The expansion of urban centers and peri-urban zones significantly impacts both the natural world and human well-being, leading to issues such as increased air pollution, the formation of urban heat islands, and challenges in water management. The concept of multifunctional greening serves as a cornerstone, emphasizing the interconnectedness of ecological, social, and health-related factors. This study aimed to identify potential locations for three specific types of blue-green infrastructure (BGI): bioswales, infiltration trenches, and green bus stops. Leveraging geospatial datasets, Geographic Information System (GIS) technology, and remote sensing methodologies, this study conducted a comprehensive analysis and modeling of spatial information. Initial cartographic representations were developed to identify specific locations within Olsztyn, a city in Poland, deemed appropriate for the implementation of the designated blue-green infrastructure (BGI) components. Following this, these models were combined with two additional models created by the researchers: a surface urban heat island (SUHI) model and a demographic model that outlined the age structure of the city’s population. This synergistic approach resulted in the development of a detailed map, which identified potential locations for the implementation of blue-green infrastructure. This was achieved by utilizing vector data acquired with a precision of 1 m. The high level of detail on the map allows for an extremely accurate representation of geographical features and infrastructure layouts, which are essential for precise planning and implementation. This infrastructure is identified as a key strategy for strengthening ecosystem resilience, improving urban livability, and promoting public health and well-being.

Renewable energy sources provide an important solution in environmental protection activities and in the process of shaping sustainable development. The search for optimal locations enabling full exploitation of the energy intensity of real estate presents a significant challenge in terms of geoinformation analysis methods in a GIS environment. The aim of the study was to develop a capabilities matrix for the location of photovoltaic farms and, based on this, to compile a map of decision alternatives for these locations. The first stage involved the determination of the spatial features (stimulants and destimulants), which were significant in the context of photovoltaic (PV) farm location. During the analysis, the scope of the necessary data and their sources, which included topographic vector studies, aerial images, and the OpenStreetMap open data, were determined. The next stage was to determine the weights of the features which were significant in the context of photovoltaic (PV) farm location. To this end, the Multicriteria Decision Making (MCDM) method, including the Analytic Hierarchy Process (AHP) method, was employed. For the verification of the results, the entropy measure was also used. Entropy was calculated based on the diversity of previously identified geospatial features that shape the optimum conditions for their location, based on the photovoltaic farms already existing in Poland. A total of 555 photovoltaic farms were analysed. The next stage assumed the performance of geoinformation analyses using GIS tools and the development of a capabilities matrix for the PV farm location for the selected commune in Poland. The final stage involved the compilation of a PV decision alternative map for the selected commune based on the capabilities matrix. As a result, as an example, a ranking of plots was developed as decision-making alternatives for the municipality of Czarnia located in the northeastern part of Poland. It shows which parcels of land primarily have the dimension of spatial features that are favourable for the location of PV. More than 6900 parcels were analysed, in which 176 presented the highest value of the index of optimal PV location generated using the AHP method. This method provides a basis for further work by identifying optimal locations taking into account existing spatial conditions. The analyses carried out can be an important document in the future for spatial management, in particular for the location of new PV farms. As a continuation of the research, the authors will work on expanding the scope of the analyses and automating the entire process.

The problems covered by this article refer to the possibility of connecting Barabási’s theory of scale-free networks with the concept of growth poles and the theory of spatial self-organization, the optimization of the landscape potential. It concerns also the possibility of transforming existing random networks of spatial connections into scale-free networks. The authors analyzed the possibilities for the formation (merging) of networks in geographical space according to agreed rules and simulated the spatial distribution of networks in regions to define their type and stages of formation of the centers. They also defined the correlation between the phenomenon of spatial self-organization and existing growth poles and the formation of spatial connection networks. The analyses carried out have shown that the spatial diffusion of rules of formation of networks leads to the formation of scale-free structures. The application of Barabási’s theory of scale-free networks in spatial analyses will make it possible to describe two hitherto separated issues relating to spatial management—the theory of spatial self-organization and the concept of growth poles—by means of one research tool. The use of assumptions of the scale-free network theory combined with the theory of spatial self-organization and the concept of growth poles should streamline the process of sustainable development planning for the area under analysis and make it possible to optimize the formation of the landscape.

The main aim of this study was to evaluate the applicability of the co-kriging method for modeling the vertical movements of the Earth’s crust based on data acquired with the use of precision leveling techniques and measurements conducted by permanent Global Navigation Satellite System (GNSS) stations. Data were processed with the use of empirical, theoretical, and directional variograms (semivariograms), as well as variogram maps. Large-scale spatial variability was determined using polynomial regression. The relationships between the length of the semi-major and semi-minor axes vs. the root mean square (RMS) and the standard error of the estimate were analyzed. The relationships between the anisotropic direction and the number of lags were determined, and other parameters were calculated. Preliminary data fitting produced non-stationary surfaces. The leveling data were anisotropic, and the GNSS data were isotropic. Nugget effects were observed in both datasets, in particular in the GNSS data. The size of the ellipse was strongly correlated with the RMS and σ (average standard deviation of prediction). The anisotropy angle was determined using the number of lags. Co-kriging was found to not be a suitable method for modeling the vertical movements of the Earth’s crust based on data from various sources. The final result was strongly influenced by the initial dataset. The obtained results show how the method of combining data sets (interpolation, network adjustment) affected the final cartographic model.

The development of cities and peri-urban areas is exerting an increasingly strong impact on the natural environment and, at the same time, on the living conditions and health of people. Problems and challenges that need to be addressed include increasing air pollution in these areas, formation of a surface urban heat island (SUHI), water management disruptions (water scarcity or excess), and the destruction of natural habitats. One of the solutions that contributes to climate change mitigation is the introduction of blue-green infrastructure into the city space and urbanised areas. The research objective was to identify spatial features (geodata) that determine the optimum location of selected blue-green infrastructure (BGI) components, acquire them, and then use the Geographical Information System (GIS) to determine their optimum locations. As the first step, cartographic models were developed which indicated areas that enable the development of selected blue-green infrastructure components in the Olsztyn city area, Warmińsko-Mazurskie Province, Poland. The models were juxtaposed with other two models developed by the authors, i.e. a surface urban heat island model and a demographic model that showed the age structure of the city’s population. Consequently, maps with potential locations for the blue-green infrastructure were developed, while taking into account reference data from the National Land Surveying and Cartographic Resource and Landsat 8 images.