information systems, have brought a lot of spatial data in most disciplines, which are carefully stored in recent decades and allow us spatiotemporal analysis and visualization. However, for long-term analyses and the synthesis of analysed data, including cartographic synthesis processes, it is a big problem when spatiotemporal data have different topologies at different times. And this does not only mean the correction of borders in the sense of clarification or other distinction. The problem is when, for example, the small administrative units is subject to significant temporal changes. Municipalities are divided and merged, while data are always stored for the topology of a particular year or period.The contribution presents a way to solve such a situation on the example of the Czech Republic. Data from 25 years’ period are adjusted so that it is possible to calculate and visualize long-term trends and analyses. In the case of the case study, these are spatial data of more than 6,000 municipalities, with changes in more than 200 cases during the observed period. In addition to the spatial component, there were also changes in identifiers, which are a common means of joining data. For example, when the name of the municipality was changed and the newly "created" municipality also received new identification code. All these problems are solved by the so-called "super layer", which represents aggregation to the smallest possible extent so that the analyses performed are carried out on the most detailed possible scale without missing data in partial periods.The project goal is to explore various geodemographic processes at a very detailed level, specifically at NUTS (Nomenclature of Territorial Units for Statistics) local administrative units 2 (LAU2) commonly used in European Union for statistical purposes. Our presented dataset/approach unified municipal administrative units allowing analyses of data as they change over time. We used a principle of "common spatial denominator", i.e. we used data aggregation into larger units with stable boundary.

Quality of life and life satisfaction are topics that currently receive a great deal of attention across the globe. Many approaches exist, which use both qualitative and quantitative methods, to capture these phenomena. Historically, quality of life was measured exclusively by economic indicators. However, it is indisputable that other factors influence people&rsquo, s life satisfaction, which is captured by subjective survey-based data. By contrast, objective data can easily be obtained and cover a wider range, in terms of population and area. In this research, the multiple fuzzy linear regression model is applied in order to explain the relationship between subjective life satisfaction and selected objective indicators used to evaluate quality of life. The great advantage of the fuzzy model lies in its ability to capture uncertainty, which is undoubtedly associated with the vague concept of subjective life satisfaction. The main outcome of the paper is the detection of indicators that have a statistically significant relationship with life satisfaction. Subsequently, a pan-European sub-national prediction of life satisfaction after the consideration of the most relevant input indicators was proposed, including the uncertainty associated with the prediction of such a phenomenon. The study revealed significant regional differences and similarities between the originally reported satisfaction of life and the predicted one. With the help of spatial and non-spatial statistics supported by visual analysis, it is possible to assess life satisfaction more precisely, while taking into account the ambiguity of the perception of life satisfaction. Additionally, predicted values supplemented with the uncertainty measure (fuzzy approach) and the synthesis of results in the form of European typology help to compare and contrast the results in a more useful manner than in existing studies.

Urban planning and decision-making can often be elitist and non-participatory processes. Citizens are frequently a neglected part of these activities and are usually only involved in and considered before elections, or are informed about the planned changes at the very last stage, often beyond the possibility to change anything. Nevertheless, citizens have a relevant role in the processes of town planning and administration; and this chapter describes the implementation of a web-based crowdsourcing tool for the collection and visualisation of emotion-based and subjective information on maps used in the neighbourhood planning and revitalisation. The tool was used in four case studies of neighbourhood development consultations in various locations in the Czech Republic in 2015-2018. The results presented in this chapter allow for the replication of the research methodology in other areas, both location-wise as well as topic-wise.

City planning, decision-making and participation in local administration can be sometimes elitist, closed to the public and non-participatory processes. Citizens are frequently a neglected part of these activities and are usually only involved and considered prior to elections. Yet citizens have a relevant role in the processes of town planning and administration. This paper describes the implementation of a web-based crowdsourcing tool for the collection and visualisation of emotion-based and subjective information on maps. The tool was used in a case study of neighbourhood development consultation in the city of Příbram, the Czech Republic. Visual, textual and statistical analyses showed a similar spatial distribution of some topics within the Křižáky neighbourhood and provide results, combining qualitative and quantitative approaches in the process of e-participation in urban e-planning. The results presented in this paper allow replication of the research methodology in other areas as well as its implementation.

The aim of this paper is to describe SMEs performance on the regional level with respect to their geographical location. As there is no such research on the regional level, we calculate and describe traditional indicators of performance, financial position and changes in the financial position represented by ROE, Financial Leverage and Cash Flow on Sales. These indicators are then compared with the results of previous research which has been conducted on Eastern Europe and also spatially projected on the map to demonstrate spatial distribution of companies’ performance. Our findings from the observation of 170 SMEs allow us compare the regional level of financial indicators with the European one, and our findings suggest that the Olomoucký region is weaker.

City planning, decision-making and participation in local administration can be sometimes elitist, closed to the public and non-participatory processes. Citizens are frequently a neglected part of these activities and are usually only involved and considered prior to elections. Yet citizens have a relevant role in the processes of town planning and administration. This paper describes the implementation of a web-based crowdsourcing tool for the collection and visualisation of emotion-based and subjective information on maps. The tool was used in a case study of neighbourhood development consultation in the city of Príbram, the Czech Republic. Visual, textual and statistical analyses showed a similar spatial distribution of some topics within the Križáky neighbourhood and provide results, combining qualitative and quantitative approaches in the process of e-participation in urban e-planning. The results presented in this paper allow replication of the research methodology in other areas as well as its implementation.

It was probably more valuable to use user-generated data from location services than ever before during the COVID-19 pandemic. The newly emerged situation caused by the disease’s rapid spread found most of the European countries not ready in many aspects. From the geospatial point of view, it was a tremendous lack of open-data about people’s behaviour. Many public and private services were closed as a preventive measure to slow down COVID-19 spread, which caused an unprecedented change in citizens’ mobility and overall behaviour. It is not possible to acquire such information using traditional ways of data collection (e.g. surveys and questionnaires), or it would be extremely expensive and time-consuming. In order to obtain information about people’s change in behaviour, location-based data from mobile phone users could be used. Although individual user-generated data are highly sensitive, they could be aggregated (e.g. for higher administrative units) and thus anonymised; while still possessing valuable insight into the behaviour change. This contribution deals with COVID-19 Community Mobility Reports dataset released in early April 2020 by Google (available at https://www.google.com/covid19/mobility), which offers unique information about a change of human activities due to the pandemic. This data is in principle very similar to data from mobile operators; however, more accurate, because the location of the device is determined also by connection to Wi-Fi networks and especially via GPS – besides nearby BTS stations. Information carried by the data gives the average decline in population activity in spatial resolution, either only for the entire country or for the country and its main regions. The data is constantly being updated, but Google provides no information on the date of future dissemination of data. Behavioural changes as such are monitored in six categories according to the location of the activity, which was determined by Google as useful for maintaining social distancing or from the point of view of the availability of basic services. The categories are as follows – a) residential, b) transit stations, c) retail and recreation, d) grocery and pharmacy, e) parks, and f) workplaces. Key information is the percentage decrease or increase in the number of individuals present compared to the usual state of affairs (baseline – 3 January to 6 February 2020) in these six fundamental localities. We analysed and visualized data during the peak of the pandemic (from 5 March to 11 April 2020) by processing the average value from that period. In total, 567 European regions with six pieces of information on activity in the above categories was analyzed and visualized. First, we displayed average values in given categories in separate maps, which allowed us to understand a spatial pattern of data. This procedure is relatively common step in the data visual analytics process. We present resulting maps in the contribution of the conference.From the analytical part, we applied a cluster analysis, which served as the processing input for the cartographical synthesis. Methodologically, we created a typology based on the results of cluster analysis as multiple variables can be analyzed simultaneously to provide groups of types of regions with common properties. The k-medoid method was chosen for processing data on human activity type due to its lower sensitivity to outliers. By calculating auxiliary numerical statistics (Pseudo F-index, gap statistics, silhouette method) and after expert assessment, we chose to analyse and display results into five categories (Figure 1), although the optimum number of clusters was originally determined to be two, which was cartographically senseless. This also gives readers a finer breakdown of European regions, which more appropriately shows the diversity/similarity of individual types of regions; especially in the case of areas adjacent to the countries most severely afflicted by COVID-19 - for example Portugal, southern Austria, Slovenia, and others.Besides analytical processing of data (e.g. geovisual and cluster analysis), we also explored a more advance cartographic approach. Cartographic synthesis methods allow the visualization of various types of data with qualitative and quantitative resolution. Therefore, the synthetic processing of the above-mentioned data has been implemented. During the pandemic, a lot of maps were created that are cartographically incorrect, as well as a lot of maps that are cartographically correct. In most cases, however, it was only a simple visualization of the amount of positive-tested people or any other dealing issues in the form of analytical maps. Thus, synthetic maps can provide a new perspective on the issue and can be useful in unconventionally providing information about Covid-19 pandemic. We show how this dataset can be utilised in terms of cartographic synthesis in form of a regional typology in order to reveal the spatial pattern of such change in citizens‘ behaviour.The resulting typology categorizes the relevant administrative units into five clusters (types) in the space defined by the change in behaviour. These clusters can also be named according to the impact of the COVID-19 pandemic on behaviour of citizens: 1) moderate – regions with a relatively small change in population activity. For this type, it must especially be emphasized that this involves a mild impact of the pandemic compared to other types and that COVID-19 and related restrictions have had a visible effect on human behaviour in these regions as well; 2) substantial (secondary activities) – regions with a generally statistically average change in population activity with a more significant change in secondary activities (Grocery and Pharmacy, Parks); 3) substantial (main activities) – regions with a generally statistically average change in population activity with a more significant change in main activities (Residential, Transit Stations, Workplaces); 4) significant – regions with a statistically significant change in behaviour (values in the fourth quartile outside the range of the box graph); 5) extreme – regions with an unprecedented change in behaviour in outliers or near their borders.Five types of regions were identified according to the impact of COVID-19 and related restrictions - from the type moderately impacted by COVID-19 (e.g. Sweden, Latvia, Hungary), to those impacted by the pandemic in a substantial (Ireland, regions of Greece, the Czech Republic, Norway, Switzerland), significant (e.g. regions of France, Belgium, Austria, Slovenia, Portugal), and extreme manner (Spain, Italy, and the Paris region). As a conclusion, The current global situation clearly shows that positional and individual data can also be useful in the case of pandemics or any other situations involving the safety and health of the population. The use of Google Location data proves to be valuable in analysis and evaluation of citizens’ behaviour during any pandemic crisis, though, in this case, aggregated into higher territorial units.

The main goal of this chapter is to present the development of selected economic and environmental indicators in EU28 countries connected with climate protection in the period from 2005 to 2015. European Union Emission Trading Scheme (EU ETS) was introduced in 2005. Currently, the EU ETS is in operation for more than a decade; moreover, in 2018, the European Commission adopts rules for the next 4th trading period. Is there visible any improvement in CO2 emissions development? Can it be connected to the changes in the macroeconomic indicators? The methodological part presents the data sources and methodological background of the research. Possible geographical patterns in the development of selected indicators within the EU28 are indicated. Detailed analysis of economic and environmental data of EU28 countries is provided, with the use of (geo)visual analysis of the data. The results of the (geo)visual analysis show that CO2 emissions within selected EU countries were decreasing in the chosen period 2005–2015, with some exceptions (e.g. Iceland and Latvia). As the development of CO2 emissions in all selected EU countries is not similar, the other economic and environmental indicators were included (e.g. GDP, Investments) into the analysis to reveal a typical (geographical) pattern and explain the current situation.

The last part of the book is dedicated to the simulation game framework. What is meant by the simulation game within the Spationomy project will be described in the following Sect. 16.1. Conceptual thoughts, as well as the motivation and goals of the Spationomy simulation game, will be briefly introduced as well. Also, the overall theoretical framing about educational aspects of simulation games (or gamification in general) with a focus on geography or economy will be provided with the first section. After this introductory chapter, each of the Spationomy simulation game rounds will be described in detail and also original instructions assigned to the students will be presented (Sect. 16.2). The reason for this chapter is to give a chance to the reader to use, extend and employ these materials in his/her teaching activity. The last two chapters are devoted to feedback and evaluation of the simulation game. Section 16.3 is about teacher/project staff experiences during the creation of simulation game, playing the game itself, modifying it during its evolution and also about “what worked and what did not”. In the same section (Sect. 16.3), we will provide the reader with students’ feedback and evaluation. Students’ authentic commentaries and suggestion are essential for further improvements of the game, and their insights also prove that the whole idea of making spatial economy playful is highly appreciated and fruitful. The main goal of Part IV of the book is to allow anyone to adopt and replicate the simulation game framework in the learning and teaching process.

In the introductory chapter, firstly, the summary of how this chapter evolved is provided as well as the organisation of the whole Part III of this book is described. Then, the main body serves as an overview of how the spatial exploration of economic data and respective methods of interdisciplinary analytics can be approached. Based on the authors experiences up to this date, five levels or stages of an analytical approach to spatially analyse economic data are defined. At the lowest level, the author focuses on a “simple” visualisation of data, following by the level of merging (multivariate) statistics of economic data with their spatial component. In the middle, as a third level, spatial statistics – as an implicit use of statistics in the spatial analysis – is mentioned. As the fourth stage, a workflow of the previous ones is depicted, which leads to the final and most advanced level of spatial-economic modelling. This chapter strives to define a universal workflow in economic data analysis. The conceptual framework introduced in this chapter is based on 3 years of interdisciplinary cooperation within the Spationomy project.

In this study, we first develop a hypothesis that existing quantitative visual complexity measures will overall reflect the level of cartographic generalization, and test this hypothesis. Specifically, to test our hypothesis, we first selected common geovisualization types (i.e., cartographic maps, hybrid maps, satellite images and shaded relief maps) and retrieved examples as provided by Google Maps, OpenStreetMap and SchweizMobil by swisstopo. Selected geovisualizations vary in cartographic design choices, scene contents and different levels of generalization. Following this, we applied one of Rosenholtz et al.’s (2007) visual clutter algorithms to obtain quantitative visual complexity scores for screenshots of the selected maps. We hypothesized that visual complexity should be constant across generalization levels, however, the algorithm suggested that the complexity of small-scale displays (less detailed) is higher than those of large-scale (high detail). We also observed vast differences in visual complexity among maps providers, which we attribute to their varying approaches towards the cartographic design and generalization process. Our efforts will contribute towards creating recommendations as to how the visual complexity algorithms could be optimized for cartographic products, and eventually be utilized as a part of the cartographic design process to assess the visual complexity.

This article reviews the potential of kite-mapping as part of an interdisciplinary and multinational field course. It situates kite-mapping as a low cost, high quality, participatory approach, in relation to field use of maps, arguing that research might usefully attend to more than simply the skills acquired during the field exercise and the technicalities of the innovation. Evidenced from two day-long workshops on the Maltese island of Gozo we describe practical issues involved in making the mapping work. We draw on descriptions of field practice and deploy a multi-method evaluation of staff and student reactions and experiences to explore pedagogic aspects of kite-mapping. We conclude that the practices of kite-mapping significantly impact upon the value of the mapping technique, and highlight how creative and playful mapping processes implicit in the technique might usefully be deployed in other field contexts.

The subject of the article is also determination of key issues related to accounting and tax legislation, including the monitoring of financial indicators, for example return on equity (ROE) and return on assets (ROA), indicators of cash flow (CF). Financial analysis is an essential resource accounting, as a basis for calculating the indicators, which provide a complete picture of the economic situation of the company. The quality of accounting (financial) statements of the company depends on the quality of financial indicators and their predictive ability. It is therefore important for the company and the financial analysis that is part of the financial statements. In this context are also explored areas: performance-production (power and influence of accounting legislation), organizational and sectoral level (NACE) and the production (number of employees), building on existing theory and practice in relation to accounting and tax legislation in terms of the Czech Republic and its changes since 2016.

Subjective and participatory mapping has become an important tool for urban planners and city administrations. Perceptions of safety can affect the quality of life and property prices. The presented paper describes participatory mapping exercises in three Czech cities, where questions were asked concerning people’s subjective perceptions of safety during the day and at night. Respondents in Olomouc (n = 661), Opava (n = 901) and Jihlava (n = 106) respectively indicated 1516 (Olomouc); 3491 (Opava) and 894 (Jihlava) places as unsafe. The data was gathered over a period of two years; in Olomouc data was collected between 1st October and 2nd December 2015, in Opava the survey took place between 19th October 2016 and 30th December 2016, and in Jihlava in the period from 30th November 2015 until 4th March 2016. The data collected included information about the gender of the respondents and the day-time/night-time division of perceived safety, so it was possible to analyse gender differences as well as time specifics from the collected data. The results suggest that there are certain areas in all three cities that have similar patterns (train stations, city parks, dark and narrow streets, excluded communities).

Innovation is widely considered as an engine of economic growth, competitiveness and job creation. As such it has become a hot policy topic over the last years, especially on regional and national levels. Thus, it is vital to analyze and evaluate innovation activities, whether they are dispersed or concentrated in space. This article address the question: are innovations spatially clustered or not? In this article, we obtain a clear picture of the geographical distribution of innovation activities carried out by companies in Czech Republic during 2010-2012. Visual and statistical analysis of firm-level survey data concerning innovation activities of 5,449 (22% of the statistical population) Czech companies has been performed. Using four newly calculated innovation measures, their overall scores in Local Administrative Units 1 and consequent geovisualization allowed us revealing clusters of innovating districts and evaluating their spatial patterns. The main findings are that low innovative regions are located mainly (1) in the borderlands, (2) in rural areas, and (3) in mountainous areas. On the other hand, highly innovative regions tend to be in major cities and their neighborhoods, which indicates a positive spillover effect.

Crisis mapping is a legitimate component of both crisis informatics and disaster risk management. It has become an effective tool for humanitarian workers, especially after the earthquake in Haiti in 2010. Ushahidi is among the many mapping platforms on offer in the growing field of crisis mapping, and involves the application of crowdsourcing to create online and interactive maps of areas in turmoil. This paper presents the Crisis Map of the Czech Republic, which is the first such instrument to be deployed nationwide in Central Europe. It describes the methodologies used in the preparatory work phase and details some practices identified during the creation and actual employment of the map. In addition, the paper assesses its structure and technological architecture, as well as its potential possible development in the future. Lastly, it evaluates the utilisation of the Crisis Map during the floods in the Czech Republic in 2013.

Many cities have started to engage their citizens in collaborative planning and in re-creating the urban spaces where they live. The case study presented in this paper crowdsourced subjective/emotional responses concerning the environment of the city of Brno in the Czech Republic, from 2,087 respondents, who marked 24,065 points on six different topics and added over 3,000 further comments. The paper presents not only the case study, but also the authors’ own web-application called Emotional Maps—built for crowdsourcing spatial data, mainly about city development and subjective perceptions of urban spaces. The collected data showed three main hot-spots in the city, with a significant number of marked points reported by citizens. The paper comments on the reasons these spots are important for the citizens of Brno and their suggestions for future activities there. Furthermore, the authors argue that ad hoc flexible web-mapping platforms such as the one presented in the paper can be an asset to city administrations as well as urban planners.

Places are strongly linked with emotions and can be considered safe or unsafe, pleasant or ugly, favourite or boring among other emotions. Subjective perceptions of a city can be valuable sources of information for city planners and a local administration. Among the above-mentioned emotions that have an effect on the quality of life for people in a city, the perception of safety has a prominent position. Safety and fear of criminality affect our interaction with public spaces the most. But criminality does not have to be the only reason people feel uncomfortable in a city, they may also be afraid of the darkness or the friendlessness of a place. The paper describes the mapping of unsafe places in the city of Olomouc via a paper-based questionnaire and a web-based crowdsourcing tool PocitoveMapy.cz. In total, the authors collected answers from 661 respondents; 144 used the online tool and 517 used the paper-based version. The final dataset comprises 1516 places (453 online/1063 questionnaire). The data were gathered over the period between 1st October and 2nd December 2015. The authors collected data that are gender specific as well and time of day specific, therefore it was possible to analyse the differences between daytime and night-time fearful places in the city as well as places that are perceived unsafe by women and men. The spatial density analysis, local correlations and hexagonal aggregation revealed hot-spots that are felt by the citizens of Olomouc to be unsafe. The strongest agreement in votes can mainly be found in the three localities with the densest localisation of votes. In these localities, a strong correlation exists also between the perception of fear during the daytime and the night-time. The results of the case study can be used by the local police department or administration authorities in the future development of safety strategies for the city.

The concept of entropy connected with GIS is relatively new. Its mathematical background was defined after World War II by Claude E. Shannon, well-known mathematician, electronic engineer and founder of information theory. Information theory deals with entropy as measure of information which every single message has. And thus entropy quantifies the amount of information in a message. The paper is based on entropy applications in cartography and demonstrates its usage as a measure of information in GIS. The authors provide an algorithm for setting number of intervals in thematic maps with using entropy calculations. Finally, the obtained knowledge is applied to sample datasets for creating climatic maps within GIS environment.

Due to the suburbanisation process, it is becoming more difficult to properly define rural and urban areas in the Czech Republic. This delimitation problem has been intensively studied in Europe, including the Czech Republic, for decades, but only so-called ‘crisp’ rules have been set for the categorisation of urban and rural. This is no longer satisfactory because of substantial population movements. Our research focuses on applying fuzzy set theory to the delimitation of rural and urban areas and on the subsequent advanced cartographic visualisation. We used the principles of fuzzy regulation, or fuzzy inference systems, on socio-economic data to show the transitional character of municipalities. The generated Main map is at scale of 1:500,000, whereas secondary maps are at scale of 1:2,500,000. Map visualisation of municipalities in the Czech Republic provides a very unique combination of geographical information science, cartography and modern geo-computational methods. Information perception via a map is an adequate way to analyse geographic information, and the problem of delimiting rural and urban areas can be suitably visualised using these methods.

There are various methods, algorithms and automated tools for geometry generalization, however cartographers’ knowledge and experiences are crucial for a decision of what generalization level is suitable for the final visualization. This decision of experienced cartographer is usually correct but still quite subjective. Due to this reason we introduce the shape metrics as a tool to support objective evaluation of a generalization level. Shape metrics were originally applied in the landscape ecology in order to quantify landscape patches. Since then many scientific disciplines, including Geosciences, have adopted their principles. Generally, shape metrics serves as a quantitative description of any planar object (e.g. ground projection of a building) in order to measure its shape complexity or compactness. We used 15 shape metrics to calculate complexity of four buildings ground plans at 22 generalization levels in our case study. First, we performed shape generalization of four architecturally different buildings ground plans in consecutive levels. Then, we calculated shape metrics for each generalization level to quantify generalized shape complexity. We found out that shape metrics confirmed the fact that the higher level of geometry generalization the lower shape complexity. Nevertheless some shape metrics revealed that, in certain levels of generalization, the shape was not simplified. Aim of this paper is to introduce shape metrics application on geometry generalization in a case study and to propose suitable shape metrics to identify particular levels of an improper geometry generalization.

Disease mapping, the visualization of disease rates and the clustering of disease data are still one of the most interesting topics in geosciences. This is because of the nature of the data, which are often purely spatial with a rich descriptive part and which are easy to combine with other data (demographic, economic, etc.). This contribution aims to present the usage of empirical Bayesian methods in disease mapping and the subsequent creation of disease maps. Bayesian methods incorporate prior knowledge about the phenomenon (or underlying processes) to provide a more accurate and easily understandable description of the situation. Empirical Bayesian procedures are used for disease rates smoothing in the case of a choropleth map. They also help to identify local clusters of more/less affected areas. The main topic of the case study in this paper is the analysis of the spatial distribution of a disease called campylobacteriosis in the Czech Republic between the years 2008 and 2012 with the usage of global empirical Bayesian estimates based on binomial distribution and local empirical Bayesian estimates based on first order queen contiguity.