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Data Details

A Software for Geospatial Data Similarity Calculation (GDSCS V1.0)


DAI Xiaoliang1,2ZHU Yunqiang*1,3YANG Jie1SUN Kai1LI Jidong4SONG Jia1,3
1 State Key Laboratory of Resources and Environmental Information System，Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China2 University of Chinese Academy of Sciences，Beijing 100049，China3 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application，Nanjing 210023，China4 Dongying Ecology and Environment Bureau，Dongying 257091，China

DOI:10.3974/geodb.2022.10.02.V1

Published:Oct. 2022

Visitors：8150       Data Files Downloaded：274      
Data Downloaded：32210.96 MB      Citations：

Key Words：

A Software for Geospatial Data Similarity Calculation (GDSCS V1.0)

Abstract：

Geospatial data similarity calculation is one of key issues for intelligent checking of geospatial data. To effectively improve the accuracy and efficiency of geospatial data similarity calculation, the authors' team proposed a geospatial data similarity calculation method by integrating the metadata, information and dataset and developed Geospatial Data Similarity Calculation Software (GDSCS 1.0). The tool is developed based on PyCharm software for Windows 10 platform, and uses text similarity, raster data similarity and vector data similarity methods. Both Chinese version and English version are available. It has the following requirements for input data formats: metadata for. json format, dataset for GeoTIFF (.tif) and Shapefile (.shp) formats only.Browse

Foundation Item：

National Natural Science Foundation of China (42050101), Chinese Academy of Sciences (XDA23100100)

Data Citation：

DAI Xiaoliang, ZHU Yunqiang*, YANG Jie, SUN Kai, LI Jidong, SONG Jia. A Software for Geospatial Data Similarity Calculation (GDSCS V1.0)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.10.02.V1.

DAI Xiaoliang, ZHU Yunqiang, YANG Jie, et al. Research and implementation of geospatial data similarity calculation method [J]. Journal of Global Change Data & Discovery, 2022, 6(4): 501–512.

References：

[1] Rachmawati, D., Tarigan, J. T., Ginting, A B C. A comparative study of Message Digest 5 (MD5) and SHA256 algorithm [C]. Journal of Physics: Conference Series. IOP Publishing, 2018, 978(1): 012116.
     [2] Strube, M., Rapp, S., Müller, C. The influence of minimum edit distance on reference resolution [C]. Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002). 2002: 312-319.
     [3] Zhao, Z. P., Yin, Z. M., Wang, Q. P., et al. An improved algorithm of Levenshtein Distance and its application in data processing [J]. Journal of computer applications, 2009, 29(2): 424-426.
     [4] Zhu, Y., Zhu, A. X., Feng, M., et al. A similarity-based automatic data recommendation approach for geographic models [J]. International Journal of Geographical Information Science, 2017, 31(7): 1403-1424.
     [5] Zhu, Y., Zhu, A. X., Song, J., et al. Multidimensional and quantitative interlinking approach for linked geospatial data [J]. International Journal of Digital Earth, 2017, 10(9): 923-943.
     [6] Chen, Z., Song, J., Yang, Y. Similarity measurement of metadata of geospatial data: An artificial neural network approach [J]. ISPRS International Journal of Geo-Information, 2018, 7(3): 90.
     [7] Boubenia, M., Belkhir, A., Bouyakoub, F. M. Combining linked open data similarity and relatedness for cross OSN recommendation [J]. International Journal on Semantic Web and Information Systems (IJSWIS), 2020, 16(2): 59-90.
     [8] Rao, Y. R., Prathapani, N., Nagabhooshanam, E. Application of normalized cross correlation to image registration [J]. International Journal of Research in Engineering and Technology, 2014, 3(5): 12-16.
     [9] Duan, X. Q., Liu, T., Wu, D. Spatial similarity assessment of point clusters in multi-scale map spaces based on analytic hierarchy process [J]. Journal of Geo-information Science, 2016, 18(10): 1312-1321.
     [10] Liu, T., Du, Q. Y., Mao, H. C. Spatial similarity assessment model and its application in line groups [J]. Geomatics and Information Science of Wuhan University, 2012, 37(8): 992-995.
     [11] Liu, C., Yan, G. W. Geometry similarity assessment model of spatial polygon groups [J]. Journal of Geo- Information Science, 2013, 15(5): 635-642.
     [12] Tan, Y. B., Tang, Y., Li, X. L., et al. Semantic-based geographic feature property similarity measurement model [J]. Remote Sensing Information, 2017, 32(1): 126-133.

Data Product:

ID Data Name Data Size Operation
0Datapaper_GDSCS.pdf567.00kbDownLoad
1 GDSCS_CN_V1.0.exe 120379.67KB
2 GDSCS_EN_V1.0.exe 120379.58KB
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