Abstract
As geospatial data is increasingly massive and complex, large data volume and rich data source query retrieval are among the urgent issues in need of resolution. Spatial indices are widely used to organize data and optimize queries. However, tree-based indices are increasingly difficult to adapt to a high-efficiency query, and the combination of a grid index and space-filling curve can help decrease the dimensions to improve the query efficiency, but can also lead to data redundancy as one object can cover several grids. To solve the aforementioned problems, this paper proposes a method to manage and query data using a grid-code-array spatial index based on a GeoSOT global subdivision model. For the first time, a grid code was organized in a code-array format and an inverted index was constructed on the column of the code-array. By adding a grid-code-array data structure, we verified the feasibility and efficiency and compared the R-tree index in the Oracle Spatial system and the grid index in the ArcSDE geodatabase for Oracle, which are the most widely used. Experimental results showed that the spatial index we proposed has obvious advantages, which could solve the problem of storage redundancy and query results, and effectively improve spatial queries particularly when the data volume is large.
Similar content being viewed by others
References
Amiri AM, Samavati F, Peterson P (2015) Categorization and conversions for indexing methods of discrete global grid systems. ISPRS Int J Geo-Inf 4:320–336. https://doi.org/10.3390/ijgi4010320
Bartholomé E, Belward AS (2005) GLC2000: a new approach to global land cover mapping from earth observation data. Int J Remote Sens 26:1959–1977. https://doi.org/10.1080/01431160412331291297
Bunce RGH, Metzger MJ, Jongman RHG, Brandt J, De Blust G, Elena-Rossello R, Kovář P (2008) A standardized procedure for surveillance and monitoring European habitats and provision of spatial data. Landsc Ecol 23:11–25. https://doi.org/10.1007/s10980-007-9173-8
Chen D (2016) Subdivision data model of GIS. PhD dissertation. In: Peking University. Beijing, China
Cheng CQ, Ren FH, Pu GL, Wang H, Chen B (2012) An Introduction to Spatial Information Subdivision Organization. Science Press, Beijing, China, (In Chinese)
Cheng CQ, Tong XC, Chen B, Zhai WX (2016) A subdivision method to unify the existing latitude and longitude grids. ISPRS Int J Geo-Inf 5:161–183. https://doi.org/10.3390/ijgi5090161
Ficklin DL, Letsinger SL, Gholizadeh H, Maxwell JT (2015) Incorporation of the penman–Monteith potential evapotranspiration method into a palmer drought severity index tool. Comput Geosci 85:136–141. https://doi.org/10.1016/jcageo201509013
Goodchild MF (2007) Citizens as sensors: the world of volunteered geography. GeoJ 69:211–221. https://doi.org/10.1002/9780470979587ch48
Goodchild MF (2009) Geographic information systems and science: today and tomorrow. Procedia Earth Planet Sci 1:1037–1043. https://doi.org/10.1016/jproeps200909160
Innerebner M, Costa A, Chuprikova E, Monsorno R, Ventura B (2017) Organizing earth observation data inside a spatial data infrastructure. Earth Sci Inf 10:55–68. https://doi.org/10.1007/s12145-016-0276-0
Jagadish HV, Ooi BC, Tan KL, Yu C, Zhang R (2005) iDistance: an adaptive B+−tree based indexing method for nearest neighbor search. ACM Trans Database Syst (TODS) 30:364–397. https://doi.org/10.1145/10716101071612
Li D, Shao Z, Zhu X, Zhu Y (2004) From digital map to spatial information multi-grid. In Proc of IEEE Int geoscience and remote sensing Symp, Anchorage, Alaska, USA, pp 2933–2936. DOI: https://doi.org/10.1109/igarss20041370309
Li JY, Meng LK, Wang FZ, Zhang W, Cai Y (2014) A map-reduce-enabled SOLAP cube for large-scale remotely sensed data aggregation. Comput Geosci 70:110–119. https://doi.org/10.1016/jcageo201405008
Li W, Wu S, Song M, Zhou X (2016a) A scalable cyberinfrastructure solution to support big data management and multivariate visualization of time-series sensor observation data. Earth Sci Inf 9:449–464. https://doi.org/10.1080/1369118X.2018.1485722
Li Z, Yang C, Liu K, Hu F, Jin B (2016b) Automatic scaling hadoop in the cloud for efficient process of big geospatial data. ISPRS Int J Geo-Inf 5:173–186. https://doi.org/10.3390/ijgi5100173
Li Y, Kim D, Shin BS (2016c) Geohashed spatial index method for a location-aware WBAN data monitoring system based on NoSQL. J Inform Process Syst 12:263–274. https://doi.org/10.3745/jips040025
Li S, Cheng CQ, Chen B, Meng L (2016d) Integration and management of massive remote-sensing data based on GeoSOT subdivision model. J Appl Remote Sens 034003-034003:10. https://doi.org/10.1117/1JRS10034003
Lv XF, Cheng CQ, Gong JY (2011) Review of data storage and management technologies for massive remote sensing data. Sci China Ser E 54:3220–3232. https://doi.org/10.1007/s11431-011-4549-z
Machado-Machado EA, Neeti N, Eastman JR, Chen H (2011) Implications of space-time orientation for principal components analysis of earth observation image time series. Earth Sci Inf 4:117–124. https://doi.org/10.1007/s12145-011-0082-7
Oracle (2017) 11g Spatial and Locator: Location features for Oracle database 11g Available online: http://www.oracle.com/technology/products/spatial/index.html (accessed on 28 January 2017)
Song SH, Cheng CQ, Pu GL, An FG, Luo X (2014) Global remote sensing data subdivision organization based on GeoSOT. Acta Geod Cartographica Sin 43:869–876. https://doi.org/10.13485/jcnki11-208920140103
Tan Z, Yue P, Gong J (2017) An array database approach for earth observation data management and processing. ISPRS Int J Geo-Inf 6(7):220–237. https://doi.org/10.3390/ijgi6070220
Traviglia A, Torsello A (2017) A landscape pattern detection in archaeological remote sensing. Geosci 7:128–143. https://doi.org/10.3390/geosciences7040128
Wu HC, Lu CN (2002) A data mining approach for spatial modeling in small area load forecast. IEEE T Power Syst 17:516–521. https://doi.org/10.1109/tpwrs20021007927
Zhang Y (2016) The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering. ISPRS J Photogramm Remote Sens 120:25–36. https://doi.org/10.1016/jisprsjprs201608002
Zhou MY, Chen J (2013) A pole-oriented discrete global grid system: quaternary quadrangle mesh. Comput Geosci 61:133–143. https://doi.org/10.1016/jcageo201308012
Acknowledgments
This study was financially supported by the National Key Research and Development Plan (Grant No. 2018YFB0505300) and the High-Resolution Earth Observation System National Key Foundation of China (Grant Nos. 11-Y20A02-9001-16/17 and 30-Y20A01-9003-16/17). The authors would like to thank Beijing Kingbase Information Technologies Inc., for providing the Kingbase database management system. Finally, the authors are sincerely thankful for the comments and contributions of anonymous reviewers and members of the editorial team.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by: H. A. Babaie
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, S., Pu, G., Cheng, C. et al. Method for managing and querying geo-spatial data using a grid-code-array spatial index. Earth Sci Inform 12, 173–181 (2019). https://doi.org/10.1007/s12145-018-0362-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-018-0362-6