Spatial scale analysis for the relationships between the built environment and cardiovascular disease based on multi-source data

Xu, Jun; Jing, Ying; Xu, Ximing; Zhang, Xinyi; He, Hangen; Chen, Fei; Liu, Yanfang

doi:10.1016/j.healthplace.2023.103048

Cited by 7 publications

(1 citation statement)

References 120 publications

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“…To address this deficiency, Fotheringham et al (2017) (Fotheringham, et al [ 25 ]) proposed the multiscale GWR (MGWR) model, which fits an appropriate bandwidth for each explanatory variable. MGWR has been successfully used in the analysis of local impact factors on housing prices [ 26 ], the urban built environment [ 27 ], COVID-19 incidence rates [ 28 ], landslide susceptibility [ 29 ], ecological environment quality [ 30 ], and soil carbon storage [ 31 ]. Therefore, MGWR has great potential in determining the local pollution sources of As and proposing targeted pollution control measures.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Zhu,

Liu,

Jin

et al. 2024

Toxics

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Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran’s I of residuals and a higher R2 (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants.

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Section: Introductionmentioning

confidence: 99%

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Zhu,

Liu,

Jin

et al. 2024

Toxics

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Climate-Driven Variations in Cardiovascular Events

Stewart

2024

Sustainable Development Goals Series

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Previous chapters have focussed on the broader picture of global health in the context of climate change, before examining why climatic conditions/acute weather events can influence an individual’s heart and broader cardiovascular health through the lens of ‘vulnerability to resilience’. In doing so, an argument for why and how we should alter our collective thinking around the role of climate and health (essentially embracing a new paradigm in providing clinical care) is urgently needed. However, such a radical change would be pointless, or at least a low priority, if there was little evidence that health outcomes are indeed—(1) Shaped and influenced by the weather/climatic conditions, thereby resulting in clinically significant variations in event rates and, (2) Climate change is likely to exacerbate the problem in terms of provoking more events that might be preventable. Thus, in the context of a growing body of research and published data (much of which is gravitating towards a more simplistic “heat is bad” mindset), this chapter provides hard evidence that the timing and frequency of concrete events such as hospital admissions and deaths linked to cardiovascular disease and the main subtypes of heart disease are not random. Instead, they ebb and flow according to both predictable climatic transitions (seasons) and unpredictable weather conditions (heatwaves and cold spells) in different ways.

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Investigating thermal exposure during daily walking through a human-scale approach: An analysis of a hot summer in Wuhan

Xu,

Wang

et al. 2024

Building and Environment

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Spatial scale analysis for the relationships between the built environment and cardiovascular disease based on multi-source data

Cited by 7 publications

References 120 publications

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Climate-Driven Variations in Cardiovascular Events

Investigating thermal exposure during daily walking through a human-scale approach: An analysis of a hot summer in Wuhan

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