Spatiotemporal Evolution Analysis of Habitat Quality under High-Speed Urbanization: A Case Study of Urban Core Area of China Lin-Gang Free Trade Zone (2002–2019)

Chen, Lisu; Wei, Qiong; Fu, Qiang; Feng, Dong

doi:10.3390/land10020167

Cited by 21 publications

(10 citation statements)

References 51 publications

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“…Population (PD) is, as expected, positively correlated with PM 2.5 (Figure S3b). As shown in Figure 10b, the highly industrialized cities of Hangzhou and Wenzhou, in particular, were subject to rapid industrial agglomeration and increased production activities, thereby exhibiting increased pollutant emissions and energy consumption [63,64]. Dense housing and increased traffic aggravate PM 2.5 pollution, further highlighting the positive effects of population on PM 2.5 in cities [65].…”

Section: Socioeconomic Factorsmentioning

confidence: 99%

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Meadows

et al. 2021

Remote Sensing

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Fine particulate matter in the lower atmosphere (PM2.5) continues to be a major public health problem globally. Identifying the key contributors to PM2.5 pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM2.5 values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM2.5 in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM2.5 varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) > socioeconomic factors (e.g., gross domestic product, GDP) > topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM2.5 concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM2.5 concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM2.5 values and offers a reliable reference for pollution control strategies.

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Section: Socioeconomic Factorsmentioning

confidence: 99%

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Meadows

et al. 2021

Remote Sensing

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“…Bivariate spatial autocorrelation can be divided into global spatial autocorrelation and local spatial autocorrelation [78]. Bivariate global spatial autocorrelation was described by global Moran's I, which measured the overall spatial correlation across all spatial units for the total study area [79]. Bivariate local spatial autocorrelation was measured by local Moran's I, and the aggregation and differentiation characteristics of local spatial elements were analyzed by depicting the local indicators of spatial association (LISA) map [80].…”

Section: Bivariate Spatial Autocorrelation Analysismentioning

confidence: 99%

Spatiotemporal Differentiation of Territorial Space Development Intensity and Its Habitat Quality Response in Northeast China

Yang

Wang

Guo

et al. 2021

Land

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Studying the spatiotemporal distribution characteristics of territorial space development intensity (TSDI) and its habitat quality (HQ) response is of substantial theoretical and practical significance to optimize regional development patterns and coordinate the relationship between territorial space development and eco-environmental protection. This study establishes a comprehensive assessment model across various aspects, including land, population, economy, and input, to assess the TSDI of each county in Northeast China. We used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to evaluate the HQ of each county and investigated the HQ response to TSDI. The results showed that the TSDI in Northeast China was high in the south and west, low in the north and east, and prominent in urban agglomeration areas, which increased between 2000 and 2015. The spatial pattern of HQ was low in the east and south, high in the west and north, and the HQ was degraded as a whole. Bivariate spatial autocorrelation analysis showed a significant negative spatial correlation between TSDI and HQ, and distinct patterns of local spatial agglomeration were identified. Our findings provide guidelines for territorial space planning and may offer a reference for the ecological civilization construction and the coordinated development of Northeast China.

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“…First, to test and measure in general the spatial autocorrelation and heterogeneous relationship of public attention in adjacent areas, the global Moran's I index was adopted [47,57,58], which can be expressed as follows:…”

Section: Spatial Autocorrelation Testmentioning

confidence: 99%

Understanding Public Attention towards the Beautiful Village Initiative in China and Exploring the Influencing Factors: An Empirical Analysis Based on the Baidu Index

Yang

et al. 2021

Land

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The implementation of China’s Beautiful Village Initiative was an extraordinary achievement and aroused extensive public attention. However, existing research mostly focuses on the construction and seldom on public attention towards the Beautiful Village Initiative. For this reason, this paper investigated the spatiotemporal characteristics of public attention based on the Baidu index using time-constrained clustering and the spatial autocorrelation test. Our results showed that the evolutionary process can be divided into three stages: very little national attention (2011–2012), injection of a strong impetus (2013–2015), and rooted in the people’s minds (2016–2020). Spatially, provincial public attention demonstrated obvious spatial differentiation and stable spatial autocorrelation, with Low–Low clusters in Northwest China and High–High Clusters in East, Central, and North China. Spatial econometric models were further utilized to quantify the effects of socioeconomic factors on public attention. The results of the SEM model proved the existence of spatial spillover effects and indicated that the urbanization rate, population density, education level, and network popularity rate all positively affected public attention. The relationship between Beautiful Village construction and public attention was uncoordinated and, in most provinces, advances in public attention were ahead of the construction level. Our findings contribute to the understanding of public attention towards the Beautiful Village Initiative, and policy suggestions we proposed would be applied to increasing public awareness and participation.

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Spatiotemporal Evolution Analysis of Habitat Quality under High-Speed Urbanization: A Case Study of Urban Core Area of China Lin-Gang Free Trade Zone (2002–2019)

Cited by 21 publications

References 51 publications

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Spatiotemporal Differentiation of Territorial Space Development Intensity and Its Habitat Quality Response in Northeast China

Understanding Public Attention towards the Beautiful Village Initiative in China and Exploring the Influencing Factors: An Empirical Analysis Based on the Baidu Index

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