Skip to main content

The Pervasive Challenge of Error and Uncertainty in Geospatial Data

  • Chapter
  • First Online:

Part of the book series: Key Challenges in Geography ((KCHGE))

Abstract

Understanding, quantifying and communicating uncertainty in spatial data, and its propagation through geospatial analyses have been a challenge long recognized in the geospatial community. Over the decades, extensive research has contributed to our understanding of geospatial uncertainty. However, consistent agreed upon methods for addressing, managing, and communicating uncertainty have not been integrated into common geospatial practice. Understanding and accepting the challenge that uncertainty presents to practitioners in the twenty-first century is a step forward in ensuring results of spatial analyses are communicated with greater accuracy and validity for responsible geospatial practice. The mission of this chapter is to provide readers with an appreciation of the varied nature of uncertainty in spatial data, its sources, propagation, and communication.

“…human knowledge is personal and responsible, an unending adventure at the edge of uncertainty.”

Bronowski (1974, p. 367).

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Ahlqvist O (2008) Extending post-classification change detection using semantic similarity metrics to overcome class heterogeneity: a study of 1992 and 2001 US National Land Cover Database changes. Remote Sens Environ 112(3):1226–1241

    Article  Google Scholar 

  • Ban H, Ahlqvist O (2009) Representing and negotiating uncertain geospatial concepts—where are the exurban areas? Comput Environ Urban Syst 33(4):233–246

    Article  Google Scholar 

  • Berube A, Singer A, Watson J, Frey W (2006) Finding Exurbia: America’s fast-growing communities at the metropolitan fringe. The Brookings Institution, Living Cities Census Series, Washington, DC

    Google Scholar 

  • Bishr Y (1998) Overcoming the semantic and other barriers to GIS interoperability. Int J Geogr Inf Sci 12(4):299–314

    Article  Google Scholar 

  • Brown JD (2010) Prospects for the open treatment of uncertainty in environmental research. Prog Phys Geogr 34(1):75–100

    Article  Google Scholar 

  • Camponovo ME, Freundschuh SM (2014) Assessing uncertainty in VGI for emergency response. Cartogr Geogr Inf Sci 41(5):440–455

    Article  Google Scholar 

  • Carroll L (2002) Alice’s adventures in wonderland & through the looking-glass (modern library classics). Random House Inc., Modern Library Paperback Edition, USA

    Google Scholar 

  • Census U (2010) US 2010 Census Summary File 1

    Google Scholar 

  • Chisholm M (2012) Definitions in semantics: the Humpty-Dumpty principle in definitions. http://definitionsinsemantics.blogspot.com/2012/03/humpty-dumpty-principle-in-definitions.html

  • Chow E, Kar B (2017) Error and accuracy assessment for fused data: remote sensing and GIS. Integrating scale in remote sensing and GIS, p 125

    Chapter  Google Scholar 

  • Chrisman N (1991) The error component in spatial data. Geogr Inf Syst 1:165–174

    Google Scholar 

  • Couclelis H (2003) The certainty of uncertainty: GIS and the limits of geographic knowledge. Trans GIS 7(2):165–175

    Article  Google Scholar 

  • Daniels T (1999) When city and country collide: managing growth in the metropolitan fringe. Island Press

    Google Scholar 

  • Devos W, Milenov P (2015) Applying Tegon, the elementary physical land cover feature, for data interoperability. Land use and land cover semantics: principles, best practices, and prospects, p 243

    Chapter  Google Scholar 

  • Ehlschlaeger CR, Shortridge AM, Goodchild MF (1997) Visualizing spatial data uncertainty using animation. Comput Geosci 23(4):387–395

    Article  Google Scholar 

  • Estima J, Fonte CC, Painho M (2014) Comparative study of Land Use/Cover classification using Flickr photos, satellite imagery and Corine Land Cover database

    Google Scholar 

  • FGDC (1998) Content standard for digital geospatial metadata. http://gis.sam.usace.army.mil/General_Information/Standards_And_Reports/Metadata%20Content%20Standard.pdf

  • Fisher PF (1999) Models of uncertainty in spatial data. Geogr Inf Syst 1:191–205

    Google Scholar 

  • Fisher PF, Tate Nicholas J (2006) Causes and consequences of error in digital elevation models. Prog Phys Geogr 30(4):467–489

    Article  Google Scholar 

  • Foote K, Huebner D (2000) Error, accuracy and precision—the geographer’s craft project. Department of geography, University of Texas, Austin

    Google Scholar 

  • GISCI Code of Ethics (2018) https://www.gisci.org/Ethics/CodeofEthics.aspx. Accessed 28 Aug 2018

  • GISCI Rules of Conduct (2018) https://www.gisci.org/Ethics/RulesofConduct.aspx. Accessed 28 Aug 2018

  • Goodchild MF (2007) Citizens as sensors: the world of volunteered geography. GeoJournal 69(4):211–221

    Article  Google Scholar 

  • Goodchild MF (2011) Scale in GIS: an overview. Geomorphology 130(1):5–9

    Article  Google Scholar 

  • Goodchild MF, Gopal S (1989) The accuracy of spatial databases. CRC Press, Florida

    Google Scholar 

  • Haklay M (2010) How good is volunteered geographical information? A comparative study of OpenStreetMap and ordnance survey datasets. Environ Plan 37(4):682–703

    Article  Google Scholar 

  • Heuvelink GB, Burrough PA (2002) Developments in statistical approaches to spatial uncertainty and its propagation. Int J Geogr Inf Sci 16(2):111–113

    Article  Google Scholar 

  • Hunsaker CT, Goodchild MF, Friedl MA, Case TJ (2013) Spatial uncertainty in ecology: implications for remote sensing and GIS applications. Springer, New York

    Google Scholar 

  • Hunter G, Goodchild M (1997) Modeling the uncertainty of slope and aspect estimates derived from spatial databases. Geogr Anal 29(1):35–49

    Article  Google Scholar 

  • Kwan M-P (2012) Uncertain geographic context problem: implications for environmental health research. In: 142nd APHA Annual Meeting and Exposition (November 15–19 Nov 2014), 2012. APHA

    Google Scholar 

  • Laris P, Dadashi S, Jo A, Wechsler S (2016) Buffering the savanna: fire regimes and disequilibrium ecology in West Africa. Plant Ecol 217(5):583–596

    Article  Google Scholar 

  • Li L, Ban H, Wechsler SP, Xu B (2018) 1.22—Spatial Data Uncertainty A2—Huang, Bo. In: Comprehensive geographic information systems. Elsevier, Oxford, pp 313–340. https://doi.org/10.1016/B978-0-12-409548-9.09610-X

    Chapter  Google Scholar 

  • Li L, Goodchild MF (2012) Constructing places from spatial footprints. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on crowdsourced and volunteered geographic information, 2012. ACM, pp 15–21

    Google Scholar 

  • Li L, Goodchild MF, Xu B (2013) Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartogr Geogr Inf Sci 40(2):61–77

    Article  Google Scholar 

  • Li L, Valdovinos J (2018) Optimized conflation of authoritative and crowd-sourced geographic data: creating an integrated bike map. In: Information fusion and intelligent geographic information systems (IF&IGIS’17). Springer, pp 227–241

    Google Scholar 

  • McBratney AB (1992) On variation, uncertainty and informatics in environmental soil management. Soil Res 30(6):913–935

    Article  Google Scholar 

  • Miller MD (2016) The modifiable conceptual unit problem demonstrated using pollen and seed dispersal. Glob Ecol Conserv 6:93–104

    Article  Google Scholar 

  • Neprash JA (1934) Some problems in the correlation of spatially distributed variables. J Am Stat Assoc 29(185A):167–168

    Article  Google Scholar 

  • Openshaw S (1984) The modifiable areal unit problem. In: Geo Abstracts. University of East Anglia

    Google Scholar 

  • See L, Comber A, Salk C, Fritz S, van der Velde M, Perger C, Schill C, McCallum I, Kraxner F, Obersteiner M (2013) Comparing the quality of crowdsourced data contributed by expert and non-experts. PLoS ONE 8(7):e69958

    Article  Google Scholar 

  • Shi W, Fisher P, Goodchild MF (2003) Spatial data quality. CRC Press, Florida

    Google Scholar 

  • Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ geogr 46(sup1):234–240

    Article  Google Scholar 

  • Wechsler SP (2000) Effect of DEM uncertainty on topographic parameters, DEM scale and terrain evaluation.PhD. Dissertation State University of New York College of Environmental Science and Forestry

    Google Scholar 

  • Wechsler SP, Kroll CN (2006) Quantifying DEM uncertainty and its effect on topographic parameters. Photogram Eng Remote Sens 72(9):1081–1090

    Article  Google Scholar 

  • Wechsler S (2007) Uncertainties associated with digital elevation models for hydrologic applications: a review, Hydrol. Earth Sys Sci, Spec Issue: Uncertainties Hydrol Obs 11:1481–1500

    Article  Google Scholar 

  • Widener MJ, Li W (2014) Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US. Appl Geogr 54:189–197

    Article  Google Scholar 

  • Wikimapia (2018) Wikimapia Statistics. http://wikimapia.org/#lang=en&lat=33.784300&lon=-118.115700&z=12&m=b&show=/stats/action_stats/?fstat=101&period=1&year=2018&month=1. Accessed Jan 18 2018

  • Woodcock CE, Gopal S (2000) Fuzzy set theory and thematic maps: accuracy assessment and area estimation. Int J Geogr Inf Sci 14(2):153–172

    Article  Google Scholar 

  • Yang W, Mu L (2015) GIS analysis of depression among Twitter users. Appl Geogr 60:217–223

    Article  Google Scholar 

  • Zadeh LA (1965) Information and control. Fuzzy Sets 8(3):338–353

    Google Scholar 

  • Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1:3–28

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Suzanne Perlitsh Wechsler .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wechsler, S.P., Ban, H., Li, L. (2019). The Pervasive Challenge of Error and Uncertainty in Geospatial Data. In: Koutsopoulos, K., de Miguel González, R., Donert, K. (eds) Geospatial Challenges in the 21st Century. Key Challenges in Geography. Springer, Cham. https://doi.org/10.1007/978-3-030-04750-4_16

Download citation

Publish with us

Policies and ethics