An Adaptive IDW Algorithm Involving Spatial Heterogeneity

YAN Jinbiao; DUAN Xiaoqi; ZHENG Wenwu; LIU Yuan; DENG Yunyuan; HU Zui

doi:10.13203/j.whugis20180213

Volume 45 Issue 1

Jan. 2020

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2020 > 45(1): 97-104. > DOI: 10.13203/j.whugis20180213

YAN Jinbiao, DUAN Xiaoqi, ZHENG Wenwu, LIU Yuan, DENG Yunyuan, HU Zui. An Adaptive IDW Algorithm Involving Spatial Heterogeneity[J]. Geomatics and Information Science of Wuhan University, 2020, 45(1): 97-104. DOI: 10.13203/j.whugis20180213

Citation:

PDF (3199 KB)

An Adaptive IDW Algorithm Involving Spatial Heterogeneity

YAN Jinbiao^{1, 2,},
DUAN Xiaoqi³,
ZHENG Wenwu^{2, 4, ,},
LIU Yuan^{2, 4},
DENG Yunyuan^{2, 4},
HU Zui^{2, 4}

1.
School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
2.
National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang Normal University, Hengyang 421002, China
3.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
4.
Cooperative Innovation Center for Digitalization of Cultural Heritage in Traditional Villages and Towns, Hengyang Normal University, Hengyang 421002, China

Funds:

Major Program of National Social Science Foundation of China 16ZDA159

More Information

Author Bio:
YAN Jinbiao, PhD candidate, specializes in the theory and application of spatial‐temporal data mining. E-mail:715829216@qq.com
Corresponding author:
ZHENG Wenwu, PhD, professor. E-mail:13766108@qq.com
Received Date: October 31, 2018
Published Date: January 04, 2020

Graphical Abstract

Abstract

Abstract

An adaptive inverse distance weighted(IDW) algorithm involving spatial heterogeneity to solve some problems existed in the classical IDW is proposed.The first problem is that classical IDW algorithms are heavily dependent on the spatial stability. Another one is that the initial parameters are determined by the users empirically, such as the number of stratums or sample points. The k-nearest neighbor IDW (kAIDW) algorithm can take both spatial correlation and heterogeneity into account simultaneously without the needs of parameters input for users.Firstly, kAIDW sets the classification threshold adaptively for each sample point according to the statistical characteristics of the sample data and then divides the reference points into high, medium and low categories. Secondly, the k-nearest neighbor algorithm is used to determine the category of the interpolation point. According to the classification result, different weight adjustment coefficients are adaptively determined for the first-order neighboring samples of the point to be interpolated. Finally, an IDW interpolation algorithm model integrating spatial correlation and heterogeneity is constructed.In order to validate the effectiveness of the algorithm, two different practical applications are adopted. By comparing with three classical IDW algorithms, we find out that the kAIDW can effectively improve the accuracy of the IDW interpolation algorithm without the user providing any empirical parameters.
- spatial heterogeneity,
- adaptive inverse distance weighted algorithm,
- k-nearest neighbor,
- adjustment factor

FullText(HTML)

References (19)

References

[1]	段平, 盛业华, 李佳, 等.自适应的IDW插值方法及其在气温场中的应用[J].地理研究, 2014, 33(8):1 417-1 426 http://d.old.wanfangdata.com.cn/Periodical/dlyj201408003 Duan Ping, Sheng Yehua, Li Jia, et al. Adaptive IDW Interpolation Method and Its Application in the Temperature Field[J]. Geographical Research, 2014, 33(8):1 417-1 426 http://d.old.wanfangdata.com.cn/Periodical/dlyj201408003
[2]	Lu G Y, Wong D W. An Adaptive Inverse Distance Weighting Spatial Interpolation Technique[J]. Computers and Geosciences, 2008, 34(9):1 044-1 055 doi: 10.1016/j.cageo.2007.07.010
[3]	樊子德, 李佳霖, 邓敏.顾及多因素影响的自适应反距离加权插值方法[J].武汉大学学报·信息科学版, 2016, 41(6):842-847 http://ch.whu.edu.cn/CN/abstract/abstract5473.shtml Fan Zide, Li Jialin, Deng Min.An Adaptive Inverse-Distance Weighting Spatial Interpolation Method with the Consideration of Multiple Factors[J]. Geomatics and Information Science of Wuhan University, 2016, 41(6):842-847 http://ch.whu.edu.cn/CN/abstract/abstract5473.shtml
[4]	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 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=chxb201402003 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 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=chxb201402003
[5]	Rühaak W. A Java Application for Quality Weighted 3-D Interpolation[J]. Computers and Geosciences, 2006, 32(1):43-51 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=a6f84be22dc5f0bfb4e9b5fb8d565de8
[6]	樊子德, 龚健雅, 刘博, 等.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 http://d.old.wanfangdata.com.cn/Periodical/chxb201604011 Fan Zide, Gong Jianya, Liu Bo, et al.A Space Time Interpolation Method of Missing Data Based on Spatio-Temporal Heterogeneity[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4):458-465 http://d.old.wanfangdata.com.cn/Periodical/chxb201604011
[7]	Wang J F, Christakos G, Hu M G. Modeling Spatial Means of Surfaces with Stratified Nonhomogeneity[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(12):4 167-4 174 doi: 10.1109/TGRS.2009.2023326
[8]	李佳霖, 樊子德, 邓敏.基于空间异质分区的残差IDW插值方法[J].地理与地理信息科学, 2015, 31(5):25-29 doi: 10.3969/j.issn.1672-0504.2015.05.006 Li Jialin, Fan Zide, Deng Min. Residual Inverse Distance Weighting Spatial Interpolation Method Based on Spatial Heterogeneity Subregion[J]. Geography and Geo-Information Science, 2015, 31(5):25-29 doi: 10.3969/j.issn.1672-0504.2015.05.006
[9]	Lam N. Spatial Interpolation Methods:A Review[J]. The American Cartographer, 1983, 10(2):129-150 doi: 10.1559/152304083783914958
[10]	Declercq F A N. Interpolation Methods for Scattered Sample Data: Accuracy, Spatial Patterns, Processing Time[J]. Cartographer and Geographic Information Systems, 1996, 23(3):128-144 doi: 10.1559/152304096782438882
[11]	熊亚军, 廖晓农, 李梓铭, 等.KNN数据挖掘算法在北京地区霾等级预报中的应用[J].气象, 2015, 41(1):98-104 http://d.old.wanfangdata.com.cn/Periodical/qx201501012 Xiong Yajun, Liao Xiaonong, Li Ziming, et al. Application of KNN Data Mining Algorithm to Haze Grade Forecasting in Beijing[J]. Meteorological Monthly, 2015, 41(1):98-104 http://d.old.wanfangdata.com.cn/Periodical/qx201501012
[12]	李洋, 方滨兴, 郭莉, 等.基于主动学习和TCM-KNN方法的有指导入侵检测技术[J].计算机学报, 2007, 30(8):1 464-1 473 http://d.old.wanfangdata.com.cn/Periodical/jsjxb200708029 Li Yang, Fang Binxing, Guo Li, et al. Supervised Intrusion Detection Based on Active Learning and TCM KNN Algorithm[J]. Chinese Journal of Computers, 2007, 30(8):1 464-1 473 http://d.old.wanfangdata.com.cn/Periodical/jsjxb200708029
[13]	Cover T, Hart P. Nearest Neighbor Pattern Classification[J]. IEEE Transactions on Information Theory, 1967, 13(1):21-27 doi: 10.1109/TIT.1967.1053964
[14]	李航.统计学习方法[M].北京:清华大学出版社, 2012:40 Li Hang.Statistical Learning Method[M]. Beijing:Tsinghua University Press, 2012:40
[15]	Tobler W R.A Computer Movie Simulating Urban Growth in the Detroit Region[J]. Economic Geography, 1970, 46(2):234-240 doi: 10.2307-143141/
[16]	杨伟, 艾廷华.运用约束Delaunay三角网从众源轨迹线提取道路边界[J].测绘学报, 2017, 46(2):237-245 http://d.old.wanfangdata.com.cn/Periodical/chxb201702013 Yang Wei, Ai Tinghua. The Extraction of Road Boundary from Crowdsourcing Trajectory Using Constrained Delaunay Triangulation[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(2):237-245 http://d.old.wanfangdata.com.cn/Periodical/chxb201702013
[17]	Wang J F, Zhang T L, Fu B J. A Measure of Spatial Stratified Heterogeneity[J]. Ecological Indicators, 2016, 67:250-256 doi: 10.1016/j.ecolind.2016.02.052
[18]	Wang J F, Haining R, Liu T J, et al. Sandwich Estimation for Multi-unit Reporting on a Stratified Heterogeneous Surface[J]. Environment and Planning A, 2013, 45(10):2 515-2 534 doi: 10.1068/a44710
[19]	王劲峰, 廖一兰, 刘鑫.空间数据分析教程[M].北京:科学出版社, 2010 Wang Jinfeng, Liao YiLan, Liu Xin.The Tutorial of the Spatial Data Analysis[M]. Beijing: Science Press, 2010

[1]	LU Binbin, TIAN Xiaoxi, QIN Sixian, SHI Yilin, LI Jiansong. Urban Land Carrying Capacity Evaluation of Wuhan City with Geographically Weighted Techniques[J]. Geomatics and Information Science of Wuhan University, 2025, 50(3): 430-438. DOI: 10.13203/j.whugis20220778
[2]	LU Binbin, GE Yong, QIN Kun, ZHENG Jianghua. A Review on Geographically Weighted Regression[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1356-1366. DOI: 10.13203/j.whugis20190346
[3]	LUO Minghai, JIANG Zilong, CHENG Qi, QIN Sixian, YANG Nana, ZHANG Xinyue. Carrying Capacity Evaluation of Land Resources in Wuhan Based on the Geographical Condition Monitoring[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2317-2324. DOI: 10.13203/j.whugis20180389
[4]	NIE Lei, SHU Hong, LIU Yan. Interpolation of Monthly Average Temperature by Using (Mixed) Geographically Weighted Regression Kriging in the Complex Terrain Region[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1553-1559. DOI: 10.13203/j.whugis20160433
[5]	LIU Xiaojing, GUI Zhipeng, CAO Jun, LI Rui, WU Huayi. Spatiotemporal-Aware Hybrid Prediction Model for Response Time of Web Map Services by Integrating GWR and STARMA[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 951-958. DOI: 10.13203/j.whugis20160370
[6]	LU Binbin, YANG Huan, SUN Huabo, YU Qingde. Algorithm of Approximating Road Network Distance Via the Minkowski Distance[J]. Geomatics and Information Science of Wuhan University, 2017, 42(10): 1373-1380. DOI: 10.13203/j.whugis20160225
[7]	WANG Xu, LIN Zheng, ZHANG Zhi, LI Dan. Modelling the Spatial Distribution of Lake Surface Water Temperature of the Thaw Lakes in Arctic Coastal Plain Using Geographically Weighted Regression Model[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 918-924. DOI: 10.13203/j.whugis20140411
[8]	MA Ronghua, HE Zengyou, . Mining Complete and Correct Frequent Neighboring Class Sets from Spatial Databases[J]. Geomatics and Information Science of Wuhan University, 2007, 32(2): 115-119.
[9]	YU Leiyi. Study on the Architecture of GIS Grid[J]. Geomatics and Information Science of Wuhan University, 2004, 29(2): 153-156.
[10]	LIU Yanxiong, CHEN Yongqi, LIU Jingnan. Determination of Weighted Mean Tropospheric Temperature Using Ground Meteorological Measurement[J]. Geomatics and Information Science of Wuhan University, 2000, 25(5): 400-404.

Cited By

Get Citation

PDF

XML

Article views PDF downloads

An Adaptive IDW Algorithm Involving Spatial Heterogeneity

Abstract

References

Related Articles

Catalog

Related

An Adaptive IDW Algorithm Involving Spatial Heterogeneity

Abstract

References

Related Articles

Catalog

Related

Export File

Citation

Format

Content