The indicators and methods used for measuring urban liveability: a scoping review

Greenspace and children's lung function in China: A cross-sectional study between 2013 and 2015

This study aims to investigate how surrounding greenspace density and proximity were associated with children's lung function. Between 2013 and 2015, spirometry and a parental survey were performed with children from four primary schools in two Chinese cities. Forced expiratory volume in 1 s (FEV₁, L), forced vital capacity (FVC, L), peak expiratory flow rate (PEF, L/s), and forced expiratory flow at 25 % (FEF₂₅, L/s) and 75 % (FEF₇₅, L/s) of FVC were measured. Outdoor surrounding greenspace index incorporated residential, school and commuting greenness and was weighted using assumed daytime hours spent at home and school. Residential proximity to greenspace index was defined as walking distance to nearest park. A total of 913 children (48.7 % female) were included. Overall, an interquartile range (IQR) increase in outdoor surrounding greenspace was associated with decreased FVC (0.06 L, 95%CI 0.01-0.11), but increased FEV₁/FVC (2.03, 1.13-3.04), PEF (0.26 L/s, 0.13-0.39), and FEF₂₅ (0.30 L/s, 0.17-0.42). Residential proximity to greenspace was associated with increased FEV₁ (0.05 L, 0.02-0.08) and FVC (0.04 L, 0.01-0.07) per IQR decrease in distance. Protective effect was significant for children whose parents were less educated and those living in higher road density areas, though interactions were not significant. Structural equation modeling showed that higher level of surrounding greenspace was associated with lower ambient air pollution and in turn with better lung function. Greenspace density and proximity could influence children's lung function differently.

Trajectories of neighborhood environmental factors and their associations with asthma symptom trajectories among children in Australia: evidence from a national birth cohort study

PURPOSE

This study aims to investigate the prospective associations of neighborhood environmental exposure trajectories with asthma symptom trajectories during childhood developmental stages.

METHODS

We considered asthma symptom, neighborhood environmental factors, and socio-demographic data from the "Longitudinal Study of Australian Children (LSAC)". Group-based trajectory modeling was applied to identify the trajectories of asthma symptom, neighborhood traffic conditions, and neighborhood livability scales (considered for safety and facilities). We used multivariable logistic regression models to assess associations between various neighborhood environmental factors and asthma symptom trajectories.

RESULTS

We included 4,174 children from the LSAC cohort in our study. Three distinct trajectories for asthma symptom were the outcome variables of this study. Among the neighborhood environmental factors, we identified two distinct trajectories for the prevalence of heavy traffic on street, and two trajectories of neighborhood liveability scale. Compared to the 'Low/no' asthma symptoms trajectory group, children exposed to a 'persistently high' prevalence of heavy traffic on street was also significantly associated with both 'transient high' [relative risk ratio (RRR):1.40, 95% CI:1.25,1.58) and 'persistent high' (RRR: 1.33, 95% CI:1.17,1.50)] asthma symptom trajectory groups. Trajectory of moderate and static neighborhood liveability score was at increased risk of being classified as 'transient high' (RRR:1.16, 95% CI:1.07,1.25) and 'persistent high' (RRR:1.38, 95% CI:1.27,1.50) trajectories of asthma symptom.

CONCLUSION

Exposure to heavy traffic and poor neighborhood liveability increased the risk of having an unfavourable asthma symptom trajectory in childhood. Reducing neighborhood traffic load and improving neighborhood safety and amenities may facilitate a favorable asthma symptom trajectory among these children.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-022-00824-z.

Neural Network Model Design for Landscape Ecological Planning Assessment Based on Hierarchical Analysis

In this paper, an in-depth study and analysis of landscape ecological planning and evaluation are carried out using the analytic hierarchy process (AHP) algorithm that integrates neural networks. The application of AHP in the field of tree species planning and the introduction of quantitative analysis methods can effectively change the subjectivity of previous qualitative analysis in tree species selection and make it objective, scientific, and reasonable. The research can provide a reference for other urban tree species planning. From the connotation of landscape ecological service process and ecological space structure, the analysis of landscape ecological service process involves service supply area and service association area, which correspond to different key components of ecological space structure. With the help of the platform, based on the identification and identification methods and theories of ecological spatial structure, the key components of ecological spatial structure in different environments are identified and extracted by using the representation model, binary suitability model, weighted suitability model, and process model. The type of service is based on the different service processes supported by the key components of the ecological spatial structure, forming the ecological spatial structure under different service types. Spatial structure; on this basis, the basic characteristics of the key components of the ecological spatial structure are analyzed, and the correlation characteristics of the ecological spatial structure are analyzed based on the correlation classification system of ecological spatial structure. A backpropagation (BP) neural network-based state assessment method of the grid structure is established. The method takes the parameters of the autoregressive model constructed by the acceleration signals of different working conditions as the feature quantity and the results of the fuzzy hierarchical analysis method as the labels, divides the data set into a training set and a test set, and uses the BP neural network learning method and the training set to supervised train the BP neural network learning assessment model. The test set is used to test the effectiveness and accuracy of the BP neural network-based learning method. The study shows that the evaluation system established by the BP neural network structure is fast and accurate and can substantially reduce the cost of manual testing.

Evaluation of Community Livability Using Gridded Basic Urban Geographical Data—A Case Study of Wuhan

The evaluation of community livability quantifies the demands of human settlement at the micro scale, supporting urban governance decision-making at the macro scale. Big data generated by the urban management of government agencies can provide an accurate, real-time, and rich data set for livability evaluation. However, these data are intertwined by overlapping geographical management boundaries of different government agencies. It causes the difficulty of data integration and utilization when evaluating community livability. To address this problem, this paper proposes a scheme of partitioning basic geographical space into grids by optimally integrating various geographical management boundaries relevant to enterprise-level big data. Furthermore, the system of indexes on community livability is created, and the evaluation model of community livability is constructed. Taking Wuhan as an example, the effectiveness of the model is verified. After the evaluation, the experimental results show that the livability evaluation with reference to our basic geographic grids can effectively make use of governmental big data to spatially identify the multi-dimensional characteristics of a community, including management, environment, facility services, safety, and health. Our technical solution to evaluate community livability using gridded basic urban geographical data is of large potential in producing thematic data of community, constructing a 15-min community living circle of Wuhan, and enhancing the ability of the community to resist risks.