Geographically weighted regression to measure spatial variations in correlations between water pollution versus land use in a coastal watershed

Huang, Jinliang; Huang, Yaling; Pontius, Robert Gilmore; Zhang, Zhenyu

doi:10.1016/j.ocecoaman.2014.10.007

Cited by 91 publications

(48 citation statements)

References 55 publications

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“…Yet scattered settlements did not emerge as a significant explanatory variable, possibly due to its correlation with arable land. Built land has explained nutrient losses elsewhere (Basnyat et al, ; Huang, Huang, Pontius Jr, & Zhang, ; Pratt & Chang, ; Uuemaa et al, ; Zampella, Procopio, Lathrop, & Dow, ), and highly urbanized areas have been shown to locally affect pollutant loads in Finland (Taka, ; Valtanen, Sillanpää, & Setälä, ). In our study, however, urban land cover was generally low, and its effect, like that of scattered settlements, may have been dwarfed by arable land due to their mutual correlation.…”

Section: Discussionmentioning

confidence: 99%

Catchment characteristics predicting nitrogen and phosphorus losses in Finland

Röman

Ekholm

Tattari

et al. 2018

River Research & Apps

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Empirical relationships between catchment properties and nutrient losses are often derived from spatially restricted data, which hampers the ability to more widely generalize the results. In this study, we regressed the catchment characteristics and the losses of total nitrogen (TN) and total phosphorus (TP) for all monitored catchments in Finland with adequate data (n = 59, from the year 2000 to 2011). TN loss was found to increase with the percentage of arable land and amount of point source loading and to decrease with increasing lake percentage. TP loss also increased not only with the percentage of arable land but also with fine‐grained soils (Cambisols) in the catchment and decreased with the percentage of lakes. Urban areas, scattered settlements, and N deposition were not significant explanatory variables, possibly due to their spatial covariation with arable land in Finland. Despite the fact that only a few significant explanatory variables were identified, the models explained the TN and TP losses by up to 91% and 92%, respectively.

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Section: Discussionmentioning

confidence: 99%

Catchment characteristics predicting nitrogen and phosphorus losses in Finland

Röman

Ekholm

Tattari

et al. 2018

River Research & Apps

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“…Geographically weighted regression (GWR) and GTWR were applied to analyze the temporal and spatial differentiation characteristics of variables [39][40][41][42]. For instance, GWR was used to explore the spatial and temporal patterns of land cover, land use, and population change dynamics [43]; the urban land efficiency [44]; and the impact of the agro-environmental policy initiative on the economic landscape [45].…”

Section: Methodsmentioning

confidence: 99%

Evaluation and Influencing Factors of Sustainable Development Capability of Agriculture in Countries along the Belt and Road Route

Wang

Chen

2019

Sustainability

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Agriculture is increasingly facing major challenges such as climate change, scarcity of natural resources, and changing societal demands. To tackle these challenges, there is a pressing need to evolve towards more sustainable agricultural practices. As a result, sustainability stands among the most relevant topics in agricultural research worldwide, and countries along the Belt and Road (B&R) route are no exception. This paper selected 25 indicators from the five subsystems of population, society, economy, environment, and resources in order to build an evaluation index system of agricultural sustainable development capability, and then it used an improved entropy weight method, technique for ordering preference by similarity to an ideal solution (TOPSIS), and coordination degree method to measure the comprehensive capability and coordination of agricultural sustainable development of all countries along the B&R route from 2006 to 2015. First, according to the time dimension, the comprehensive score of sustainable development capability of agriculture along the B&R route: This had an average annual score of 0.3195 which initially decreased, then increased in a fluctuating manner, before finally falling again. Second, according to the spatial dimension, the average comprehensive score of agricultural sustainable development capability showed an evolutionary trend of ‘high–low–high–low–high’ from west to east, which showed an obvious basic spatial pattern of the ‘W’ type. Third, from the perspective of the subsystems of agriculture, although the coordination degree among subsystems in the main grain-producing areas increased continually from 2006 to 2015, the overall level of development needed to be further improved. In order to further clarify the main factors affecting the capability of agricultural sustainable development, this paper selected six explanatory variables: The level of economic development, financial expenditure for agriculture, agricultural foreign direct investment, agricultural labor force, the intensity of agricultural R&D investment, and the level of agricultural informatization. Then, geographically and temporally weighted regression was applied to evaluate the direction and degree of influences of selected factors on sustainability development capability of agriculture. The results showed that the regression coefficients of each variable in 53 countries were positive or negative, which indicated that the influencing factors of agricultural sustainable development capacity had the characteristics of geospatial nonstationarity.

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“…The model has a considerably better fit with empirical data than the global model, which makes it possible to draw detailed conclusions [24,25]. For OLS (ordinary least squares), GWR is an improved and processed OLS model where the spatial position of the influencing factor variable is fully considered and the parameter estimation of local regression is allowed [26,27]. Therefore, in this paper, the GWR model is selected as the main method to analyse the influencing factors of IVQC.According to Anselin [18,28], the GWR model is specified as:…”

Section: Gwrmentioning

confidence: 99%

Influencing Factors for the Promotion of International Vocational Qualification and Certification: Evidences from International Project Manager Professionals in China

et al. 2020

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Globalization has driven the promotion of international vocational qualification and certification (IVQC) to unify certification systems and standards. We explore IVQC promotion paths through the introduction and development of China's International Project Manager Professional (IPMP) certification and identify and analyse IVQC processes' influencing factors. Four factors (economic level, education level, employment level, sex ratio) are proposed; their impacts are hypothesized. Geographically weighted regression (GWR) is employed to identify factor impact relationships and validate assumptions. The results show that the four factors are positive for the promotion of IVQC. Economic level, employment level, and sex ratio contribute to the promotion of IVQC; employment level contributes most. Education level has relatively small impact. Therefore, IVQC is more likely to enter areas with developed economies, high employment rates, and more males. The promotion of IVQC can be facilitated by continuous social progress and international development. However, areas where salient factor levels are too low still present challenges.

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Geographically weighted regression to measure spatial variations in correlations between water pollution versus land use in a coastal watershed

Cited by 91 publications

References 55 publications

Catchment characteristics predicting nitrogen and phosphorus losses in Finland

Catchment characteristics predicting nitrogen and phosphorus losses in Finland

Evaluation and Influencing Factors of Sustainable Development Capability of Agriculture in Countries along the Belt and Road Route

Influencing Factors for the Promotion of International Vocational Qualification and Certification: Evidences from International Project Manager Professionals in China

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