Editing Geographic information system (section)

===Data quality===
{{further|Data quality}}
While no digital model can be a perfect representation of the real world, it is important that GIS data be of a high quality. In keeping with the principle of [[homomorphism]], the data must be close enough to reality so that the results of GIS procedures correctly correspond to the results of real world processes. This means that there is no single standard for data quality, because the necessary degree of quality depends on the scale and purpose of the tasks for which it is to be used. Several elements of data quality are important to GIS data:

;[[Accuracy and precision|Accuracy]]
:The degree of similarity between a represented measurement and the actual value; conversely, ''error'' is the amount of difference between them.<ref name="bolstad">{{cite book |last1=Bolstad |first1=Paul |title=GIS Fundamentals: A First Text on Geographic Information Systems |date=2019 |publisher=XanEdu |isbn=978-1-59399-552-2 |edition=6th}}</ref>{{rp|page=623}} In GIS data, there is concern for accuracy in representations of location (''positional accuracy''), property (''attribute accuracy''), and time. For example, the US 2020 Census says that the population of [[Houston]] on April 1, 2020 was 2,304,580; if it was actually 2,310,674, this would be an error and thus a lack of attribute accuracy.
;[[Accuracy and precision|Precision]]
:The degree of refinement in a represented value. In a quantitative property, this is the number of significant digits in the measured value.<ref name="longley2015"/>{{rp|page=115}} An imprecise value is vague or ambiguous, including a range of possible values. For example, if one were to say that the population of Houston on April 1, 2020 was "about 2.3 million," this statement would be imprecise, but likely accurate because the correct value (and many incorrect values) are included. As with accuracy, representations of location, property, and time can all be more or less precise. ''[[Spatial resolution|Resolution]]'' is a commonly used expression of positional precision, especially in [[Raster graphics|raster]] data sets. [[map scale|Scale]] is closely related to precision in maps, as it dictates a desirable level of spatial precision, but is problematic in GIS, where a data set can be shown at a variety of display scales (including scales that would not be appropriate for the quality of the data).
;[[Uncertainty]]
:A general acknowledgement of the presence of error and imprecision in geographic data.<ref name="longley2015" />{{rp|page=99}} That is, it is a degree of general doubt, given that it is difficult to know exactly how much error is present in a data set, although some form of estimate may be attempted (a [[confidence interval]] being such an estimate of uncertainty). This is sometimes used as a collective term for all or most aspects of data quality.
;[[Fuzzy concept|Vagueness or fuzziness]]
:The degree to which an aspect (location, property, or time) of a phenomenon is inherently imprecise, rather than the imprecision being in a measured value.<ref name="longley2015"/>{{rp|page=103}} For example, the spatial extent of the [[Houston]] [[metropolitan area]] is vague, as there are places on the outskirts of the city that are less connected to the central city (measured by activities such as [[commuting]]) than places that are closer. Mathematical tools such as [[fuzzy set theory]] are commonly used to manage vagueness in geographic data.
;Completeness
:The degree to which a data set represents all of the actual features that it purports to include.<ref name="bolstad"/>{{rp|page=623}} For example, if a layer of "roads in [[Houston]]" is missing some actual streets, it is incomplete.
;Currency
:The most recent point in time at which a data set claims to be an accurate representation of reality. This is a concern for the majority of GIS applications, which attempt to represent the world "at present," in which case older data is of lower quality.
;[[Consistency]]
:The degree to which the representations of the many phenomena in a data set correctly correspond with each other.<ref name="bolstad"/>{{rp|page=623}} Consistency in [[Geospatial topology|topological relationships]] between spatial objects is an especially important aspect of consistency.<ref name="jensenjensen">{{cite book |last1=Jensen |first1=John R. |last2=Jensen |first2=Ryan R. |title=Introductory Geographic Information Systems |date=2013 |publisher=Pearson |isbn=978-0-13-614776-3}}</ref>{{Rp|page=117}} For example, if all of the lines in a street network were accidentally moved 10 meters to the East, they would be inaccurate but still consistent, because they would still properly connect at each intersection, and [[Transport network analysis|network analysis]] tools such as shortest path would still give correct results.
;[[Propagation of uncertainty]]
:The degree to which the quality of the results of [[Spatial analysis]] methods and other processing tools derives from the quality of input data.<ref name="jensenjensen"/>{{rp|page=118}} For example, [[interpolation]] is a common operation used in many ways in GIS; because it generates estimates of values between known measurements, the results will always be more precise, but less certain (as each estimate has an unknown amount of error).

The quality of a dataset is very dependent upon its sources, and the methods used to create it. Land surveyors have been able to provide a high level of positional accuracy utilizing high-end [[GPS]] equipment, but GPS locations on the average smartphone are much less accurate.<ref>{{cite web|url=http://www.fgdc.gov/standards/projects/FGDC-standards-projects/accuracy/part3/chapter3|title=Geospatial Positioning Accuracy Standards Part 3: National Standard for Spatial Data Accuracy |archive-url=https://web.archive.org/web/20181106172527/http://www.fgdc.gov/standards/projects/FGDC-standards-projects/accuracy/part3/chapter3|archive-date=6 November 2018|url-status=dead}}</ref> Common datasets such as digital terrain and aerial imagery<ref>{{cite web |url=https://njgin.state.nj.us/NJ_NJGINExplorer/IW.jsp |title=NJGIN's Information Warehouse |publisher=Njgin.state.nj.us |access-date=13 May 2012 |archive-date=10 October 2011 |archive-url=https://web.archive.org/web/20111010091429/https://njgin.state.nj.us/NJ_NJGINExplorer/IW.jsp |url-status=dead }}</ref> are available in a wide variety of levels of quality, especially spatial precision. Paper maps, which have been digitized for many years as a data source, can also be of widely varying quality.

A quantitative analysis of maps brings accuracy issues into focus. The electronic and other equipment used to make measurements for GIS is far more precise than the machines of conventional map analysis. All geographical data are inherently inaccurate, and these inaccuracies will propagate through GIS&nbsp;operations in ways that are difficult to predict.<ref>{{Cite journal|last=Couclelis|first=Helen|date=March 2003|title=The Certainty of Uncertainty: GIS and the Limits of Geographic Knowledge|url=http://doi.wiley.com/10.1111/1467-9671.00138|journal=Transactions in GIS|language=en|volume=7|issue=2|pages=165–175|doi=10.1111/1467-9671.00138|bibcode=2003TrGIS...7..165C |s2cid=10269768 |issn=1361-1682}}</ref>