Editing Geographic information system

{{short description|System to capture, manage, and present geographic data}}
{{Redirect|GIS}}
{{more citations needed|date=March 2023}}
[[File:Fig 4.4.svg|thumb|Basic GIS concept]]

A '''geographic information system''' ('''GIS''') consists of integrated computer hardware and [[Geographic information system software|software]] that store, manage, [[Spatial analysis|analyze]], edit, output, and [[Cartographic design|visualize]] [[Geographic data and information|geographic data]].<ref name="DeMers1">{{cite book |last1=DeMers |first1=Michael |title=Fundamentals of Geographic Information Systems |date=2009 |publisher=John Wiley & Sons, inc. |isbn=978-0-470-12906-7 |edition=4th}}</ref><ref name="chang2016">{{cite book |last1=Chang |first1=Kang-tsung |title=Introduction to Geographic Information Systems |date=2016 |publisher=McGraw-Hill |isbn=978-1-259-92964-9 |page=1 |edition=9th}}</ref> Much of this often happens within a [[spatial database]]; however, this is not essential to meet the definition of a GIS.<ref name="DeMers1"/> In a broader sense, one may consider such a system also to include human users and support staff, procedures and workflows, the [[Geographic Information Science and Technology Body of Knowledge|body of knowledge]] of relevant concepts and methods, and institutional organizations.

The uncounted plural, ''geographic information systems'', also abbreviated GIS, is the most common term for the industry and profession concerned with these systems. The academic discipline that studies these systems and their underlying geographic principles, may also be abbreviated as GIS, but the unambiguous [[GIScience]] is more common.<ref>{{cite journal|doi=10.5311/JOSIS.2010.1.2|title=Twenty years of progress: GIScience in 2010|year=2010|last1=Goodchild|first1=Michael F|journal=Journal of Spatial Information Science|issue=1|doi-access=free}}</ref> GIScience is often considered a subdiscipline of [[geography]] within the branch of [[technical geography]].

Geographic information systems are utilized in multiple technologies, processes, techniques and methods. They are attached to various operations and numerous applications, that relate to: engineering, planning, management, transport/logistics, insurance, telecommunications, and business,<ref name="Maliene V, Grigonis V, Palevičius V, Griffiths S 2011 1–6">{{cite journal| vauthors=Maliene V, Grigonis V, Palevičius V, Griffiths S|title=Geographic information system: Old principles with new capabilities |journal=Urban Design International |volume=16 |issue= 1 |pages= 1–6 |year= 2011 |doi= 10.1057/udi.2010.25 |s2cid=110827951 }}</ref> as well as the natural sciences such as forestry, ecology, and Earth science. For this reason, GIS and [[location intelligence]] applications are at the foundation of location-enabled services, which rely on geographic analysis and visualization.

GIS provides the ability to relate previously unrelated information, through the use of location as the "key index variable". Locations and extents that are found in the Earth's [[spacetime]] are able to be recorded through the date and time of occurrence, along with x, y, and z [[coordinate]]s; representing, [[longitude]] (''x''), [[latitude]] (''y''), and [[elevation (geography)|elevation]] (''z''). All Earth-based, spatial–temporal, location and extent references should be relatable to one another, and ultimately, to a "real" physical location or extent. This key characteristic of GIS has begun to open new avenues of scientific inquiry and studies.

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==History and development==
While digital GIS dates to the mid-1960s, when [[Roger Tomlinson]] first coined the phrase "geographic information system",<ref>{{cite web|title=The 50th Anniversary of GIS|url=http://www.esri.com/news/arcnews/fall12articles/the-fiftieth-anniversary-of-gis.html|publisher=ESRI|access-date=18 April 2013}}</ref> many of the geographic concepts and methods that GIS automates date back decades earlier.

[[File:Snow-cholera-map.jpg|thumb|right|300px|[[E. W. Gilbert]]'s version (1958) of [[John Snow (physician)|John Snow]]'s 1855&nbsp;map of the [[Soho]] cholera outbreak showing the clusters of cholera cases in the [[1854 Broad Street cholera outbreak|London epidemic of 1854]]]]

One of the first known instances in which spatial analysis was used came from the field of [[epidemiology]] in the {{Lang|fr|Rapport sur la marche et les effets du choléra dans Paris et le département de la [[Seine]]}} (1832).<ref>{{cite web|url=http://gallica.bnf.fr/ark:/12148/bpt6k842918/f353.image|title=Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine. Année 1832|publisher=Gallica|access-date=10 May 2012}}</ref> French [[Cartography|cartographer]] and geographer [[Charles Picquet]] created a map outlining the [[Arrondissements of Paris|forty-eight&nbsp;districts in Paris]], using [[halftone]] color gradients, to provide a visual representation for the number of reported deaths due to [[cholera]] per&nbsp;every 1,000&nbsp;inhabitants.

In 1854, [[John Snow]], an epidemiologist and physician, was able to determine the source of a [[1854 Broad Street cholera outbreak|cholera outbreak in London]] through the use of spatial analysis. Snow achieved this through plotting the residence of each casualty on a map of the area, as well as the nearby water sources. Once these points were marked, he was able to identify the water source within the cluster that was responsible for the outbreak. This was one of the earliest successful uses of a geographic methodology in pinpointing the source of an outbreak in epidemiology. While the basic elements of [[topography]] and theme existed previously in [[cartography]], Snow's map was unique due to his use of cartographic methods, not only to depict, but also to analyze clusters of geographically dependent phenomena.

The early&nbsp;20th&nbsp;century saw the development of [[photozincography]], which allowed maps to be split into layers, for example one layer for vegetation and another for water. This was particularly used for printing contours&nbsp;– drawing these was a labour-intensive task but having them on a separate layer meant they could be worked on without the other layers to confuse the [[Drafter|draughtsman]]. This work was initially drawn on glass plates, but later [[plastic film]] was introduced, with the advantages of being lighter, using less storage space and being less brittle, among others. When all the layers were finished, they were combined into one image using a large process camera. Once color printing came in, the layers idea was also used for creating separate printing plates for each color. While the use of layers much later became one of the typical features of a contemporary GIS, the photographic process just described is not considered a GIS in itself&nbsp;– as the maps were just images with no database to link them to.

Two additional developments are notable in the early days of GIS: [[Ian McHarg]]'s publication ''Design with Nature''<ref>{{Cite book|title=Design with nature|last=MacHarg |first=Ian L.|date=1971|publisher=Natural History Press|oclc=902596436}}</ref> and its map overlay method and the introduction of a street network into the U.S. Census Bureau's DIME ([[Dual Independent Map Encoding]]) system.<ref>{{Cite journal|last1=Broome|first1=Frederick R.|last2=Meixler|first2=David B.|date=January 1990|title=The TIGER Data Base Structure|journal=Cartography and Geographic Information Systems|volume=17|issue=1|pages=39–47|doi=10.1559/152304090784005859|bibcode=1990CGISy..17...39B |issn=1050-9844}}</ref>

The first publication detailing the use of computers to facilitate cartography was written by [[Waldo Tobler]] in 1959.<ref>{{cite journal |last1=Tobler |first1=Waldo |title=Automation and Cartography |journal=Geographical Review |date=1959 |volume=49 |issue=4 |pages=526–534 |doi=10.2307/212211 |jstor=212211 |bibcode=1959GeoRv..49..526T |url=https://www.jstor.org/stable/212211 |access-date=10 March 2022}}</ref> Further [[computer hardware]] development spurred by [[nuclear weapon]] research led to more widespread general-purpose computer "mapping" applications by the early 1960s.<ref name="map_printing_methods">{{cite web |url=http://www.broward.org/library/bienes/lii14009.htm |title=Map Printing Methods |first=Joseph H. |last=Fitzgerald |access-date=9 June 2007 |archive-url = https://web.archive.org/web/20070604194024/http://www.broward.org/library/bienes/lii14009.htm <!-- Bot retrieved archive --> |archive-date = 4 June 2007}}</ref>

In 1963, the world's first true operational GIS was developed in [[Ottawa, Ontario]], Canada, by the federal Department of Forestry and Rural Development. Developed by [[Roger Tomlinson]], it was called the [[Canada Geographic Information System]] (CGIS) and was used to store, analyze, and manipulate data collected for the [[Canada Land Inventory]], an effort to determine the land capability for rural Canada by mapping information about [[soil]]s, agriculture, recreation, wildlife, [[waterfowl]], [[forestry]] and land use at a scale of 1:50,000. A rating classification factor was also added to permit analysis.<ref>{{Cite web|title=History of GIS {{!}} Early History and the Future of GIS – Esri|url=https://www.esri.com/en-us/what-is-gis/history-of-gis|website=esri.com|language=en-us|access-date=2020-05-02}}</ref><ref name=":1">{{cite web |author=<!-- no author given --> |url=http://ucgis.org/ucgis-fellow/roger-tomlinson |title=Roger Tomlinson |publisher=UCGIS |date=21 February 2014 |access-date=16 December 2015|url-status=dead |archive-url=https://web.archive.org/web/20151217012639/http://ucgis.org/ucgis-fellow/roger-tomlinson |archive-date=17 December 2015}}</ref>

CGIS was an improvement over "computer mapping" applications as it provided capabilities for data storage, overlay, measurement, and [[digitizing]]/scanning. It supported a national coordinate system that spanned the continent, coded lines as [[Directed edge|arcs]] having a true embedded [[topology]] and it stored the attribute and locational information in separate files. As a result of this, Tomlinson has become known as the "father of GIS", particularly for his use of overlays in promoting the spatial analysis of convergent geographic data.<ref name="Tomlinson">{{cite web |url=http://www.urisa.org/node/395 |title=GIS Hall of Fame&nbsp;– Roger Tomlinson |publisher=URISA |access-date=9 June 2007 |url-status=dead |archive-url=https://web.archive.org/web/20070714083049/http://www.urisa.org/node/395 |archive-date=14 July 2007}}</ref> CGIS lasted into the 1990s and built a large digital land resource database in Canada. It was developed as a [[Mainframe computer|mainframe]]-based system in support of federal and provincial resource planning and management. Its strength was continent-wide analysis of complex [[data set|dataset]]s. The CGIS was never available commercially.

In&nbsp;1964, Howard T. Fisher formed the Laboratory for Computer Graphics and Spatial Analysis at the [[Harvard Graduate School of Design]] (LCGSA 1965–1991), where a number of important theoretical concepts in spatial data handling were developed, and which by the 1970s had distributed seminal software code and systems, such as SYMAP, GRID, and ODYSSEY, to universities, research centers and corporations worldwide.<ref name="Fisher">{{cite web |url = http://www.gis.dce.harvard.edu/fisher/HTFisher.htm |title = Howard T. Fisher |first = Lucia |last = Lovison-Golob |publisher = Harvard University |access-date = 9 June 2007 |url-status = dead |archive-url = https://web.archive.org/web/20071213234339/http://www.gis.dce.harvard.edu/fisher/HTFisher.htm |archive-date = 13 December 2007}}</ref> These programs were the first examples of general-purpose GIS software that was not developed for a particular installation, and was very influential on future commercial software, such as [[Esri]] [[ARC/INFO]], released in 1983.

By the late 1970s, two public domain GIS systems ([[Map Overlay and Statistical System|MOSS]] and [[GRASS GIS]]) were in development, and by the early 1980s, M&S Computing (later [[Intergraph]]) along with Bentley Systems Incorporated for the [[Computer-aided design|CAD]]&nbsp;platform, Environmental Systems Research Institute ([[Environmental Systems Research Institute|ESRI]]), [[Teledyne CARIS|CARIS]]&nbsp;(Computer Aided Resource Information System), and ERDAS&nbsp;(Earth Resource Data Analysis System) emerged as commercial vendors of GIS&nbsp;software, successfully incorporating many of the CGIS&nbsp;features, combining the first-generation approach to separation of spatial and attribute information with a second-generation approach to organizing attribute data into database structures.<ref name="wiki.osgeo.org">{{cite web |url=http://wiki.osgeo.org/wiki/Open_Source_GIS_History |title=Open Source GIS History – OSGeo Wiki Editors |access-date=21 March 2009}}</ref>

In 1986, Mapping Display and Analysis System (MIDAS), the first desktop GIS product,<ref>{{Cite book|title=GIS for Environmental Applications A practical approach|last=Xuan|first=Zhu|year=2016|publisher=Routledge |isbn=9780415829069|oclc=1020670155}}</ref> was released for the [[DOS]] operating system. This was renamed in 1990 to MapInfo for Windows when it was ported to the [[Microsoft Windows]] platform. This began the process of moving GIS from the research department into the business environment.

By the end of the 20th&nbsp;century, the rapid growth in various systems had been consolidated and standardized on relatively few platforms and users were beginning to explore viewing GIS&nbsp;data over the [[Internet]], requiring data format and transfer standards. More recently, a growing number of [[List of GIS software#Open source software|free, open-source GIS packages]] run on a range of operating systems and can be customized to perform specific tasks. The major trend of the 21st Century has been the integration of GIS capabilities with other [[Information technology]] and [[Internet]] infrastructure, such as [[relational database]]s, [[cloud computing]], [[software as a service]] (SAAS), and [[mobile computing]].<ref>Fu, P., and J. Sun. 2010. ''Web GIS: Principles and Applications''. ESRI Press. Redlands, CA. {{ISBN|1-58948-245-X}}.</ref>

==GIS software==
{{main|Geographic information system software}}
{{See also|List of free and open-source software packages#Maps|l1=List of open source GIS software}}
The distinction must be made between a singular ''geographic information system'', which is a single installation of software and data for a particular use, along with associated hardware, staff, and institutions (e.g., the GIS for a particular city government); and ''[[Geographic information system software|GIS software]]'', a general-purpose [[Application software|application program]] that is intended to be used in many individual geographic information systems in a variety of application domains.<ref name="bolstad" />{{rp|page=16}} Starting in the late 1970s, many software packages have been created specifically for GIS applications. [[Esri|Esri's]] [[ArcGIS]], which includes [[ArcGIS Pro]] and the legacy software [[ArcMap]], currently dominates the GIS market.{{As of?|date=September 2024}} Other examples of GIS include [[Autodesk]] and [[MapInfo Professional]] and open-source programs such as [[QGIS]], [[GRASS GIS]], [[MapGuide]], and [[Apache Hadoop|Hadoop-GIS]].<ref>{{Cite conference |author1=Ablimit Aji |author2=Hoang Vo |author3=Qiaoling Liu |author4=Fusheng Wang |author5=Joel Saltz |author6=Rubao Lee |author7=Xiaodong Zhang | title="Hadoop GIS: a high performance spatial data warehousing system over mapreduce" |journal=Proceedings of the VLDB Endowment International Conference on Very Large Data Bases |conference= The 39th International Conference on Very Large Data Bases | pages=1009–1020|year=2013 |volume=6 |issue=11 |pmid=24187650 |pmc=3814183 }}</ref> These and other desktop GIS applications include a full suite of capabilities for entering, managing, analyzing, and visualizing geographic data, and are designed to be used on their own.

Starting in the late 1990s with the emergence of the [[Internet]], as computer network technology progressed, GIS infrastructure and data began to move to [[Server (computing)|server]]s, providing another mechanism for providing GIS capabilities.<ref name="longley2015" />{{rp|page=216}} This was facilitated by standalone software installed on a server, similar to other server software such as [[HTTP server]]s and [[relational database management system]]s, enabling clients to have access to GIS data and processing tools without having to install specialized desktop software. These networks are known as [[distributed GIS]].<ref name=Zhong1>{{cite book |last1=Peng |first1=Zhong-Ren |last2=Tsou |first2=Ming-Hsiang |title=Internet GIS: Distributed Information Services for the Internet and Wireless Networks |year=2003 |location=Hoboken, NJ |publisher=John Wiley and Sons |isbn=0-471-35923-8 |oclc=50447645 |url=https://archive.org/details/internetgisdistr0000peng |url-access=registration}}</ref><ref name=Moretz1>{{cite encyclopedia |last1=Moretz |first1=David |title=Internet GIS |year=2008 |encyclopedia=Encyclopedia of GIS |editor1-last=Shekhar |editor1-first=Shashi |editor2-last=Xiong |editor2-first=Hui |location=New York |publisher=Springer |pages=[https://archive.org/details/encyclopediaofgi0000unse_i4o0/page/591 591–596] |isbn=978-0-387-35973-1 |oclc=233971247 |url=https://archive.org/details/encyclopediaofgi0000unse_i4o0/page/591 |url-access=registration |doi=10.1007/978-0-387-35973-1_648}}</ref> This strategy has been extended through the Internet and development of [[cloud computing|cloud-based]] GIS platforms such as ArcGIS Online and GIS-specialized [[software as a service]] (SAAS). The use of the Internet to facilitate distributed GIS is known as [[Internet GIS]].<ref name=Zhong1/><ref name=Moretz1/>

An alternative approach is the integration of some or all of these capabilities into other software or [[information technology]] architectures. One example is a [[Spatial database|spatial extension]] to [[Object-relational database]] software, which defines a geometry datatype so that spatial data can be stored in relational tables, and extensions to [[SQL]] for spatial analysis operations such as [[Vector overlay|overlay]]. Another example is the proliferation of geospatial libraries and [[application programming interface]]s (e.g., [[GDAL]], [[Leaflet (software)|Leaflet]], [[D3.js]]) that extend programming languages to enable the incorporation of GIS data and processing into custom software, including [[web mapping]] sites and [[location-based service]]s in [[smartphone]]s.

==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}}</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.

==Spatial analysis{{anchor|Spatial analysis}}==
{{further|Spatial analysis}}

GIS spatial analysis is a rapidly changing field, and GIS packages are increasingly including analytical tools as standard built-in facilities, as optional toolsets, as add-ins or 'analysts'. In many instances these are provided by the original software suppliers (commercial vendors or collaborative non commercial development teams), while in other cases facilities have been developed and are provided by third parties. Furthermore, many products offer software development kits (SDKs), programming languages and language support, scripting facilities and/or special interfaces for developing one's own analytical tools or variants. The increased availability has created a new dimension to [[business intelligence]] termed "[[Spatial intelligence (business method)|spatial intelligence]]" which, when openly delivered via intranet, democratizes access to geographic and social network data. [[Geospatial intelligence]], based on GIS spatial analysis, has also become a key element for security. GIS as a whole can be described as conversion to a vectorial representation or to any other digitisation process.

{{anchor|Geoprocessing}}'''Geoprocessing'''<!--boldface per [[WP:R#PLA]]--> is a GIS operation used to manipulate spatial data. A typical geoprocessing operation takes an input [[dataset]], performs an operation on that dataset, and returns the result of the operation as an output dataset. Common geoprocessing operations include geographic feature overlay, feature selection and analysis, [[topology]] processing, [[raster graphics|raster]] processing, and data conversion. Geoprocessing allows for definition, management, and analysis of information used to form decisions.<ref>Wade, T. and Sommer, S. eds. ''[http://store.esri.com/esri/showdetl.cfm?SID=2&Product_ID=868&Category_ID=49 A to Z GIS]''</ref>

===Terrain analysis===
[[File:Dem.jpg|thumb|right|300px|Hillshade model derived from a [[digital elevation model]] of the Valestra area in the northern Apennines (Italy)]]
{{main|Geomorphometry}}
{{see also|Surface gradient}}
Many geographic tasks involve the [[terrain]], the shape of the surface of the earth, such as [[hydrology]], [[Earthworks (engineering)|earthworks]], and [[biogeography]]. Thus, terrain data is often a core dataset in a GIS, usually in the form of a raster [[Digital elevation model]] (DEM) or a [[Triangulated irregular network]] (TIN). A variety of tools are available in most GIS software for analyzing terrain, often by creating derivative datasets that represent a specific aspect of the surface. Some of the most common include:
* [[Grade (slope)|Slope or grade]] is the steepness or gradient of a unit of terrain, usually measured as an angle in degrees or as a percentage.<ref name=Jones1998>{{cite journal |doi= 10.1016/S0098-3004(98)00032-6 |author = Jones, K.H.|year=1998 |title=A comparison of algorithms used to compute hill slope as a property of the DEM |journal=Computers and Geosciences |volume=24 |issue=4 |pages =315–323 |bibcode=1998CG.....24..315J}}</ref> 
* [[Aspect (geography)|Aspect]] can be defined as the direction in which a unit of terrain faces. Aspect is usually expressed in degrees from north.<ref name="Skidmore1989">{{cite journal |author1 =Chang, K. T.|year=1989|title=A comparison of techniques for calculating gradient and aspect from a gridded digital elevation model|journal=International Journal of Geographical Information Science|volume=3|issue=4|pages =323–334 |doi=10.1080/02693798908941519|url=https://research.utwente.nl/en/publications/470392f6-30d7-45df-806b-a29aa84aaa72 }}</ref> 
* Cut and fill is a computation of the difference between the surface before and after an [[Earthworks (engineering)|excavation]] project to estimate costs.
* [[Hydrological model]]ing can provide a spatial element that other hydrological models lack, with the analysis of variables such as slope, aspect and watershed or [[Catchment area (human geography)|catchment area]].<ref name="Heywood">{{cite book |vauthors=Heywood I, Cornelius S, Carver S |year=2006 |title=An Introduction to Geographical Information Systems |publisher=Prentice Hall |place=Essex, England |edition = 3rd}}</ref> Terrain analysis is fundamental to hydrology, since water always flows down a slope.<ref name="Heywood" /> As basic terrain analysis of a [[digital elevation model]] (DEM) involves calculation of slope and aspect, DEMs are very useful for hydrological analysis. Slope and aspect can then be used to determine direction of [[surface runoff]], and hence flow accumulation for the formation of streams, rivers and lakes. Areas of divergent flow can also give a clear indication of the boundaries of a catchment. Once a flow direction and accumulation matrix has been created, queries can be performed that show contributing or dispersal areas at a certain point.<ref name="Heywood" /> More detail can be added to the model, such as terrain roughness, vegetation types and soil types, which can influence infiltration and evapotranspiration rates, and hence influencing surface flow. One of the main uses of hydrological modeling is in [[GIS in environmental contamination|environmental contamination research]]. Other applications of hydrological modeling include [[GIS and hydrology|groundwater and surface water mapping]], as well as flood risk maps.
* [[Viewshed analysis]] predicts the impact that terrain has on the visibility between locations, which is especially important for wireless communications.
* [[Shaded relief]] is a depiction of the surface as if it were a three dimensional model lit from a given direction, which is very commonly used in maps.

Most of these are generated using algorithms that are discrete simplifications of [[vector calculus]]. Slope, aspect, and surface curvature in terrain analysis are all derived from neighborhood operations using elevation values of a cell's adjacent neighbours.<ref name="Chang">{{cite book |author=Chang, K. T. |year=2008 |title=Introduction to Geographical Information Systems |publisher=McGraw Hill |place=New York |page =184}}</ref> Each of these is strongly affected by the level of detail in the terrain data, such as the resolution of a DEM, which should be chosen carefully.<ref name="Longley2005">{{cite journal |author1 =Longley, P. A. |author2 =Goodchild, M. F. |author3 =McGuire, D. J. |author4 = Rhind, D. W. |year=2005 |title=Analysis of errors of derived slope and aspect related to DEM data properties |journal=Geographic Information Systems and Science |publisher=John Wiley and Sons |place=West Sussex, England |page=328}}</ref>

===Proximity analysis===
{{main|Proximity analysis}}
Distance is a key part of solving many geographic tasks, usually due to the [[friction of distance]]. Thus, a wide variety of analysis tools have analyze distance in some form, such as [[Buffer analysis|buffers]], [[Voronoi diagram|Voronoi or Thiessen polygons]], [[Cost distance analysis]], and [[Transport network analysis|network analysis]].

===Data analysis===
It is difficult to relate [[wetlands]] maps to [[rainfall]] amounts recorded at different points such as airports, television stations, and schools. A GIS, however, can be used to depict two- and three-dimensional characteristics of the Earth's surface, subsurface, and atmosphere from information points. For example, a GIS can quickly generate a map with [[isopleth]] or [[contour line]]s that indicate differing amounts of rainfall. Such a map can be thought of as a rainfall contour map. Many sophisticated methods can estimate the characteristics of surfaces from a limited number of point measurements. A two-dimensional contour map created from the surface modeling of rainfall point measurements may be overlaid and analyzed with any other map in a GIS covering the same area. This GIS derived map can then provide additional information - such as the viability of [[water power]] potential as a [[renewable energy]] source. Similarly, GIS can be used to compare other [[renewable energy]] resources to find the best geographic potential for a region.<ref>K. Calvert, J. M. Pearce, W.E. Mabee, "Toward renewable energy geo-information infrastructures: Applications of GIScience and remote sensing that can build institutional capacity" ''Renewable and Sustainable Energy Reviews'' 18, pp. 416–429 (2013). [https://www.academia.edu/2180165/Toward_renewable_energy_geo-information_infrastructures_Applications_of_GIScience_and_remote_sensing_that_build_institutional_capacity open access]</ref>

Additionally, from a series of three-dimensional points, or [[digital elevation model]], isopleth lines representing elevation contours can be generated, along with slope analysis, [[shaded relief]], and other elevation products. Watersheds can be easily defined for any given reach, by computing all of the areas contiguous and uphill from any given point of interest. Similarly, an expected [[thalweg]] of where surface water would want to travel in intermittent and permanent streams can be computed from elevation data in the GIS.

===Topological modeling===
A GIS can recognize and analyze the spatial relationships that exist within digitally stored spatial data. These [[topological]] relationships allow complex spatial modelling and analysis to be performed. Topological relationships between geometric entities traditionally include adjacency (what adjoins what), containment (what encloses what), and proximity (how close something is to something else).

===Geometric networks===
{{main | Transport network analysis }}

[[Geometric networks]] are linear networks of objects that can be used to represent interconnected features, and to perform special spatial analysis on them. A geometric network is composed of edges, which are connected at junction points, similar to [[Graph (discrete mathematics)|graphs]] in mathematics and computer science. Just like graphs, networks can have weight and flow assigned to its edges, which can be used to represent various interconnected features more accurately. Geometric networks are often used to model road networks and [[public utility]] networks, such as electric, gas, and water networks. Network modeling is also commonly employed in [[transportation planning]], [[hydrology]] modeling, and [[infrastructure]] modeling.

===Cartographic modeling===
{{main | Map algebra}}
[[File:gislayers.jpg|300px|right|thumb| An example of use of layers in a GIS application. In this example, the forest-cover layer (light green) forms the bottom layer, with the [[topography|topographic]] layer (contour lines) over it. Next up is a standing water layer (pond, lake) and then a flowing water layer (stream, river), followed by the boundary layer and finally the road layer on top. The order is very important in order to properly display the final result. Note that the ponds are layered under the streams, so that a stream line can be seen overlying one of the ponds.]]

[[Dana Tomlin]] coined the term ''cartographic modeling'' in his PhD dissertation (1983); he later used it in the title of his book, ''Geographic Information Systems and Cartographic Modeling'' (1990).<ref>{{cite book | last1 = Tomlin | first1 = C. Dana | author-link1 = Dana Tomlin | title = Geographic information systems and cartographic modeling | url = https://archive.org/details/geographicinform00toml | url-access = registration | series = Prentice Hall series in geographic information science | publisher = Prentice Hall | date = 1990 | isbn = 9780133509274 | access-date = 5 January 2017}}</ref> [[cartographic design|Cartographic modeling]] refers to a process where several thematic [[layer (disambiguation)|layers]] of the same area are produced, processed, and analyzed. Tomlin used raster layers, but the overlay method (see below) can be used more generally. Operations on map layers can be combined into algorithms, and eventually into simulation or optimization models.

===Map overlay===
{{main | Vector overlay | Map algebra }}
The combination of several spatial datasets (points, lines, or [[polygons]]) creates a new output vector dataset, visually similar to stacking several maps of the same region. These overlays are similar to mathematical [[Venn diagram]] overlays. A [[union (set theory)|union]] overlay combines the geographic features and attribute tables of both inputs into a single new output. An [[intersection (set theory)|intersect]] overlay defines the area where both inputs overlap and retains a set of attribute fields for each. A [[symmetric difference]] overlay defines an output area that includes the total area of both inputs except for the overlapping area.

Data extraction is a GIS process similar to vector overlay, though it can be used in either vector or raster data analysis. Rather than combining the properties and features of both datasets, data extraction involves using a "clip" or "mask" to extract the features of one data set that fall within the spatial extent of another dataset.

In raster data analysis, the overlay of datasets is accomplished through a process known as "local operation on multiple rasters" or "[[map algebra]]", through a function that combines the values of each raster's [[matrix (mathematics)|matrix]]. This function may weigh some inputs more than others through use of an "index model" that reflects the influence of various factors upon a geographic phenomenon.

===Geostatistics===
{{Main|Geostatistics}}

[[Geostatistics]] is a branch of statistics that deals with field data, spatial data with a continuous index. It provides methods to model spatial correlation, and predict values at arbitrary locations (interpolation).

When phenomena are measured, the observation methods dictate the accuracy of any subsequent analysis. Due to the nature of the data (e.g. traffic patterns in an urban environment; weather patterns over the [[Pacific Ocean]]), a constant or dynamic degree of precision is always lost in the measurement. This loss of precision is determined from the scale and distribution of the data collection.

To determine the statistical relevance of the analysis, an average is determined so that points (gradients) outside of any immediate measurement can be included to determine their predicted behavior. This is due to the limitations of the applied statistic and data collection methods, and interpolation is required to predict the behavior of particles, points, and locations that are not directly measurable.

[[Interpolation]] is the process by which a surface is created, usually a raster dataset, through the input of data collected at a number of sample points. There are several forms of interpolation, each which treats the data differently, depending on the properties of the data set. In comparing interpolation methods, the first consideration should be whether or not the source data will change (exact or approximate). Next is whether the method is subjective, a human interpretation, or objective. Then there is the nature of transitions between points: are they abrupt or gradual. Finally, there is whether a method is global (it uses the entire data set to form the model), or local where an algorithm is repeated for a small section of terrain.

Interpolation is a justified measurement because of a spatial autocorrelation principle that recognizes that data collected at any position will have a great similarity to, or influence of those locations within its immediate vicinity.

[[Digital elevation model]]s, [[triangulated irregular network]]s, edge-finding algorithms, [[Thiessen polygons]], [[Fourier analysis]], [[Weighted moving average|(weighted) moving averages]], [[inverse distance weighting]], [[kriging]], [[Spline (mathematics)|spline]], and [[Trend estimation|trend surface analysis]] are all mathematical methods to produce interpolative data.

===Address geocoding===
{{Main|Geocoding}}
Geocoding is interpolating spatial locations (X,Y coordinates) from street addresses or any other spatially referenced data such as [[ZIP&nbsp;Code]]s, [[Land lot|parcel lots]] and address locations. A reference theme is required to [[Geocoding|geocode]] individual addresses, such as a road centerline file with address ranges. The individual address locations have historically been interpolated, or estimated, by examining address ranges along a road segment. These are usually provided in the form of a table or database. The software will then place a dot approximately where that address belongs along the segment of centerline. For example, an address point of 500 will be at the midpoint of a line segment that starts with address&nbsp;1 and ends with address&nbsp;1,000. Geocoding can also be applied against actual parcel data, typically from municipal tax maps. In this case, the result of the geocoding will be an actually positioned space as opposed to an interpolated point. This approach is being increasingly used to provide more precise location information.

===Reverse geocoding===
{{main|Reverse geocoding}}
Reverse geocoding is the process of returning an estimated [[street address]] number as it relates to a given coordinate. For example, a user can click on a road centerline theme (thus providing a coordinate) and have information returned that reflects the estimated house number. This house number is interpolated from a range assigned to that road segment. If the user clicks at the [[midpoint]] of a segment that starts with address&nbsp;1 and ends with&nbsp;100, the returned value will be somewhere near 50. Note that reverse geocoding does not return actual addresses, only estimates of what should be there based on the predetermined range.

===Multi-criteria decision analysis===
{{main | Multiple-criteria decision analysis}}
Coupled with GIS, [[multi-criteria decision analysis]] methods support decision-makers in analysing a set of alternative spatial solutions, such as the most likely ecological habitat for restoration, against multiple criteria, such as vegetation cover or roads. MCDA uses decision rules to aggregate the criteria, which allows the alternative solutions to be ranked or prioritised.<ref name="Greene">{{cite journal | last1 = Greene | first1 = R. | last2 = Devillers | first2 = R. | last3 = Luther | first3 = J.E. | last4 = Eddy | first4 = B.G. | title = GIS-based multi-criteria analysis | journal = Geography Compass | year = 2011 | volume = 5/6 | issue = 6 | pages = 412–432 | doi = 10.1111/j.1749-8198.2011.00431.x }}</ref> GIS MCDA may reduce costs and time involved in identifying potential restoration sites.

===GIS data mining===
GIS or spatial [[data mining]] is the application of data mining methods to spatial data. Data mining, which is the partially automated search for hidden patterns in large databases, offers great potential benefits for applied GIS-based&nbsp;decision&nbsp;making. Typical applications include [[environmental monitoring]]. A characteristic of such applications is that spatial correlation between data measurements require the use of specialized algorithms for more efficient data analysis.<ref>{{Cite journal | last1 = Ma | first1 = Y. | last2 = Guo | first2 = Y. | last3 = Tian | first3 = X. | last4 = Ghanem | first4 = M. | title = Distributed Clustering-Based Aggregation Algorithm for Spatial Correlated Sensor Networks | doi = 10.1109/JSEN.2010.2056916 | journal = IEEE Sensors Journal | volume = 11 | issue = 3 | page = 641 | year = 2011 | url = http://www.inf.ufpr.br/carmem/oficinaBD/artigos1s2014/dist-clustering.pdf | bibcode = 2011ISenJ..11..641M | citeseerx = 10.1.1.724.1158 | s2cid = 1639100 }}</ref>

==Data output and cartography==
{{main | Cartographic design| Digital mapping}}
[[Cartography]] is the design and production of maps, or visual representations of spatial data. The vast majority of modern cartography is done with the help of computers, usually using GIS but production of quality cartography is also achieved by importing layers into a design program to refine it. Most GIS&nbsp;software gives the user substantial control over the appearance of the data.

Cartographic work serves two major functions:

First, it produces graphics on the screen or on paper that convey the results of analysis to the people who make decisions about resources. Wall maps and other graphics can be generated, allowing the viewer to visualize and thereby understand the results of analyses or simulations of potential events. [[Web Map Server]]s facilitate distribution of generated maps through web browsers using various implementations of web-based application programming interfaces ([[AJAX]], [[Java programming|Java]], [[Adobe Flash|Flash]], etc.).

Second, other database information can be generated for further analysis or use. An example would be a list of all addresses within one&nbsp;mile (1.6&nbsp;km) of a toxic spill.

An archeochrome is a new way of displaying spatial data. It is a thematic on a 3D&nbsp;map that is applied to a specific building or a part of a building. It is suited to the visual display of heat-loss data.

===Terrain depiction===
{{main | Terrain cartography }}
[[File:Swisstopo Eiger - Grindelwald.jpg|thumb|300px|A traditional topographic map rendered in 3D]]
Traditional maps are abstractions of the real world, a sampling of important elements portrayed on a sheet of paper with symbols to represent physical objects. People who use maps must interpret these symbols. [[Topographic map]]s show the shape of land surface with [[contour line]]s or with [[Cartographic relief depiction|shaded relief]].

Today, graphic display techniques such as [[shading]] based on [[altitude]] in a GIS can make relationships among map elements visible, heightening one's ability to extract and analyze information. For example, two types of data were combined in a GIS to produce a perspective view of a portion of [[San&nbsp;Mateo County]], [[California]].
*The [[digital elevation model]], consisting of surface elevations recorded on a 30-meter horizontal grid, shows high elevations as white and low elevation as black.
*The accompanying [[Landsat]] Thematic Mapper image shows a false-color infrared image looking down at the same area in 30-meter&nbsp;pixels, or picture elements, for the same coordinate points, pixel&nbsp;by&nbsp;pixel, as the elevation information.

A GIS was used to register and combine the two images to [[Rendering (computer graphics)|render]] the three-dimensional [[perspective view]] looking down the [[San Andreas Fault]], using the Thematic Mapper image pixels, but shaded using the elevation of the [[landform]]s. The GIS&nbsp;display depends on the viewing point of the [[observation|observer]] and time of day of the display, to properly render the shadows created by the sun's rays at that latitude, longitude, and time of day.

===Web mapping===
{{Main|Web mapping}}

In recent years there has been a proliferation of free-to-use and easily accessible mapping software such as the [[proprietary software|proprietary]] web applications [[Google&nbsp;Maps]] and [[Bing&nbsp;Maps]], as well as the [[free and open-source software|free and open-source]] alternative [[OpenStreetMap]]. These services give the public access to huge amounts of geographic data, perceived by many users to be as trustworthy and usable as professional information.<ref name = activities>{{cite journal|last1=Parker|first1=Christopher J.|last2=May|first2=Andrew J.|last3=Mitchell|first3=Val |title=The role of VGI and PGI in supporting outdoor activities|journal=Applied Ergonomics|date=2013|volume=44|issue=6|pages=886–94|doi= 10.1016/j.apergo.2012.04.013 |pmid=22795180|s2cid=12918341 |url=https://dspace.lboro.ac.uk/2134/10350}}</ref> For example, during the COVID-19 pandemic, web maps hosted on dashboards were used to rapidly disseminate case data to the general public.<ref name=Everts1>{{cite journal |last1=Everts |first1=Jonathan |title=The dashboard pandemic |journal=Dialogues in Human Geography |year=2020 |volume=10 |issue=2 |pages=260–264 |doi=10.1177/2043820620935355 |s2cid=220418162 |doi-access=free }}</ref>

Some of them, like Google Maps and [[OpenLayers]], expose an [[application programming interface]] (API) that enable users to create custom applications. These toolkits commonly offer street maps, aerial/satellite imagery, geocoding, searches, and routing functionality. Web mapping has also uncovered the potential of [[crowdsourcing]] geodata in projects like [[OpenStreetMap]], which is a collaborative project to create a free editable map of the world. These [[Mashup (web application hybrid)|mashup]] projects have been proven to provide a high level of value and benefit to end users outside that possible through traditional geographic information.<ref>{{cite journal|last1=Parker|first1=Christopher J.|last2=May|first2=Andrew J.|last3=Mitchel|first3=Val|title=User Centred Design of Neogeography: The Impact of Volunteered Geographic Information on Trust of Online Map 'Mashups|journal=Ergonomics|date=2014|volume=57|issue=7|pages=987–997|doi=10.1080/00140139.2014.909950|pmid=24827070|s2cid=13458260|url=https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/23845/3/Parker%2c%20May%2c%20Mitchell%20-%202014%20-%20User-centred%20design%20of%20neogeography%20the%20impact%20of%20volunteered%20geographic%20information%20on%20users%27%20perception.pdf |archive-url=https://web.archive.org/web/20170830034140/https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/23845/3/Parker%2c%20May%2c%20Mitchell%20-%202014%20-%20User-centred%20design%20of%20neogeography%20the%20impact%20of%20volunteered%20geographic%20information%20on%20users%27%20perception.pdf |archive-date=2017-08-30 |url-status=live}}</ref><ref>{{cite journal|last1=May|first1=Andrew|last2=Parker|first2=Christopher J.|last3=Taylor|first3=Neil|last4=Ross|first4=Tracy|title=Evaluating a concept design of a crowd-sourced 'mashup' providing ease-of-access information for people with limited mobility|journal=Transportation Research Part C: Emerging Technologies|date=2014|volume=49|pages=103–113|doi=10.1016/j.trc.2014.10.007|doi-access=free|bibcode=2014TRPC...49..103M }}</ref>

Web mapping is not without its drawbacks. Web mapping allows for the creation and distribution of maps by people without proper cartographic training.<ref name="Plewe1">{{cite journal |last1=Plewe |first1=Brandon |title=Web Cartography in the United States |journal=Cartography and Geographic Information Science |date=2007 |volume=34 |issue=2 |pages=133–136 |doi=10.1559/152304007781002235|bibcode=2007CGISc..34..133P |s2cid=140717290 }}</ref> This has led to maps that ignore cartographic conventions and are potentially misleading, with one study finding that more than half of United States state government COVID-19 dashboards did not follow these conventions.<ref name=Adams1>{{cite journal |last1=Adams |first1=Aaron |last2=Xiang |first2=Chen |last3=Weidong |first3=Li |last4=Zhang |first4=Chuanrong |title=The disguised pandemic: The importance of data normalization in COVID-19 web mapping |journal=Public Health |date=May 2020 |volume=183 |issue=3 |pages=36–37 |doi = 10.1016/j.puhe.2020.04.034|pmid=32416476 |pmc=7203028 }}</ref><ref name="Adams2">{{cite journal |last1=Adams |first1=Aaron M. |last2=Chen |first2=Xiang |last3=Li |first3=Weidong |last4=Chuanrong |first4=Zhang |title=Normalizing the pandemic: exploring the cartographic issues in state government COVID-19 dashboards |journal=Journal of Maps |date=27 July 2023 |volume=19 |issue=5 |pages=1–9 |doi=10.1080/17445647.2023.2235385|bibcode=2023JMaps..19Q...1A |doi-access=free }}</ref>

==Uses==
{{see also | Category:Applications of geographic information systems}}

Since its origin in the 1960s, GIS has been used in an ever-increasing range of applications, corroborating the widespread importance of location and aided by the continuing reduction in the barriers to adopting geospatial technology. The perhaps hundreds of different uses of GIS can be classified in several ways:
* ''Goal'': the purpose of an application can be broadly classified as either ''scientific research'' or ''[[resource management]]''. The purpose of [[research]], defined as broadly as possible, is to discover new knowledge; this may be performed by someone who considers themself a scientist, but may also be done by anyone who is trying to learn why the world appears to work the way it does. A study as practical as deciphering why a business location has failed would be research in this sense. Management (sometimes called operational applications), also defined as broadly as possible, is the application of knowledge to make practical decisions on how to employ the resources one has control over to achieve one's goals. These resources could be time, capital, labor, equipment, land, mineral deposits, wildlife, and so on.<ref>{{Cite journal |last1=Chouhan |first1=Avinash Kumar |last2=Kumar |first2=Rakesh |last3=Mishra |first3=Abhishek Kumar |date=June 2024 |title=Assessment of the geothermal potential zone of India utilizing GIS-based multi-criteria decision analysis technique |url=https://linkinghub.elsevier.com/retrieve/pii/S0960148124006177 |journal=Renewable Energy |language=en |volume=227 |pages=120552 |doi=10.1016/j.renene.2024.120552|bibcode=2024REne..22720552C }}</ref><ref name="bigbook">{{cite book |editor1-last=Longley |editor1-first=Paul |editor2-last=Goodchild |editor2-first=Michael F. |editor3-last=Maguire |editor3-first=David J. |editor4-last=Rhind |editor4-first=David W. |title=Geographical Information Systems, V.2: Management Issues and Applications |date=1999 |publisher=Wiley |isbn=0471-32182-6 |edition=2nd}}</ref>{{rp|791}}
** ''Decision level'': Management applications have been further classified as ''strategic'', ''tactical'', ''operational'', a common classification in [[business management]].<ref name="grimshaw1994">{{cite book |last1=Grimshaw |first1=D.J. |title=Bringing geographical information systems into business |date=1994 |publisher=GeoInformation International |location=Cambridge, UK}}</ref> Strategic tasks are long-term, visionary decisions about what goals one should have, such as whether a business should expand or not. Tactical tasks are medium-term decisions about how to achieve strategic goals, such as a national forest creating a grazing management plan. Operational decisions are concerned with the day-to-day tasks, such as a person finding the shortest route to a pizza restaurant.
* ''Topic'': the domains in which GIS is applied largely fall into those concerned with [[human geography|the human world]] (e.g., [[Economic geography|economics]], [[Political geography|politics]], [[Transportation geography|transportation]], [[education]], [[landscape architecture]], [[archaeology]], [[urban planning]], [[real estate]], [[public health]], [[crime mapping]], [[defense (military)|national defense]]), and those concerned with [[Physical geography|the natural world]] (e.g., [[Geological map|geology]], [[Biogeography|biology]], [[oceanography]], [[Climatology|climate]]). That said, one of the powerful capabilities of GIS and the spatial perspective of geography is their integrative ability to compare disparate topics, and many applications are concerned with multiple domains. Examples of integrated human-natural application domains include [[deep map]]ping,<ref>{{Cite journal |last1=Butts |first1=Shannon |last2=Jones |first2=Madison |date=2021-05-20 |title=Deep mapping for environmental communication design |url=https://doi.org/10.1145/3437000.3437001 |journal=Communication Design Quarterly |volume=9 |issue=1 |pages=4–19 |doi=10.1145/3437000.3437001|s2cid=234794773 }}</ref> [[Natural hazard]] mitigation, [[wildlife management]], [[sustainable development]],<ref>{{Cite journal |last1=Chouhan |first1=Avinash Kumar |last2=Harsh |first2=Anuranjan |last3=Mishra |first3=Abhishek Kumar |last4=Kumar |first4=Vikram |last5=Kumar |first5=Rakesh |last6=Kumar |first6=Satyam |date=August 2024 |title=Delineation of groundwater vulnerable zone for sustainable development in the southwestern part of Bihar, India |url=https://linkinghub.elsevier.com/retrieve/pii/S2352801X24001632 |journal=Groundwater for Sustainable Development |language=en |volume=26 |pages=101240 |doi=10.1016/j.gsd.2024.101240|bibcode=2024GSusD..2601240C }}</ref><ref>{{cite web |url=http://continuingeducation.construction.com/article.php?L=5&C=879 |archive-url=https://web.archive.org/web/20120308044542/http://continuingeducation.construction.com/article.php?L=5&C=879 |url-status=dead |archive-date=8 March 2012 |title=Off the Map &#124; From Architectural Record and Greensource &#124; Originally published in the March 2012 issues of Architectural Record and Greensource &#124; McGraw-Hill Construction - Continuing Education Center |publisher=Continuingeducation.construction.com |date=11 March 2011 |access-date=13 May 2012 }}</ref> [[natural resources]], and [[climate change]] response.<ref>{{cite web|url= http://www.nasa.gov/topics/earth/features/seaicemin09.html|title= Arctic Sea Ice Extent is Third Lowest on Record|access-date= 2009-10-20|archive-date= 2017-05-17|archive-url= https://web.archive.org/web/20170517222956/http://www.nasa.gov/topics/earth/features/seaicemin09.html|url-status= dead}}</ref>
* ''Institution'': GIS has been implemented in a variety of different kinds of institutions: ''government'' (at all levels from municipal to international), ''business'' (of all types and sizes), ''non-profit organizations'' (even churches), as well as ''personal'' uses. The latter has become increasingly prominent with the rise of location-enabled smartphones.
* ''Lifespan'': GIS implementations may be focused on a ''project'' or an ''enterprise''.<ref name="huisman">{{cite book |last1=Huisman |first1=Otto |last2=de By |first2=Rolf A. |title=Principles of Geographic Information Systems: An introductory textbook |date=2009 |publisher=ITC |location=Enschede, The Netherlands |isbn=978-90-6164-269-5 |page=44 |url=https://webapps.itc.utwente.nl/librarywww/papers_2009/general/principlesgis.pdf |archive-url=https://web.archive.org/web/20180514113013/https://webapps.itc.utwente.nl/librarywww/papers_2009/general/principlesgis.pdf |archive-date=2018-05-14 |url-status=live}}</ref> A Project GIS is focused on accomplishing a single task: data is gathered, analysis is performed, and results are produced separately from any other projects the person may perform, and the implementation is essentially transitory. An Enterprise GIS is intended to be a permanent institution, including a database that is carefully designed to be useful for a variety of projects over many years, and is likely used by many individuals across an enterprise, with some employed full-time just to maintain it.<ref name="longley2011">{{cite book |last1=Longley |first1=Paul A. |last2=Goodchild |first2=Michael F. |last3=Maguire |first3=David J. |last4=Rhind |first4=David W. |title=Geographic Information Systems & Science |date=2011 |publisher=Wiley |page=434 |edition=3rd}}</ref>
* ''Integration'': Traditionally, most GIS applications were ''standalone'', using specialized GIS software, specialized hardware, specialized data, and specialized professionals. Although these remain common to the present day, ''integrated'' applications have greatly increased, as geospatial technology was merged into broader enterprise applications, sharing IT infrastructure, databases, and software, often using enterprise integration platforms such as [[SAP]].<ref>{{Cite web|url=http://www.esri.com/news/arcnews/spring09articles/integrating-gis.html|title=Integrating GIS with SAP—The Imperative|last=Benner|first=Steve|archive-url=https://web.archive.org/web/20091022085822/http://www.esri.com/news/arcnews/spring09articles/integrating-gis.html|archive-date=22 October 2009|url-status=dead|access-date=28 March 2017 |date=Spring 2009 |website=Esri }}</ref>

The implementation of a GIS is often driven by jurisdictional (such as a city), purpose, or application requirements. Generally, a GIS implementation may be custom-designed for an organization. Hence, a GIS deployment developed for an application, jurisdiction, enterprise, or purpose may not be necessarily [[Interoperability|interoperable]] or compatible with a GIS that has been developed for some other application, jurisdiction, enterprise, or purpose.<ref>{{Cite SSRN|last1=Kumar|first1=Deepak|last2=Das|first2=Bhumika|date=23 May 2015|title=Recent Trends in GIS Applications|language=en|ssrn=2609707}}</ref>

GIS is also diverging into [[location-based service]]s, which allows GPS-enabled mobile devices to display their location in relation to fixed objects (nearest restaurant, gas station, fire hydrant) or mobile objects (friends, children, police&nbsp;car), or to relay their position back to a central server for display or other processing.

GIS is also used in digital marketing and SEO for audience segmentation based on location.<ref>{{Cite web |last=Haywood |first=Leeann |date=2023-03-15 |title=Location Data: How GIS is Used in Digital Marketing and SEO |url=https://hennessey.com/blog/location-data-how-gis-is-used-in-digital-marketing-and-seo/ |access-date=2024-01-15 |website=Hennessey Digital |language=en}}</ref><ref>{{Cite web |last=Liaquat |first=Ali |date=2021-08-01 |title=How To Use Geofencing for Targeted Digital Marketing Campaigns |url=https://aliliaquat.com/how-to-geofencing-digital-marketing/ |access-date=2024-01-15 |language=en-US}}</ref>

===Topics===
====Aquatic science====
{{Excerpt|GIS and aquatic science|hat=yes|paragraphs=1}}

====Archaeology====
{{Excerpt|GIS in archaeology|hat=yes|paragraphs=1}}

====Disaster response====
[[File:I just got off the phone with @GovJoshGreenMD – following a call with @FEMA Deanne – to discuss Hawai'i's recovery after the deadliest wildfire in a century that has claimed 99 lives.jpg|thumb|right|280px|Aboard [[Air Force One]] enroute to the disaster, President Biden reviews maps of damage assessments, made by [[Federal Emergency Management Agency|FEMA]] and the [[Civil Air Patrol]]'s [[Geographic data and information|geospatial]] team in response to the [[2023 Hawaii wildfires]]<ref>{{cite web |title=he president reviews maps... |url=https://x.com/CivilAirPatrol/status/1691607732954997124 |website=X |access-date=12 January 2025}}</ref>]] Geospatial disaster response uses geospatial data and tools to help emergency responders, land managers, and scientists respond to disasters. Geospatial data can help save lives, reduce damage, and improve communication. Geospatial data can be used by federal authorities like [[Federal Emergency Management Agency|FEMA]] to create maps that show the extent of a disaster, the location of people in need, and the location of debris, create models that estimate the number of people at risk and the amount of damage, improve communication between emergency responders, land managers, and scientists, as well as help determine where to allocate resources, such as emergency medical resources or search and rescue teams and plan evacuation routes and identify which areas are most at risk.

In the United States, FEMA's Response Geospatial Office is responsible for the agency's capture, analysis and development of GIS products to enhance situational awareness and enable expeditions and effective decision making. The RGO's mission is to support decision makers in understanding the size, scope, and extent of disaster impacts so they can deliver resources to the communities most in need.<ref>{{cite web |title=Response Geospatial Office |url=https://www.fema.gov/about/offices/response/response-geospatial |website=FEMA |access-date=22 January 2025}}</ref>

====Environmental governance====
{{Excerpt|GIS and environmental governance|hat=yes|paragraphs=1}}

====Environmental contamination====
{{Excerpt|GIS in environmental contamination|hat=yes|paragraphs=1}}

====Geological mapping ====
{{Excerpt|Digital geological mapping|hat=yes|paragraphs=1}}

====Geospatial intelligence====
{{Excerpt|Geographic information systems in geospatial intelligence|hat=yes|paragraphs=1}}

====History====
{{Excerpt|Historical GIS|hat=yes|paragraphs=1}}The use of digital maps generated by GIS has also influenced the development of an academic field known as spatial humanities.<ref>{{Cite news |last=Cohen |first=Patricia |date=2011-07-26 |title=Digital Maps Are Giving Scholars the Historical Lay of the Land |url=https://www.nytimes.com/2011/07/27/arts/geographic-information-systems-help-scholars-see-history.html |access-date=2024-01-15 |work=The New York Times |language=en-US |issn=0362-4331}}</ref>

====Hydrology====
{{Excerpt|GIS and hydrology|hat=yes|paragraphs=1}}

====Participatory GIS====
{{Excerpt|Participatory GIS|hat=yes|paragraphs=1}}

====Public health====
{{Excerpt|GIS and public health|references=Chouhan, A.K., Harsh, A., Mishra, A.K., Kumar, V., Kumar, R.; Delineation of groundwater vulnerable zone for sustainable development in the southwestern part of Bihar, India. Groundwater for Sustainable Development. doi.org/10.1016/j.gsd.2024.101240.|hat=yes|paragraphs=1}}

====Traditional knowledge GIS====
{{Excerpt|Traditional knowledge GIS|hat=yes|paragraphs=1}}

==Other aspects==
===Open Geospatial Consortium standards===
{{Main|Open Geospatial Consortium}}
The [[Open Geospatial Consortium]] (OGC) is an international industry consortium of 384&nbsp;companies, government agencies, universities, and individuals participating in a consensus process to develop publicly available geoprocessing specifications. Open interfaces and protocols defined by OpenGIS&nbsp;Specifications support interoperable solutions that "geo-enable" the Web, wireless and location-based services, and mainstream&nbsp;IT, and empower technology developers to make complex spatial information and services accessible and useful with all kinds of applications. Open Geospatial Consortium protocols include [[Web Map Service]], and [[Web Feature Service]].<ref>{{cite web|url=http://www.opengeospatial.org/ogc/members |title=OGC Members &#124; OGC(R) |publisher=Opengeospatial.org |access-date=13 May 2012}}</ref>

GIS&nbsp;products are broken down by the&nbsp;OGC into two categories, based on how completely and accurately the software follows the OGC&nbsp;specifications.
[[File:Geoservices server with apps.png|thumb|347px|OGC standards help GIS tools communicate.]]

''Compliant products'' are software products that comply to OGC's&nbsp;OpenGIS&nbsp;Specifications. When a product has been tested and certified as compliant through the OGC&nbsp;Testing Program, the product is automatically registered as "compliant" on this site.

''Implementing products'' are software products that implement OpenGIS&nbsp;Specifications but have not yet passed a compliance test. Compliance tests are not available for all specifications. Developers can register their products as implementing draft or approved specifications, though OGC&nbsp;reserves the right to review and verify each entry.

===Adding the dimension of time===
<!--This section is linked from [[Historical geographic information system]] and [[Time geography]] ([[MOS:HEAD]])-->
{{See also|Historical geographic information system|Time geography}}
The condition of the Earth's surface, atmosphere, and subsurface can be examined by feeding satellite data into a GIS. GIS&nbsp;technology gives researchers the ability to examine the variations in Earth processes over days, months, and years through the use of cartographic visualizations.<ref>{{cite journal |last1=Monmonier |first1=Mark |title=Strategies For The Visualization Of Geographic Time-Series Data |journal=Cartographica: The International Journal for Geographic Information and Geovisualization |date=1990 |volume=27 |issue=1 |pages=30–45 |doi=10.3138/U558-H737-6577-8U31}}</ref> As an example, the changes in vegetation vigor through a growing season can be animated to determine when drought was most extensive in a particular region. The resulting graphic represents a rough measure of plant health. Working with two variables over time would then allow researchers to detect regional differences in the lag between a decline in rainfall and its effect on vegetation.

GIS&nbsp;technology and the availability of digital data on regional and global scales enable such analyses. The satellite sensor output used to generate a vegetation graphic is produced for example by the [[advanced very-high-resolution radiometer]] (AVHRR). This sensor system detects the amounts of energy reflected from the Earth's surface across various bands of the spectrum for surface areas of about {{Convert|1|km2|sqmi|abbr=on}}. The satellite sensor produces images of a particular location on the Earth twice a day. AVHRR and more recently the [[moderate-resolution imaging spectroradiometer]] (MODIS) are only two of many sensor systems used for Earth surface analysis.

In addition to the integration of time in environmental studies, GIS is also being explored for its ability to track and model the progress of humans throughout their daily routines. A concrete example of progress in this area is the recent release of time-specific population data by the [[U.S.&nbsp;Census]]. In this data set, the populations of cities are shown for daytime and evening hours highlighting the pattern of concentration and dispersion generated by North American commuting patterns. The manipulation and generation of data required to produce this data would not have been possible without&nbsp;GIS.

Using models to project the data held by a GIS&nbsp;forward in time have enabled planners to test policy decisions using [[spatial decision support system]]s.

===Semantics===
Tools and technologies emerging from the [[World Wide Web Consortium]]'s [[Semantic Web]] are proving useful for [[data integration]] problems in information systems. Correspondingly, such technologies have been proposed as a means to facilitate [[interoperability]] and data reuse among GIS&nbsp;applications and also to enable new analysis mechanisms.<ref>{{cite book |last1=Zhang |first1=Chuanrong |last2=Zhao |first2=Tian |last3=Li |first3=Weidong |title=Geospatial Semantic Web |date=2015 |publisher=Springer International Publishing |isbn=978-3-319-17801-1}}</ref><ref>{{Cite journal |last1=Fonseca |first1=Frederico | last2 = Sheth | first2 = Amit |journal=UCGIS White Paper | title= The Geospatial Semantic Web |year=2002 |url = http://www.personal.psu.edu/faculty/f/u/fuf1/Fonseca-Sheth.pdf }}</ref><ref>{{Cite journal |last1=Fonseca |first1=Frederico | last2 = Egenhofer | first2 = Max |journal=Proc. ACM International Symposium on Geographic Information Systems |title= Ontology-Driven Geographic Information Systems |year=1999 |pages=14–19 |citeseerx=10.1.1.99.5206 }}</ref><ref>{{Cite journal | last1 = Perry | first1 = Matthew | last2 = Hakimpour | first2 = Farshad | last3 = Sheth | first3 = Amit | journal = Proc. ACM International Symposium on Geographic Information Systems | title = Analyzing Theme, Space and Time: an Ontology-based Approach | url = http://knoesis.wright.edu/library/download/ACM-GIS_06_Perry.pdf | year = 2006 | pages = 147–154 | access-date = 2007-05-29 | archive-date = 2007-06-14 | archive-url = https://web.archive.org/web/20070614120623/http://knoesis.wright.edu/library/download/ACM-GIS_06_Perry.pdf | url-status = dead }}</ref>

[[Ontology (computer science)|Ontologies]] are a key component of this semantic approach as they allow a formal, machine-readable specification of the concepts and relationships in a given domain. This in turn allows a GIS to focus on the intended meaning of data rather than its syntax or structure. For example, [[reasoning]] that a land cover type classified as ''deciduous needleleaf trees'' in one dataset is a specialization or subset of land cover type ''forest'' in another more roughly classified dataset can help a GIS automatically merge the two datasets under the more general land cover classification. Tentative ontologies have been developed in areas related to GIS&nbsp;applications, for example the hydrology ontology<ref>{{cite web|url=http://www.ordnancesurvey.co.uk/oswebsite/ontology/|title=Ordnance Survey Ontologies |archive-url=https://web.archive.org/web/20070521025424/http://www.ordnancesurvey.co.uk/oswebsite/ontology/|archive-date=21 May 2007}}</ref> developed by the [[Ordnance Survey]] in the [[United Kingdom]] and the SWEET ontologies<ref>{{cite web |url=http://sweet.jpl.nasa.gov/ontology/ |title=Semantic Web for Earth and Environmental Terminology |url-status=dead |archive-url=https://web.archive.org/web/20070529200940/http://sweet.jpl.nasa.gov/ontology/ |archive-date=29 May 2007 }}</ref> developed by [[NASA]]'s [[Jet Propulsion Laboratory]]. Also, simpler ontologies and semantic metadata standards are being proposed by the W3C Geo Incubator Group<ref>{{cite web|url= http://www.w3.org/2005/Incubator/geo/|title= W3C Geospatial Incubator Group}}</ref> to represent geospatial data on the web. [[GeoSPARQL]] is a standard developed by the Ordnance Survey, [[United States Geological Survey]], [[Natural Resources Canada]], Australia's [[Commonwealth Scientific and Industrial Research Organisation]] and others to support ontology creation and reasoning using well-understood OGC literals (GML, WKT), topological relationships (Simple Features, RCC8, DE-9IM), RDF and the [[SPARQL]] database query protocols.

Recent research results in this area can be seen in the International Conference on Geospatial Semantics<ref>{{cite web|url= http://www.geosco.org/|title= International Conferences on Geospatial Semantics}}</ref> and the Terra Cognita – Directions to the Geospatial Semantic Web<ref>{{cite web|url=http://www.ordnancesurvey.co.uk/oswebsite/partnerships/research/research/terracognita.html|title=Terra Cognita 2006 – Directions to the Geospatial Semantic Web |archive-url=https://web.archive.org/web/20070518054232/http://www.ordnancesurvey.co.uk/oswebsite/partnerships/research/research/terracognita.html|archive-date=18 May 2007}}</ref> workshop at the International Semantic Web Conference.

==Societal implications==
{{Main|Neogeography|Public participation GIS}}
With the popularization of GIS in decision making, scholars have begun to scrutinize the social and political implications of GIS.<ref>{{Cite journal|last=Haque|first=Akhlaque|date=1 May 2001|title=GIS, Public Service, and the Issue of Democratic Governance|journal=Public Administration Review|language=en|volume=61|issue=3|pages=259–265|doi=10.1111/0033-3352.00028|issn=1540-6210}}</ref><ref>{{Cite journal|last=Haque |first= Akhlaque|date=2003|title=Information technology, GIS and democraticvalues: Ethical implications for IT professionals in public service|journal=Ethics and Information Technology|doi=10.1023/A:1024986003350|volume=5|pages=39–48|s2cid= 44035634}}</ref><ref name="activities" /> GIS can also be misused to distort reality for individual and political gain.<ref>{{Cite journal|last=Monmonier|first=Mark|date=2005|title=Lying with Maps|jstor=20061176|journal=Statistical Science|doi=10.1214/088342305000000241|volume=20|issue=3|pages=215–222|doi-access=free}}</ref><ref>{{Cite book|title=How to Lie with Maps|last= Monmonier |first=Mark|publisher=University of Chicago Press|year=1991|isbn=978-0226534213|location=Chicago, Illinois}}</ref> It has been argued that the production, distribution, utilization, and representation of geographic information are largely related with the social context and has the potential to increase citizen trust in government.<ref>{{Cite book|title=Surveillance, Transparency and Democracy: Public Administration in the Information Age |last= Haque|first=Akhlaque|publisher=University of Alabama Press|year=2015|isbn=978-0817318772|location=Tuscaloosa, AL|pages=70–73}}</ref> Other related topics include discussion on [[copyright]], [[privacy]], and [[censorship]]. A more optimistic social approach to GIS&nbsp;adoption is to use it as a tool for public participation.

===In education===
{{see also|Esri Education User Conference}}
At the end of the 20th century, GIS began to be recognized as tools that could be used in the classroom.<ref>{{cite book |editor1-last=Sinton |editor1-first=Diana Stuart |editor2-last=Lund |editor2-first=Jennifer J. |date=2007 |title=Understanding place: GIS and mapping across the curriculum |location= Redlands, CA |publisher=[[ESRI Press]] |isbn=9781589481497 |oclc=70866933}}</ref><ref>{{cite book |editor1-last=Milson |editor1-first=Andrew J. |editor2-last=Demirci |editor2-first=Ali |editor3-last=Kerski |editor3-first=Joseph J. |date=2012 |title=International perspectives on teaching and learning with GIS in secondary schools |location=Dordrecht; New York |publisher=[[Springer-Verlag]] |isbn=9789400721197 |oclc=733249695 |doi=10.1007/978-94-007-2120-3 |url= http://dergipark.gov.tr/rigeo/issue/11187/133647 |type=Submitted manuscript }}</ref><ref>{{cite book |editor1-last=Solari |editor1-first=Osvaldo Muñiz |editor2-last=Demirci |editor2-first=Ali |editor3-last=Schee |editor3-first=Joop van der |date=2015 |title=Geospatial technologies and geography education in a changing world: geospatial practices and lessons learned |location=Tōkyō; New York |publisher=[[Springer-Verlag]] |isbn=9784431555186 |oclc=900306594 |doi=10.1007/978-4-431-55519-3|series=Advances in Geographical and Environmental Sciences |s2cid=130174652 }}</ref> The benefits of GIS in education seem focused on developing [[spatial cognition]], but there is not enough bibliography or statistical data to show the concrete scope of the use of GIS in education around the world, although the expansion has been faster in those countries where the curriculum mentions them.<ref name="Nieto">{{cite thesis |type=Ph.D. thesis |last1=Nieto Barbero |first1=Gustavo |title=Análisis de la práctica educativa con SIG en la enseñanza de la Geografía de la educación secundaria: un estudio de caso en Baden-Württemberg, Alemania |date=2016 |publisher=[[University of Barcelona]] |location=Barcelona |hdl=10803/400097 }}</ref>{{rp|36}}

GIS seems to provide many advantages in teaching [[geography]] because it allows for analysis based on real geographic data and also helps raise research questions from teachers and students in the classroom. It also contributes to improvement in learning by developing spatial and geographical thinking and, in many cases, student motivation.<ref name="Nieto"/>{{rp|38}}

Courses in GIS are also offered by educational institutions.<ref>{{Cite web |title=New Geographic Information Systems (GIS) Certificate Now Offered to USD Undergraduate Students - University of San Diego |url=https://www.sandiego.edu/news/detail.php?_focus=90416 |access-date=2024-01-15 |website=www.sandiego.edu}}</ref><ref>{{Cite web |title=Minor in Geographic Information Systems (GIS) {{!}} University of New England in Maine |url=https://www.une.edu/cas/schools/marine-environment/minors/minor-geographic-information-systems-gis |access-date=2024-01-15 |website=www.une.edu |language=en}}</ref>

===In local government===
GIS is proven as an organization-wide, enterprise and enduring technology that continues to change how local government operates.<ref name=":0">{{Cite web|url=https://www.crcpress.com/Strategic-GIS-Planning-and-Management-in-Local-Government/Holdstock/p/book/9781466556508|title=Strategic GIS Planning and Management in Local Government|website=CRC Press|language=en|access-date=25 October 2017}}</ref> Government agencies have adopted GIS technology as a method to better manage the following areas of government organization:
* Economic development departments use interactive GIS mapping tools, aggregated with other data (demographics, labor force, business, industry, talent) along with a database of available commercial sites and buildings in order to attract investment and support existing business. Businesses making location decisions can use the tools to choose communities and sites that best match their criteria for success.
* Public safety<ref>{{Cite web|url=http://safecitygis.com/|title=Home - SafeCity|website=SafeCity|language=en-US|access-date=25 October 2017}}</ref> operations such as emergency operations centers, fire prevention, police and sheriff mobile technology and dispatch, and mapping weather risks.
* Parks and recreation departments and their functions in asset inventory, land conservation, land management, and cemetery management
* Public works and utilities, tracking water and stormwater drainage, electrical assets, engineering projects, and public transportation assets and trends
* Fiber network management for interdepartmental network assets
* School analytical and demographic data, asset management, and improvement/expansion planning
* Public administration for election data, property records, and zoning/management

The [[open data]] initiative is pushing local government to take advantage of technology such as GIS technology, as it encompasses the requirements to fit the open data/[[open government]] model of transparency.<ref name=":0" /> With open data, local government organizations can implement citizen engagement applications and online portals, allowing citizens to see land information, report potholes and signage issues, view and sort parks by assets, view real-time crime rates and utility repairs, and much more.<ref>{{Cite web|url=http://www.esri.com/industries/localgov/open-government|title=GIS for Local Government{{!}} Open Government|website=www.esri.com|language=en|access-date=25 October 2017}}</ref><ref>{{cite journal | last1 = Parker | first1 = C.J. | last2 = May | first2 = A. | last3 = Mitchell | first3 = V. | last4 = Burrows | first4 = A. | year = 2013 | title = Capturing Volunteered Information for Inclusive Service Design: Potential Benefits and Challenges | url = https://dspace.lboro.ac.uk/2134/11589| journal = The Design Journal | volume = 16 | issue = 2| pages = 197–218 | doi=10.2752/175630613x13584367984947| s2cid = 110716823 | type = Submitted manuscript }}</ref> The push for open data within government organizations is driving the growth in local government GIS technology spending, and database management.

== See also ==
<!-- Please keep this list alphabetized. Thanks! -->
{{Div col|colwidth=15em}}
* [[AM/FM/GIS]]
* [[Climate Information Service]]
* [[Comparison of GIS software]]
* [[Concepts and Techniques in Modern Geography]]
* [[Dialogue-Assisted Visual Environment for Geoinformation]]
* [[Distributed GIS]]
* [[Geodatabase (Esri)]]
* [[Geomatics]]
* [[GISCorps]]
* [[GIS Day]]
* [[Integrated Geo Systems]]
* [[List of GIS data sources]]
* [[List of GIS software]]
* [[Map database management]]
* [[Open Source Geospatial Foundation]]
* [[Quantitative geography]]
* [[Technical geography]]
* [[Tobler's first law of geography]]
* [[Tobler's second law of geography]]
* [[Virtual globe]]
{{div col end}}

== References ==
{{reflist|30em}}

== Further reading ==
* Bolstad, P. (2019). ''GIS Fundamentals: A first text on Geographic Information Systems, Sixth Edition''. Ann Arbor: XanEdu, 764 pp.
* Burrough, P. A. and McDonnell, R. A. (1998). ''Principles of geographical information systems''. [[Oxford University Press]], Oxford, 327 pp.
* [[Michael N. DeMers|DeMers, M.]] (2009). ''Fundamentals of Geographic Information Systems, 4th Edition''. Wiley, {{ISBN|978-0-470-12906-7}}
* Harvey, Francis (2008). ''A Primer of GIS, Fundamental geographic and cartographic concepts.'' The Guilford Press, 31 pp.
* Heywood, I., Cornelius, S., and Carver, S. (2006). ''An Introduction to Geographical Information Systems''. Prentice Hall. 3rd edition.
* Ott, T. and Swiaczny, F. (2001) .''Time-integrative GIS. Management and analysis of Spatio-temporal data'', Berlin / Heidelberg / New York: Springer.
* Thurston, J., Poiker, T.K. and J. Patrick Moore. (2003). ''Integrated Geospatial Technologies: A Guide to GPS, GIS, and Data Logging''. Hoboken, New Jersey: Wiley.
* {{cite book|last1=Worboys|first1=Michael|last2=Duckham|first2=Matt|year=2004|title=GIS: a computing perspective|place=Boca Raton|publisher=CRC Press|isbn=978-0415283755}}

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