Using urban landscape pattern to understand and evaluate infectious disease risk

Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling

Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted [Formula: see text] of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.

Green space and the health of the older adult during pandemics: a narrative review on the experience of COVID-19

Introduction

Aging is an inevitable process that leads to changes in various dimensions of older adult life, including physical, psychological, and social aspects. Unfortunately, older adults are more susceptible to health problems caused by adverse experiences such as the Corona outbreak.

Aim

The current study examines the lived experience of older adults in facing the conditions of the Corona epidemic to see how green spaces at various scales can influence the physical and mental health of this group.

Method

Relevant articles published, from 2019 to February 17, 2023, were searched using in the Scopus and Web of Science databases. Eligible studies published in English and all studies passed a quality evaluation.

Result

In the final search, 40 articles were selected and analyzed. The majority of studies conducted during the pandemic categorized the impact of green spaces on the health of older adults into three main categories: Place-based attribute, Process, and Function.

Conclusion

The findings of this study demonstrated that people were using private green spaces (gardens, balconies, etc.), small local green public spaces, sitting and gathering spaces in the neighborhood, nearby open spaces, and urban green-blue spaces throughout the epidemic era. They visited green spaces outside the city and urban areas, including urban gardens, agricultural areas, forestlands, and pastures. In this research, we investigated the characteristics of these spaces and classified them into four physical categories: urban landscape, land use, activity, movement, and accessibility. The results showed that exposure to nature or green space improved physical and mental health and increased attention and decision-making quality in older people. We have proposed design implications recommendations for crises to improve safety, security, and social capital by increasing the safe access of older adults to diverse and high-quality green spaces on different scales, which will ultimately enhance the physical and mental health of people in different age groups.

Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review

The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.

Impacts of Urban Blue-Green Space on Residents' Health: A Bibliometric Review

Urban blue-green space (UBGS), as an important component of the urban environment, is found to closely relate to human health. An extensive understanding of the effects of UBGS on human health is necessary for urban planning and intervention schemes towards healthy city development. However, a comprehensive review and discussion of relevant studies using bibliometric methods is still lacking. This paper adopted the bibliometric method and knowledge graph visualization technology to analyze the research on the impact of UBGS on residents' health, including the number of published papers, international influence, and network characteristics of keyword hotspots. The key findings include: (1) The number of articles published between 2001 and 2021 shows an increasing trend. Among the articles collected from WoS and CNKI, 38.74% and 32.65% of the articles focus on physical health, 38.32% and 30.61% on mental health, and 17.06% and 30.61% on public health, respectively. (2) From the analysis of international partnerships, countries with high levels of economic development and urbanization have closer cooperation than other countries. (3) UBGS has proven positive effects on residents' physical, mental, and public health. However, the mediating effects of UBGS on health and the differences in the health effects of UBGS on different ages and social classes are less studied. Therefore, this study proposes several future research directions. First, the mediating effect of UBGS on health impacts should be further examined. Furthermore, the interactive effects of residents' behaviors and the UBGS environment should be emphasized. Moreover, multidisciplinary integration should be strengthened. The coupling mechanism between human behavior and the environment should also be studied in depth with the help of social perception big data, wearable devices, and human-computer interactive simulation. Finally, this study calls for developing health risk monitoring and early warning systems, and integrating health impact assessment into urban planning, so as to improve residents' health and urban sustainability.

The temporal response of dissolved heavy metals to landscape indices in the Le'an river, China

Heavy metals in watersheds are a serious concern due to their toxicity, abundance, and persistence in the environment, especially in mining areas. Source analyses and exploration of other related factors are one of the most important methods to help with effective prevention and control of heavy metal pollution in watersheds. In this study, the concentrations of Cr, Co, Ni, Cu, Zn, As, Cd, Sb, Ba and Pb were measured in the Le'an River, and PCA (principal component analysis) and APCS-MLR (absolute principal component scores - multivariate linear regression) methods were used to identify the sources of the dissolved heavy metals. Additionally, a CA (correlation analysis) method was used to explore the correlations between landscape indices and concentrations of heavy metals. Results show that the main sources for these dissolved heavy metals are mining activities, fertilizers, pesticides, and natural sources. Specific results of PCA and APCS-MLR suggest that Cu, Zn, Cd, Ba are mainly related to mining activities, Cr and Pb are due to fertilizers and pesticides, and Co and Ni are mainly due to natural sources. Correlations between landscapes and heavy metals revealed significant temporal variations, with the strongest responses of dissolved heavy metals to landscape indices appearing in December and March. The propensity of positive or negative responses of the heavy metals to landscape indices are determined by the sources, and their temporal variations may be related to the seasonal changes of rainfall and plant metabolism.

On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest

Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing’s metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect.

Attitudes and Behaviors toward the Use of Public and Private Green Space during the COVID-19 Pandemic in Iran

This paper reports the results of an exploratory study carried out in Birjand, Iran, during the first year of the COVID-19 pandemic. The aim of the study was to explore the behavioral change in the use and the motivation to visit a green space (public or private) during the pandemic as compared to the pre-pandemic period, the effect of green spaces (private and public) on users’ feelings, the relations between the extent to which the access to green spaces was missed, and characteristics of respondents and the place they live. A survey was carried out through an online questionnaire in winter 2020 and about 400 responses were collected. The results showed a decrease in visitation of public green spaces during the pandemic, and higher visitation of private green spaces such as gardens or courtyards by those with access. In addition, both public and private green spaces enhance positive feelings and decrease the negative ones. Respondents missed access to green spaces, especially when their visitation before the pandemic was high, and women missed them more than men. Therefore, private green spaces might represent an opportunity for psychological respite in time of a pandemic, but also for socialization. The study reports respondents’ useful suggestions for urban landscape planning for the city of Birjand that might also be useful for other cities in dry lands; improving the quality of green spaces beyond the quantity may play a role in enhancing the connection to nature in the time of a pandemic, with positive effects on mental health, and this can also can improve recreation opportunities and reduce inequalities.