Editing Geographic information system (section)

==Geospatial data management==
The core of any GIS is a [[database]] that contains representations of geographic phenomena, modeling their ''geometry'' (location and shape) and their ''properties'' or ''attributes''. A GIS database may be stored in a variety of forms, such as a collection of separate [[GIS file formats|data files]] or a single [[Spatial database|spatially-enabled]] [[relational database]]. Collecting and managing these data usually constitutes the bulk of the time and financial resources of a project, far more than other aspects such as analysis and mapping.<ref name="longley2015">{{cite book |last1=Longley |first1=Paul A. |last2=Goodchilde |first2=Michael F. |last3=Maguire |first3=David J. |last4=Rhind |first4=David W. |title=Geographic Information Systems & Science |date=2015 |publisher=Wiley |edition=4th}}</ref>{{rp|page=175}}

===Aspects of geographic data===
GIS uses spatio-temporal ([[space-time]]) location as the key index variable for all other information. Just as a relational database containing text or numbers can relate many different tables using common key index variables, GIS can relate otherwise unrelated information by using location as the key index variable. The key is the location and/or extent in space-time.

Any variable that can be located spatially, and increasingly also temporally, can be referenced using a GIS. Locations or extents in Earth space–time may be recorded as dates/times of occurrence, and x, y, and z [[coordinate]]s representing, [[longitude]], [[latitude]], and [[elevation (geography)|elevation]], respectively. These GIS coordinates may represent other quantified systems of temporo-spatial reference (for example, film frame number, stream gage station, highway mile-marker, surveyor benchmark, building address, street intersection, entrance gate, water depth sounding, [[Point of sale|POS]] or [[Computer-aided design|CAD]] drawing origin/units). Units applied to recorded temporal-spatial data can vary widely (even when using exactly the same data, see [[map projection]]s), but all Earth-based spatial–temporal location and extent references should, ideally, be relatable to one another and ultimately to a "real" physical location or extent in space–time.

Related by accurate spatial information, an incredible variety of real-world and projected past or future data can be analyzed, interpreted and represented.<ref>{{cite journal|last=Cowen|first=David |url=http://funk.on.br/esantos/doutorado/GEO/igce/DBMS.pdf |title=GIS versus CAD versus DBMS: What Are the Differences? |access-date=17 September 2010 |year=1988| journal=Photogrammetric Engineering and Remote Sensing|volume=54|number=11|pages=1551–1555|url-status=dead |archive-url=https://web.archive.org/web/20110424091317/http://funk.on.br/esantos/doutorado/GEO/igce/DBMS.pdf |archive-date=24 April 2011}}</ref> This key characteristic of GIS has begun to open new avenues of scientific inquiry into behaviors and patterns of real-world information that previously had not been systematically [[correlation|correlated]].

===Data modeling===
{{Main|Data model (GIS) | GIS file formats}}
GIS data represents phenomena that exist in the real world, such as roads, land use, elevation, trees, waterways, and states. The most common types of phenomena that are represented in data can be divided into two conceptualizations: [[Geographical feature|discrete objects]] (e.g., a house, a road) and [[Field (geography)|continuous fields]] (e.g., rainfall amount or population density).<ref name="longley2015" /> {{rp|pages=62–65}} Other types of geographic phenomena, such as events (e.g., location of [[World War II]] battles), processes (e.g., extent of [[suburbanization]]), and masses (e.g., types of [[soil]] in an area) are represented less commonly or indirectly, or are modeled in analysis procedures rather than data.

Traditionally, there are two broad methods used to store data in a GIS for both kinds of abstractions mapping references: [[raster images]] and [[Vector graphics|vector]]. Points, lines, and polygons represent vector data of mapped location attribute references.

A new hybrid method of storing data is that of identifying point clouds, which combine three-dimensional points with [[RGB]] information at each point, returning a [[Anaglyph 3D|3D color image]]. GIS thematic maps then are becoming more and more realistically visually descriptive of what they set out to show or determine.

===Data acquisition===
[[File:Field-Map birdie.jpg|thumb|right|Example of hardware for mapping ([[GPS]] and [[laser rangefinder]]) and data collection ([[rugged computer]]). The current trend for geographical information system (GIS) is that accurate mapping and data analysis are completed while in the field. Depicted hardware ([[field-map]] technology) is used mainly for [[forest inventory|forest inventories]], monitoring and mapping.]]
GIS data acquisition includes several methods for gathering spatial data into a GIS database, which can be grouped into three categories: ''primary data capture'', the direct measurement phenomena in the field (e.g., [[remote sensing]], the [[global positioning system]]); ''secondary data capture'', the extraction of information from existing sources that are not in a GIS form, such as paper maps, through [[digitization]]; and ''[[List of GIS data sources|data transfer]]'', the copying of existing GIS data from external sources such as government agencies and private companies. All of these methods can consume significant time, finances, and other resources.<ref name="longley2015"/>{{rp|page=173}}

====Primary data capture====
[[Surveying|Survey]] data can be directly entered into a GIS from digital data collection systems on survey instruments using a technique called [[coordinate geometry]] (COGO). Positions from a global navigation satellite system ([[Satellite navigation|GNSS]]) like the [[Global Positioning System]] can also be collected and then imported into a GIS. A current trend{{As of?|date=September 2024}} in data collection gives users the ability to utilize [[Rugged computer|field computers]] with the ability to edit live data using wireless connections or disconnected editing sessions.<ref>{{cite journal|last1=Marwick|first1=Ben|last2=Hiscock|first2=Peter|last3=Sullivan|first3=Marjorie|last4=Hughes|first4=Philip|title=Landform boundary effects on Holocene forager landscape use in arid South Australia|journal=Journal of Archaeological Science: Reports|volume=19|pages=864–874|date=July 2017|doi=10.1016/j.jasrep.2017.07.004|s2cid=134572456}}</ref> The current trend{{As of?|date=September 2024}} is to utilize applications available on smartphones and [[Personal digital assistant|PDAs]] in the form of mobile GIS.<ref>{{citation |last1=Buławka |first1=Nazarij |last2=Chyla |first2=Julia Maria |chapter=Mobile GIS in Archaeology – Current Possibilities, Future Needs. Position Paper |chapter-url=https://publikationen.uni-tuebingen.de/xmlui/handle/10900/101847 |title=CAA: Digital Archaeologies, Material Worlds (Past and Present) |year=2020 |publisher=Tübingen University Press |location=Tübingen |isbn=978-3-947-25115-5 |s2cid=246410784}}</ref> This has been enhanced by the availability of low-cost mapping-grade GPS units with decimeter accuracy in real time. This eliminates the need to post process, import, and update the data in the office after fieldwork has been collected. This includes the ability to incorporate positions collected using a [[laser rangefinder]]. New technologies also allow users to create maps as well as analysis directly in the field, making projects more efficient and mapping more accurate.

[[Remote sensing|Remotely sensed]] data also plays an important role in data collection and consist of sensors attached to a platform. Sensors include cameras, digital scanners and [[lidar]], while platforms usually consist of aircraft and [[satellite]]s. In England in the mid-1990s, hybrid kite/balloons called [[Allsopp Helikite|helikites]] first pioneered the use of compact airborne digital cameras as airborne geo-information systems. Aircraft measurement software, accurate to 0.4&nbsp;mm, was used to link the photographs and measure the ground. Helikites are inexpensive and gather more accurate data than aircraft. Helikites can be used over roads, railways and towns where [[unmanned aerial vehicle]]s (UAVs) are banned.

Recently, aerial data collection has become more accessible with [[miniature UAV]]s and drones. For example, the [[Aeryon Scout]] was used to map a 50-acre&nbsp;area with a [[ground sample distance]] of {{convert|1|in|cm|2}} in only 12&nbsp;minutes.<ref>{{cite web |url=http://www.aeryon.com/news/pressreleases/248-softwareversion5.html |title=Aeryon Announces Version 5 of the Aeryon Scout System &#124; Aeryon Labs Inc |publisher=Aeryon.com |date=6 July 2011 |access-date=13 May 2012 |archive-date=10 June 2020 |archive-url=https://web.archive.org/web/20200610142031/http://www.aeryon.com/news/pressreleases/248-softwareversion5.html |url-status=dead }}</ref>

The majority of digital data currently comes from [[photo interpretation]] of aerial photographs. Soft-copy workstations are used to digitize features directly from [[Stereoscopy|stereo pairs]] of digital photographs. These systems allow data to be captured in two and three dimensions, with elevations measured directly from a stereo pair using principles of [[photogrammetry]]. Analog aerial photos must be scanned before being entered into a soft-copy system, for high-quality digital cameras this step is skipped.

Satellite [[remote sensing]] provides another important source of spatial data. Here satellites use different sensor packages to passively measure the reflectance from parts of the [[electromagnetic spectrum]] or radio waves that were sent out from an active sensor such as radar. Remote sensing collects raster data that can be further processed using different bands to identify objects and classes of interest, such as land cover.

====Secondary data capture====
{{further|Digitizing}}
The most common method of data creation is [[Digitizing|digitization]], where a [[hard copy]] map or survey plan is transferred into a digital medium through the use of a CAD program, and geo-referencing capabilities. With the wide availability of [[Orthophoto|ortho-rectified imagery]] (from satellites, aircraft, Helikites and UAVs), heads-up digitizing is becoming the main avenue through which geographic data is extracted. Heads-up digitizing involves the tracing of geographic data directly on top of the aerial imagery instead of by the traditional method of tracing the geographic form on a separate [[Graphics tablet|digitizing tablet]] (heads-down digitizing). Heads-down digitizing, or manual digitizing, uses a special magnetic pen, or stylus, that feeds information into a computer to create an identical, digital map. Some tablets use a mouse-like tool, called a puck, instead of a stylus.<ref>{{Cite journal|last=Puotinen|first=Marji|date=June 2009|title=A Primer of GIS: Fundamental Geographic and Cartographic Concepts - By Francis Harvey|journal=Geographical Research|volume=47|issue=2|pages=219–221|doi=10.1111/j.1745-5871.2009.00577.x|bibcode=2009GeoRs..47..219P |issn=1745-5863|doi-access=free}}</ref><ref name=":2">{{Cite web|title=Digitizing - GIS Wiki {{!}} The GIS Encyclopedia|url=http://wiki.gis.com/wiki/index.php/Digitizing|access-date=2021-01-29|website=wiki.gis.com}}</ref> The puck has a small window with cross-hairs which allows for greater precision and pinpointing map features. Though heads-up digitizing is more commonly used, heads-down digitizing is still useful for digitizing maps of poor quality.<ref name=":2" />

Existing data printed on paper or [[PET film (biaxially oriented)|PET film]] maps can be [[digitizer|digitized]] or scanned to produce digital data. A digitizer produces [[Vector graphics|vector]] data as an operator traces points, lines, and polygon boundaries from a map. [[Image scanner|Scanning]] a map results in raster data that could be further processed to produce vector data.

When data is captured, the user should consider if the data should be captured with either a relative accuracy or absolute accuracy, since this could not only influence how information will be interpreted but also the cost of data capture.

After entering data into a GIS, the data usually requires editing, to remove errors, or further processing. For vector data it must be made "topologically correct" before it can be used for some advanced analysis. For example, in a road network, lines must connect with nodes at an intersection. Errors such as undershoots and overshoots must also be removed. For scanned maps, blemishes on the source map may need to be removed from the resulting [[Raster graphics|raster]]. For example, a fleck of dirt might connect two lines that should not be connected.

===Projections, coordinate systems, and registration===
{{Main|Spatial reference system}}
The earth can be represented by various models, each of which may provide a different set of coordinates (e.g., latitude, longitude, elevation) for any given point on the Earth's surface. The simplest model is to assume the earth is a perfect sphere. As more measurements of the earth have accumulated, the models of the earth have become more sophisticated and more accurate. In fact, there are models called [[datum (geodesy)|datums]] that apply to different areas of the earth to provide increased accuracy, like [[NAD83|North American Datum of 1983]] for U.S. measurements, and the [[World Geodetic System]] for worldwide measurements.

The latitude and longitude on a map made against a local datum may not be the same as one obtained from a [[GPS receiver]]. Converting coordinates from one datum to another requires a [[Geographic coordinate conversion#Datum transformations|datum transformation]] such as a [[Helmert transformation]], although in certain situations a simple [[Translation (geometry)|translation]] may be sufficient.<ref name=Irish>{{cite web |url = http://www.osi.ie/GetAttachment.aspx?id=25113681-c086-485a-b113-bab7c75de6fa |title=Making maps compatible with GPS |publisher=Government of Ireland 1999 |access-date=15 April 2008 |archive-url = https://web.archive.org/web/20110721130505/http://www.osi.ie/GetAttachment.aspx?id=25113681-c086-485a-b113-bab7c75de6fa |archive-date=21 July 2011 |url-status=dead }}</ref>

In popular GIS software, data projected in latitude/longitude is often represented as a [[Geographic coordinate system]]. For example, data in latitude/longitude if the datum is the '[[North American Datum]] of 1983' is denoted by 'GCS North American 1983'.

===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|url-access=subscription}}</ref>

===Raster-to-vector translation===
Data restructuring can be performed by a GIS to convert data into different formats. For example, a GIS may be used to convert a satellite image map to a vector structure by generating lines around all cells with the same classification, while determining the cell spatial relationships, such as adjacency or inclusion.

More advanced data processing can occur with [[image processing]], a technique developed in the late&nbsp;1960s by [[NASA]] and the private sector to provide contrast enhancement, false color rendering and a variety of other techniques including use of two dimensional [[Fourier transforms]]. Since digital data is collected and stored in various ways, the two data sources may not be entirely compatible. So a GIS must be able to convert [[geographic data]] from one structure to another. In so doing, the implicit assumptions behind different ontologies and classifications require analysis.<ref>{{cite journal|last=Winther|first=Rasmus G.|year=2014|title=Mapping Kinds in GIS and Cartography|journal=Natural Kinds and Classification in Scientific Practice| editor=C. Kendig|url=http://philpapers.org/archive/WINMKI.pdf |archive-url=https://web.archive.org/web/20140808044350/http://philpapers.org/archive/WINMKI.pdf |archive-date=2014-08-08 |url-status=live}}</ref> Object ontologies have gained increasing prominence as a consequence of [[object-oriented programming]] and sustained work by [[Barry Smith (academic and ontologist)|Barry Smith]] and co-workers.

===Spatial ETL===
[[Spatial ETL]] tools provide the data processing functionality of traditional [[extract, transform, load]]&nbsp;(ETL) software, but with a primary focus on the ability to manage spatial data. They provide GIS&nbsp;users with the ability to translate data between different standards and proprietary formats, whilst geometrically transforming the data en route. These tools can come in the form of add-ins to existing wider-purpose software such as [[spreadsheet]]s.