Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain foc...Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.展开更多
Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interest...Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and brushing.This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix.Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the Simultaneous-Scatterplots.While the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more accurate.Moreover,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study.Overall,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.展开更多
We investigate task performance and reading characteristics for scatterplots(Cartesian coordinates)and parallel coordinates.In a controlled eye-tracking study,we asked 24 participants to assess the relative distance o...We investigate task performance and reading characteristics for scatterplots(Cartesian coordinates)and parallel coordinates.In a controlled eye-tracking study,we asked 24 participants to assess the relative distance of points in multidimensional space,depending on the diagram type(parallel coordinates or a horizontal collection of scatterplots),the number of data dimensions(2,4,6,or 8),and the relative distance between points(15%,20%,or 25%).For a given reference point and two target points,we instructed participants to choose the target point that was closer to the reference point in multidimensional space.We present a visual scanning model that describes different strategies to solve this retrieval task for both diagram types,and propose corresponding hypotheses that we test using task completion time,accuracy,and gaze positions as dependent variables.Our results show that scatterplots outperform parallel coordinates significantly in 2 dimensions,however,the task was solved more quickly and more accurately with parallel coordinates in 8 dimensions.The eye-tracking data further shows significant differences between Cartesian and parallel coordinates,as well as between different numbers of dimensions.For parallel coordinates,there is a clear trend toward shorter fixations and longer saccades with increasing number of dimensions.Using an area-of-interest(AOI)based approach,we identify different reading strategies for each diagram type:For parallel coordinates,the participants’gaze frequently jumped back and forth between pairs of axes,while axes were rarely focused on when viewing Cartesian coordinates.We further found that participants’attention is biased:toward the center of the whole plot for parallel coordinates and skewed to the center/left side for Cartesian coordinates.We anticipate that these results may support the design of more effective visualizations for multidimensional data.展开更多
A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At pre...A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At present,spatial autocorrelation presents two types of measures:continuous and discrete.Is it possible to use Moran’s I and the Moran scatterplot with continuous data?Is it possible to use the same methodology with discrete data?A particular and cumbersome problem is the choice of the spatial-neighborhood matrix(W)for points data.This paper addresses these issues by introducing the concept of covariogram contiguity,where each weight is based on the variogram model for that particular dataset:(1)the variogram,whose range equals the distance with the highest Moran I value,defines the weights for points separated by less than the estimated range and(2)weights equal zero for points widely separated from the variogram range considered.After the W matrix is computed,the Moran location scatterplot is created in an iterative process.In accordance with various lag distances,Moran’s I is presented as a good search factor for the optimal neighborhood area.Uncertainty/transition regions are also emphasized.At the same time,a new Exploratory Spatial Data Analysis(ESDA)tool is developed,the Moran variance scatterplot,since the conventional Moran scatterplot is not sensitive to neighbor variance.This computer-mapping framework allows the study of spatial patterns,outliers,changeover areas,and trends in an ESDA process.All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb#(or,in the near future,myGeooffice.org).展开更多
Visualizing high-dimensional data on a 2D canvas is generally challenging.It becomes significantly more difficult when multiple time-steps are to be presented,as the visual clutter quickly increases.Moreover,the chall...Visualizing high-dimensional data on a 2D canvas is generally challenging.It becomes significantly more difficult when multiple time-steps are to be presented,as the visual clutter quickly increases.Moreover,the challenge to perceive the significant temporal evolution is even greater.In this paper,we present a method to plot temporal high-dimensional data in a static scatterplot;it uses the established PCA technique to project data from multiple time-steps.The key idea is to extend each individual displacement prior to applying PCA,so as to skew the projection process,and to set a projection plane that balances the directions of temporal change and spatial variance.We present numerous examples and various visual cues to highlight the data trajectories,and demonstrate the effectiveness of the method for visualizing temporal data.展开更多
Objective:To study the value of the wearable single-lead remote monitoring device with the scatterplot in chronic disease management.Methods:dmitted into 435 residents accord with the inclusion criteria of 20 primary ...Objective:To study the value of the wearable single-lead remote monitoring device with the scatterplot in chronic disease management.Methods:dmitted into 435 residents accord with the inclusion criteria of 20 primary medical institutions of Yinchuan city,and grouped voluntarily by the implementation schemes were grouped voluntarily according to the implementation schemes.According to one of the three implementation schemes selected,the general practitioner guided the subjects to take on the wearable single-lead remote monitoring device,collecting and uploading the EEG data,then diagnosed and analyzed by the synchronously generated ECG scatterplot,finally,summarized the incidence and the categories,analyzed the differences among these three groups.Results:Among 435 subjects,there were 61 normal patients and 374 arrhythmias with the detection rate of 85.98%;and among the 1672 data collected,there were 606 normal data and 1066 arrhythmia with the detection rate of 63.76%;880 data in total 333 cases with atrial premature beat;442 data in total 215 cases with occasional ventricular premature beat;37 data of 22 cases with frequent atrial beat;65 data of 28 cases with frequent ventricular premature beat;13 data of 6 cases with atrial fibrillation;25 data of 15 cases with excitation conduction disorder;2 data of 2 cases with atrial flutter;31 data of 19 cases with ventricular tachycardia;30 data of 16 cases with conduction block;and 14 data of 8 cases with Para systolic rhythm.comparing the detection rate of arrhythmia in three groups,the difference was not statistically significant(P>0.05).Conclusion:The wearable singlelead remote monitoring device with the scatterplot has high application value in cardiovascular chronic disease management.Its effectively screening,validly diagnosing and detailed classifying are helpful to the early intervention,and the protection of the patients’lives.展开更多
Optical and visual measurement technology is used widely in fields that involve geometric measurements,and among such technology are laser and vision-based displacement measuring modules(LVDMMs).The displacement trans...Optical and visual measurement technology is used widely in fields that involve geometric measurements,and among such technology are laser and vision-based displacement measuring modules(LVDMMs).The displacement transformation coefficient(DTC)of an LVDMM changes with the coordinates in the camera image coordinate system during the displacement measuring process,and these changes affect the displacement measurement accuracy of LVDMMs in the full field of view(FFOV).To give LVDMMs higher accuracy in the FFOV and make them adaptable to widely varying measurement demands,a new calibration method is proposed to improve the displacement measurement accuracy of LVDMMs in the FFOV.First,an image coordinate system,a pixel measurement coordinate system,and a displacement measurement coordinate system are established on the laser receiving screen of the LVDMM.In addition,marker spots in the FFOV are selected,and the DTCs at the marker spots are obtained from calibration experiments.Also,a fitting method based on locally weighted scatterplot smoothing(LOWESS)is selected,and with this fitting method the distribution functions of the DTCs in the FFOV are obtained based on the DTCs at the marker spots.Finally,the calibrated distribution functions of the DTCs are applied to the LVDMM,and experiments conducted to verify the displacement measurement accuracies are reported.The results show that the FFOV measurement accuracies for horizontal and vertical displacements are better than±15μm and±19μm,respectively,and that for oblique displacement is better than±24μm.Compared with the traditional calibration method,the displacement measurement error in the FFOV is now 90%smaller.This research on an improved calibration method has certain significance for improving the measurement accuracy of LVDMMs in the FFOV,and it provides a new method and idea for other vision-based fields in which camera parameters must be calibrated.展开更多
The analysis of environmental daily evaporation plays a vital role in the field of agriculture. It is very essential to know the daily evaporation rate of a particular area for proper cultivation. So, we need a standa...The analysis of environmental daily evaporation plays a vital role in the field of agriculture. It is very essential to know the daily evaporation rate of a particular area for proper cultivation. So, we need a standard prediction model which can predict the daily evaporation. In this paper, we use subtractive clustering and Fuzzy logic to predict daily evaporation of a particular area. The input data used in the paper are: maximum soil temperature, average soil temperature, average air temperature, minimum relative humidity, average relative humidity and total wind, which are related to the daily evaporation of a particular area as the output. The accuracy of output of the paper is compared with the previous model of Artificial Neural Network (ANN) and we get better result towards the target value. The finding of the paper is applicable in environmental science, geological science and agriculture.展开更多
Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (...Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.展开更多
X-ray images obtained from synchrotron beamlines are large-scale,high-resolution and high-dynamic-range grayscale data encoding multiple complex properties of the measured materials.They are typically associated with ...X-ray images obtained from synchrotron beamlines are large-scale,high-resolution and high-dynamic-range grayscale data encoding multiple complex properties of the measured materials.They are typically associated with a variety of metadata which increases their inherent complexity.There is a wealth of information embedded in these data but so far scientists lack modem exploration tools to unlock these hidden treasures.To bridge this gap,we propose MultiSciView,a multivariate scientific x-ray image visualization and exploration system for beamline-generated x-ray scattering data.Our system is composed of three complementary and coordinated interactive visualizations to enable a coordinated exploration across the images and their associated attribute and feature spaces.The first visualization features a multi-level scatterplot visualization dedicated for image exploration in attribute,image,and pixel scales.The second visualization is a histogram-based attribute cross filter by which users can extract desired subset patterns from data.The third one is an attribute projection visualization designed for capturing global attribute correlations.We demonstrate our framework by ways of a case study involving a real-world material scattering dataset.We show that our system can efficiently explore large-scale x-ray images,accurately identify preferred image patterns,anomalous images and erroneous experimental settings,and effectively advance the comprehension of material nanostructure properties.展开更多
文摘Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.
文摘Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and brushing.This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix.Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the Simultaneous-Scatterplots.While the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more accurate.Moreover,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study.Overall,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.
基金We would like to thank the Carl-Zeiss-Foundation(Carl-Zeiss-Stiftung)the German Research Foundation(DFG)for financial support within project B01 of SFB/Transregio 161.
文摘We investigate task performance and reading characteristics for scatterplots(Cartesian coordinates)and parallel coordinates.In a controlled eye-tracking study,we asked 24 participants to assess the relative distance of points in multidimensional space,depending on the diagram type(parallel coordinates or a horizontal collection of scatterplots),the number of data dimensions(2,4,6,or 8),and the relative distance between points(15%,20%,or 25%).For a given reference point and two target points,we instructed participants to choose the target point that was closer to the reference point in multidimensional space.We present a visual scanning model that describes different strategies to solve this retrieval task for both diagram types,and propose corresponding hypotheses that we test using task completion time,accuracy,and gaze positions as dependent variables.Our results show that scatterplots outperform parallel coordinates significantly in 2 dimensions,however,the task was solved more quickly and more accurately with parallel coordinates in 8 dimensions.The eye-tracking data further shows significant differences between Cartesian and parallel coordinates,as well as between different numbers of dimensions.For parallel coordinates,there is a clear trend toward shorter fixations and longer saccades with increasing number of dimensions.Using an area-of-interest(AOI)based approach,we identify different reading strategies for each diagram type:For parallel coordinates,the participants’gaze frequently jumped back and forth between pairs of axes,while axes were rarely focused on when viewing Cartesian coordinates.We further found that participants’attention is biased:toward the center of the whole plot for parallel coordinates and skewed to the center/left side for Cartesian coordinates.We anticipate that these results may support the design of more effective visualizations for multidimensional data.
文摘A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At present,spatial autocorrelation presents two types of measures:continuous and discrete.Is it possible to use Moran’s I and the Moran scatterplot with continuous data?Is it possible to use the same methodology with discrete data?A particular and cumbersome problem is the choice of the spatial-neighborhood matrix(W)for points data.This paper addresses these issues by introducing the concept of covariogram contiguity,where each weight is based on the variogram model for that particular dataset:(1)the variogram,whose range equals the distance with the highest Moran I value,defines the weights for points separated by less than the estimated range and(2)weights equal zero for points widely separated from the variogram range considered.After the W matrix is computed,the Moran location scatterplot is created in an iterative process.In accordance with various lag distances,Moran’s I is presented as a good search factor for the optimal neighborhood area.Uncertainty/transition regions are also emphasized.At the same time,a new Exploratory Spatial Data Analysis(ESDA)tool is developed,the Moran variance scatterplot,since the conventional Moran scatterplot is not sensitive to neighbor variance.This computer-mapping framework allows the study of spatial patterns,outliers,changeover areas,and trends in an ESDA process.All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb#(or,in the near future,myGeooffice.org).
基金the Israel Science Foundation(Grant No.2366/16 and 2472/17)。
文摘Visualizing high-dimensional data on a 2D canvas is generally challenging.It becomes significantly more difficult when multiple time-steps are to be presented,as the visual clutter quickly increases.Moreover,the challenge to perceive the significant temporal evolution is even greater.In this paper,we present a method to plot temporal high-dimensional data in a static scatterplot;it uses the established PCA technique to project data from multiple time-steps.The key idea is to extend each individual displacement prior to applying PCA,so as to skew the projection process,and to set a projection plane that balances the directions of temporal change and spatial variance.We present numerous examples and various visual cues to highlight the data trajectories,and demonstrate the effectiveness of the method for visualizing temporal data.
基金The Special Project of Science and technology benefit the people of Ningxia Hui Autonomous Region(2018CMG03015)。
文摘Objective:To study the value of the wearable single-lead remote monitoring device with the scatterplot in chronic disease management.Methods:dmitted into 435 residents accord with the inclusion criteria of 20 primary medical institutions of Yinchuan city,and grouped voluntarily by the implementation schemes were grouped voluntarily according to the implementation schemes.According to one of the three implementation schemes selected,the general practitioner guided the subjects to take on the wearable single-lead remote monitoring device,collecting and uploading the EEG data,then diagnosed and analyzed by the synchronously generated ECG scatterplot,finally,summarized the incidence and the categories,analyzed the differences among these three groups.Results:Among 435 subjects,there were 61 normal patients and 374 arrhythmias with the detection rate of 85.98%;and among the 1672 data collected,there were 606 normal data and 1066 arrhythmia with the detection rate of 63.76%;880 data in total 333 cases with atrial premature beat;442 data in total 215 cases with occasional ventricular premature beat;37 data of 22 cases with frequent atrial beat;65 data of 28 cases with frequent ventricular premature beat;13 data of 6 cases with atrial fibrillation;25 data of 15 cases with excitation conduction disorder;2 data of 2 cases with atrial flutter;31 data of 19 cases with ventricular tachycardia;30 data of 16 cases with conduction block;and 14 data of 8 cases with Para systolic rhythm.comparing the detection rate of arrhythmia in three groups,the difference was not statistically significant(P>0.05).Conclusion:The wearable singlelead remote monitoring device with the scatterplot has high application value in cardiovascular chronic disease management.Its effectively screening,validly diagnosing and detailed classifying are helpful to the early intervention,and the protection of the patients’lives.
基金supported financially by the National Natural Science Foundation of China (NSFC) (Grant No.51775378)the Key Projects in Tianjin Science&Technology Support Program (Grant No.19YFZC GX00890).
文摘Optical and visual measurement technology is used widely in fields that involve geometric measurements,and among such technology are laser and vision-based displacement measuring modules(LVDMMs).The displacement transformation coefficient(DTC)of an LVDMM changes with the coordinates in the camera image coordinate system during the displacement measuring process,and these changes affect the displacement measurement accuracy of LVDMMs in the full field of view(FFOV).To give LVDMMs higher accuracy in the FFOV and make them adaptable to widely varying measurement demands,a new calibration method is proposed to improve the displacement measurement accuracy of LVDMMs in the FFOV.First,an image coordinate system,a pixel measurement coordinate system,and a displacement measurement coordinate system are established on the laser receiving screen of the LVDMM.In addition,marker spots in the FFOV are selected,and the DTCs at the marker spots are obtained from calibration experiments.Also,a fitting method based on locally weighted scatterplot smoothing(LOWESS)is selected,and with this fitting method the distribution functions of the DTCs in the FFOV are obtained based on the DTCs at the marker spots.Finally,the calibrated distribution functions of the DTCs are applied to the LVDMM,and experiments conducted to verify the displacement measurement accuracies are reported.The results show that the FFOV measurement accuracies for horizontal and vertical displacements are better than±15μm and±19μm,respectively,and that for oblique displacement is better than±24μm.Compared with the traditional calibration method,the displacement measurement error in the FFOV is now 90%smaller.This research on an improved calibration method has certain significance for improving the measurement accuracy of LVDMMs in the FFOV,and it provides a new method and idea for other vision-based fields in which camera parameters must be calibrated.
文摘The analysis of environmental daily evaporation plays a vital role in the field of agriculture. It is very essential to know the daily evaporation rate of a particular area for proper cultivation. So, we need a standard prediction model which can predict the daily evaporation. In this paper, we use subtractive clustering and Fuzzy logic to predict daily evaporation of a particular area. The input data used in the paper are: maximum soil temperature, average soil temperature, average air temperature, minimum relative humidity, average relative humidity and total wind, which are related to the daily evaporation of a particular area as the output. The accuracy of output of the paper is compared with the previous model of Artificial Neural Network (ANN) and we get better result towards the target value. The finding of the paper is applicable in environmental science, geological science and agriculture.
文摘Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.
基金This research was supported by two Lab Directed Research and Development projects 16-041 and 17-029 of Brookhaven National Laboratory.
文摘X-ray images obtained from synchrotron beamlines are large-scale,high-resolution and high-dynamic-range grayscale data encoding multiple complex properties of the measured materials.They are typically associated with a variety of metadata which increases their inherent complexity.There is a wealth of information embedded in these data but so far scientists lack modem exploration tools to unlock these hidden treasures.To bridge this gap,we propose MultiSciView,a multivariate scientific x-ray image visualization and exploration system for beamline-generated x-ray scattering data.Our system is composed of three complementary and coordinated interactive visualizations to enable a coordinated exploration across the images and their associated attribute and feature spaces.The first visualization features a multi-level scatterplot visualization dedicated for image exploration in attribute,image,and pixel scales.The second visualization is a histogram-based attribute cross filter by which users can extract desired subset patterns from data.The third one is an attribute projection visualization designed for capturing global attribute correlations.We demonstrate our framework by ways of a case study involving a real-world material scattering dataset.We show that our system can efficiently explore large-scale x-ray images,accurately identify preferred image patterns,anomalous images and erroneous experimental settings,and effectively advance the comprehension of material nanostructure properties.
