The goal of the article was to develop a serious geogame that allows for the activation of the Warsaw University of Technology’s (WUT) academic community’s involvement in the participatory planning process for campus revitalization and development. The Campus Changer geogame is implemented as a PC‐based application that uses a detailed 3D model of the WUT campus and the players’ real‐time physical location. The geogame allows not only virtual exploration of a detailed, 3D model of the university campus but also raises awareness of the problems associated with the functioning of the members of the academic community in a jointly used space. The Campus Changer geogame, developed using the Unity engine with detailed WUT campus models (LoD [level of detail] 3), allows users to explore the campus and propose transformations. Guided by Genius Loci, the university’s guardian spirit, players encounter challenges and interact with diverse campus stakeholders, such as academic staff, students, and local residents. Player decisions reshape the campus instantly, with changes visible in the 3D virtual world. The Campus Changer geogame allows users to manipulate detailed geospatial data (a 3D model of the WUT campus and its connection to GIS tools) and can be used as a powerful geoparticipation tool. The results of GIS analyses conducted by the authors of the article using data collected based on the results of a multi‐player game provide insight into the opinions of different groups of the WUT academic community, who propose diverse changes in different parts of the campus. Compared to traditional planning consultations, the geogame facilitates the collection of input from a significantly broader and more diverse audience, fostering inclusive and participatory decision‐making. The most significant achievement of the project is the activation of the academic community and co‐creation planning for the modernization and development of the university.

This paper discusses the use of IoT sensor networks and spatial data mining methods to support the design process in the revitalization of the university campus of the Warsaw University of Technology (WUT) in the spirit of universal design. The aim of the research was to develop a methodology for the use of IoT and edge computing for the acquisition of spatial knowledge based on spatial big data, as well as for the development of an open (geo)information society that shares the responsibility for the process of shaping the spaces of smart cities. The purpose of the article is to verify the hypothesis on whether it is possible to obtain spatial–temporal quantitative data that are useful in the process of designing the space of a university campus using low-cost Internet of Things sensors, i.e., already existing networks of CCTV cameras supported by simple installed beam-crossing sensors. The methodological approach proposed in the article combines two main areas—the use of IT technologies (IoT, big data, spatial data mining) and data-driven design based on analysis of urban space actors’ behavior for participatory revitalization of a university campus. The research method applied involves placing a network of locally communicating heterogeneous IoT sensors in the space of a campus. These sensors collect data on the behavior of urban space actors: people and vehicles. The data collected and the knowledge gained from its analysis are used to discuss the shape of the campus space. The testbed of the developed methodology was the central campus of the WUT (Warsaw University of Technology), which made it possible to analyze the time-varying use of the selected campus spaces and to identify the premises for the revitalization project in accordance with contemporary trends in the design of the space of HEIs (higher education institutions), as well as the needs of the academic community and the residents of the capital. The results are used not only to optimize the process of redesigning the WUT campus, but also to support the process of discussion and activation of the community in the development of deliberative democracy and participatory shaping of space in general.

The rapid integration of Internet of Things (IoT) technologies in smart cities enhances urban management, yet public acceptance remains crucial for successful deployment. This study examined gender-based differences in IoT acceptance through a survey of 288 respondents from Warsaw and Plock, analyzed using structural equation modeling (SEM). The results revealed that women demonstrated significantly higher trust in IoT (+0.93, p < 0.001), greater perceived safety (+0.24, p = 0.013), and stronger support for environmental IoT applications (+0.48, p = 0.007) than men. While perceived usefulness was the strongest predictor of IoT acceptance for men (β = 0.523, p < 0.001), safety (β = 0.286, p = 0.001) and environmental awareness (β = 0.507, p < 0.001) drove acceptance among women. These findings highlight the need for gender-sensitive urban technology policies, emphasizing safety and sustainability to foster inclusive smart city development. The research results can be used by city authorities to learn about the requirements and concerns of residents to design a city that meets all residents’ requirements and better communicates IoT technology. Furthermore, the study underscores the importance of targeted education and awareness campaigns to address privacy concerns and promote broader adoption of IoT-driven solutions in urban environments.

Urban revitalization processes are increasingly requiring inclusive and data-driven approaches that address spatial inequalities and support the achievement of the Sustainable Development Goals (SDGs). The article presents a methodology for utilizing social geoparticipation tools in the revitalization process of the Warsaw University of Technology campus. The study demonstrates how campus-scale geoparticipation can incorporate SDGs and spatial justice principles in micro-urban contexts, with a methodology that is transferable to city-scale projects and provides practical guidance for inclusive and sustainable urban governance. This enables the transformation of volunteered geographic information (VGI) data and spatial databases into practical spatial knowledge that supports sustainable urban development. Empirical analysis of 710 responses and nearly 1000 mapped locations revealed that 83% of respondents identified insufficient greenery as the primary spatial problem. At the same time, accessibility (β = 0.618) and green infrastructure quality (β = 0.553) were the strongest predictors of the need for change. The collected feedback from the academic community was processed using exploratory data analysis and spatial statistics into a spatial knowledge base. ESRI’s ArcGIS Experience Builder (Developer Edition version 1.16) was employed in the app’s development. A custom function was developed to meet the requirements of the geo-questionnaire fully. The application was ultimately deployed within the CENAGIS domain of the IT infrastructure at Warsaw University of Technology. Authors employed the structural equation modeling (SEM) method and provided statistical analysis of community expectations. The findings provide actionable evidence for urban planners, campus managers, and decision-makers seeking to implement data-driven, participatory revitalization strategies, demonstrating how social geoparticipation can directly inform sustainable design and policy-making at both campus and city levels.

Technological development, data growth and the complexity of decision-making processes in the economy are increasing the demand for analytical skills. There is a noticeable shortage of analysts, hence the problem of how to accelerate the development of analytical skills among young people who are still studying, representatives of the Generation Z entering the labour market. The subject of the research is to diagnose the level of these competences, expressed in the formulated objectives: to determine the relationship between the level of analytical and digital competences of Generation Z representatives and to examine the similarities (or differences) between the assessment of the level of analytical competences using self-assessment and objectified knowledge tests. The selected competences of 1,870 secondary technical school students were analysed. For this purpose, a CART decision tree model was used, supplementing the data presentation with spatial mapping. In the sample examined, analytical and digital competences are strongly correlated. It has been confirmed that self-assessment, as a method of examining the level of students' competences, is justified and reflects their real analytical thinking skills relatively well. The article makes an important contribution to the development of talent management and analytical competences in the conditions of dynamic digital transformation. Using the results will help in decision-making processes in education and on the labour market, support the development of analytical and digital competences, shorten training processes in companies, reducing their costs, and improve competence assessment processes.

In the era of the digital revolution, there is a great emphasis on implementing new technologies including AI. In the context of the changes strategic resource planning becomes necessary. This also applies to the provision of human resources and strategic competencies, and in the context of the implementation of new, future technologies also to have the company’s competence if the future to implement them. The new approach in HR that the authors propose, which allows planning and forecasting the availability of competencies of the future relating to AI, is the well-known customer segmentation used here for the segmentation of potential candidates. The purpose of the article is to explore the possibility of modeling the segmentation of candidates with selected competencies of the future. Data on young generation Z's characteristics, competencies possessed, candidates’ job expectations, or location were used for analysis. In this article ML and CART are used as examples of AI methods for data analysis. The analysis was conducted on a sample of 2,197 16–19 year olds, male and female students studying 12 technical subjects. The key finding is that ML and AI are widely applicable in segmenting candidates, and that the competencies of the future can be linked to their expectations of where and how they work, including such things as work atmosphere, company innovation, and the ability to work remotely. The implementation of similar modeling in practice can contribute in organizations to the optimization of management decision-making including increasing the efficiency of resources and recruitment processes.

Objective: The purpose of this paper was to develop a model for accelerating the acquisition of the selected transversal competence of teamwork. Based on data from four EU countries, four models were developed and the best of them was selected, describing the results and variables relevant to that model. Research Design & Methods: Data on improving transversal competences were collected from students in four countries, i.e. Poland, Slovakia, Slovenia and Finland. 26 variables were taken into account in the modelling which was based on four methods. They included the Multiple Linear Regression Model, Multivariate Adaptive Regression Splines, Support Vector Machine and two Artificial Neural Network methods. Findings: The analyses show that the method of educating students and young employees, e.g. during training courses, can be a catalyst for accelerating teamwork competence acquisition. Other transversal competences including creativity, communicativeness and entrepreneurship correlate positively with growth in teamwork competence. Implications / Recommendations: The study was conducted on an international group, also taking into account cross-cultural variables. However, to deepen the results, it is suggested that the sample size be increased and the research updated. The ranking of the education method is indicated to have an impact on the growth of transversal competences, including teamwork. Contribution: New approaches in the paper include the analytical approach to modelling the growth in teamwork competence in relation to many variables describing students and young workers in the labour market in the UE. The use of multiple analytical and statistical methods allows the most fitting model to be selected and the error to be minimised.

Companies should choose competitive markets for their products to maximize the efficiency of their resources. The younger generation has increasing demands for personalized products. The way their needs are met and understanding their consumer behaviors should allow companies to continuously analyze trends within the target group. In the current era of globalization, the Internet, and Big Data, using traditional methods alone may lead to companies selecting the wrong markets, resulting in significant financial and resource losses. Therefore, this article proposes the utilization of machine learning and CART for modeling customer needs to facilitate market segmentation among the younger generation, focusing on young individuals studying in the IT field. Implementing such modeling in practice can contribute to optimizing decision-making, minimizing financial losses, and resource efficiency. The study analyzed the needs of 1149 individuals aged 16–19 in the Wielkopolska region. The developed results provide an explanation of the “high salary” and “low salary” values in the form of a decision tree. Additionally, a spatial distribution map of expected salaries was visualized using the CART model. The extracted rules, which can be explicitly interpreted for each terminal node of the CART model, not only allow for spatial differentiation of the model but, most importantly, enable understanding of the motivations driving the survey respondents. The conducted research demonstrated that to comprehend the motivations of the surveyed individuals, it is crucial to consider several completely different independent variables in the process of modeling the spatial distribution of expected remuneration, including economic parameters. The conducted analyses have shown that machine learning and AI have broad applications in marketing, including customer and market segmentation.

This study aimed to design a cartographic visualization for mobile navigation applications around indoor spaces, including metro stations, with persons with special needs in mind. The visualization will be used in a dedicated application for mobile devices supporting the navigation of people around the Warsaw metro. The app was developed for any metro users and includes functions that help persons with special needs navigate the metro. It is not possible to meet the needs of persons with every type, degree, or complexity of disability, but the methodology has been developed based on the principles of designing cartographic presentations, accessibility guidelines and existing solutions, taking into account the needs of persons with special needs and the specifics of metro navigation. As a result, it differs significantly from the visualizations that currently exist in the Warsaw metro. The developed methodology was tested by designing a map of the Świętokrzyska metro station and subjected to user testing, including persons with special needs. The types of special needs included mobility disabilities, colour vision deficiency, and persons, who often travel with baby strollers. The results obtained allowed to improve the proposed solution.

Around 20% of the population is disabled. Many people have mobility problems, including the elderly and people with young children. It is crucial to adapt cities to the needs of these people and, at the same time, to the needs of all residents. This is the subject of universal design, which should consider inhabitants’ needs and habits. This information can be collected by Internet of Things (IoT) devices that observe and listen to residents. Residents do not accept constant surveillance, so the public may not accept data collection by IoT sensors. This study aimed to identify and evaluate factors influencing the acceptance of data collection by IoT devices for universal design. For this purpose, an online survey was prepared by the Warsaw University of Technology. The following statistical methods were used to analyze the data: descriptive statistics, exploratory factor analysis, confirmatory factor analysis, reliability analysis and structural equation modeling. This paper identifies key factors influencing the acceptance of IoT devices for universal design. The statistically significant factors are the perceived usefulness of data collection, trust in city authorities, the perceived security of data collected by IoT devices and empathy for people with disabilities. The original achievement of this study is its indication that empathy for the disabled moderates and increases the positive relationship between the perceived usefulness of IoT devices and their acceptance. It was also found that trust in city authorities mediates the relationship between the perceived usability and acceptance of IoT devices. City authorities can use the results of this analysis in the implementation of IoT devices in smart cities.

This article describes an original methodology for integrating global SIR-like epidemic models with spatial interaction models, which enables the forecasting of COVID-19 dynamics in Poland through time and space. Mobility level, estimated by the regional population density and distances among inhabitants, was the determining variable in the spatial interaction model. The spatiotemporal diffusion model, which allows the temporal prediction of case counts and the possibility of determining their spatial distribution, made it possible to forecast the dynamics of the COVID-19 pandemic at a regional level in Poland. This model was used to predict incidence in 380 counties in Poland, which represents a much more detailed modeling than NUTS 3 according to the widely used geocoding standard Nomenclature of Territorial Units for Statistics. The research covered the entire territory of Poland in seven weeks of early 2021, just before the start of vaccination in Poland. The results were verified using official epidemiological data collected by sanitary and epidemiological stations. As the conducted analyses show, the application of the approach proposed in the article, integrating epidemiological models with spatial interaction models, especially unconstrained gravity models and destination (attraction) constrained models, leads to obtaining almost 90% of the coefficient of determination, which reflects the quality of the model’s fit with the spatiotemporal distribution of the validation data.

In this paper, we consider the problem of planning non-pharmaceutical interventions to control the spread of infectious diseases. We propose a new model derived from classical compartmental models; however, we model spatial and population-structure heterogeneity of population mixing. The resulting model is a large-scale non-linear and non-convex optimisation problem. In order to solve it, we apply a special variant of covariance matrix adaptation evolution strategy. We show that results obtained for three different objectives are better than natural heuristics and, moreover, that the introduction of an individual's mobility to the model is significant for the quality of the decisions. We apply our approach to a six-compartmental model with detailed Poland and COVID-19 disease data. The obtained results are non-trivialand sometimes unexpected; therefore, we believe that our model could be applied to support policy-makers in fighting diseases at the long-term decision-making level.

Human resources (HR) have a key impact on the creation and implementation of modern products, solutions and concepts. Relatively new and rarely undertaken research challenge in enterprise is optimization of HR in the context of their location and requirements for working conditions. A great challenge here is the transparency and reliability of the collected data. In the article, we present a modern approach to knowledge extraction based on Artificial Intelligence (AI) and Multivariate Adaptive Regression Splines opti-mizing the availability of HR with a high innovation rate, taking into account their availability time and location. This study was conducted on a group of 5095 young people from the Z generation. A total of 11 variables were ana-lyzed in the context of innovation and presented in this article. The effect of research using machine learning methods is the analysis of the characteristics of generation Z representatives, whose desire is to work in innovative compa-nies. Research results indicate that some regions offer candidates with a higher level and commitment to innovation, and thus make HR more available for the development of innovative products. Chosen models designed by using AI and Operational Research Analytics were presented in the graphic visualization, which is a novelty in the presentation of similar issues in relation to HR.

Challenges connected with neuroscience and the use of machine learning to support analytical processes encompass more and more areas, thus supporting practitioners and managerial decisions. These changes can also be seen in the area of human resource management and support for decisions on key future spending on the remuneration of future employees. The article presents an original spatial data enrichment and spatial data mining methodology used for the analysis of primary data based on a sample of 1149 young candidates from generation Z to measure the effectiveness of data mining learning methods. The studies used data collected directly from surveys that were “enriched” with spatial geolocation. The fact that the spatial context was taken into account in the studies made it possible to develop a model explaining the spatio-temporal differentiation of professional expectations of respondents from generation Z who were studying professions connected with broadly understood IT. The analyzes used modeling with linear polynomial regression, the neural network of a multi-layer perceptron type and the multivariate adaptive regression splines method in the variant with and without spatial data filtration. The use of different spatial data mining methods made it possible to compare the reliability of models of knowledge extraction from the data and to explain the significance of individual factors which affected the respondents' beliefs. The analysis shows that spatial filtering of the data generates twice lower mean squared error while effective application of machine learning methods requires the use of explanatory spatial data.

In the article, the authors present a multi-agent model that simulates the development of the COVID-19 pandemic at the regional level. The developed what-if system is a multi-agent generalization of the SEIR epidemiological model, which enables predicting the pandemic's course in various regions of Poland, taking into account Poland's spatial and demographic diversity, the residents' level of mobility, and, primarily, the level of restrictions imposed and the associated compliance. The developed simulation system considers detailed topographic data and the residents' professional and private lifestyles specific to the community. A numerical agent represents each resident in the system, thus providing a highly detailed model of social interactions and the pandemic's development. The developed model, made publicly available as free software, was tested in three representative regions of Poland. As the obtained results indicate, implementing social distancing and limiting mobility is crucial for impeding a pandemic before the development of an effective vaccine. It is also essential to consider a given community's social, demographic, and topographic specificity and apply measures appropriate for a given region.

This paper proposes a methodology for numerical modeling of terraforming Mars’ atmosphere using high-energy asteroid impact and greenhouse gas production processes. The developed simulation model uses a spatial data science approach to analyze the Global Climate Model of Mars and cellular automata to model the changes in Mars’ atmospheric parameters. The developed model allows estimating the energy required to raise the planet’s temperature by sixty degrees using different variations of the terraforming process. Using a data science approach for spatial big data analysis has enabled successful numerical simulations of global and local atmospheric changes on Mars and an analysis of the energy potential required for this process.

The aim of the research was to analyze the possibilities of using deep learning methods for classifying multisource image data for Mars. It should be emphasized that the main goal of the research was to develop a methodology for integrating image data acquired from orbiters (MRO mission's HIRISE camera) and in situ (opportunity rover's NAVCAM camera) and to use their combined analytical potential. We used a VGG-16-based network for this article, which is well-characterized in the literature and has been successfully applied in a wide range of applications. The article proposes a methodology for the supervised classification of landforms on Mars. The proposed solution was evaluated using the Meridiani Planum area, utilizing neural network deep learning and was based on multisource image data. We found that our approach classified aeolian reliefs correctly for more than 94% of the test dataset. The classification accuracy increased to almost 96% when using panoramas developed from opportunity's images and the derivatives of the digital terrain models used during the classification process. It is possible to broaden the proposed concept of multisource classification and the customized deep learning system to the analysis of other regions of Mars and to multispectral imaging without losing the generalizability of the solution.

: Deep learning analysis of multisource Martian data (both from orbiter and rover) allows for the separation and classification of different geomorphological settings. However, it is difficult to determine the optimal neural network model for unambiguous semantic segmentation due to the specificity of Martian data and blurring of the boundary of individual settings (which is its immanent property). In this paper, the authors describe several variants of multisource deep learning processing system for Martian data and develop a methodology for semantic segmentation of geomorphological settings for this planet based on the combination of selected solutions output. Network ensemble with use of the weighted averaging method improved results comparing to single network. The paper also discusses the decision rule extraction method of individual Martian geomorphological landforms using fuzzy inference systems. The results obtained using FIS tools allow for the extraction of single geomorphological forms, such as ripples.