Climate crisis risks to elderly health: strategies for effective promotion and response.

The climate crisis significantly impacts the health and well-being of older adults, both directly and indirectly. This issue is of growing concern in Canada due to the country's rapidly accelerating warming trend and expanding elderly population. This article serves a threefold purpose: (i) outlining the impacts of the climate crisis on older adults, (ii) providing a descriptive review of existing policies with a specific focus on the Canadian context, and (iii) promoting actionable recommendations. Our review reveals the application of current strategies, including early warning systems, enhanced infrastructure, sustainable urban planning, healthcare access, social support systems, and community engagement, in enhancing resilience and reducing health consequences among older adults. Within the Canadian context, we then emphasize the importance of establishing robust risk metrics and evaluation methods to prepare for and manage the impacts of the climate crisis efficiently. We underscore the value of vulnerability mapping, utilizing geographic information to identify regions where older adults are most at risk. This allows for targeted interventions and resource allocation. We recommend employing a root cause analysis approach to tailor risk response strategies, along with a focus on promoting awareness, readiness, physician training, and fostering collaboration and benchmarking. These suggestions aim to enhance disaster risk management for the well-being and resilience of older adults in the face of the climate crisis.

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis.

Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extract hierarchical information from input images. Each down sample step reduces spatial dimensions, and increases feature depth, aiding context acquisition. Batch normalization and dropout layers stabilize models and prevent overfitting during encoding. The decoder reconstructs higher-resolution feature maps using "UpSampling2D" layers. Through extensive experimentation and evaluation of the Cityscapes dataset, this study demonstrates the effectiveness of the U-Net model in achieving state-of-the-art results in image segmentation. The results clearly shown that, the proposed model has high accuracy, mean IOU and mean DICE compared to existing models.

Effects of urban green space landscape pattern on flood retention efficiency from "urban-block" scale perspective.

Under the background of frequent flood disasters and stock planning challenges, clarifying the relationship and mechanism of urban green space landscape patterns and flood retention efficiency at multiple spatial scales has become a critical scientific issue in realizing the maximum flood retention efficiency of limited urban green spaces and improving the capabilities of urban flood control. We reviewed and summarized the factors, mechanisms, and scale differences in the influence of green space landscape patterns on flood retention efficacy at the urban and block scales. Based on the causes for differences in conclusions and research deficiencies, we suggested that future studies should focus on watershed-scale research and expand the investigation into three-dimensional green space landscape patterns. Additionally, attention should be paid to urban and suburban areas separately, and a set of research indices with indicative significance for the flooding process should be established for different flood-sensitive areas and block structures. These measures will help quantitatively reveal how green space landscape patterns of urban and block scales affect flooding process, providing theoretical guidance for urban planning and establishing urban flood safety patterns.

Disappearing cities on US coasts.

The sea level along the US coastlines is projected to rise by 0.25-0.3 m by 2050, increasing the probability of more destructive flooding and inundation in major cities1-3. However, these impacts may be exacerbated by coastal subsidence-the sinking of coastal land areas4-a factor that is often underrepresented in coastal-management policies and long-term urban planning2,5. In this study, we combine high-resolution vertical land motion (that is, raising or lowering of land) and elevation datasets with projections of sea-level rise to quantify the potential inundated areas in 32 major US coastal cities. Here we show that, even when considering the current coastal-defence structures, further land area of between 1,006 and 1,389 km2 is threatened by relative sea-level rise by 2050, posing a threat to a population of 55,000-273,000 people and 31,000-171,000 properties. Our analysis shows that not accounting for spatially variable land subsidence within the cities may lead to inaccurate projections of expected exposure. These potential consequences show the scale of the adaptation challenge, which is not appreciated in most US coastal cities.

Tool for fast assessment of stormwater flood volumes for urban catchment: A machine learning approach.

Specific flood volume is an important criterion for evaluating the performance of sewer networks. Currently, mechanistic models - MCMs (e.g., SWMM) are usually used for its prediction, but they require the collection of detailed information about the characteristics of the catchment and sewer network, which can be difficult to obtain, and the process of model calibration is a complex task. This paper presents a methodology for developing simulators to predict specific flood volume using machine learning methods (DNN - Deep Neural Network, GAM - Generalized Additive Model). The results of Sobol index calculations using the GSA method were used to select the ML model as an alternative to the MCM model. It was shown that the DNN model can be used for flood prediction, for which high agreement was obtained between the results of GSA calculations for rainfall data, catchment and sewer network characteristics, and calibrated SWMM parameters describing land use and sewer retention. Regression relationships (polynomials and exponential functions) were determined between Sobol indices (retention depth of impervious area, correction factor of impervious area, Manning's roughness coefficient of sewers) and sewer network characteristics (unit density of sewers, retention factor - the downstream and upstream of retention ratio) obtaining R2 = 0. 55-0.78. The feasibility of predicting sewer network flooding and modernization with the DNN model using a limited range of input data compared to the SWMM was shown. The developed model can be applied to the management of urban catchments with limited access to data and at the stage of urban planning.

Integrated assessment and prediction of ecological security in typical ecologically fragile areas.

In order to safeguard and restore ecological security in ecologically fragile regions, a regionally appropriate land use structure and ecological security pattern should be constructed. Previous ecological security research models for ecologically fragile areas are relatively homogenous, and it is necessary to establish a multi-modeling framework to consider integrated ecological issues. This study proposes a coupled "PLUS-ESI-Circuit Theory" framework for multi-scenario ecological security assessment of the Ningxia Hui Autonomous Region (NHAR). Firstly, the PLUS model was used to complete the simulation of four future development scenarios. Secondly, a new ecological security index (ESI) is constructed by synthesizing ecological service function, ecological health, and ecological risk. Finally, the Circuit Theory is applied to construct the ecological security pattern under multiple scenarios, and the optimization strategy of ecological security zoning is proposed. The results show that (1) from 2000 to 2030, the NHAR has about 80% of grassland and farmland. The built-up area is consistently growing. (2) Between 2000 and 2030, high ecological security areas are primarily located in Helan Mountain, Liupan Mountain, and the central part of NHAR, while the low ecological security areas are dominated by Shapotou District and Yinchuan City. (3) After 2010, the aggregation of high-security areas decreases, and the fragmentation of patches is obvious. Landscape fragmentation would increase under the economic development (ED) scenario and would be somewhat ameliorated by the ecological protection (EP) and balanced development (BD) scenarios. (4) The number of sources increases but the area decreases from 2000 to 2020. The quantity of ecological elements is on the rise. Ecological restoration and protection of this part of the country will improve its ecological security.

Digitally supported participation in the nexus between public health and urban planning.

The nexus between urban planning and public health acknowledges the importance of creating cities that contribute to their residents' physical, mental, and social well-being. The Healthy Cities movement underlines that community participation and intersectoral work are important to create sustainable, equitable, and healthy cities.Several theoretical and practical participatory approaches form the foundation for participation in public health and urban planning. Growing digitalization has significantly transformed how participation is conducted in various fields. Digital technologies not only play a large role in daily life, but they have opened more opportunities for individuals to interact, share, and collaborate in the planning and design of cities.This article explores how digital technologies enable participation among residents and stakeholders in order to support the health-oriented planning of cities and neighborhoods. From the selective case studies presented in the paper, it can be ascertained that digital technologies can support various forms of participation by enabling different levels of engagement as well as both one-way and two-way interactions. Some forms of engagement can be supported entirely within digital platforms. However, in the case of higher engagement, which requires deeper insights into the problems and the codevelopment of solutions, other nondigital formats and traditional methods such as follow-up workshops and focus group discussions are necessary to complement the digital form of participation.

Associations between community green view index and fine particulate matter from Airboxes.

Urban greenery can help to improve air quality, reduce health risks and create healthy livable urban communities. This study aimed to explore the role of urban greenery in reducing air pollution at the community level in Tainan City, Taiwan, using air quality sensors and street-view imagery. We also collected the number of road trees around each air quality sensor site and identified the species that were best at absorbing PM2.5. Three greenness metrics were used to assess community greenery in this study: two Normalized Difference Vegetation Indices (NDVI) from different satellites and the Green View Index (GVI) from Google Street View (GSV) images. Land-use Regression (LUR) was used for statistical analysis. The results showed that a higher GVI within a 500 m buffer was significantly associated with decreased PM2.5. Neither NDVI metrics within a 500 m circular buffer were significantly associated with decreased PM2.5. Evergreen trees were significantly associated with lower ambient PM2.5, compared with deciduous and semi-deciduous trees. Because localized changes in air quality profoundly affect public health and environmental equity, our findings provide evidence for future urban community greenspace planning and its beneficial impacts on reducing air pollution.

Study of medium and long-term free flow capacity and queue discharge rates on roads.

With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban road capacity exceedingly crucial. The research on the medium and long-term free-flowing capacity and queue emission rate of roads takes an in-depth exploration of this issue from a cutting-edge perspective, aiming to find solutions adaptable to the progression of the times. The purpose of this study is to understand and predict the road capacity and queue emission rate more accurately, thus improving the urban traffic condition. Existing literature primarily focuses on short-term forecasts of road capacity, leaving a notable void in the research of medium and long-term road capacity and queue emission rate. This gap often results in a lack of sufficient foresight when urban traffic planning faces practical issues. To fill this void, this study undertook an in-depth examination of the road capacity and queue emission rate over the medium and long term (10 years) based on big data analysis and artificial intelligence theories. This paper employs a Radial Basis Function (RBF) neural network, combined with twelve other parameters that could potentially impact road capacity, such as traffic volume, road width, number of lanes, traffic signal control methods, etc., to analyze the relationship between each parameter and free-flow traffic and queue emission rate. These analyses are grounded in extensive road data, encompassing not only the city's main roads but also secondary roads and community roads. The study results show a continuous downward trend in the free-flowing capacity of roads and a slight upward trend in the queue emission rate over the past decade. Further analysis reveals the extent of impact each factor has on the free-flow traffic and queue emission rate, providing a scientific basis for future urban traffic planning.

Making cities mental health friendly for adolescents and young adults.

Urban life shapes the mental health of city dwellers, and although cities provide access to health, education and economic gain, urban environments are often detrimental to mental health1,2. Increasing urbanization over the next three decades will be accompanied by a growing population of children and adolescents living in cities3. Shaping the aspects of urban life that influence youth mental health could have an enormous impact on adolescent well-being and adult trajectories4. We invited a multidisciplinary, global group of researchers, practitioners, advocates and young people to complete sequential surveys to identify and prioritize the characteristics of a mental health-friendly city for young people. Here we show a set of ranked characteristic statements, grouped by personal, interpersonal, community, organizational, policy and environmental domains of intervention. Life skills for personal development, valuing and accepting young people's ideas and choices, providing safe public space for social connection, employment and job security, centring youth input in urban planning and design, and addressing adverse social determinants were priorities by domain. We report the adversities that COVID-19 generated and link relevant actions to these data. Our findings highlight the need for intersectoral, multilevel intervention and for inclusive, equitable, participatory design of cities that support youth mental health.

Machine learning-based techniques for land subsidence simulation in an urban area.

Understanding and mitigating land subsidence (LS) is critical for sustainable urban planning and infrastructure management. We introduce a comprehensive analysis of LS forecasting utilizing two advanced machine learning models: the eXtreme Gradient Boosting Regressor (XGBR) and Long Short-Term Memory (LSTM). Our findings highlight groundwater level (GWL) and building concentration (BC) as pivotal factors influencing LS. Through the use of Taylor diagram, we demonstrate a strong correlation between both XGBR and LSTM models and the subsidence data, affirming their predictive accuracy. Notably, we applied delta-rate (Δr) calculus to simulate a scenario with an 80% reduction in GWL and BC impact, revealing a potential substantial decrease in LS by 2040. This projection emphasizes the effectiveness of strategic urban and environmental policy interventions. The model performances, indicated by coefficients of determination R2 (0.90 for XGBR, 0.84 for LSTM), root-mean-squared error RMSE (0.37 for XGBR, 0.50 for LSTM), and mean-absolute-error MAE (0.34 for XGBR, 0.67 for LSTM), confirm their reliability. This research sets a precedent for incorporating dynamic environmental factors and adapting to real-time data in future studies. Our approach facilitates proactive LS management through data-driven strategies, offering valuable insights for policymakers and laying the foundation for sustainable urban development and resource management practices.

Optimization of land use planning under multi-objective demand-the case of Changchun City, China.

Modeling and scenario analysis are the core elements of land use change research, and in the face of the increasingly serious ecological and environmental problems in urbanization, it is important to carry out land use simulation studies under different protection constraints for scientific planning and policy formulation. Taking Changchun City, the capital of Jilin Province, a pilot national eco-province, as an example, a CLUE-S model with coupled landscape ecological security patterns was constructed to predict and simulate the land use structure and layout under multi-objective optimization scenarios in the planning target year (2030), and the results were analyzed based on landscape index evaluation. The study found the following: (i) the proportion of ecological land area under low, medium, and high security levels in the study area was 8.7%, 64.8%, and 26.5%, respectively; (ii) under the current development trend scenario, the trend of increasing fragmentation of cultivated land patches in Changchun in 2030 will remain unchanged, with construction land spreading along the periphery in a compact and continuous pattern, while ecological land will be seriously encroached upon; and (iii) in the 2030 multi-objective optimization scenario, land use patches of all types will begin to show a tendency to cluster, with less landscape fragmentation and more connectivity, while cultivated land and construction land will also begin to converge and do not deteriorate as a result of spatial conflicts over ecological land.

Road extraction through Yangwang-1 nighttime light data: A case study in Wenzhou, China.

The extraction of roadways from remote sensing imagery constitutes a pivotal task, with far-reaching implications across diverse domains such as urban planning, management of transportation systems, emergency response initiatives, and environmental monitoring endeavors. Satellite images captured during daytime have customarily served as the primary resource for this extraction process. However, the emergence of Nighttime Light (NTL) remote sensing data introduces an innovative dimension to this arena. The exploration of NTL data for road extraction remains in its nascent stage, and this study seeks to bridge this gap. We present a refined U-Net model (CA U-Net) integrated with Cross-Attention Mechanisms, meticulously designed to extract roads from Yangwang-1 NTL images. This model incorporates several enhancements, thereby improving its proficiency in identifying and delineating road networks. Through extensive experimentation conducted in the urban landscape of Wenzhou City, the model delivers highly accurate results, achieving an F1 score of 84.46%. These outcomes significantly surpass the performance benchmarks set by Support Vector Machines (SVM) and the Optimal Threshold (OT) method. This promising development paves the way towards maximizing the utility of NTL data for comprehensive mapping and analysis of road networks. Furthermore, the findings underscore the potential of utilizing Yangwang-1 data as a reliable source for road extraction and reaffirm the viability of deploying deep learning frameworks for road extraction tasks utilizing NTL data.

How to Achieve a Healthy City: a Scoping Review with Ten City Examples.

This scoping review of the literature explores the following question: what systematic measures are needed to achieve a healthy city? The World Health Organization (WHO) suggests 11 characteristics of a healthy city. Measures contributing to these characteristics are extracted and classified into 29 themes. Implementation of some of these measures is illustrated by examples from Freiburg, Greater Vancouver, Singapore, Seattle, New York City, London, Nantes, Exeter, Copenhagen, and Washington, DC. The identified measures and examples indicate that a healthy city is a system of healthy sectors. A discussion section suggests healthy directions for nine sectors in a healthy city. These sectors include transportation, housing, schools, city planning, local government, environmental management, retail, heritage, and healthcare. Future work is advised to put more focus on characteristic 5 (i.e., the meeting of basic needs for all the city's people) and characteristic 10 (i.e., public health and sick care services accessible to all) of a healthy city.

Spatial-temporal pattern of change in production-living-ecological space of Nanchong City from 2000 to 2020 and underlying factors.

This study analyzed the spatial-temporal change pattern and underlying factors in production-living-ecological space (PLES) of Nanchong City, China, over the past 20 years using historical land use data (2000, 2010, 2020). A land use transfer matrix was calculated from the historical land use maps, and spatial analysis was conducted to analyze changes in the land use dynamics degree, standard deviation ellipse, and center of gravity. The results showed that there was a rapid spatial evolution of the PLES in Nanchong from 2000 to 2010, followed by a stabilization in the second decade. The transfer of ecological-production space occurred mainly in the Jialing and Yilong River basins, while the reduction of production space and the increase of living space were most prominent in the intersection of three districts (Shunqing, Jialing, and Gaoping districts). The return of production-ecological space was observed in the south and northeast of Yingshan, and there was little notable transfer of other types. The distribution of production space in Nanchong evolved in a north-south to east-west trend, with the center of gravity moving from Yilong to Peng'an County. The living space and production space expanded in a north-south direction, and the center of gravity position was in Nanbu, indicating a more balanced growth or decrease in the last 20 years. The changes in the spatial-temporal pattern of PLES in Nanchong were attributed to the intertwined factors of national policies, economic development, population growth, and the natural environment. This study introduced a novel approach towards rational planning of land resources in Nanchong, which may facilitate more sustainable urban planning and development.

[City Planning, Urban Initiatives and Health]. / Stadtplanung, urbane Initiativen und Gesundheit.

Urban planning and public health are main fields of action when looking at urban development from a health perspective. In both fields of action, politics and administration as well as urban initiatives play a formative role. Action is oriented towards common overarching themes of sustainability, social justice and environmental justice. These commonalities are reflected in different memoranda. Despite the common basis of urban planning and health, there are areas of tension that are rooted, among other things, in different legal frameworks and logic of action. Against this complex background, recommendations are formulated for science, the funding landscape, practice as well as education and training in these areas.