Multi-source sensor based urban habitat and resident health sensing: A case study of Wuhan, China

The first urban open space product of global 169 megacities using remote sensing and geospatial data

Urban open space (UOS) plays an important environmental role, especially in areas characterized by intense social and economic activity. However, the high interclass similarities, complex surroundings, and scale variations of UOS lead to unsatisfactory UOS mapping performance, and UOS mapping products for major cities around the world are lacking. To fill this gap, we used a deep learning-based method based on a tiny-manual annotation strategy and optical remote sensing imagery to produce a 1.19 m resolution UOS map of 169 megacities, namely the OpenspaceGlobal product. We generated the OpenspaceGlobal product with five urban open space categories. To obtain the final OpenspaceGlobal product, we processed over 8.5 TB of remote sensing images and nearly 90 million polygons in crowdsourced geospatial data. The validation results showed that the OpenspaceGlobal product had an overall accuracy of 79.13 % and a kappa coefficient of 73.47 %. The OpenspaceGlobal product can promote a better understanding of human-made space surfaces in major cities around the world.

The influence of weather and urban environment characteristics on upper respiratory tract infections: a systematic review

Background

Weather can independently affect the occurrence of respiratory tract infections (RTIs) in urban areas. Built environments of cities could further modify exposure to weather and consequently the risk of RTIs, but their combined effects on infections are not known.

Objectives

Our aim was to synthesize evidence of the influence of weather on RTIs in urban areas and to examine whether urban built environments are associated with both weather and RTIs.

Methods

A systematic search of Scopus, PubMed, and Web of Science databases was conducted on 9th of August 2022 following PRISMA guidelines. Studies were included in the review based on predefined criteria by screening 5,789 articles and reviewing reference lists of relevant studies. The quality of the studies was assessed using the AXIS appraisal tool, and the results analyzed by narrative synthesis.

Results

Twenty-one eligible studies focusing on COVID-19 and influenza transmissions, were included in the review. All studies were register based ecological studies by design. Low temperature (11/19 studies) was most often associated with increased risk of RTI. Humidity showed either negative (5/14 studies), positive (3/14 studies) or no (6/14 studies) relation with RTIs. The association between wind and solar radiation on infections was inconclusive. Population density was positively associated with RTIs (14/15 studies).

Conclusions

Our review shows that exposure to low temperature increases the occurrence of RTIs in urban areas, and where also high population density increases the infection risk. The study highlights the need to further assess the relationship between built environment characteristics, weather, and RTIs.

Relationship of screen time with anxiety, depression, and sleep quality among adolescents: a cross-sectional study

Introduction

In the current digital age, people's use of electronic devices has significantly increased screen time, which may have an impact on different aspects of their lives. Adolescents today are exposed to excessive screen time, which may affect their sleep and contribute to anxiety and depression. The purpose of this study is to examine the relationship between screen time with sleep quality, anxiety and depression, among adolescents in Klang Valley, Malaysia.

Methods

This study is a cross-sectional study information was gathered from among 353 secondary school students in the Klang Valley using a questionnaire. The instrument that was used in this study was Pittsburgh Sleep Quality Index (PSQI) Malay version, screen-based media usage (SCREENS-Q) and Hopkins Symptom Check List-25 (HSCL-25) Malay version. The sampling method was stratified and convenience sampling method. The analysis study used the Smart Partial least squares (PLS) method to analyze the data.

Results

Using the Smart PLS technique, we examined the relationship between these variables and identified revealed that screen time has a direct, positive, and significant impact on anxiety level (Mean = 0.134, β = 0.123, p < 0.01) and depression levels (Mean = 0.202, β = 0.194, p < 0.01). Moreover, screen time has a low effect on sleep quality (Mean = 0.128, β = 0.117, p < 0.05). However, the mediating factor, sleep quality, was not significant in the indirect effect of screen time with anxiety (Mean = 0.047, β = 0.040, p  > 0.05) and depression (Mean = 0.044, β = 0.043, p  < 0.05).

Discussion

This study highlights the importance of understanding the association between screen use, sleep quality, anxiety and depression. Notably, excessive screen time appears to be associated with poorer sleep quality, ultimately increasing anxiety and depression. Understanding the effects of excessive screen time on sleep and well-being may have a substantial impact on public health policies and interventions. Enacting policies that promote better screen habits and sleep hygiene could improve people's overall quality of life and well-being in the digital age. However, more longitudinal research is needed to confirm the causality of these relationships and investigate potential intervention strategies.

Exploring the spatial pattern of community urban green spaces and COVID-19 risk in Wuhan based on a random forest model

Since 2019, COVID-19 has triggered a renewed investigation of the urban environment and disease outbreak. While the results have been inconsistent, it has been observed that the quantity of urban green spaces (UGS) is correlated with the risk of COVID-19. However, the spatial pattern has largely been ignored, especially on the community scale. In high-density communities where it is difficult to increase UGS quantity, UGS spatial pattern could be a crucial predictive variable. Thus, this study investigated the relative contribution of quantity and spatial patterns of UGS on COVID-19 risk at the community scale using a random forest (RF) regression model based on (n = 44) communities in Wuhan. Findings suggested that 8 UGS indicators can explain 35% of the risk of COVID-19, and the four spatial pattern metrics that contributed most were core, edge, loop, and branch whereas UGS quantity contributed least. The potential mechanisms between UGS and COVID-19 are discussed, including the influence of UGS on residents' social distance and environmental factors in the community. This study offers a new perspective on optimizing UGS for public health and sustainable city design to combat pandemics and inspire future research on the specific relationship between UGS spatial patterns and pandemics and therefore help establish mechanisms of UGS and pandemics.

Study on the coupling and coordination degree between urban tourism development and habitat environment in the Yangtze River Delta in China

By constructing the two evaluation systems of urban tourism development (TD) and habitat environment (HE), the dynamic response relationship between the two systems in the Yangtze River Delta urban agglomeration from 2001 to 2020 is explored by using panel vector autoregression (PVAR) model and coupled coordination degree model. The study unearthed four intriguing findings: (1) the level of TD in the study area has been continuously rising from 2001 to 2020, with an initial slow growth rate and then fast. The level of HE is increasing steadily at an average annual rate of 7.05%. There exists a reciprocal response relationship between the two systems, with a strong shock effect in the short term and a synergistic evolution in the long term. (2) The coupling degree between the urban TD and HE has an increasing trend, and the coupling coordination degree of the two systems has an average annual rate of 4.165%, implying the interaction and promotion effect between the two systems is improving. (3) Most of the cities in the Yangtze River Delta gradually realize the evolution from dysfunctional type to coordinated type, but the overall coordination intensity is low. (4) The barrier degrees of TD system indicators show a small annual increasing trend, while the barrier degrees of HE system indicators show a substantial and continuous decreasing trend. In terms of the barrier degree factors, some important factors that restrict the coupled and coordinated development of the two systems are also reported. This research can provide a useful reference for the synergistic improvement of urban tourism economy and habitat environment.

Will carbon trading reduce spatial inequality? A spatial analysis of 200 cities in China

The rising concentration of carbon in the atmosphere leads to increasing climate change, and it has become a worldwide consensus to reduce emissions. Considering the degree of economic development and industrial structure of different regions and the vast differences in the spatial distribution of clean energy reserves, it is essential to develop localized emission reduction programs. This study investigates the changes in city GDP after implementing carbon pricing policies. The results show that the carbon pricing policy could effectively reduce inequalities between "rich" and "poor" regions. The Moran index before and after the implementation of the policy decreases from 0.416 to 0.401. We also found spatial clustering patterns of carbon emissions, with the main drivers of carbon emissions differing significantly between developed and developing cities, resource-based and industrial cities, and southern and northern cities in China. The most crucial driver of carbon emissions is still the demand for economic development, which can explain more than 30% of carbon emissions. This study focuses on the impact of carbon market & carbon pricing on poverty alleviation and carbon reduction, makes up for the lack of "spatial justice" in the existing studies and provides a feasible carbon reduction plan for different cities.

Mechanisms influencing the factors of urban built environments and coronavirus disease 2019 at macroscopic and microscopic scales: The role of cities

In late 2019, the coronavirus disease 2019 (COVID-19) pandemic soundlessly slinked in and swept the world, exerting a tremendous impact on lifestyles. This study investigated changes in the infection rates of COVID-19 and the urban built environment in 45 areas in Manhattan, New York, and the relationship between the factors of the urban built environment and COVID-19. COVID-19 was used as the outcome variable, which represents the situation under normal conditions vs. non-pharmacological intervention (NPI), to analyze the macroscopic (macro) and microscopic (micro) factors of the urban built environment. Computer vision was introduced to quantify the material space of urban places from street-level panoramic images of the urban streetscape. The study then extracted the microscopic factors of the urban built environment. The micro factors were composed of two parts. The first was the urban level, which was composed of urban buildings, Panoramic View Green View Index, roads, the sky, and buildings (walls). The second was the streets' green structure, which consisted of macrophanerophyte, bush, and grass. The macro factors comprised population density, traffic, and points of interest. This study analyzed correlations from multiple levels using linear regression models. It also effectively explored the relationship between the urban built environment and COVID-19 transmission and the mechanism of its influence from multiple perspectives.

Correlations between the urban built environmental factors and the spatial distribution at the community level in the reported COVID-19 samples: A case study of Wuhan

COVID-19 has dramatically changed the lifestyle of people, especially in urban environments. This paper investigated the variations of built environments that were measurably associated with the spread of COVID-19 in 150 Wuhan communities. The incidence rate in each community before and after the lockdown (January 23, 2020), as respective dependent variables, represented the situation under normal circumstances and non-pharmaceutical interventions (NPI). After controlling the population density, floor area ratio (FAR), property age and sociodemographic factors, the built environmental factors in two spatial dimensions, the 15-minute walking life circle and the 10-minute cycling life circle, were brought into the Hierarchical Linear Regression Model and the Ridge Regression Model. The results indicated that before lockdown, the number of markets and schools were positively associated with the incidence rate, while community population density and FAR were negatively associated with COVID-19 transmission. After lockdown, FAR, GDP, the number of hospitals (in the 15-minute walking life circle) and the bus stations (in the 10-minute cycling life circle) became negatively correlated with the incidence rate, while markets remained positive. This study effectively extends the discussions on the association between the urban built environment and the spread of COVID-19. Meanwhile, given the limitations of sociodemographic data sources, the conclusions of this study should be interpreted and applied with caution.

Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review

Street view imagery (SVI) provides efficient access to data that can be used to research spatial quality at the human scale. The previous reviews have mainly focused on specific health findings and neighbourhood environments. There has not been a comprehensive review of this topic. In this paper, we systematically review the literature on the application of SVI in the built environment, following a formal innovation–decision framework. The main findings are as follows: (I) SVI remains an effective tool for automated research assessments. This offers a new research avenue to expand the built environment-measurement methods to include perceptions in addition to physical features. (II) Currently, SVI is functional and valuable for quantifying the built environment, spatial sentiment perception, and spatial semantic speculation. (III) The significant dilemmas concerning the adoption of this technology are related to image acquisition, the image quality, spatial and temporal distribution, and accuracy. (IV) This research provides a rapid assessment and provides researchers with guidance for the adoption and implementation of SVI. Data integration and management, proper image service provider selection, and spatial metrics measurements are the critical success factors. A notable trend is the application of SVI towards a focus on the perceptions of the built environment, which provides a more refined and effective way to depict urban forms in terms of physical and social spaces.