顾及时空异质性的缺失数据时空插值方法

樊子德; 龚健雅; 刘博; 李佳霖; 邓敏

doi:10.11947/j.AGCS.2016.20150123

测绘学报 >

2016 , Vol. 45 >Issue 4: 458 - 465

DOI: https://doi.org/10.11947/j.AGCS.2016.20150123

地图学与地理信息

顾及时空异质性的缺失数据时空插值方法

樊子德 ,
龚健雅 ,
刘博 ,
李佳霖 ,
邓敏

展开

1. 中南大学地球科学与信息物理学院, 湖南长沙 410083;
2. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
3. 日电(NEC)中国研究院, 北京 100084

樊子德(1988-),男,博士生,研究方向为时空数据插值与建模。

收稿日期: 2015-03-09

修回日期: 2016-02-02

网络出版日期: 2016-04-28

基金资助

国家863计划(2013AA122301);湖南省博士生优秀学位论文(CX2014B050);中南大学研究生创新项目(2015zzts067)

收起

A Space-time Interpolation Method of Missing Data Based on Spatio-temporal Heterogeneity

FAN Zide ,
GONG Jianya ,
LIU Bo ,
LI Jialin ,
DENG Min

Expand

1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
3. NEC Labs, Beijing 100084, ChinaAbstract

Received date: 2015-03-09

Revised date: 2016-02-02

Online published: 2016-04-28

Supported by

The National High Technology Research and Development Program of China(863 Program)(No.2013AA122301);The Hunan Funds for Excellent Doctoral Dissertation(No.CX2014B050);The Central South University Funds for Excellent Doctoral Dissertation(No.2015zzts067)

Fold

摘要

时空插值方法被广泛应用于缺失时空数据集的插值与估计。时空插值是时空建模与分析的一个重要内容,当前该研究关注的热点之一是异质条件下的时空插值与估计问题。因此,本文从时空数据的异质性出发,提出了一种顾及时空异质性的缺失数据时空插值方法。该方法首先对数据集进行时空分区,然后分别在时间和空间按照异质协方差模型计算缺失数据的估计值,进而利用相关系数确定时空权重、融合时间和空间估计值得到缺失数据的最终估计结果。最后通过两组气象数据集进行交叉验证对比分析试验。试验结果表明本文方法对比其他插值方法具有更高的精度和适用性。

关键词： 时空插值; 分区; 异质性; 缺失数据

本文引用格式

樊子德 , 龚健雅 , 刘博 , 李佳霖 , 邓敏 . 顾及时空异质性的缺失数据时空插值方法[J]. 测绘学报, 2016 , 45(4) : 458 -465 . DOI: 10.11947/j.AGCS.2016.20150123

Abstract

Space-time interpolation is widely used to estimate missing data in a dataset integrating both spatial and temporal records. Although space-time interpolation plays a key role in space-time modeling, it is still challenging to model heterogeneity of space-time data in the interpolation model.To overcome this limitation, in this study, a novel space-time interpolation method based on spatio-temporal heterogeneity is proposed to estimate missing data of space-time datasets. Firstly, space partitioning and time slicing of space-time data was implemented. Then the estimates of missing data are computed using space-time surrounding records with heterogeneous spatio-temporal covariance model.Further the weights of space and time are determined using the correlation coefficient and the finally estimates of missing data is combined integrating time and space estimates. Finally, two datasets are selected to verify the accuracy of this method. Experimental results show that the proposed method outperforms the four state-of-the-art methods with higher accuracy and applicability.

Key words： spatio-temporal interpolation; partitioning; heterogeneity; missing data

参考文献

[1] SIMOLO C, BRUNETTI M, MAUGERI M, et al. Improving Estimation of Missing Values in Daily Precipitation Series by a Probability Density Function-Preserving Approach[J]. International Journal of Climatology, 2010, 30(10):1564-1576.
[2] 王劲峰, 葛咏, 李连发, 等. 地理学时空数据分析方法[J]. 地理学报, 2014, 69(9):1326-1345. WANG Jinfeng, GE Yong, LI Lianfa, et al. Spatiotemporal Data Analysis in Geography[J]. Acta Geographica Sinica, 2014, 69(9):1326-1345.
[3] 叶近天, 季世民, 杨勇. 时空地统计学方法研究及进展[J]. 测绘与空间地理信息, 2014, 37(1):38-43. YE Jintian, JI Shimin, YANG Yong. Spatio-Temporal Geotatistics Method Research and Progress[J]. Geomatics & Spatial Information Technology, 2014, 37(1):38-43.
[4] XU Chengdong, WANG Jinfeng, HU Maogui, et al. Interpolation of Missing Temperature Data at Meteorological Stations Using P-BSHADE[J]. Journal of Climate, 2013, 26(19):7452-7463.
[5] DE CESARE L, MYERS D E, POSA D. Estimating and Modeling Space-time Correlation Structures[J]. Statistics & Probability Letters, 2001, 51(1):9-14.
[6] KYRIAKIDIS P C, JOURNEL A G. Geostatistical Space-Time Models:a Review[J]. Mathematical Geology, 1999, 31(6):651-684.
[7] KILIBARDA M, TADIC' M P, HENGL T, et al. Global Geographic and Feature Space Coverage of Temperature Data in the Context of Spatio-Temporal Interpolation[J]. Spatial Statistics, 2015, 14(Part A):22-38.
[8] WANG Jinfeng, XU Chengdong, HU Maogui, et al. A New Estimate of the China Temperature Anomaly Series and Uncertainty Assessment in 1900-2006[J]. Journal of Geophysical Research:Atmospheres, 2014, 119(1):1-9.
[9] DE IACO S,MYERS D E,POSA D.Space-Time Variograms and a Functional Form for Total Air Pollution Measurements[J]. Computational Statistics & Data Analysis, 2002, 41(2):311-328.
[10] LI Lixin, REVESZ P. Interpolation Methods for Spatio-Temporal Geographic Data[J]. Computers, Environment and Urban Systems, 2004, 28(3):201-227.
[11] HUANG Bo, WU Bo, BARRY M. Geographically and Temporally Weighted Regression for Modeling Spatio-Temporal Variation in House Prices[J]. International Journal of Geographical Information Science, 2010, 24(3):383-401.
[12] 杨朝晖, 余洁, 陈江平. 基于聚类与空间自回归模型的缺失数据填补方法[C]//亚太环境科学研究中心. 2010年国际遥感会议论文集.[S.l.]:智能信息技术应用学会, 2010, 3:4. YANG Zhaohui, YU Jie, CHEN Jiangping. A Missing Data Imputation Method Based on Cluster and Spatial Autoregressive Model[C]//Asia Pacific Environmental Science Research Center. Proceedings of 2010 International Conference on Remote Sensing (ICRS 2010).[S.l.]:Application of Intelligent Information Technology Institute, 2010, 3:4.
[13] 李正泉, 吴尧祥. 顾及方向遮蔽性的反距离权重插值法[J]. 测绘学报, 2015, 44(1):91-98. DOI:10.11947/j.AGCS.2015.20130349. LI Zhengquan, WU Yaoxiang. Inverse Distance Weighted Interpolation Involving Position Shading[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(1):91-98. DOI:10.11947/j.AGCS.2015.20130349.
[14] 张锦明, 游雄, 万刚. DEM插值参数优选的试验研究[J]. 测绘学报, 2014, 43(2):178-185, 192. DOI:10.13485/j.cnki.11-2089.2014.0026 ZHANG Jinming, YOU Xiong, WAN Gang. Experimental Research on Optimization of DEM Interpolation Parameters[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(2):178-185, 192. DOI:10.13485/j.cnki.11-2089.2014.0026
[15] 董箭, 彭认灿, 郑义东, 等. 局部动态最优Voronoi图的NNI算法及其在格网数字水深模型中的应用[J]. 测绘学报, 2013, 42(2):284-289, 303. DONG Jian, PENG Rencan, ZHENG Yidong, et al. An Algorithm of Natural Neighbor Interpolation Based on Local Dynamic Optimal Voronoi Diagram and Its Application in Grid Digital Depth Model[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(2):284-289, 303.
[16] LU Binbin, CHARLTON M, HARRIS P, et al. Geographically Weighted Regression with a Non-Euclidean Distance Metric:A Case Study Using Hedonic House Price Data[J]. International Journal of Geographical Information Science, 2014, 28(4):660-681.
[17] WU Bo, LI Rongrong, HUANG Bo. A Geographically and Temporally Weighted Autoregressive Model with Application to Housing Prices[J]. International Journal of Geographical Information Science, 2014, 28(4):1186-1204.
[18] HUBBARD K G, YOU Jinsheng. Sensitivity Analysis of Quality Assurance Using the Spatial Regression Approach-A Case Study of the Maximum/Minimum Air Temperature[J]. Journal of Atmospheric and Oceanic Technology, 2005, 22(10):1520-1530.
[19] LU G Y, WONG D W. An Adaptive Inverse-Distance Weighting Spatial Interpolation Technique[J]. Computers & Geosciences, 2008, 34(9):1044-1055.
[20] 徐爱萍, 圣文顺, 舒红. 时空积和模型的数据插值与交叉验证[J]. 武汉大学学报(信息科学版), 2012, 37(7):766-769. XU Aiping, SHENG Wenshun, SHU Hong. Spatiotemporal Interpolation and Cross Validation Based on Product-Sum Model[J]. Geomatics and Information Science of Wuhan University, 2012, 37(7):766-769.
[21] BILONICK R A. The Space-Time Distribution of Sulfate Deposition in the Northeastern United States[J]. Atmospheric Environment (1967), 1985, 19(11):1829-1845.
[22] DE IACOS S,MYERS D E,POSA D.Space-Time Analysis Using a General Product-Sum Model[J]. Statistics & Probability Letters, 2001, 52(1):21-28.
[23] WANG Jinfeng, HAINING R,LIU Tiejun,et al.Sandwich Estimation for Multi-Unit Reporting on a Stratified Heterogeneous Surface[J]. Environment and Planning A, 2013, 45(10):2515-2534.
[24] WANG Jinfeng, HAINING R, CAO Zhidong. Sample Surveying to Estimate the Mean of a Heterogeneous Surface:Reducing the Error Variance through Zoning[J]. International Journal of Geographical Information Science, 2010, 24(4):523-543.
[25] WANG Jingfeng, REIS B Y, HU Maogui, et al. Area Disease Estimation Based on Sentinel Hospital Records[J]. PLoS One, 2011, 6(8):e23428.
[26] HU Maogui, WANG Jinfeng, ZHAO Yu, et al. A B-SHADE Based Best Linear Unbiased Estimation Tool for Biased Samples[J]. Environmental Modelling & Software, 2013, 48:93-97.
[27] GUO D. Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (REDCAP)[J]. International Journal of Geographical Information Science, 2008, 22(7):801-823.
[28] MA Chunsheng. Spatio-temporal Covariance Functions Generated by Mixtures[J]. Mathematical Geology, 2002, 34(8):965-975.
[29] MA Chunsheng. Recent Developments on the Construction of Spatio-Temporal Covariance Models[J]. Stochastic Environmental Research and Risk Assessment, 2008, 22(1):39-47.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献