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Yumin Chen
School of Resource and Environment Science, Wuhan University, Wuhan 430079, China

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Journal article
Published: 05 August 2021 in Remote Sensing
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High-resolution DEMs are important spatial data, and are used in a wide range of analyses and applications. However, the high cost to obtain high-resolution DEM data over a large area through sensors with higher precision poses a challenge for many geographic analysis applications. Inspired by the convolution neural network (CNN) excellent performance in super-resolution (SR) image analysis, this paper investigates the use of deep residual neural networks and low-resolution DEMs to generate high-resolution DEMs. An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic high-resolution DEM. The results were compared with those generated with the bicubic, bilinear, and EDSR methods. The numerical accuracy and terrain feature preserving effects of the EDEM-SR method can generate reconstructed DEMs that better match the original DEMs, show lower MAE and RMSE, and improve the accuracy of the terrain parameters. MAE is reduced by about 30 to 50% compared with traditional interpolation methods. The results show how the EDEM-SR method can generate high-resolution DEMs using low-resolution DEMs.

ACS Style

Annan Zhou; Yumin Chen; John Wilson; Heng Su; Zhexin Xiong; Qishan Cheng. An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs. Remote Sensing 2021, 13, 3089 .

AMA Style

Annan Zhou, Yumin Chen, John Wilson, Heng Su, Zhexin Xiong, Qishan Cheng. An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs. Remote Sensing. 2021; 13 (16):3089.

Chicago/Turabian Style

Annan Zhou; Yumin Chen; John Wilson; Heng Su; Zhexin Xiong; Qishan Cheng. 2021. "An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs." Remote Sensing 13, no. 16: 3089.

Research article
Published: 15 July 2021 in Environmental Science and Pollution Research
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PM2.5 concentrations are commonly estimated using geographically weighted regression (GWR) models, but these models may suffer from multi-collinearity and over-focus on local feature problems. To overcome these shortcomings, a self-adaptive bandwidth eigenvector spatial filtering (SA-ESF) model utilizing the golden section search (GO-ESF) and genetic algorithm (GA-ESF) was proposed. The SA-ESF model was applied to estimate ground PM2.5 concentrations in the Yangtze River Delta (YRD) region of China both seasonally and annually from December 2015 to November 2016 using remotely sensing data, factory locations, and road networks. The results of the original eigenvector spatial filtering (ESF), GO-ESF, GA-ESF, and GWR models show that the GA-ESF model offers better performance and exhibits a better average adjusted R2 which is 26.6%, 15.3%, and 10.8% higher than for the ESF, GO-ESF, and GWR models, respectively. We next calculated stochastic site indicators that can describe characteristics of regional concentration from interpolated concentration maps derived from the GA-ESF and GWR models. The concentration maps and stochastic site indicators point to major differences in the PM2.5 concentrations in mountainous areas. There are notably high concentrations in those areas using the GWR model, in contrast with the GA-ESF results, indicating that there may be overfitting problems using the GWR model. Overall, the proposed SA-ESF model with the genetic algorithm technique can capture both global and local features and achieve promising results.

ACS Style

Huangyuan Tan; Yumin Chen; John P. Wilson; Annan Zhou; Tianyou Chu. Self-adaptive bandwidth eigenvector spatial filtering model for estimating PM2.5 concentrations in the Yangtze River Delta region of China. Environmental Science and Pollution Research 2021, 1 -14.

AMA Style

Huangyuan Tan, Yumin Chen, John P. Wilson, Annan Zhou, Tianyou Chu. Self-adaptive bandwidth eigenvector spatial filtering model for estimating PM2.5 concentrations in the Yangtze River Delta region of China. Environmental Science and Pollution Research. 2021; ():1-14.

Chicago/Turabian Style

Huangyuan Tan; Yumin Chen; John P. Wilson; Annan Zhou; Tianyou Chu. 2021. "Self-adaptive bandwidth eigenvector spatial filtering model for estimating PM2.5 concentrations in the Yangtze River Delta region of China." Environmental Science and Pollution Research , no. : 1-14.

Journal article
Published: 04 December 2020 in Remote Sensing
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Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27%–77.09% compared with the raw feature. In addition, GFS has a 5.27%–23.59% higher precision than other methods.

ACS Style

Tianyou Chu; Yumin Chen; Liheng Huang; Zhiqiang Xu; Huangyuan Tan. A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features. Remote Sensing 2020, 12, 3978 .

AMA Style

Tianyou Chu, Yumin Chen, Liheng Huang, Zhiqiang Xu, Huangyuan Tan. A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features. Remote Sensing. 2020; 12 (23):3978.

Chicago/Turabian Style

Tianyou Chu; Yumin Chen; Liheng Huang; Zhiqiang Xu; Huangyuan Tan. 2020. "A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features." Remote Sensing 12, no. 23: 3978.

Journal article
Published: 24 August 2020 in ISPRS International Journal of Geo-Information
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This paper proposes a flow-path-network-based (FPN-based) algorithm, constructed from a square-grid digital elevation model (DEM) to improve the simulation of the flow path curvature (C). First, the flow-path network model was utilized to obtain an FPN. Then, a flow-path-network-flow-path-curvature (FPN-C) algorithm was proposed to estimate C from the FPN. The experiments consisted of two sections: (1) quantitatively evaluating the accuracy using 5 m DEMs generated from the mathematical ellipsoid and Gauss models, and (2) qualitatively assessing the accuracy using a 30 m DEM of a real-world complex region. The three algorithms proposed by Evans (1980), Zevenbergen and Throne (1987), and Shary (1995) were used to validate the accuracy of the new algorithm. The results demonstrate that the C value of the proposed algorithm was generally closer to the theoretical C value derived from two mathematical surfaces. The root mean standard error (RMSE) and mean absolute error (MAE) of the new method are 0.0014 and 0.0002 m, reduced by 42% and 82% of that of the third algorithm on the ellipsoid surface, respectively. The RMSE and MAE of the presented method are 0.0043 and 0.0025 m at best, reduced by up to 35% and 14% of that of the former two algorithms on the Gauss surface, respectively. The proposed algorithm generally produces better spatial distributions of C on different terrain surfaces.

ACS Style

Qianjiao Wu; Yumin Chen; Hongyan Zhou; Shujie Chen; Han Wang. A New Algorithm for Calculating the Flow Path Curvature (C) from the Square-Grid Digital Elevation Model (DEM). ISPRS International Journal of Geo-Information 2020, 9, 510 .

AMA Style

Qianjiao Wu, Yumin Chen, Hongyan Zhou, Shujie Chen, Han Wang. A New Algorithm for Calculating the Flow Path Curvature (C) from the Square-Grid Digital Elevation Model (DEM). ISPRS International Journal of Geo-Information. 2020; 9 (9):510.

Chicago/Turabian Style

Qianjiao Wu; Yumin Chen; Hongyan Zhou; Shujie Chen; Han Wang. 2020. "A New Algorithm for Calculating the Flow Path Curvature (C) from the Square-Grid Digital Elevation Model (DEM)." ISPRS International Journal of Geo-Information 9, no. 9: 510.

Journal article
Published: 11 April 2020 in ISPRS International Journal of Geo-Information
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The recognition of postearthquake scenes plays an important role in postearthquake rescue and reconstruction. To overcome the over-reliance on expert visual interpretation and the poor recognition performance of traditional machine learning in postearthquake scene recognition, this paper proposes a postearthquake multiple scene recognition (PEMSR) model based on the classical deep learning Single Shot MultiBox Detector (SSD) method. In this paper, a labeled postearthquake scenes dataset is constructed by segmenting acquired remote sensing images, which are classified into six categories: landslide, houses, ruins, trees, clogged and ponding. Due to the insufficiency and imbalance of the original dataset, transfer learning and a data augmentation and balancing strategy are utilized in the PEMSR model. To evaluate the PEMSR model, the evaluation metrics of precision, recall and F1 score are used in the experiment. Multiple experimental test results demonstrate that the PEMSR model shows a stronger performance in postearthquake scene recognition. The PEMSR model improves the detection accuracy of each scene compared with SSD by transfer learning and data augmentation strategy. In addition, the average detection time of the PEMSR model only needs 0.4565s, which is far less than the 8.3472s of the traditional Histogram of Oriented Gradient + Support Vector Machine (HOG+SVM) method.

ACS Style

Zhiqiang Xu; Yumin Chen; Fan Yang; Tianyou Chu; Hongyan Zhou. A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning. ISPRS International Journal of Geo-Information 2020, 9, 238 .

AMA Style

Zhiqiang Xu, Yumin Chen, Fan Yang, Tianyou Chu, Hongyan Zhou. A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning. ISPRS International Journal of Geo-Information. 2020; 9 (4):238.

Chicago/Turabian Style

Zhiqiang Xu; Yumin Chen; Fan Yang; Tianyou Chu; Hongyan Zhou. 2020. "A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning." ISPRS International Journal of Geo-Information 9, no. 4: 238.

Journal article
Published: 05 March 2020 in Remote Sensing
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Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R2 = 0.98). The ordinary least squares (OLS) (mean R2 = 0.86) and spatial error model (SEM) (mean pseudo R2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.

ACS Style

Jiping Cao; Yumin Chen; John P. Wilson; Huangyuan Tan; Jiaxin Yang; Zhiqiang Xu. Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods. Remote Sensing 2020, 12, 839 .

AMA Style

Jiping Cao, Yumin Chen, John P. Wilson, Huangyuan Tan, Jiaxin Yang, Zhiqiang Xu. Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods. Remote Sensing. 2020; 12 (5):839.

Chicago/Turabian Style

Jiping Cao; Yumin Chen; John P. Wilson; Huangyuan Tan; Jiaxin Yang; Zhiqiang Xu. 2020. "Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods." Remote Sensing 12, no. 5: 839.

Journal article
Published: 04 December 2019 in Atmospheric Environment
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Ordinary interpolation using PM2.5 ground monitoring observations can seldom reveal the PM2.5 concentration distribution characteristics due to the uneven distribution of monitoring stations and because ordinary linear regression often neglects the spatial autocorrelation among geographical locations. In this study, we developed an eigenvector spatial filtering based spatially varying coefficient (ESF-SVC) model to estimate ground PM2.5 concentration. To generate and analyze the spatiotemporal distribution of PM2.5 concentration in the China's Yangtze River Delta (YRD) region, ESF-SVC model which uses a set of satellite remote sensing data, factory locations, and road networks, was fitted at different time scales from December, 2015 to November, 2016. Comparisons among the ESF-SVC, eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models suggest that the ESF-SVC model with an average annual and seasonal adjusted R2 of 0.684, is 10.3 and 13.8% higher than the GWR and ESF models, respectively. The average annual and seasonal cross validation root mean square error (RMSE) of the ESF-SVC models lower than the GWR and ESF models. PM2.5 concentration distribution maps for annual and seasonal were produced to illustrate YRD region's spatiotemporal characteristics. In summary, an ESF-SVC model offers a reliable approach PM2.5 concentrations estimation in large area.

ACS Style

Huangyuan Tan; Yumin Chen; John P. Wilson; Jingyi Zhang; Jiping Cao; Tianyou Chu. An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China. Atmospheric Environment 2019, 223, 117205 .

AMA Style

Huangyuan Tan, Yumin Chen, John P. Wilson, Jingyi Zhang, Jiping Cao, Tianyou Chu. An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China. Atmospheric Environment. 2019; 223 ():117205.

Chicago/Turabian Style

Huangyuan Tan; Yumin Chen; John P. Wilson; Jingyi Zhang; Jiping Cao; Tianyou Chu. 2019. "An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China." Atmospheric Environment 223, no. : 117205.

Journal article
Published: 08 November 2019 in Atmosphere
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After the release of the high-resolution downscaled National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, it is worth exploiting this dataset to improve the simulation and projection of local precipitation. This study developed support vector regression (SVR) and quantile mapping (SVR_QM) ensemble and correction models on the basis of historic precipitation in the Han River basin and the 21 NEX-GDDP models. The generated SVR_QM models were applied to project changes of precipitation during the 21st century for the region. Several statistical metrics, including Pearson’s correlation coefficient (PCC), root mean squared error (RMSE), and relative bias (Rbias), were used for evaluation and comparative analyses. The results demonstrated the superior performance of SVR_QM compared with multi-layer perceptron (MLP), SVR, and random forest (RF), as well as simple model average (MME) ensemble methods and single NEX-GDDP models. PCC was up to 0.84 from 0.61–0.71 for the single NEX-GDDP models, RMSE was up to 34.02 mm from 48–51 mm, and Rbias values were almost removed. Additionally, the projected precipitation changes during the 21st century in most stations had an increasing trend under both Representative Concentration Pathway RCP4.5 and RCP8.5 emissions scenarios; the regional average precipitation during the middle (2040–2059) and late (2070–2089) 21st century increased by 3.54% and 5.12% under RCP4.5 and by 7.44% and 9.52% under RCP8.5, respectively.

ACS Style

Ren Xu; Yumin Chen; Zeqiang Chen. Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method. Atmosphere 2019, 10, 688 .

AMA Style

Ren Xu, Yumin Chen, Zeqiang Chen. Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method. Atmosphere. 2019; 10 (11):688.

Chicago/Turabian Style

Ren Xu; Yumin Chen; Zeqiang Chen. 2019. "Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method." Atmosphere 10, no. 11: 688.

Journal article
Published: 21 September 2019 in Water
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A Poisson regression based on eigenvector spatial filtering (ESF) is proposed to evaluate the flood risk in the middle reaches of the Yangtze River in China. Regression analysis is employed to model the relationship between the frequency of flood alarming events observed by hydrological stations and hazard-causing factors from 2005 to 2012. Eight factors, including elevation (ELE), slope (SLO), elevation standard deviation (ESD), river density (DEN), distance to mainstream (DIST), NDVI, annual mean rainfall (RAIN), mean annual maximum of three-day accumulated precipitation (ACC) and frequency of extreme rainfall (EXE) are selected and integrated into a GIS environment for the identification of flood-prone basins. ESF-based Poisson regression (ESFPS) can filter out the spatial autocorrelation. The methodology includes construction of a spatial weight matrix, testing of spatial autocorrelation, decomposition of eigenvectors, stepwise selection of eigenvectors and calculation of regression coefficients. Compared with the pseudo R squared obtained by PS (0.56), ESFPS exhibits better fitness with a value of 0.78, which increases by approximately 39.3%. ESFPS identifies six significant factors including ELE, DEN, EXE, DIST, ACC and NDVI, in which ACC and NDVI are the first two main factors. The method can provide decision support for flood risk relief and hydrologic station planning.

ACS Style

Tao Fang; Yumin Chen; Huangyuan Tan; Jiping Cao; Jiaxin Liao; Liheng Huang. Flood Risk Evaluation in the Middle Reaches of the Yangtze River Based on Eigenvector Spatial Filtering Poisson Regression. Water 2019, 11, 1969 .

AMA Style

Tao Fang, Yumin Chen, Huangyuan Tan, Jiping Cao, Jiaxin Liao, Liheng Huang. Flood Risk Evaluation in the Middle Reaches of the Yangtze River Based on Eigenvector Spatial Filtering Poisson Regression. Water. 2019; 11 (10):1969.

Chicago/Turabian Style

Tao Fang; Yumin Chen; Huangyuan Tan; Jiping Cao; Jiaxin Liao; Liheng Huang. 2019. "Flood Risk Evaluation in the Middle Reaches of the Yangtze River Based on Eigenvector Spatial Filtering Poisson Regression." Water 11, no. 10: 1969.

Journal article
Published: 22 August 2019 in ISPRS International Journal of Geo-Information
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The 3D road network scene helps to simulate the distribution of road infrastructure and the corresponding traffic conditions. However, the existing road modeling methods have limitations such as inflexibility in different types of road construction, inferior quality in visual effects and poor efficiency for large-scale model rendering. To tackle these challenges, a template-based 3D road modeling method is proposed in this paper. In this method, the road GIS data is first pre-processed before modeling. The road centerlines are analyzed to extract topology information and resampled to improve path accuracy and match the terrain. Meanwhile, the road network is segmented and organized using a hierarchical block data structure. Road elements, including roadbeds, road facilities and moving vehicles are then designed based on templates. These templates define the geometric and semantic information of elements along both the cross-section and road centerline. Finally, the road network scene is built by the construction algorithms, where roads, at-grade intersections, grade separated areas and moving vehicles are modeled and simulated separately. The proposed method is tested by generating large-scale virtual road network scenes in the World Wind, an open source software package. The experimental results demonstrate that the method is flexible and can be used to develop different types of road models and efficiently simulate large-scale road network environments.

ACS Style

Xuequan Zhang; Ming Zhong; Shaobo Liu; Luoheng Zheng; Yumin Chen. Template-Based 3D Road Modeling for Generating Large-Scale Virtual Road Network Environment. ISPRS International Journal of Geo-Information 2019, 8, 364 .

AMA Style

Xuequan Zhang, Ming Zhong, Shaobo Liu, Luoheng Zheng, Yumin Chen. Template-Based 3D Road Modeling for Generating Large-Scale Virtual Road Network Environment. ISPRS International Journal of Geo-Information. 2019; 8 (9):364.

Chicago/Turabian Style

Xuequan Zhang; Ming Zhong; Shaobo Liu; Luoheng Zheng; Yumin Chen. 2019. "Template-Based 3D Road Modeling for Generating Large-Scale Virtual Road Network Environment." ISPRS International Journal of Geo-Information 8, no. 9: 364.

Journal article
Published: 27 July 2019 in ISPRS International Journal of Geo-Information
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Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation by implementing eigenvector spatial filtering (ESF) into logistic regression for landslide susceptibility assessment. Based on a landslide inventory map and 11 landslide predisposing factors, we developed the eigenvector spatial filtering-based logistic regression (ESFLR) model, as well as a conventional logistic regression (LR) model and an autologistic regression (ALR) model for comparison. The three models were evaluated and compared in terms of their prediction capability and model fit. The ESFLR model performed better than the other two models. The overall predictive accuracy of the ESFLR model was 90.53%, followed by the ALR model (76.21%) and the LR model (74.76%), and the areas under the ROC curves for the ESFLR, ALR and LR models were 0.957, 0.828 and 0.818, respectively. The ESFLR model adequately addressed the spatial autocorrelation of residuals by reducing the Moran’s I value of the residuals to 0.0270. In conclusion, the ESFLR model is an effective and flexible method for landslide analysis.

ACS Style

Huifang Li; Yumin Chen; Susu Deng; Meijie Chen; Tao Fang; Huangyuan Tan. Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment. ISPRS International Journal of Geo-Information 2019, 8, 332 .

AMA Style

Huifang Li, Yumin Chen, Susu Deng, Meijie Chen, Tao Fang, Huangyuan Tan. Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment. ISPRS International Journal of Geo-Information. 2019; 8 (8):332.

Chicago/Turabian Style

Huifang Li; Yumin Chen; Susu Deng; Meijie Chen; Tao Fang; Huangyuan Tan. 2019. "Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment." ISPRS International Journal of Geo-Information 8, no. 8: 332.

Journal article
Published: 30 May 2019 in International Journal of Environmental Research and Public Health
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This paper proposes a spatial difference analysis method for evaluating transit-based accessibility to hospitals using spatially adjusted ANOVA. This method specializes in examining spatial variations of accessibility to hospitals by regions (i.e. administrative districts or subdistricts). The spatial lag model is applied to adjust traditional ANOVA, which reduces spatial dependency and avoids false rejection to null hypothesis. Multiple comparison methods are used for further detection of differences in accessibility between regions. After multiple comparison, accessibility within regions is classified into three levels. The study is conducted on two scales—administrative districts and subdistricts—to discuss spatial variations in macro and micro dimensions respectively in the central part of Wuhan, China. Accessibility is calculated by using a simple model and a gravity model. The final classification results showed that the spatially adjusted method is more reliable than the traditional non spatially adjusted one and the gravity model can better detect more hidden information about the inequal distribution of medical resources. It is also found that the subdistricts, which have significantly lower accessibility to hospitals than others, are mainly distributed in Hongshan and Qingshan district. Our study hopes to shed new lights in spatial difference analysis for accessibility and provide policy recommendations that would promote equality in provisions of public health services.

ACS Style

Meijie Chen; Yumin Chen; Xiaoguang Wang; Huangyuan Tan; Fenglan Luo. Spatial Difference of Transit-Based Accessibility to Hospitals by Regions Using Spatially Adjusted ANOVA. International Journal of Environmental Research and Public Health 2019, 16, 1923 .

AMA Style

Meijie Chen, Yumin Chen, Xiaoguang Wang, Huangyuan Tan, Fenglan Luo. Spatial Difference of Transit-Based Accessibility to Hospitals by Regions Using Spatially Adjusted ANOVA. International Journal of Environmental Research and Public Health. 2019; 16 (11):1923.

Chicago/Turabian Style

Meijie Chen; Yumin Chen; Xiaoguang Wang; Huangyuan Tan; Fenglan Luo. 2019. "Spatial Difference of Transit-Based Accessibility to Hospitals by Regions Using Spatially Adjusted ANOVA." International Journal of Environmental Research and Public Health 16, no. 11: 1923.

Journal article
Published: 31 January 2019 in Computers & Geosciences
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Texture slicing based volume rendering is widely used for data visualization, but dynamic high-fidelity rendering for large-scale meteorological data is still a challenge. The traditional octree-based multiresolution method increases the slicing and rendering batches, and it also has the visual defect of brick borders. This paper introduces an efficient dynamic volume rendering method for large-scale meteorological data in a virtual globe. First, the volumetric data is resampled and transformed adaptively according to the camera view. Second, the proxy geometry is calculated by slicing the whole spherical shaped volume along the ray from the camera to Earth center based on GPU. Finally, the rendering efficiency is optimized for interactive dynamic visualization. The proposed method is tested by visualizing Typhoon Rananim in an open source software, called World Wind. The experimental results demonstrate that the proposed method is more efficient and the visual effect is smoother than the octree-based multiresolution method.

ACS Style

Xuequan Zhang; Peng Yue; Yumin Chen; Lei Hu. An efficient dynamic volume rendering for large-scale meteorological data in a virtual globe. Computers & Geosciences 2019, 126, 1 -8.

AMA Style

Xuequan Zhang, Peng Yue, Yumin Chen, Lei Hu. An efficient dynamic volume rendering for large-scale meteorological data in a virtual globe. Computers & Geosciences. 2019; 126 ():1-8.

Chicago/Turabian Style

Xuequan Zhang; Peng Yue; Yumin Chen; Lei Hu. 2019. "An efficient dynamic volume rendering for large-scale meteorological data in a virtual globe." Computers & Geosciences 126, no. : 1-8.

Journal article
Published: 18 January 2019 in Environmental Modelling & Software
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An effective parallelization algorithm based on the compute-unified-device-architecture (CUDA) is developed for DEM generalization that is critical to multi-scale terrain analysis. It aims to efficiently retrieve the critical points for generating coarser-resolution DEMs which maximally maintain the significant terrain features. CUDA is embedded into a multi-point algorithm to provide a parallel-multi-point algorithm for enhancing its computing efficiency. The outcomes are compared with the ANUDEM, compound and maximum z-tolerance methods and the results demonstrate the proposed algorithm reduces response time by up to 96% compared to other methods. As to RMSE, it performs better than ANUDEM and needs half the number of points to keep the same RMSE. The mean slope and surface roughness are reduced by less than 1% in the tested cases. The parallel algorithm provides better streamline matching. Given its high computing efficiency, the proposed algorithm can retrieve more critical points to meet the demands of higher precision.

ACS Style

Qianjiao Wu; Yumin Chen; John P. Wilson; Xuejun Liu; Huifang Li. An effective parallelization algorithm for DEM generalization based on CUDA. Environmental Modelling & Software 2019, 114, 64 -74.

AMA Style

Qianjiao Wu, Yumin Chen, John P. Wilson, Xuejun Liu, Huifang Li. An effective parallelization algorithm for DEM generalization based on CUDA. Environmental Modelling & Software. 2019; 114 ():64-74.

Chicago/Turabian Style

Qianjiao Wu; Yumin Chen; John P. Wilson; Xuejun Liu; Huifang Li. 2019. "An effective parallelization algorithm for DEM generalization based on CUDA." Environmental Modelling & Software 114, no. : 64-74.

Journal article
Published: 17 August 2018 in ISPRS International Journal of Geo-Information
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A segmented processing approach of eigenvector spatial filtering (ESF) regression is proposed to detect the relationship between NDVI and its environmental factors like DEM, precipitation, relative humidity, precipitation days, soil organic carbon, and soil base saturation in central China. An optimum size of 32 × 32 is selected through experiments as the basic unit for image segmentation to resolve the large datasets to smaller ones that can be performed in parallel and processed more efficiently. The eigenvectors from the spatial weights matrix (SWM) of each segmented image block are selected as synthetic proxy variables accounting for the spatial effects and aggregated to construct a global ESF regression model. Results show precipitation and humidity are more influential than other factors and spatial autocorrelation plays a vital role in vegetation cover in central China. Despite the increase in model complexity; the parallel ESF regression model performs best across all performance criteria compared to the ordinary least squared linear regression (OLS) and spatial autoregressive (SAR) models. The proposed parallel ESF approach overcomes the computational barrier for large data sets and is very promising in applying spatial regression modeling to a wide range of real world problem solving and forecasting.

ACS Style

Jiaxin Yang; Yumin Chen; Meijie Chen; Fan Yang; Ming Yao. A Segmented Processing Approach of Eigenvector Spatial Filtering Regression for Normalized Difference Vegetation Index in Central China. ISPRS International Journal of Geo-Information 2018, 7, 330 .

AMA Style

Jiaxin Yang, Yumin Chen, Meijie Chen, Fan Yang, Ming Yao. A Segmented Processing Approach of Eigenvector Spatial Filtering Regression for Normalized Difference Vegetation Index in Central China. ISPRS International Journal of Geo-Information. 2018; 7 (8):330.

Chicago/Turabian Style

Jiaxin Yang; Yumin Chen; Meijie Chen; Fan Yang; Ming Yao. 2018. "A Segmented Processing Approach of Eigenvector Spatial Filtering Regression for Normalized Difference Vegetation Index in Central China." ISPRS International Journal of Geo-Information 7, no. 8: 330.

Journal article
Published: 11 June 2018 in International Journal of Environmental Research and Public Health
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This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM2.5 analysis and prediction.

ACS Style

Jingyi Zhang; Bin Li; Yumin Chen; Meijie Chen; Tao Fang; Yongfeng Liu. Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data. International Journal of Environmental Research and Public Health 2018, 15, 1228 .

AMA Style

Jingyi Zhang, Bin Li, Yumin Chen, Meijie Chen, Tao Fang, Yongfeng Liu. Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data. International Journal of Environmental Research and Public Health. 2018; 15 (6):1228.

Chicago/Turabian Style

Jingyi Zhang; Bin Li; Yumin Chen; Meijie Chen; Tao Fang; Yongfeng Liu. 2018. "Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data." International Journal of Environmental Research and Public Health 15, no. 6: 1228.

Research article
Published: 06 February 2018 in Earth Science Informatics
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Landslides lead to a great threat to human life and property safety. The delineation of landslide-prone areas achieved by landslide susceptibility assessment plays an important role in landslide management strategy. Selecting an appropriate mapping unit is vital for landslide susceptibility assessment. This paper compares the slope unit and grid cell as mapping unit for landslide susceptibility assessment. Grid cells can be easily obtained and their matrix format is convenient for calculation. A slope unit is considered as the watershed defined by ridge lines and valley lines based on hydrological theory and slope units are more associated with the actual geological environment. Using 70% landslide events as the training data and the remaining landslide events for verification, landslide susceptibility maps based on slope units and grid cells were obtained respectively using a modified information value model. ROC curve was utilized to evaluate the landslide susceptibility maps by calculating the training accuracy and predictive accuracy. The training accuracies of the grid cell-based susceptibility assessment result and slope unit-based susceptibility assessment result were 80.9 and 83.2%, and the prediction accuracies were 80.3 and 82.6%, respectively. Therefore, landslide susceptibility mapping based on slope units performed better than grid cell-based method.

ACS Style

Qianqian Ba; Yumin Chen; Susu Deng; Jiaxin Yang; Huifang Li. A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Science Informatics 2018, 11, 373 -388.

AMA Style

Qianqian Ba, Yumin Chen, Susu Deng, Jiaxin Yang, Huifang Li. A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Science Informatics. 2018; 11 (3):373-388.

Chicago/Turabian Style

Qianqian Ba; Yumin Chen; Susu Deng; Jiaxin Yang; Huifang Li. 2018. "A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment." Earth Science Informatics 11, no. 3: 373-388.

Journal article
Published: 16 January 2017 in ISPRS International Journal of Geo-Information
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Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

ACS Style

Qianqian Ba; Yumin Chen; Susu Deng; Qianjiao Wu; Jiaxin Yang; Jingyi Zhang. An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping. ISPRS International Journal of Geo-Information 2017, 6, 18 .

AMA Style

Qianqian Ba, Yumin Chen, Susu Deng, Qianjiao Wu, Jiaxin Yang, Jingyi Zhang. An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping. ISPRS International Journal of Geo-Information. 2017; 6 (1):18.

Chicago/Turabian Style

Qianqian Ba; Yumin Chen; Susu Deng; Qianjiao Wu; Jiaxin Yang; Jingyi Zhang. 2017. "An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping." ISPRS International Journal of Geo-Information 6, no. 1: 18.

Articles
Published: 22 May 2014 in International Journal of Geographical Information Science
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This paper proposes a flow-path network (FPN) model to simulate complex surface flow based on a drainage-constrained triangulated irregular network (TIN). The TIN was constructed using critical points and drainage lines extracted from a digital terrain surface. Runoff generated on the surface was simplified as ‘water volumes’ at constrained random points that were then used as the starting points of flow paths (i.e. flow source points). The flow-path for each ‘water volume’ was constructed by tracing the direction of flow from the flow source point over the TIN surface to the stream system and then to the outlet of the watershed. The FPN was represented by a set of topologically defined one-dimensional line segments and nodes. Hydrologic variables, such as flow velocity and volume, were computed and integrated into the FPN to support dynamic surface flow simulation. A hypothetical rainfall event simulation on a hilly landscape showed that the FPN model was able to simulate the dynamics of surface flow over time. A real-world catchment test demonstrated that flow rates predicted by the FPN model agreed well with field observations. Overall, the FPN model proposed in this study provides a vector-based modeling framework for simulating surface flow dynamics. Further studies are required to enhance the simulations of individual hydrologic processes such as flow generation and overland and channel flows, which were much simplified in this study.

ACS Style

Yumin Chen; Qiming Zhou; Sheng Li; Fanrui Meng; Xiaomei Bi; John P. Wilson; Zisheng Xing; Junyu Qi; Qiang Li; Chengfu Zhang. The simulation of surface flow dynamics using a flow-path network model. International Journal of Geographical Information Science 2014, 28, 2242 -2260.

AMA Style

Yumin Chen, Qiming Zhou, Sheng Li, Fanrui Meng, Xiaomei Bi, John P. Wilson, Zisheng Xing, Junyu Qi, Qiang Li, Chengfu Zhang. The simulation of surface flow dynamics using a flow-path network model. International Journal of Geographical Information Science. 2014; 28 (11):2242-2260.

Chicago/Turabian Style

Yumin Chen; Qiming Zhou; Sheng Li; Fanrui Meng; Xiaomei Bi; John P. Wilson; Zisheng Xing; Junyu Qi; Qiang Li; Chengfu Zhang. 2014. "The simulation of surface flow dynamics using a flow-path network model." International Journal of Geographical Information Science 28, no. 11: 2242-2260.

Journal article
Published: 14 May 2012 in Computers & Geosciences
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In multi-scale digital terrain analysis, the main goal is to preserve the basic ‘skeleton’ with changing scales and to deliver more consistent measurements of terrain parameters at different scales. The drainage lines serve the basic morphology features and ‘skeleton’ in a basin, and therefore play an important role for most applications. Many drainage-constrained methods for DEM generalization have been proposed over the last few decades. This article compares three drainage-constrained methods: a Stream Burning algorithm, the ANUDEM algorithm as an example of a surface fitting approach, and the Compound method as an example of a constrained-TIN approach. All of these methods can be used to build coarser-scale DEMs while taking drainage features into account. The accuracy of the elevations and several terrain derivatives (slope, surface roughness) in the new digital terrain models along with the geometry or shape of key terrain features (streamline matching rate, streamline matching error) is then compared with each other to analyze the efficacy of these methods. The results show that the Compound algorithm offers the best performance over a series of generalization experiments.

ACS Style

Yumin Chen; John P. Wilson; Quansheng Zhu; Qiming Zhou. Comparison of drainage-constrained methods for DEM generalization. Computers & Geosciences 2012, 48, 41 -49.

AMA Style

Yumin Chen, John P. Wilson, Quansheng Zhu, Qiming Zhou. Comparison of drainage-constrained methods for DEM generalization. Computers & Geosciences. 2012; 48 ():41-49.

Chicago/Turabian Style

Yumin Chen; John P. Wilson; Quansheng Zhu; Qiming Zhou. 2012. "Comparison of drainage-constrained methods for DEM generalization." Computers & Geosciences 48, no. : 41-49.