An implementation evaluation framework of ecological spatial planning based on multi-dimensional data: A case study in China

Fan, Chenjing; Gai, Zhenyu; Shen, Shuanghe; Sun, Shuo

doi:10.1016/j.ufug.2021.127222

Cited by 15 publications

(3 citation statements)

References 61 publications

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“…First of all, agricultural soil pollution originating from man-made landscapes such as industries, highways, villages, and towns could be controlled by optimizing the layout of land use. Similar results could be found in previous studies [39]. According to the results of the present study and previous studies, paddy fields is more beneficial to reduce heavy metal ecological risks compared with drylands [40,41].…”

Section: Strategies For Ecological Restoration In the Futuresupporting

confidence: 93%

Potential Ecological Risks of Heavy Metals in Agricultural Soil Alongside Highways and Their Relationship with Landscape

Wen

et al. 2021

Agriculture

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The agricultural soil alongside highways has experienced multiple potential ecological risks from human activities. In this study, 100 soil samples near the highways were collected in Lishui District, Nanjing City. Using the single-factor pollution index, the Nemerow comprehensive pollution index, and the potential ecological risk index, the study investigated the heavy metal contents and distribution in roadside agricultural soil. PCA and a multiple regression model were applied to quantitatively analyze the spatial relationships between sampling soil heavy metal accumulation and the surrounding man-made landscape. The mean contents of Cu and Pb exceeded the background, while Cd, Cr, and Zn were lower than that. The potential ecological risk index exhibited a very low ecological hazard and only Cr in soils rarely showed moderate risk. Furthermore, quantitative analysis for the sources of contamination revealed that agricultural practices were the dominant contributors to the heavy metals, including Cd, Cu, and Zn, while road and heavy industrial practices contributed to Cr and Pb. The study provides sources of heavy metal pollution from human activities in roadside agricultural land and serves as a reference for ecological restoration.

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Section: Strategies For Ecological Restoration In the Futuresupporting

confidence: 93%

Potential Ecological Risks of Heavy Metals in Agricultural Soil Alongside Highways and Their Relationship with Landscape

Wen

et al. 2021

Agriculture

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“…In an urban environment, multiple spatial elements lead to substantial differences in residents' living conditions and experiences, which may reflect large sentiment differences, resulting in unbalanced regional development and unequal health of residents [5,6]. Therefore, observations and analyses of urban residents' sentiments can provide urban planning decision-makers with feedback to inform planning decisions, narrowing regional development differences and promoting environmental health and equity [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China

Gai

Fan

Shen

et al. 2022

Wireless Communications and Mobile Computing

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Differences in urban land values affect residents' living experiences and may contribute to sentiment inequality. Due to the popularity of smart mobile devices and social media platforms, online tweets with location information can be used as objective information to reflect sentiment differences of urban residents in different locations, overcoming the limitations of previous studies with small sample sizes or a lack of spatial information. Sentiment quantification based on deep learning enables the identification of spatial patterns of urban residents' sentiments. It also provides a new approach for analyzing data from big data platforms using an intelligent computing platform. This paper quantitatively analyzes the sentiment contained in social media tweets using a deep learning sentiment analysis algorithm to reveal inequalities between urban residents' sentiments and land values. The Baidu Intelligent Cloud sentiment analysis platform is used to identify 460,000 Weibo tweets in Xiamen, China, in 2020. We quantitatively analyze the positive and negative sentiments of residents and create a spatial distribution map. The concentration curve indicates sentiment inequality and the impact of high land values on residents' sentiments. The positive sentiment concentration index (CI) and correlation analysis show that the CI value is 0.07, and significant sentiment inequality exists due to the high land value. The use of social media tweet data to analyze sentiment inequality provides a reference for future interdisciplinary research in psychology, urban planning, geography, and sociology. The proposed approach of analyzing social media data using an intelligent computing platform provides new insights into multiplatform data interaction in the context of the Internet of Everything.

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“…Aside from migration, only the GDP had a significant impact on COVID-19's spread; that is, in the case of fixed migration in Wuhan, regions with higher GDPs were likely to face more serious epidemic transmission. Currently, only a few studies have focused on the role of economic factors in epidemic transmission [30][31][32][33][34][35][36][37][38][39][40][41][42]. We considered that a higher GDP was suggestive of greater human activity, closer traffic flows, and socioeconomic connections with other cities, as well as higher population movement (both between and within cities).…”

Section: Factors Influencing Epidemic Transmissionmentioning

confidence: 99%

Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis

Bardin

et al. 2021

IJGI

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COVID-19 has seriously threatened people’s health and well-being across the globe since it was first reported in Wuhan, China in late 2019. This study investigates the mechanism of COVID-19 transmission in different periods within and between cities in China to better understand the nature of the outbreak. We use Moran’s I, a measure of spatial autocorrelation, to examine the spatial dependency of COVID-19 and a dynamic spatial autoregressive model to explore the transmission mechanism. We find that the spatial dependency of COVID-19 decreased over time and that the transmission of the disease could be divided into three distinct stages: an eruption stage, a stabilization stage, and a declination stage. The infection rate between cities was close to one-third of the infection rate within cities at the eruption stage, while it reduced to zero at the declination stage. We also find that the infection rates within cities at the eruption stage and declination stage were similar. China’s policies for controlling the spread of the epidemic, specifically with respect to limiting inter-city mobility and implementing intra-city travel restrictions (social isolation), were most effective in reducing the viral transmission of COVID-19. The findings from this study indicate that the elimination of inter-city mobility had the largest impact on controlling disease transmission.

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An implementation evaluation framework of ecological spatial planning based on multi-dimensional data: A case study in China

Cited by 15 publications

References 61 publications

Potential Ecological Risks of Heavy Metals in Agricultural Soil Alongside Highways and Their Relationship with Landscape

Potential Ecological Risks of Heavy Metals in Agricultural Soil Alongside Highways and Their Relationship with Landscape

Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China

Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis

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