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A general framework for spatial data inspection and assessment

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Abstract

The quality aspects of spatial data are very important in the decision-making process. However, the quality inspection of spatial data is still dependent on manual checking, and there is an urgent need to develop an automatic or semi-automatic generic system for spatial data quality inspection. In this paper, we present a general framework that automatically copes with spatial data quality inspection based on various spatial data quality standards and specifications. The framework involves all descriptions of given spatial data, a data quality model characterized by quality elements, scheme batch checking and spatial data quality assessment based on quality control and assessment procedures. It is implemented in Unified Modeling Language with four main sets of classes: data dictionary, quality model, scheme checking and quality assessment. Accordingly, we have designed four structured Extensible Markup Language files for the framework to organize and describe the data dictionary, quality model, scheme check and quality assessment. It is very easy for users to describe the data requirements using the data dictionary file, and to extend the quality elements or check rules using the quality model file. Users can design the specified checks and quality assessment schemes without coding by configuring the scheme check files and quality assessment scheme files. The framework also incorporates a checking tool capable of solving the difficulties inherent in the diversity of spatial data quality standards and specifications. The proposed framework and its implementation, as a quality inspection system, will facilitate automatic multiple spatial data quality inspection and acceptance. As a result, the quality of diversified spatial data can be ensured and improved, which is extremely important in the era of spatial big data.

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References

  • Aronoff S (1989) Geographic information systems: a management perspective. Geocarto Int 4(4):58. doi:10.1080/10106048909354237

    Article  Google Scholar 

  • Beckert B, Hähnle R, Schmitt PH (2007) Verification of object-oriented software: The KeY approach. Springer, Berlin Heidelberg

    Google Scholar 

  • Burnicki AC, Brown DG, Goovaerts P (2007) Simulating error propagation in land-cover change analysis: the implications of temporal dependence. Comput Environ Urban Syst 31(3):282–302

    Article  Google Scholar 

  • Burrough PA (2001) GIS and geostatistics: essential partners for spatial analysis. Environ Ecol Stat 8(4):361–377

    Article  Google Scholar 

  • Cao Y, Song W (2009) Research on RSDOM and DLG quality mutual check and evaluation technique. Urban Remote Sensing Event, 2009 Joint, 2009, pp 1–6. doi:10.1109/URS.2009.5137579

  • Chen X, Pan M, Wu H, Yang J, Zhu L (2005) DEM Data Quality Detecting Based on Spatial Index. Appl Res Comput (09):28–30

  • Crosetto M, Tarantola S (2001) Uncertainty and sensitivity analysis: tools for GIS-based model implementation. Int J Geogr Inf Sci 15(5):415–437

    Article  Google Scholar 

  • De Champeaux D, Lea D, Faure P (1993) Object-oriented system development. Addison-Wesley Longman Publishing Co., Inc., Boston

    Google Scholar 

  • Delavar MR, Devillers R (2010) Spatial data quality: from process to decisions. Trans GIS 14(4):379–386. doi:10.1111/j.1467-9671.2010.01224.x

    Article  Google Scholar 

  • Department of Commerce (1992) Spatial Data Transfer Standard (SDTS). http://mcmcweb.er.usgs.gov/sdts/standard.html. Accessed 17 Sept 2013

  • Devillers R, Jeansoulin R (2006) Fundamentals of spatial data quality. ISTE, London

    Book  Google Scholar 

  • Duckham M (2002) A user-oriented perspective of error-sensitive GIS development. Trans GIS 6(2):179–193

    Article  Google Scholar 

  • Esri (2007) QA/QC for GIS Data. http://training.esri.com/gateway/index.cfm?CourseID=50099063_9.X&fa=catalog.courseDetail. Accessed 23 Feb 2014

  • Evans MR, Oliver D, Zhou X, Shekhar S (2014) Spatial big data. In: Karimi HA (ed) Big data: Techniques and technologies in geoinformatics. CRC Press, New York, pp 150–156

    Google Scholar 

  • Fisher P (1997) Book reviews. Int J Geogr Inf Sci 11(4):407–408. doi:10.1080/136588197242356

    Google Scholar 

  • Foken T, Göockede M, Mauder M, Mahrt L, Amiro B, Munger W (2005) Post-field data quality control. In: Lee X, Massman W, Law B (eds) Handbook of Micrometeorology. Springer, pp 181–208

  • Forier F, Canters F (1996) A user-friendly tool for error modelling and error propagation in a GIS environment. United States Department of Agriculture Forest Service General Technical Report Rm, pp 225–234

  • Fu H (2010) Quality control and assessment of 1:2000 DLG. China Place Name (03):70–71

  • Gong P, Mu L (2000) Error detection through consistency checking. Geogr Inf Sci 6(2):188–193

    Google Scholar 

  • Goodchild MF, Gopal S (1989) The accuracy of spatial databases. Taylor & Fancis, London

    Google Scholar 

  • Guptill SC, Morrison JLA (1995) Elements of spatial data quality. Elsevier Science, New York

    Google Scholar 

  • Hegde NP, Hegde GL (2007) Quality control in large spatial databases maintenance. In: 5th International Symposium for Spatial Data Quality. ITC, Enschede, p 3

    Google Scholar 

  • Heuvelink GBM (1993) Error propagation in quantitative spatial modelling: applications in geographical information systems. Dissertation, University of Utrecht

  • Heuvelink GBM, Brown JD, van Loon EE (2007) A probabilistic framework for representing and simulating uncertain environmental variables. Int J Geogr Inf Sci 21(5):497–513

    Article  Google Scholar 

  • ISO (2005) ISO 9000:2005: Quality management systems – Fundamentals and vocabulary. International Organization for Standardization (ISO), Geneva, p 30

    Google Scholar 

  • ISO (2013) ISO 19157:2013: Geographic information – data quality. International Organization for Standardization (ISO), Geneva, p 164

    Google Scholar 

  • Langran G, Chrisman NR (1988) A framework for temporal geographic information. Cartographica: Int J Geogr Inf Geovisualization 25(3):1–14

    Article  Google Scholar 

  • Li D, Zhang J, Wu H (2012) Spatial data quality and beyond. Int J Geogr Inf Sci 26(12):2277–2290. doi:10.1080/13658816.2012.719625

    Article  Google Scholar 

  • Malenfant J, Jacques M, Demers FN (1996) A tutorial on behavioral reflection and its implementation. In: Kiczales G (ed) Proceedings of the Reflection ‘96 Conference, San Francisco, California, USA, 1996, pp 1–20

  • McGranaghan M (1993) A cartographic view of spatial data quality. Cartographica: Int J Geogr Inf Geovisualization 30(2):8–19. doi:10.3138/310V-0067-7570-6566

    Article  Google Scholar 

  • NASMG (1994) GB 14804--93: Classification and codes for the features of 1:500,1:1000 and 1:2000 topographic maps. National Administration of Surveying, Mapping and Geoinformation (NASMG), Beijing, p 19

    Google Scholar 

  • NASMG (1995) CH 1002--1995: Specifications for inspection and acceptance of surveying and mapping product. National Administration of Surveying, Mapping and Geoinformation (NASMG), Beijing, p 24

    Google Scholar 

  • NASMG (2007) GB/T 20258.1-2007: Data dictionary for fundamental geographic information features -- Part 1:Data dictionary for fundamental geographic information features of 1:500 1:1 000 1:2 000 scale. National Administration of Surveying, Mapping and Geoinformation (NASMG), Beijing, p 498

    Google Scholar 

  • NASMG (2008) GB/T 18316–2008: Specifications for inspection and acceptance of quality of digital surveying and mapping achievements. National Administration of Surveying, Mapping and Geoinformation (NASMG), Beijing, p 27

    Google Scholar 

  • NIST (1994) Federal Information Processing Standard Publication 173. (Spatial Data Transfer Standard Part 1. Version 1.1). National Institute Of Technology (NIST), U.S. Department of Commerce, p 193

  • NMPQITC (2013) GDPJ 09–2013: Specifications for inspection and acceptance of General Survey Achievements of Geographic. National Mapping Product Quality Inspection and Testing Center (NMPQITC), Beijing, p 33

    Google Scholar 

  • OMG (2007) Unified Modeling Language: Superstructure, version 2.1.1 (non-change bar). Document formal/2007-02-05. http://www.omg.org/cgi-bin/doc?formal/2007-02-05. Accessed 16 Jan 2014

  • Oort PV (2006) Spatial data quality: from description to application. Dissertation, Wageningen University

  • Shi WZ (2008) From uncertainty description to spatial data quality control. Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Vol II: Accuracy in Geomatics, Liverpool, 2008, pp 412–417

  • Shi WZ, Fisher P, Goodchild MF (2003) Spatial data quality. Taylor & Francis, London

  • Skidmore A (2002) Accuracy assessment of spatial information. In: Stein A, Meer F, Gorte B (eds), vol 1. Remote Sensing and Digital Image Processing. Springer Netherlands, pp 197–209. doi:10.1007/0-306-47647-9_12

  • USGS (1941) United States National Map Accuracy Standards. US Geological Survey (USGS), p 1

  • Veregin H (1999) Data quality parameters. In: Goodchild MF, Maguire DJ, Rhind DW (eds) Geographical information systems. Wiley, New York, pp 177–189

    Google Scholar 

  • W3C (2008) Extensible Markup Language (XML) 1.0 (Fifth Edition). http://www.w3.org/TR/2008/REC-xml-20081126/. Accessed 24 Aug 2013

  • Whitney CW, Lind BK, Wahl PW (1998) Quality assurance and quality control in longitudinal studies. Epidemiol Rev 20(1):71–80

    Article  Google Scholar 

  • Wu D, Hu H, Yang XM, Zheng YD, Zhang LH (2010) Digital chart cartography: error and quality control. The international archives of the photogrammetry. Remote Sens Spat Inf Sci 38(Part II):255–260

    Google Scholar 

  • Wu M, Zeng J, Li Q (2012) Development of quality checking software for the Second National Land Inventory. Land Resour Informatization (04):12–18

  • Wu H, Ye L, Shi W, Clarke KC (2014) Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China. Int J Appl Earth Obs Geoinformation 32:67–78

    Article  Google Scholar 

  • Zeng J (2009) Quality control and assessment of 1:500 DLG. Beijing Surv Mapp (03):66–68

  • Zheng F, Wang X (2009) Quality control and detection for data production of 1:10000 topographic maps (DLG). Geospatial Inf (01):91–94

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Acknowledgments

This research was sponsored by the National Key Technology R&D Program of China (Grant No. 2012BAJ15B04) and the Hong Kong Polytechnic University Joint Supervision Scheme with the Chinese Mainland (Grant No. G-UA35). The authors would also like to thank the Editor and the two anonymous reviewers whose insightful suggestions have significantly improved this paper.

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Authors and Affiliations

  1. Joint Research Laboratory on Spatial Information, The Hong Kong Polytechnic University and Wuhan University, Wuhan, Hong Kong, China

    Wenzhong Shi

  2. School of Remote Sensing and Information Engineering, Wuhan University, Luoyu Road 129, Wuhan, Hubei, China

    Yiliang Wan, Lipeng Gao, Pengfei Chen & Yong Hua

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  1. Yiliang Wan
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  2. Wenzhong Shi
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  3. Lipeng Gao
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  4. Pengfei Chen
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  5. Yong Hua
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Correspondence to Wenzhong Shi.

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Communicated by: H. A. Babaie

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Wan, Y., Shi, W., Gao, L. et al. A general framework for spatial data inspection and assessment. Earth Sci Inform 8, 919–935 (2015). https://doi.org/10.1007/s12145-014-0196-9

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  • DOI: https://doi.org/10.1007/s12145-014-0196-9

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