A general framework for spatial data inspection and assessment

详细信息    查看全文

• 作者：Yiliang Wan ; Wenzhong Shi ; Lipeng Gao ; Pengfei Chen ; Yong Hua
• 关键词：Spatial data quality ; Inspection ; Data assessment ; Quality standards
• 刊名：Earth Science Informatics
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：8
• 期：4
• 页码：919-935
• 全文大小：1,366 KB
• 参考文献：Aronoff S (1989) Geographic information systems: a management perspective. Geocarto Int 4(4):58. doi:10.鈥?080/鈥?010604890935423鈥? CrossRef
  Beckert B, H盲hnle R, Schmitt PH (2007) Verification of object-oriented software: The KeY approach. Springer, Berlin Heidelberg
  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鈥?02CrossRef
  Burrough PA (2001) GIS and geostatistics: essential partners for spatial analysis. Environ Ecol Stat 8(4):361鈥?77CrossRef
  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鈥?. doi:10.鈥?109/鈥婾RS.鈥?009.鈥?137579
  Chen X, Pan M, Wu H, Yang J, Zhu L (2005) DEM Data Quality Detecting Based on Spatial Index. Appl Res Comput (09):28鈥?0
  Crosetto M, Tarantola S (2001) Uncertainty and sensitivity analysis: tools for GIS-based model implementation. Int J Geogr Inf Sci 15(5):415鈥?37CrossRef
  De Champeaux D, Lea D, Faure P (1993) Object-oriented system development. Addison-Wesley Longman Publishing Co., Inc., Boston
  Delavar MR, Devillers R (2010) Spatial data quality: from process to decisions. Trans GIS 14(4):379鈥?86. doi:10.鈥?111/鈥媕.鈥?467-9671.鈥?010.鈥?1224.鈥媥 CrossRef
  Department of Commerce (1992) Spatial Data Transfer Standard (SDTS). http://鈥媘cmcweb.鈥媏r.鈥媢sgs.鈥媑ov/鈥媠dts/鈥媠tandard.鈥媓tml . Accessed 17 Sept 2013
  Devillers R, Jeansoulin R (2006) Fundamentals of spatial data quality. ISTE, LondonCrossRef
  Duckham M (2002) A user-oriented perspective of error-sensitive GIS development. Trans GIS 6(2):179鈥?93CrossRef
  Esri (2007) QA/QC for GIS Data. http://鈥媡raining.鈥媏sri.鈥媍om/鈥媑ateway/鈥媔ndex.鈥媍fm?鈥婥ourseID=鈥?0099063_鈥?.鈥媂&鈥媐a=鈥媍atalog.鈥媍ourseDetail . 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鈥?56
  Fisher P (1997) Book reviews. Int J Geogr Inf Sci 11(4):407鈥?08. doi:10.鈥?080/鈥?36588197242356
  Foken T, G枚ockede 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鈥?08
  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鈥?34
  Fu H (2010) Quality control and assessment of 1:2000 DLG. China Place Name (03):70鈥?1
  Gong P, Mu L (2000) Error detection through consistency checking. Geogr Inf Sci 6(2):188鈥?93
  Goodchild MF, Gopal S (1989) The accuracy of spatial databases. Taylor & Fancis, London
  Guptill SC, Morrison JLA (1995) Elements of spatial data quality. Elsevier Science, New York
  Hegde NP, Hegde GL (2007) Quality control in large spatial databases maintenance. In: 5th International Symposium for Spatial Data Quality. ITC, Enschede, p 3
  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鈥?13CrossRef
  ISO (2005) ISO 9000:2005: Quality management systems 鈥?Fundamentals and vocabulary. International Organization for Standardization (ISO), Geneva, p 30
  ISO (2013) ISO 19157:2013: Geographic information 鈥?data quality. International Organization for Standardization (ISO), Geneva, p 164
  Langran G, Chrisman NR (1988) A framework for temporal geographic information. Cartographica: Int J Geogr Inf Geovisualization 25(3):1鈥?4CrossRef
  Li D, Zhang J, Wu H (2012) Spatial data quality and beyond. Int J Geogr Inf Sci 26(12):2277鈥?290. doi:10.鈥?080/鈥?3658816.鈥?012.鈥?19625 CrossRef
  Malenfant J, Jacques M, Demers FN (1996) A tutorial on behavioral reflection and its implementation. In: Kiczales G (ed) Proceedings of the Reflection 鈥?6 Conference, San Francisco, California, USA, 1996, pp 1鈥?0
  McGranaghan M (1993) A cartographic view of spatial data quality. Cartographica: Int J Geogr Inf Geovisualization 30(2):8鈥?9. doi:10.鈥?138/鈥?10V-0067-7570-6566 CrossRef
  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
  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
  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
  NASMG (2008) GB/T 18316鈥?008: Specifications for inspection and acceptance of quality of digital surveying and mapping achievements. National Administration of Surveying, Mapping and Geoinformation (NASMG), Beijing, p 27
  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鈥?013: Specifications for inspection and acceptance of General Survey Achievements of Geographic. National Mapping Product Quality Inspection and Testing Center (NMPQITC), Beijing, p 33
  OMG (2007) Unified Modeling Language: Superstructure, version 2.1.1 (non-change bar). Document formal/2007-02-05. http://鈥媤ww.鈥媜mg.鈥媜rg/鈥媍gi-bin/鈥媎oc?鈥媐ormal/鈥?007-02-05 . 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鈥?17
  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鈥?09. doi:10.鈥?007/鈥?-306-47647-9_鈥?2
  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鈥?89
  W3C (2008) Extensible Markup Language (XML) 1.0 (Fifth Edition). http://鈥媤ww.鈥媤3.鈥媜rg/鈥婽R/鈥?008/鈥婻EC-xml-20081126/鈥?/span> . Accessed 24 Aug 2013
  Whitney CW, Lind BK, Wahl PW (1998) Quality assurance and quality control in longitudinal studies. Epidemiol Rev 20(1):71鈥?0CrossRef
  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鈥?60
  Wu M, Zeng J, Li Q (2012) Development of quality checking software for the Second National Land Inventory. Land Resour Informatization (04):12鈥?8
  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鈥?8CrossRef
  Zeng J (2009) Quality control and assessment of 1:500 DLG. Beijing Surv Mapp (03):66鈥?8
  Zheng F, Wang X (2009) Quality control and detection for data production of 1:10000 topographic maps (DLG). Geospatial Inf (01):91鈥?4
  
• 作者单位：Yiliang Wan (2)
  Wenzhong Shi (1)
  Lipeng Gao (2)
  Pengfei Chen (2)
  Yong Hua (2)
  
  2. School of Remote Sensing and Information Engineering, Wuhan University, Luoyu Road 129, Wuhan, Hubei, China
  1. Joint Research Laboratory on Spatial Information, The Hong Kong Polytechnic University and Wuhan University, Wuhan, Hong Kong, China
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Computer Applications in Geosciences
  Geosciences
  Simulation and Modeling
  
• 出版者：Springer Berlin Heidelberg
• ISSN：1865-0481

文摘

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. Keywords Spatial data quality Inspection Data assessment Quality standards