Analyzing the Relationship Between Built-Environment Factors and Safety Threat Reports in Cracow, Poland

With the acceleration of urbanization, the sustainable development of cities and the improvement of residents' quality of life have become major challenges facing the world. Taking Wenzhou City and Hohhot City of China as examples, this paper uses machine learning method to build urban housing price prediction and service level evaluation models. First, the stochastic forest regression and ridge regression models are used to predict the housing price, and the forecasting effect of the two models is compared. The results show that the stochastic forest regression model has better performance in capturing nonlinear relationship and forecasting accuracy. Secondly, a multi-index comprehensive evaluation model is constructed to evaluate the urban service level, and the factors affecting the service level are determined by weighted summation method. The results show that optimizing the greening rate and property management fees can effectively enhance the service level and improve the quality of life of residents. The research results of this paper provide a scientific basis for urban planning and decision-making, and provide a reference for the sustainable development of other cities. Future studies can further explore other factors that affect housing prices and service levels, and incorporate more advanced machine learning models to improve the predictive accuracy and practicality of the models.

Rapid urbanization growth has intensified the demand for sustainable smart city solutions that optimize resource management, improve citizens’ quality of life, and reduce environmental impact. Integrating Internet of Things (IoT) technology with artificial intelligence (AI) offers transformative opportunities to address these challenges by enabling real-time data collection, analysis, and decision-making. This study explores the potential of integrating IoT with AI for sustainable smart city development, using a case study approach to examine its application in diverse urban domains, including energy management, transportation, waste management, and public safety. The research highlights innovative IoT-enabled systems such as smart grids, intelligent traffic control, predictive waste collection, and AI-driven surveillance, demonstrating their ability to improve efficiency and sustainability. Case studies from globally recognized smart cities such as Singapore, Barcelona, ​​and Copenhagen illustrate the benefits and challenges of adopting these technologies. Key findings reveal significant improvements in energy efficiency (up to 25%), reduced traffic congestion (up to 30%) and optimised waste management (up to 40%). However, challenges such as data privacy, interoperability and high implementation costs remain barriers to large-scale deployment. This study proposes a framework to address these issues, emphasizing collaborative governance, robust cybersecurity measures and scalable infrastructure design. The findings underline the transformative potential of integrating IoT and AI to achieve sustainable urban development, offering practical insights for policy makers, urban planners and technology developers. This research contributes to driving smart city initiatives by bridging technological innovation with sustainability goals, paving the way for more resilient and liveable urban environments.

Recently, much attention has been paid to sustainable development in cities. The quality of human life is directly related to environmental quality. Because many people live in cities as a place of social, economic and cultural relationships, certain issues such as environmental crises, energy, air and noise pollution and traffic jams are some of the factors that can alter the quality of human life. Therefore, in order to improve the quality of human life, attention to sustainable development (or sustainability) in cities is proposed. Sustainable building has a comprehensive significance that begins with the conception of negative and positive impacts on the environment. Several descriptions of sustainable or green buildings have been created; however, they all pursue one goal, which is to create sustainable urban developments and protection of the environment. The quality of indoor environments, materials, and energy consumption, water usage, the impact of building construction processes and building maintenance are some of the factors that affect the environment and sustainability. Sustainable building is an attempt to relieve the minus impacts on the environment that occur during a building’s lifetime. This research investigates the important factors that have relevance to green buildings and introduces several criteria of sustainable housing.

Urban safety plays an essential role in the quality of citizens' lives and in the sustainable development of cities. In recent years, researchers have attempted to apply machine learning techniques to identify the role of location-specific attributes in the development of urban safety. However, existing studies have mainly relied on limited images (e.g., map images, single- or four-directional images) of areas based on a relatively large geographical unit and have narrowly focused on severe crime rates, which limits their predictive performance and implications for urban safety. In this work, we propose a novel method that predicts "deviance," which includes formal deviant crimes (e.g., murders) and informal deviant behaviors (e.g., loud parties at night). To do this, we first collect a large-scale geo-tagged dataset consisting of incident report data for seven metropolitan cities, along with corresponding sequential images around incident sites obtained from Google Street View. We then design a convolutional neural network that learns spatio-temporal visual attributes of deviant streets. Experimental results show that our framework is able to reliably recognize real-world deviance in various cities. Furthermore, we analyze which visual attribute is important for deviance identification and severity estimation with respect to social science as well as activated feature maps in the neural network.

Air quality is directly related to people’s health and quality of life and has a profound impact on the sustainable development of cities. Good air quality is the foundation of sustainable development. To solve the current problem of air quality for sustainable development, we used high-resolution (1 km) satellite-retrieved aerosol optical depth (AOD), meteorological, nighttime light and vegetation data to develop a spatiotemporal convolution feature random forest (SCRF) model to predict the PM2.5 concentration in Shandong from 2016 to 2019. We evaluated the performance of the SCRF model and compared the results of other models, including neural network (BPNN), gradient boosting (GBDT), and random forest (RF) models. The results show that compared with the other models, the improved SCRF model performs best. The coefficient of determination (R2) and root mean square error (RMSE) are 0.83 and 9.87 µg/m3, respectively. Moreover, we discovered that the characteristic variables AOD and air temperature (TEM) data improved the accuracy of the model in Shandong Province. The annual average PM2.5 concentrations in Shandong Province from 2016 to 2019 were 74.44 µg/m3, 65.01 µg/m3, 58.32 µg/m3, and 59 µg/m3, respectively. The spatial distribution of air pollution increases from northeastern and southeastern to western Shandong inland. In general, our research has significant implications for the sustainable development of various cities in Shandong Province.

The quality of life of citizens in a city is related to the sustainable decisions made by their leaders. By using principal component analysis (PCA) and taking an intellectual capital perspective (all sources of knowledge in human, relational and structural areas), we explore which of the three dimensions used to measure the sustainability of a city—economic, social or environmental—has the greatest effect on a subjective measurement of quality of life. We propose an econometric model based on a tangible production model to study the relationship between the quality of life and sustainability. To that end, we perform an in-depth examination of the different effects on the four dimensions that comprise the measure of the subjective quality of life: satisfaction, mobility, integration and public service. The results of the estimated model of citizens’ quality of life confirm the existence of a direct relationship for the 52 European cities under study; however, the least relevant role is played by the environmental dimension, which is still unappreciated by citizens. Conversely, the economic and social dimension are found to be determinants in all cases, except for social integration. Therefore, a key requirement of the management aimed at achieving sustainable development in European cities is to activate the environmental dimension.

One of the main reasons for migration is the search for a better quality of life. The concept of quality of life is very broad, encompassing economic, social, political, and cultural factors. According to the World Migration Report 2022, 3.6 percent of the world’s population are migrants. This number is growing due to geopolitical reasons. Increasing migration flows affect the growth of the part of the population living in urban areas, that is, urbanisation. The scale of migration is growing along with the search for a better life. In Lithuania, according to the Department of Statistics, as well as throughout the world, the number of people living in cities is constantly growing; for comparison, a 3% growth has been observed over 4 years, in the European Union, according to the World Bank, 1%, and in the world—1%. The term urbanisation also describes social changes that are determined by the concentration of the population. To ensure quality of life, cities face challenges such as ensuring security, integration of migrants into society and the labour market, the functioning of the health and education system, and sustainable development of cities. Despite growing interest, the impact of migrant flows on the quality of life in cities has not been sufficiently studied in the world scientific literature. Most research is focused on the causes of migration, migrant integration, demographic changes, or labour market interactions. However, less attention is paid to how the dynamics of migrant flows affect the quality of life in cities. Comprehensive assessment is lacking. The goal is to assess the link between quality of life and the dynamics of migration flows in urbanised areas. The article, which conducted a systematic and comparative analysis of concepts published in the scientific literature, formed the concept of quality of life in urban areas, identified the factors that determine quality of life, and studied the link between the quality of life in the city and the dynamics of migration flows. This assessment will allow us to combine the factors that determine quality of life in terms of changes in migrant flows into a common system. To achieve this goal, statistical processing, correlation analysis, and CRITIC methods will be applied.

Enhancing the urban ‘quality of life’ has become a major task for actors in the public realm. The now ‘undisputed objective’ of ‘sustainable city development’ is meaningless unless we can turn our cities, towns and urban neighbourhoods into places where people actively want to live, work and play. The spatial quality of neighbourhoods is crucial in this respect. Besides housing typology and access, ‘quality of life’ is primarily defined by the nature of available open space. Public space is one of the most important and strategic instruments of local government. Thus a basic issue for those in planning practice is how to help realise public spaces which contribute to the appropriate level of quality. Although much progress has been made to assess quality of life at the larger level of scale, for example that of a city or a region, much less progress has been made at a smaller and more concrete area level. More than 25 years after Kevin Lynch's opus magnum Good City Form, an operational approach on the level of urban design is still much in need.

PROTOCOL: Participation, inclusion, transparency and accountability (PITA) to improve public services in low- and middle-income countries: a systematic review.

With the constant development of science and economy, the quality of life is gradually improving, while some city problems such as traffic congestion, fog and haze are also appearing. It has become a hot topic of concern that the improvement of urban living environment should be grown with the development of economy. Following scientific principles, this research firstly reconstructs the evaluation index system of urban living environment. Then, using the method of principal component analysis, this paper takes China’s 35 typical large and medium-sized cities as examples, studies their urban living environment conditions from 2000 to 2011, and analyzes their real urban living conditions. We find that some cities like Shenzhen, Beijing, Shanghai and so on, whose comprehensive index of urban living environment are higher than the average level, which indicate that their urban living constructions have achieved good developments, while the others did not reach that. Finally, some suggestions are put forward.

The primary objective of the city is to provide a high quality of life of the residents. It is one of the reasons behind the migration of the residents, causing an outflow of the people or attracting them. Moreover, needs and requirements connected with the inhabited area are undergoing a constant change. All of the decisions taken in the cities and concerning their development are connected with the quality of the living environment and the quality of the urban space. The condition of the urban and natural environment has an important impact on the health and safety of the residents. The promotion of the city’s development with protection of land against the urban sprawl, with respect for the cultural heritage, efficient infrastructure and social balance has become the main task of the medium-sized cities. Spatial structure of Oświęcim was determined by the development of Oświęcim Chemical Company. Multi-family housing units, together with the necessary services were created in the near vicinity of the company. The attention of the local authorities was directed on this part of the city called the estate. At that time, no investments in the historic part of the city were made. Oświęcim is an old town, with the history that dates back 800 years. The Old Town remained underinvested for a long period of time, which is visible to this day. The tragic heritage of Auschwitz – Birkenau, a former Nazi Concentration Camp has significantly shaped the image of the city, and historical legacy of the city remains in its shadow. This uneven development of the urban structure resulted in a number of social, spatial, economic and environmental problems visible today. Using sustainability in order to provide integrated development to urban areas has become one of the most important tasks of the city’s development. “Sustainable development” perceives the city as a complex, connected and dynamic system, with many interacting processes of social, economic and spatial development. Numerous activities undertaken in Oświęcim are aimed at sustainable development of the city. This article presents selected actions which increased the attractiveness of the Old Town and its surroundings. The presented program is a part of the land development strategy called “Oświęcim Space for Meetings” prepared for the area located along the Soła river and the Old Town district, which are to serve the residents and tourists visiting Auschwitz-Birkenau National Museum. The areas selected for the program will undergo revitalization activities that will improve the quality of the urban space. There will be friendly, integrating green public spaces, and the areas will be activated by making them “attractive” also for newcomers – the tourists.

Understanding the relationship between the built environment and the ride-hailing ridership is crucial to the prediction of the demand for ride-hailing and the formulation of the strategy for upgrading the built environment. However, the existing studies on ride-hailing ignore the scale effect and zone effect of the modifiable area unit problem (MAUP), and show a lack of consideration for the elastic relationship with spatial heterogeneity between built environment variables and ride-hailing ridership. Taking Chengdu as an example, this paper selects 12 independent variables based on the “5Ds” (density, diversity, design, destination accessibility and distance to transit) of the built environment, the dependent variables are the density of ride-hailing pick-ups in the morning and evening peak hours, and 11 spatial units are proposed according to different scales and zoning methods for the aggregation of built environment variables and ride-hailing pick-ups. With the goal of global optimal goodness-of-fit, we determined the optimal spatial unit by using the log-linear Ordinary Least-Squares (OLS) model. A multi-scale geographically weighted elastic regression (MGWER) model is formulated to explore the relative effect of the built environment on the ride-hailing ridership and spatial heterogeneity. The average value of positive elastic local regression coefficient of different variables is used to measure the relative positive impact of built environment factors, and the absolute value of the average value of negative elastic local regression coefficient is used to measure the relative negative impact of built environment factors. The results show that: (1) The MGWER model under the community unit division has the best global goodness-of-fit. (2) Different built environment variables have different elastic impacts on the demand for ride-hailing. For the morning peak hours and evening peak hours, the top three built environment factors with positive impacts are ranked as follows: commercial POI density > average house price > population density, and distance to CBD has the highest negative impacts on pick-up ridership. (3) The different local elasticity coefficients of the built environment factors at different stations are discussed, which indicate the spatial heterogeneity of the ride-hailing ridership. The optimal community zoning method can provide a basis for the zoning and scheduling management of ride-hailing. The results of the built environment variables with greater impact are conducive to the formulation of targeted urban renewal strategies in the process of adjusting the ridership of ride-hailing.

Understanding the determinants of spatial-temporal mobility patterns based on multi-source heterogeneous data

People identify with landscapes and landscapes contribute to a sense of place and wellbeing. The landscape is therefore an important contributor to quality of life. New developments, such as urban and infrastructure projects and the expansion of large-scale agriculture, introduce many new elements into traditional landscapes, altering their visual appearance and perceived quality. These changes may have significant influences on people’s quality of life. In order to protect or enhance the visual landscape, changes in the visual landscape should be given explicit attention in landscape planning and policy making. Current improvements of measurement techniques enabled by GIS, and of highly detailed topographic data covering large areas make it feasible to describe the visual landscape with a high degree of realism without making many concessions to generality and objectivity. The article proposes a procedure that describes the visual landscape, which takes advantage of improvements in measurement techniques, developments in GIS and availability of high-resolution topographic data. The procedure is developed for policy making and spatial planning purposes, and provides information about one specific aspect of the visual landscape, landscape openness . In the remainder of the article, first the concept of landscape openness is explained, then a method to model landscape openness is proposed. Subsequently, a procedure to use this model for policy making purposes is demonstrated. Finally the results of an evaluation of the procedure with policy makers are discussed.

Objective: To investigate the safety hazards in nursing and explore mitigation strategies for elderly patients in the endocrinology department. Methods: A total of 240 bedridden elderly patients from February 2021 to January 2023 were recruited and divided into two groups using the random number table method. The control group (n = 120) received conventional nursing care, and the observation group (n = 120) received personalized nursing care. Patients’ quality of life, the incidence rate of pressure sores, and patient satisfaction with nursing care were observed and analyzed. Results: The incidence of pressure ulcers in the observation group was significantly lower at 17.50% compared to 30.00% in the control group (P < 0.05). Quality of life in the observation group was significantly higher than that in the control group (P < 0.05). The satisfaction rate in the observation group was significantly higher at 93.33% compared to 84.17% in the control group (P < 0.05). Conclusion: These findings emphasize that only through the implementation of scientific and rational nursing measures can effectively reduce nursing risks, enhance therapeutic outcomes, and improve the quality of life for elderly patients.