The aim of this study is to provide a comprehensive view of the issue of open data in Czech cities and thus give the world community an insight into the state of open data in the Czech Republic. It serves as a basis for further research and implementation of open data in urban planning. Its results can be used not only for the benefit of the professional community but can also serve as a basis for decision-making by city authorities in the planning and development of urban space. The open data are therefore integral part of developing smart cities (Ojo, Curry, Zeleti, 2015). This extensive study deals with the issue of the availability of open data in Czech cities and to what extent are they used use in the framework of urban planning and development of urban space. In the context of rapid digitization and technological progress, open data is becoming increasingly important for the effective management and design of urban infrastructure. This study systematically analyses the current state of open data in Czech cities, identifies key aspects of their availability and examines their potential applications in urban planning. The study focuses in more detail on Brno, which is the second largest city in the Czech Republic and provides freely available data on its website data.brno.cz.

The first part of the study focuses on the theoretical framework of open data and its significance for modern urban planning. The basic principles of open data are introduced, including the standards and formats currently in use. The advantages of open data in the context of transparent decision-making, citizen participation and sustainable urban development are also discussed. In the Czech Republic, the possibilities of providing and using open data has been more and more discussed in the last ten years, especially at the level of data from state organizations. Nevertheless, the term open data is not understood in the same way by all organizations, when for example PDF format is considered as open data format. At the same time, we also perceive the problem of the completeness, data quality and consistency of open data, as well as missing metadata for easier understanding of lineage.

The analysis of available data in specific Czech cities follows up in the second part of the study. The analysis performed includes the identification of existing data sources such as geographic data, traffic information, demographic data and other relevant information for urban planning. Each data source is subject to a detailed evaluation, including assessment of quality, topicality and availability. The Czech regional cities try to provide open data using geoportals., The largest geoportals are data.brno.cz, geoportalpraha.cz, mapy.ostrava.cz, but there exist others. However, state-government institutions also provide data. The geoportal.cuzk.cz and subsequently geoportal.gov.cz might be considered as the largest provider of data (including open data). A large amount of basic statistical data is provided by the Czech Statistical Office, including the last census from 2021, published mainly as open data.

Regular hackathons are already organized to increase awareness about open data on these portals, to illustrate the range of possible use of data and the power of making data available to a wide professional and general public. One of the most creative examples can be the Minecraft world derived from a 3D model of the city of Brno. Such an unconventional method may better attract general public to think more about their city and how to contribute to its improvement.

In the following part of the study, concrete examples of the use of open data in urban planning are presented. The making available of 3D data of cities became one of the most significant step for the needs of architectural or urban studies. We cannot forget the making accessible of the basic map study (The Fundamental Base of Geographic Data of the Czech Republic) including Digital model of relief, Digital model of surface and orthophoto in the form of open data last year by the State Administration of Land Surveying and Cadastre. Some potential can be hidden in the emerging Digital Technical Map. Different insights onto the location can arise with the optics of the crime rate published by police office. In short, successful projects are described where open data played a key role in optimizing traffic, planning public spaces, and improving the quality of life of residents. Based on these examples, recommendations are proposed for the further development and use of open data in the urban planning environment.

In the final part of the study, the challenges and opportunities associated with the implementation of open data in Czech cities are discussed. Potential strategies for improving the availability of open data are presented, including collaboration between city authorities, the academic sector and civil society. In addition, ethical and security issues related to the handling and sharing of sensitive data in an urban context are stressed.

The popularity of participative mapping continuously grows and is becoming an essential tool to involve citizens in urban planning, architectural solutions and transport design. Citizens can quickly and easily review proposals and variants, explore models and visualizations, express their opinions, pin comments, and vote on their favourites (Ribeiro and Ribeiro 2016). Emotional maps and similar mapping tools are frequently used in Czechia, especially for mapping citizens’ attitudes towards both physical and social features of the urban environment. Quantitative assessment of mapping results can help urban planners better understand citizens’ perception and improve the targeting of planned measures (Camara, Camboim, and Bravo 2021). Discussion sometimes arises about the validity of such mapping, complementarity or substitution of traditional questionnaire surveys. The objective of the paper is to discuss benefits and weaknesses of such tools and to compare them with questionnaire surveys.
The case study is focused on two middle-sized Czech cities, Ostrava (OV) and Hradec Kralove (HK), and selected rural municipalities in their surroundings. Participants are all seniors (age 65+) due to the project aim of understanding seniors’ spatial mobility, accessibility and perception.
The questionnaire survey was conducted in 2022 by the Research Agency STEM/MARK (n=536, PAPI method 86%, CAWI method 14%). Quota sampling used stratification by age, gender, territory, and urbanization based on census data.
At the same time, two web map applications were launched - the emotional and mobility maps. We used the platform EmotionalMaps.eu which utilizes a Leaflet library (Pánek et al. 2021).
In the map application, respondents indicate their age group and health limitations, and mark one or more locations: attractive locations, repulsive locations, barriers to movement, attractive paths, repulsive paths, and approximate residence location. Each marked target can be further specified by 16 reasons with a multiple-choice option, visiting frequency, schedule, and weather and social constraints (Horak et al. 2022).
In the mobility map, respondents specify one or more of their favourite locations in the following categories: home, workplace, retail, pharmacy, post office, doctor, supermarket, ATM, worship, services, park, restaurant, visiting family or friends, garden or cottage, or other place. After marking each point, they may specify frequency of attendance and transport mode.
The main advantages for emotional and mobility web mapping are cost effectiveness, flexibility of use, usually large sample size, attractiveness of design, ease of use for people with computer or mobile skill, ability for accurate positioning of the targets, customized map design (zoom, pan, etc.), larger extent, ability to describe more specific conditions, use of illustrative pictures or icons, interactive help, consistency monitoring, integrity constraints, and selection from specified options. Disadvantages include no validation of the respondent profile, bias of respondents towards more technically skilled and wealthier people, privacy concerns, and duplicate responses (Wikstrøm 2023).
The biggest problems were encountered when drawing lines to specify attractive and repulsive paths. We obtained only 32 records from OV and 29 records from HK and evident errors represent 19% and 40%, respectively.
Quota sampling was not applied on the web mapping data, only a basic selection of the relevant age group and residence in HK or OV. The differences of the respondents’ profiles between the three methods of survey show clear bias towards younger and more healthy seniors in the case of web mapping and CAWI.
Any surveys’ raw data contains some inaccuracies, errors, or odd responses from people misunderstanding questions, misusing tools, trial responses, intention to damage data or outputs, or having concerns (e.g., losing privacy). Deviations from planned quota shares in the quota-based survey may result in the removal of some respondents and/or the need to conduct an additional survey (in our case, 40-46% in two villages). The data's temporal consistency is deteriorated by such changes.
The primary aim of the survey was to discover seniors’ mobility targets. We asked for their dwelling location and up to four of their most important targets, listed in descending order by their perceived importance, written as a free text. To specify the locations of residence and targets we asked for addresses or another useful specification. Respondents identified 23 kinds of important targets in HK and 24 in OV with the following main priorities: shopping (37 and 24%, resp.), doctor (19 and 22%), family (10 and 13%), walking (8 and 6%), and friends (5 and 4%). An additional problem is that 5% of free-text destinations had multiple targets.
The web mobility mapping requested specification of favourite locations for one or more targets in the 13 categories, the residence and the “other” target (specified by free text). Respondents identified 16 kinds of important targets in HK and 12 in OV with the following priorities: retail (15 and 12%, respectively), supermarket (12 both), pharmacy (12 and 10%), post office (11 and 10%). Such a flat distribution is caused by the respondents’ tendency to mark only one target per category.
The accuracy of location is variable. While the web mapping application instantly provides coordinates for each location, the targets from questionnaires require geocoding. In our case, geocoding was successful only for 65% of records. Among these, 18% were geocoded by utilizing the complete address, 53% were geocoded by finding the nearest matching destination, 24% were geocoded manually with interpretation, and 5% were geocoded but only to the center of the street
Further, the spatial distributions of targets were compared. The clustering of both indicated targets and all targets available in OpenStreetMap is confirmed by the M-function in both variants (questionnaire and web mapping). The analysis of distances from a residence to an indicated real target shows more clustering for questionnaire targets around a residence than for those from web mobility mapping. However, the selection of closer destinations in the questionnaire is influenced by the age bias of respondents and by the limited number of requested targets (up to four).
The study contributes to the discussion on the validity of participative mapping and sheds a light on the importance of carefully preparing such surveys and pre-processing data comprehensively.