About 15,000 years ago[4]​ in the caves of Lascaux (France) the Cro-Magnon men painted the animals they hunted on the walls, associating these drawings with linear traces that, it is believed, corresponded to the migration routes of those species.[5]​

While this example is simplistic compared to modern technologies, these early antecedents mimic two elements of modern geographic information systems: an image associated with an information attribute.[6]​.

In 1832,[7] French geographer and cartographer Charles Picquet created a map to visually show the spread of the cholera epidemic in 48 Parisian districts.[8] He colored the affected districts according to the number of deaths.[9] This is one of the first uses of spatial statistics in relation to epidemiology. This representation was published in 1834 in a report by the head of the commission, Louis-François Benoiston de Châteauneuf, who was an economist, statistician and demographer.[10][11]​.

In 1854, epidemiology pioneer Dr. John Snow would provide another classic example of this concept when he mapped, on a now-famous map, the incidence of cholera cases in the Soho district of London. outbreak.

Although topographic and thematic cartography already existed previously, John Snow's map was the only one to date that, using cartographic methods, not only represented reality, but for the first time analyzed sets of dependent geographical phenomena.

At the beginning of the century, photolithography was developed, where maps were separated into layers. The advancement of hardware driven by nuclear weapons research would lead, in the early 1960s, to the development of cartographic applications for general-purpose computers.[14]​.

The year 1962 saw the first real use of GIS in the world, specifically in Ottawa (Ontario, Canada) and by the Federal Department of Forestry and Rural Development. Developed by English geographer Roger Tomlinson, the Canadian Geographic Information System (CGIS) was used to store, analyze and manipulate data collected for the Canada Land Inventory (CLI) - an initiative aimed at managing the country's vast natural resources with cartographic information relating to land types and uses, agriculture, recreation, wildlife, waterfowl and forestry, all at once. scale "Scale (cartography)") of 1:50,000. A classification factor was also added to allow the analysis of the information.

The Canada Geographic Information System was the first GIS in the world similar to what we know today, and a considerable advance with respect to the cartographic applications that existed until then, since it allowed layers of information to be superimposed, measurements to be made, and data to be digitized and scanned. It also supported a continent-spanning national coordinate system, a coding of lines into "arcs" that had a truly integrated topology, and that stored each element's attributes and location information in separate "File (computing)" files. As a consequence of this, Tomlinson is considered "the father of GIS", in particular for the use of convergent geographical information structured in layers, which facilitates its spatial analysis.[15]​ The CGIS was operational until the 1990s, becoming the largest territorial resource database in Canada. It was developed as a mainframe-based system and its strength was that it allowed complex analyzes of data sets spanning the entire continent. The software, the dean of geographic information systems, was never commercially available.

In 1964, Howard T. Fisher formed the Laboratory for Computer Graphics and Spatial Analysis at Harvard University at the Harvard Graduate School of Design (LCGSA 1965-1991), where a series of important theoretical concepts in spatial data management were developed, and in the 1970s he had disseminated software code and germ systems, such as SYMAP, GRID, and ODYSSEY. - which served as sources of conceptual inspiration for their subsequent commercial developments - to universities, research centers and companies around the world.[16]​.

In the 1980s, M&S Computing (later Intergraph), Environmental Systems Research Institute (ESRI) and CARIS") (Computer Aided Resource Information System) would emerge as commercial providers of GIS software. They successfully incorporated many of the features of CGIS, combining the first-generation geographic information systems approach to separating spatial information and attributes of represented geographic features with a second-generation approach that organizes and structures these attributes in databases.

In the decade of the 70s and early 80s, the development of two public domain systems began in parallel. The Map Overlay and Statistical System (MOSS) project began in 1977 in Fort Collins, Colorado, USA, under the auspices of the Western Energy and Land Use Team (WELUT) and the US Fish and Wildlife Service. In 1982, the United States Army Construction Engineering Research Laboratory Corps of Engineers (USA-CERL) developed GRASS as a tool for environmental supervision and management of the territories under administration of the Department of Defense.

This stage of development is characterized, in general, by the decrease in the importance of individual initiatives and an increase in interests at the corporate level, especially by government agencies and administration.

The 80s and 90s were years of strong increase in companies that marketed these systems, due to the growth of GIS on UNIX workstations and personal computers. It is the period in which it has come to be known in GIS as the commercial phase. The interest of the different large industries directly or indirectly related to GIS is growing enormously due to the great avalanche of products in the international computer market that made this technology widespread.

In the 1990s, a commercial stage began for professionals, where geographic information systems began to spread to the level of the home user due to the generalization of personal computers or microcomputers.

At the end of the beginning of the 21st century, rapid growth in different systems has been consolidated, being restricted to a relatively small number of platforms. Users are beginning to export the concept of GIS data visualization to the Internet, requiring standardization of data formats and transfer standards. More recently, there has been an expansion in the number of open source GIS software developments, which, unlike commercial software, typically span a wider range of operating systems, allowing them to be modified to carry out specific tasks.

Data creation

Modern GIS technologies work with digital information, for which there are several methods used in creating digital data. The most used method is digitization, where a printed map or information taken in the field is transferred to a digital medium through the use of a Computer Aided Design (DAO or CAD) program with georeferencing capabilities.

Given the wide availability of ortho-rectified images (both satellite and aerial), digitization in this way is becoming the main source of geographic data extraction. This form of digitization involves searching for geographic data directly in aerial images rather than the traditional method of locating geographic features on a digitizing board.

The representation of the data

GIS data represents real-world objects (roads, land use, altitudes). Real-world objects can be divided into two abstractions: discrete objects (a house) and continuous objects (amount of rain fallen, an elevation). There are two ways to store data in a GIS: raster and vector.

GIS that focus on handling data in vector format are more popular in the market. However, raster GIS are widely used in studies that require the generation of continuous layers, necessary in non-discrete phenomena; also in environmental studies where excessive spatial precision is not required (atmospheric pollution, temperature distribution, location of marine species, geological analysis, etc.).

A type of raster data is essentially any type of digital image represented in meshes. The raster or grid GIS model focuses on the properties of space rather than the accuracy of location. Divides the space into regular "Cell (geometry)") cells where each one represents a single value. It is a data model that is very suitable for the representation of continuous variables in space.

Anyone familiar with digital photography recognizes the pixel as the smallest unit of information in an image. A combination of these pixels will create an image, unlike the common use of scalable vector graphics that are the basis of the vector model. While a digital image refers to the output as a representation of reality, in a photograph or art transferred to the computer, the raster type of data will reflect an abstraction of reality. Aerial photographs are a form of raster data commonly used for one purpose: to display a detailed image of a base map upon which digitization will be performed. Other raster data sets may contain information regarding terrain elevations (a Digital Terrain Model), or the reflection "Reflection (physics)") of light of a particular wavelength (for example those obtained by the LandSat satellite), among others.

Raster data is made up of rows and columns of cells, each cell stores a unique value. Raster data can be images (raster images), with a color value in each cell (or pixel). Other values ​​recorded for each cell can be a discrete value, such as land use, continuous values, such as temperatures, or a null value if no data is available. Although a cell raster stores a single value, the cells can be expanded by using the raster's bands to represent RGB (red, green, blue) colors, or an extended attribute table with a row for each unique cell value. The resolution of the raster data set is the width of the cell in ground units.

Raster data is stored in different formats, from a standard file based on the structure of TIFF, JPEG, etc. to Binary Large Objects (BLOBs), data stored directly in Database Management System. Database storage, when indexed, generally allows for rapid retrieval of raster data, but at the cost of requiring the storage of millions of records with significant memory size.

In a raster model, the larger the dimensions of the cells, the lower the precision or detail (resolution) of the representation of the geographical space.

In a GIS, geographic features are often expressed as vectors, maintaining the geometric characteristics of the figures.

In vector data, the interest of the representations focuses on the precision of the location of the geographical elements in space and where the phenomena to be represented are discrete, that is, with defined limits. Each of these geometries is linked to a row in a database that describes its attributes. For example, a database describing lakes may contain data on lake bathymetry, water quality, or pollution level. This information can be used to create a map that describes a particular attribute contained in the database. Lakes can have a range of colors depending on the level of pollution. Furthermore, the different geometries of the elements can also be compared. Thus, for example, GIS can be used to identify those wells (point geometry) that are around 2 kilometers from a lake (polygon geometry) and that have a high level of contamination.

Vector elements can be created respecting territorial integrity through the application of topological rules such as "polygons must not overlap." Vector data can be used to represent continuous variations of phenomena. Contour lines and irregular triangle networks (TINs) are used to represent altitude or other continually evolving values. TINs are records of values ​​at a localized point, which are connected by lines to form an irregular mesh of triangles. The face of the triangles represent, for example, the surface of the land.

To digitally model real-world entities, three geometric elements are used: the point "Point (geometry)"), the line and the polygon.[17]​.

Advantages and disadvantages of raster and vector models

There are advantages and disadvantages when using a raster or vector data model to represent reality.

Non-spatial data

Non-spatial data can also be stored along with spatial data, those represented by the coordinates of a vector geometry or by the position of a raster cell. In vector data, the additional data contains attributes of the geographic entity. For example, a forest inventory polygon can also have a value that serves as an identifier and information about tree species. In raster data the cell value can store attribute information, but it can also be used as an identifier referring to the records in a table.

Capturing the data

Capturing data and entering information into the system consumes most of the time of GIS professionals. There are a wide variety of methods used to enter data into a GIS stored in a digital format.

Data printed on paper or maps on PET film can be digitized or scanned to produce digital data.

With the digitization of cartography in analog support, vector data is produced through traces of points, lines, and polygon boundaries. This work can be carried out by a person manually or through vectorization programs that automate the work on a scanned map. However, in the latter case, manual review and editing will always be necessary, depending on the level of quality you wish to obtain.

Data obtained from topographic measurements can be entered directly into a GIS through digital data capture instruments using a technique called analytical geometry. Additionally, position coordinates taken through a Global Positioning System (GPS) can also be entered directly into a GIS.

Remote sensors also play an important role in data collection. They are sensors, such as cameras, scanners or LIDAR attached to mobile platforms such as airplanes or satellites.

Currently, most digital data comes from the interpretation of aerial photographs. To do this, workstations are used that directly digitize geographical elements through stereoscopic pairs of digital photographs.

These systems allow data to be captured in two and three dimensions, with elevations measured directly from a stereoscopic pair according to the principles of photogrammetry.

Satellite remote sensing provides another important source of spatial data. In this case, satellites use different sensors to measure the reflectance of parts of the electromagnetic spectrum, or radio waves that are sent from an active sensor such as radar. Remote sensing collects raster data that can be processed using different bands to determine classes and objects of interest, such as different land covers.

When data is captured, the user must consider whether it should be captured with relative accuracy or absolute precision. This decision is important since it not only influences the interpretation of the information, but also the cost of its capture.

Likewise, Mobile Mapping, also known as mobile mapping, is a technique that allows the collection of point clouds, geolocated 360° images and geographic data, all through technologies incorporated in a vehicle or mobile platform to tour and inventory the different elements that make up urban environments (public lighting management, sanitation and drinking water, urban signage, etc.).

In addition to capturing and entering spatial data, attribute data is also entered into a GIS.

During cartography digitization processes, it is common for involuntary topological errors (dangles, , , , , , etc.) to occur in the vector data and they must be corrected. After entering the data into a GIS, it will normally require further editing or processing to eliminate the aforementioned errors. A "topological correction" must be made before they can be used in some advanced analyzes and, for example, in a road network the lines must be connected to nodes at intersections.

Raster-vector data conversion

GIS can carry out data restructuring to transform it into different formats. For example, it is possible to convert a satellite image to a map of vector elements by generating lines around cells with the same classification, determining their spatial relationship, such as proximity or inclusion.

Unassisted vectorization of raster images using advanced algorithms is a technique that has been developed since the late 1960s. To achieve this, contrast improvement, false color images, as well as the design of filters are used through the implementation of two-dimensional Fourier transforms.

The reverse process of converting vector data to a data structure based on a raster matrix is ​​called rasterization.

Since digital data is collected and stored in both vector and raster forms, a GIS must be able to convert geographic data from one storage structure to another.

Projections, coordinate systems and reprojection

Before analyzing the data in the GIS, the cartography must all be in the same projection and coordinate systems. To do this, it is often necessary to reproject the information layers before integrating them into the geographic information system.

The Earth can be represented cartographically by several mathematical models, each of which can provide a different set of coordinates (e.g. latitude, longitude "Longitude (cartography)", altitude) for any given point on its surface. The simplest model is to assume that the Earth is a perfect sphere. As more measurements of the planet have been accumulated, models of the geoid have become more sophisticated and more precise. In fact, some of these are applied to different regions of the Earth to provide greater precision (for example, the European Terrestrial Reference System 1989 - ETRS89 - works well in Europe but not in North America).

Projection is a fundamental component when creating a map. A mathematical projection is the way to transfer information from a model of the Earth, which represents a curved surface in three dimensions, to another two-dimensional model such as paper or a computer screen. To do this, different cartographic projections are used depending on the type of map you want to create, since there are certain projections that are better adapted to some specific uses than others. For example, a projection that accurately represents the shape of the continents distorts their relative sizes.

Since much of the information in a GIS comes from existing cartography, a geographic information system uses computer processing power to transform digital information, obtained from sources with different projections or different coordinate systems, to a common projection and coordinate system. In the case of images (orthophotos, satellite images, etc.) this process is called rectification.

Spatial analysis using GIS

Given the wide range of spatial analysis techniques that have been developed over the last half century, any summary or review can only cover the topic in a limited depth. This is a rapidly changing field and GIS software packages increasingly include analysis tools, either in standard versions or as optional extensions to it. In many cases such tools are provided by the original software providers, while in other cases implementations of these new functionalities have been developed and are provided by third parties. Additionally, many products offer software development kits (SDKs), programming languages, scripting languages, etc. for the development of own analysis tools or other functions.

A GIS can recognize and analyze the spatial relationships that exist in stored geographic information. These topological relationships allow for complex spatial modeling and analysis. Thus, for example, the GIS can discern the cadastral parcel or parcels that are crossed by a high voltage line, or know which group of lines form a certain road.

In short, we can say that in the field of geographic information systems, topology is understood as the spatial relationships between the different graphic elements (node/point topology, network/arc/line topology, polygon topology) and their position on the map (proximity, inclusion, connectivity and neighborhood). These relationships, which for humans may be obvious to the naked eye, must be established by the software using a language and rules of mathematical geometry.

To carry out analyzes in which it is necessary to have topological consistency of the elements of the database, it is usually necessary to previously carry out validation and topological correction of the graphic information. To achieve this, there are GIS tools that facilitate the rectification of common errors automatically or semi-automatically.

Networks

A GIS intended for the calculation of optimal routes for emergency services is capable of determining the shortest path between two points taking into account both directions and directions of circulation and prohibited directions, etc. avoiding impassable areas. A GIS for the management of a water supply network would be able to determine, for example, how many subscribers would be affected by the interruption of service at a certain point in the network.

A geographic information system can simulate flows along a linear network. Values ​​such as slope, speed limit, service levels, etc. can be incorporated into the model in order to obtain greater accuracy. The use of GIS for network modeling is commonly used in transportation planning, hydrological planning or linear infrastructure management.

Map Overlay

Combining multiple sets of spatial data (points, lines, or polygons) can create another new set of vector data. Visually it would be similar to stacking several maps of the same region. These overlays are similar to the mathematical overlays of the Venn diagram. A join of overlay layers combines the geographic features and attribute tables from all of them into a new layer. In the case of an intersection of layers, this would define the area in which both overlap, and the result maintains the set of attributes for each of the regions. In the case of a symmetrical difference superposition, a resulting area is defined that includes the total surface of both layers except for the intersection zone.

In the analysis of raster data, the superposition of the data set is carried out through a process known as map algebra), through the application of simple mathematical methods that allow the values ​​of each raster matrix to be combined. In map algebra it is possible to weight certain coverages that assign the degree of importance of various factors in a geographic phenomenon.

Automated cartography

Both digital cartography and geographic information systems encode spatial relationships in structured formal representations. GIS are used in the creation of digital cartography as tools that allow an automated or semi-automated map-making process called automated cartography to be carried out.

In practice this would be a subset of GIS that would be equivalent to the final map composition phase, given that in most cases not all geographic information systems software have this functionality.

The resulting final cartographic product can be in both digital and printed formats. The combined use in certain GIS of powerful spatial analysis techniques together with a professional cartographic representation of the data means that high-quality maps can be created in a short period of time. The main difficulty in automated cartography is using a single data set to produce several products according to different types of scales "Scale (cartography)"), a technique known as generalization.

Geostatistics

Geostatistics analyzes spatial patterns in order to achieve predictions from specific spatial data. It is a way of viewing the statistical properties of spatial data. Unlike common statistical applications, geostatistics uses graph theory and algebraic matrices to reduce the number of parameters in the data. After that, the analysis of the data associated with geographical entity would be carried out secondly.

When phenomena are measured, 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 in the ocean, etc.), constant or dynamic degree of accuracy is always lost in the measurement. This loss of precision is determined from the scale and distribution of the data collected. GIS has tools that help carry out these analyses, highlighting the generation of spatial interpolation models.

Geocoding

Geocoding is the process of assigning geographic coordinates (latitude-longitude) to map points (addresses, points of interest, etc.). One of the most common uses is the georeferencing of postal addresses. This requires a base cartography on which to reference the geographic codes. This base layer can be, for example, a plot of street axes with street names and police numbers. The specific addresses that you want to georeference on the map, which usually come from tabulated tables, are positioned by interpolation or estimation. The GIS then locates the point in the street axes layer that is closest to reality according to the geocoding algorithms it uses.

Geocoding can also be done with more precise real data (for example, cadastral mapping). In this case, the result of the geographical coding will conform to a greater extent to that carried out, prevailing over the interpolation method.

In the case of reverse geocoding the process would be the other way around. An estimated street address with its portal number would be assigned to certain x,y coordinates. For example, a user could click "Click (computing)") on a layer that represents the road axes of a city and would obtain information about the postal address with the police number of a building. This portal number is calculated in an estimated manner by the GIS through interpolation from numbers already budgeted. If the user clicks on the midpoint of a segment that starts at portal 1 and ends with 100, the value returned for the selected location will be close to 50. Keep in mind that reverse geocoding does not return actual addresses, but only estimates of what should exist based on already known data.

Contenido

La información geográfica puede ser consultada, transferida, transformada, superpuesta, procesada y mostradas utilizando numerosas aplicaciones de software. Dentro de la industria empresas comerciales como ESRI, Intergraph, MapInfo"), Bentley Systems, Autodesk o Smallworld") son algunas de las compañías más importantes, con mucha experiencia en el ámbito de geoprocesamiento y que ofrecen aplicaciones propietarias en este campo. Por otro lado el software libre ha entrado con fuerza en la última década en el sector, captando una importante masa de usuarios y desarrolladores y siendo una opción cada vez más elegida por empresas y administraciones públicas. Bajo el paraguas de la fundación OSGeo se agrupan muchos de los mejores y más relevantes proyectos de software libre de este tipo existentes hoy en día.

El manejo de este tipo de sistemas son llevados a cabo generalmente por profesionales de diversos campos del conocimiento con experiencia en sistemas de información geográfica (cartografía, geografía, topografía, etc.), ya que el uso de estas herramientas requiere un aprendizaje previo que necesita de conocer las bases metodológicas sobre las que se fundamentan. Aunque existen herramientas gratuitas para ver información geográfica, el acceso del público en general a los geodatos está dominado por los recursos en línea, como Google Earth y otros basados en tecnología web mapping.

Originalmente hasta finales de los 90, cuando los datos del SIG se localizaban principalmente en grandes ordenadores y se utilizan para mantener registros internos, el software era un producto independiente. Sin embargo con el cada vez mayor acceso a Internet/Intranet y a la demanda de datos geográficos distribuidos, el software SIG ha cambiado gradualmente su perspectiva hacia la distribución de datos a través de redes. Los SIG que en la actualidad se comercializan son combinaciones de varias aplicaciones interoperables y APIs.

Hoy por hoy dentro del software SIG se distingue a menudo siete grandes tipos de programas informáticos:.

GIS software comparison

The following table includes only the most relevant GIS systems today, which stand out for their popularity and widespread use in industry and research.

Muchas disciplinas y especializaciones se han beneficiado de la tecnología subyacente en los SIG. El activo mercado de los sistemas de información geográfica se ha traducido en una reducción de costes y mejoras continuas en los componentes de hardware y software de los sistemas. Esto ha provocado que el uso de esta tecnología haya sido asimilada por universidades, gobiernos, empresas e instituciones que lo han aplicado a sectores como los bienes raíces, la salud pública, la criminología, la defensa nacional, el desarrollo sostenible, los recursos naturales, la arqueología, la ordenación del territorio, el urbanismo, el transporte, la sociología, el turismo sostenible e inteligente o la logística entre otros.

En la actualidad los SIG están teniendo una fuerte implantación en los llamados Servicios Basados en la Localización (LBS) debido al abaratamiento y masificación de la tecnología GPS integrada en dispositivos móviles de consumo (teléfonos móviles, PDAs, ordenadores portátiles). Los LBS permiten a los dispositivos móviles con GPS mostrar su ubicación respecto a puntos de interés fijos (restaurantes, gasolineras, cajeros, hidrantes, etc. más cercanos), móviles (amigos, hijos, autobuses, coches de policía) o para transmitir su posición a un servidor central para su visualización u otro tipo de tratamiento.

Otra de las líneas a destacar dentro de la rama de especialización de análisis de datos espaciales es el auge de las modelizaciones cartográficas. Gracias a ellas podemos modelizar y evaluar tanto aspectos o escenarios actuales como los futuribles con base en variables que les concretemos. Es una gran herramienta de predicción y evaluación y nos permiten evaluar casuísticas que reflejen el comportamiento por ejemplo de las zonas con mayor vulnerabilidad frente a determinados riesgos; la distribución potencial de especies o la dispersión de contaminantes por la atmósfera.[19]​.

Cartography in web environments

On the other hand, the world of GIS has seen in recent years an explosion of applications designed to display and edit cartography in web environments such as Google Maps, Bing Maps or OpenStreetMap among others. These websites give the public access to enormous amounts of geographic data. Some of them use software that, through an API, allows users to create custom applications. These services typically offer street maps, aerial or satellite imagery, geocoding, gazetteer searches, or routing functionality.

The development of the Internet and communication networks, as well as the emergence of OGC standards that facilitate the interoperability of spatial data, has promoted web mapping technology, with the emergence of numerous applications that allow the publication of geographic information on the web. In fact, this type of web mapping services based on map servers that are accessed through the browser itself have begun to adopt the most common characteristics in traditional GIS, which has caused the line that separates both types of software to become increasingly blurred.

The third dimension

The systems currently on the market are basically based on two-dimensional data management and analysis, with the limitations that this entails. There are hybrid systems halfway between 2D and 3D that have capabilities, mainly visualization, called two and a half dimensions (2.5D) or false 3D.

However, today more and more advanced applications are required with functionalities capable of managing complex data sets as they are perceived in the real world by the user, that is, in three dimensions. This environment provides a much better understanding of geospatial phenomena and patterns "Pattern (geography)"), whether on a small or large scale, for example in urban planning, geology, mining, supply network management, etc.[20]​.

The difficulties faced by a completely 3D GIS are great and range from the management of 3D geometries and their topology to their visualization in a simple way, through the analysis and geoprocessing of the information.

Currently the Open Geospatial Consortium is working on how to address the combination of the different types of modeling resulting from the different GIS, CIM), CAD and BIM technologies in the most comprehensive way possible. The interoperability of these data formats and models constitutes the first step towards the creation of intelligent 3D models at different scales.[21]​.

Semantics and GIS

The tools and technologies emerging from the W3C Semantic Web Activity are proving useful for data integration problems in information systems. Likewise, these technologies have been proposed as a means to facilitate interoperability and data reuse between GIS applications[22]​

[23]​ and also to allow new analysis mechanisms.[24]​

In short, the incorporation of certain artificial intelligence that provides these systems with new machine learning functionalities, such as selective information retrieval, statistical analysis, automatic generalization of maps or automatic interpretation of geospatial images.[25]​.

Ontologies are a key component of this semantic approach, as they facilitate machine readability of concepts and relationships in a given domain. This in turn allows GIS to focus on the meaning of the data rather than its syntax or structure. For example, we can reason that a land cover type classified as hardwood deciduous forests is a detailed data set of a layer over forest-type land covers with a less detailed classification, which could help a GIS to automatically merge both data sets into a more general land cover classification layer.

But the future development of GIS with the inclusion of semantics in management would not only allow the generalization or coflation of geospatial data with a certain similarity, but, for example, would facilitate the automated or semi-assisted generation of a task traditionally considered tedious and unrewarding, such as the creation of metadata for the different layers of geographic information.[26]​.

Very deep and exhaustive ontologies have been developed in areas related to the use of GIS, such as the Hydrology Ontology developed by the Ordnance Survey in the United Kingdom, the FAO geopolitical ontology,[27]​ the OWL ontologies hydroOntology and GML Ontology and the SWEET ontologies carried out by NASA's Jet Propulsion Laboratory.

Temporary GIS

Temporal GIS incorporates the three spatial dimensions (X, Y and Z) also adding time in a 4D representation that more closely resembles reality. Temporality in GIS collects the dynamic processes of the represented elements. For example, let's imagine the possibilities that a geographic information system would offer that allows us to slow down and accelerate the time of the geomorphological processes that are modeled in it and analyze the different morphogenetic sequences of a given land relief; or to model the urban development of a given area over a given period.[21]​.

Within raster file management, the time factor also plays an important role. For example when visualizing changes on the earth's surface. With the availability of satellite images for free from platforms such as Land Viewer, it is possible to have a wide repertoire of satellite images through which to make timelapses and see the evolution of information over time.

GIS and Spatial Data Infrastructures (SDI)

The exponential growth of Geographic Information Systems, their tools and the ease of access to them, has produced an undesirable effect on government agencies, which is the excessive dispersion and divergence of information, as well as the poor standardization of data. To solve this problem and achieve unified, quality, standardized, sustainable and publicly accessible information, so-called local and regional Spatial Data Infrastructures (SDI) have been developed. Through these, the aim is to obtain a convergence of efforts on the management of public information, as well as that corresponding to research organizations.

Educational GIS

At the end of the century, GIS began to be recognized as tools that promoted learning, mainly through research, constructivism and problem-based learning. The benefits of GIS seem focused on developing the so-called spatial thinking"), but there is not enough bibliography or statistical data to show the specific scope of the use of GIS in education around the world, although in those countries where the curriculum mentions them, its expansion has been faster.[28]​.

GIS seems to provide many advantages in the teaching of Geography because they allow a truthful analysis based on real geographical data and also pose many research questions by teachers and students in the classrooms, as well as contribute to the improvement of learning by developing spatial and geographical thinking and, in many cases, the motivation of the students.[29]​.

GIS and technologies

Technology has evolved hand in hand with Geographic Information Systems, with GIS often being an additional complement to the geographic information itself. An example of this is found with the arrival of tools such as Augmented Reality or the incorporation of drones into our lives. The management of Augmented Reality has allowed the understanding of GIS and has been an additional tool in the management of the territory in addition to an educational tool. As an example we find Sand Box. Other technologies such as unmanned aerial vehicles, or drones, have made it possible to map the territory, obtaining high-resolution images that can be taken at any time without the need to fly in small planes or use satellites.

The analysis of the metric properties of GIS reveals that they lead to erroneous assessments, evaluations and conclusions. These errors have their origin when GIS consider the concept of enlargement, zoom or zoom as synonymous with scale. This error leads to an impossible precision at a scale of 1:5,000 (plan creation scale) to: georeferencing, distance measurement, the surface of enclosures and graphic representation. In the Agricultural Parcels GIS (SIGPAC) and the Cadastre GIS (SIGCA), it is possible to visualize at a pseudoscale (magnification) 1:400, obtain UTM coordinates and make measurements with precision of one centimeter, on a plan and an orthophotography at a 1:5,000 scale. All this is incorrect, it lacks technical rigor.

We call it pseudoscale because:.

The origin of the error may be in the digital (virtual) methodology of the GIS without analog and distant references (the monitor and orthophotography), which has not been tested (calibrated).

Everything is very easy and intuitive, it does not require technical knowledge. We come to think that everything can be done from the monitor and with precision of one cm, better than with a topographic survey. In conclusion, current GIS needs a review that includes: reporting on the scale, adjusting the significant figures, giving different treatment to the elements of the symbology when we make enlargements according to it,..., until then, be extremely cautious with numerical data and visual information (when we superimpose the plan on orthophotography).[30]​.

About 15,000 years ago[4]​ in the caves of Lascaux (France) the Cro-Magnon men painted the animals they hunted on the walls, associating these drawings with linear traces that, it is believed, corresponded to the migration routes of those species.[5]​

While this example is simplistic compared to modern technologies, these early antecedents mimic two elements of modern geographic information systems: an image associated with an information attribute.[6]​.

In 1832,[7] French geographer and cartographer Charles Picquet created a map to visually show the spread of the cholera epidemic in 48 Parisian districts.[8] He colored the affected districts according to the number of deaths.[9] This is one of the first uses of spatial statistics in relation to epidemiology. This representation was published in 1834 in a report by the head of the commission, Louis-François Benoiston de Châteauneuf, who was an economist, statistician and demographer.[10][11]​.

In 1854, epidemiology pioneer Dr. John Snow would provide another classic example of this concept when he mapped, on a now-famous map, the incidence of cholera cases in the Soho district of London. outbreak.

Although topographic and thematic cartography already existed previously, John Snow's map was the only one to date that, using cartographic methods, not only represented reality, but for the first time analyzed sets of dependent geographical phenomena.

At the beginning of the century, photolithography was developed, where maps were separated into layers. The advancement of hardware driven by nuclear weapons research would lead, in the early 1960s, to the development of cartographic applications for general-purpose computers.[14]​.

The year 1962 saw the first real use of GIS in the world, specifically in Ottawa (Ontario, Canada) and by the Federal Department of Forestry and Rural Development. Developed by English geographer Roger Tomlinson, the Canadian Geographic Information System (CGIS) was used to store, analyze and manipulate data collected for the Canada Land Inventory (CLI) - an initiative aimed at managing the country's vast natural resources with cartographic information relating to land types and uses, agriculture, recreation, wildlife, waterfowl and forestry, all at once. scale "Scale (cartography)") of 1:50,000. A classification factor was also added to allow the analysis of the information.

The Canada Geographic Information System was the first GIS in the world similar to what we know today, and a considerable advance with respect to the cartographic applications that existed until then, since it allowed layers of information to be superimposed, measurements to be made, and data to be digitized and scanned. It also supported a continent-spanning national coordinate system, a coding of lines into "arcs" that had a truly integrated topology, and that stored each element's attributes and location information in separate "File (computing)" files. As a consequence of this, Tomlinson is considered "the father of GIS", in particular for the use of convergent geographical information structured in layers, which facilitates its spatial analysis.[15]​ The CGIS was operational until the 1990s, becoming the largest territorial resource database in Canada. It was developed as a mainframe-based system and its strength was that it allowed complex analyzes of data sets spanning the entire continent. The software, the dean of geographic information systems, was never commercially available.

In 1964, Howard T. Fisher formed the Laboratory for Computer Graphics and Spatial Analysis at Harvard University at the Harvard Graduate School of Design (LCGSA 1965-1991), where a series of important theoretical concepts in spatial data management were developed, and in the 1970s he had disseminated software code and germ systems, such as SYMAP, GRID, and ODYSSEY. - which served as sources of conceptual inspiration for their subsequent commercial developments - to universities, research centers and companies around the world.[16]​.

In the 1980s, M&S Computing (later Intergraph), Environmental Systems Research Institute (ESRI) and CARIS") (Computer Aided Resource Information System) would emerge as commercial providers of GIS software. They successfully incorporated many of the features of CGIS, combining the first-generation geographic information systems approach to separating spatial information and attributes of represented geographic features with a second-generation approach that organizes and structures these attributes in databases.

In the decade of the 70s and early 80s, the development of two public domain systems began in parallel. The Map Overlay and Statistical System (MOSS) project began in 1977 in Fort Collins, Colorado, USA, under the auspices of the Western Energy and Land Use Team (WELUT) and the US Fish and Wildlife Service. In 1982, the United States Army Construction Engineering Research Laboratory Corps of Engineers (USA-CERL) developed GRASS as a tool for environmental supervision and management of the territories under administration of the Department of Defense.

This stage of development is characterized, in general, by the decrease in the importance of individual initiatives and an increase in interests at the corporate level, especially by government agencies and administration.

The 80s and 90s were years of strong increase in companies that marketed these systems, due to the growth of GIS on UNIX workstations and personal computers. It is the period in which it has come to be known in GIS as the commercial phase. The interest of the different large industries directly or indirectly related to GIS is growing enormously due to the great avalanche of products in the international computer market that made this technology widespread.

In the 1990s, a commercial stage began for professionals, where geographic information systems began to spread to the level of the home user due to the generalization of personal computers or microcomputers.

At the end of the beginning of the 21st century, rapid growth in different systems has been consolidated, being restricted to a relatively small number of platforms. Users are beginning to export the concept of GIS data visualization to the Internet, requiring standardization of data formats and transfer standards. More recently, there has been an expansion in the number of open source GIS software developments, which, unlike commercial software, typically span a wider range of operating systems, allowing them to be modified to carry out specific tasks.

Data creation

Modern GIS technologies work with digital information, for which there are several methods used in creating digital data. The most used method is digitization, where a printed map or information taken in the field is transferred to a digital medium through the use of a Computer Aided Design (DAO or CAD) program with georeferencing capabilities.

Given the wide availability of ortho-rectified images (both satellite and aerial), digitization in this way is becoming the main source of geographic data extraction. This form of digitization involves searching for geographic data directly in aerial images rather than the traditional method of locating geographic features on a digitizing board.

The representation of the data

GIS data represents real-world objects (roads, land use, altitudes). Real-world objects can be divided into two abstractions: discrete objects (a house) and continuous objects (amount of rain fallen, an elevation). There are two ways to store data in a GIS: raster and vector.

GIS that focus on handling data in vector format are more popular in the market. However, raster GIS are widely used in studies that require the generation of continuous layers, necessary in non-discrete phenomena; also in environmental studies where excessive spatial precision is not required (atmospheric pollution, temperature distribution, location of marine species, geological analysis, etc.).

A type of raster data is essentially any type of digital image represented in meshes. The raster or grid GIS model focuses on the properties of space rather than the accuracy of location. Divides the space into regular "Cell (geometry)") cells where each one represents a single value. It is a data model that is very suitable for the representation of continuous variables in space.

Anyone familiar with digital photography recognizes the pixel as the smallest unit of information in an image. A combination of these pixels will create an image, unlike the common use of scalable vector graphics that are the basis of the vector model. While a digital image refers to the output as a representation of reality, in a photograph or art transferred to the computer, the raster type of data will reflect an abstraction of reality. Aerial photographs are a form of raster data commonly used for one purpose: to display a detailed image of a base map upon which digitization will be performed. Other raster data sets may contain information regarding terrain elevations (a Digital Terrain Model), or the reflection "Reflection (physics)") of light of a particular wavelength (for example those obtained by the LandSat satellite), among others.

Raster data is made up of rows and columns of cells, each cell stores a unique value. Raster data can be images (raster images), with a color value in each cell (or pixel). Other values ​​recorded for each cell can be a discrete value, such as land use, continuous values, such as temperatures, or a null value if no data is available. Although a cell raster stores a single value, the cells can be expanded by using the raster's bands to represent RGB (red, green, blue) colors, or an extended attribute table with a row for each unique cell value. The resolution of the raster data set is the width of the cell in ground units.

Raster data is stored in different formats, from a standard file based on the structure of TIFF, JPEG, etc. to Binary Large Objects (BLOBs), data stored directly in Database Management System. Database storage, when indexed, generally allows for rapid retrieval of raster data, but at the cost of requiring the storage of millions of records with significant memory size.

In a raster model, the larger the dimensions of the cells, the lower the precision or detail (resolution) of the representation of the geographical space.

In a GIS, geographic features are often expressed as vectors, maintaining the geometric characteristics of the figures.

In vector data, the interest of the representations focuses on the precision of the location of the geographical elements in space and where the phenomena to be represented are discrete, that is, with defined limits. Each of these geometries is linked to a row in a database that describes its attributes. For example, a database describing lakes may contain data on lake bathymetry, water quality, or pollution level. This information can be used to create a map that describes a particular attribute contained in the database. Lakes can have a range of colors depending on the level of pollution. Furthermore, the different geometries of the elements can also be compared. Thus, for example, GIS can be used to identify those wells (point geometry) that are around 2 kilometers from a lake (polygon geometry) and that have a high level of contamination.

Vector elements can be created respecting territorial integrity through the application of topological rules such as "polygons must not overlap." Vector data can be used to represent continuous variations of phenomena. Contour lines and irregular triangle networks (TINs) are used to represent altitude or other continually evolving values. TINs are records of values ​​at a localized point, which are connected by lines to form an irregular mesh of triangles. The face of the triangles represent, for example, the surface of the land.

To digitally model real-world entities, three geometric elements are used: the point "Point (geometry)"), the line and the polygon.[17]​.

Advantages and disadvantages of raster and vector models

There are advantages and disadvantages when using a raster or vector data model to represent reality.

Non-spatial data

Non-spatial data can also be stored along with spatial data, those represented by the coordinates of a vector geometry or by the position of a raster cell. In vector data, the additional data contains attributes of the geographic entity. For example, a forest inventory polygon can also have a value that serves as an identifier and information about tree species. In raster data the cell value can store attribute information, but it can also be used as an identifier referring to the records in a table.

Capturing the data

Capturing data and entering information into the system consumes most of the time of GIS professionals. There are a wide variety of methods used to enter data into a GIS stored in a digital format.

Data printed on paper or maps on PET film can be digitized or scanned to produce digital data.

With the digitization of cartography in analog support, vector data is produced through traces of points, lines, and polygon boundaries. This work can be carried out by a person manually or through vectorization programs that automate the work on a scanned map. However, in the latter case, manual review and editing will always be necessary, depending on the level of quality you wish to obtain.

Data obtained from topographic measurements can be entered directly into a GIS through digital data capture instruments using a technique called analytical geometry. Additionally, position coordinates taken through a Global Positioning System (GPS) can also be entered directly into a GIS.

Remote sensors also play an important role in data collection. They are sensors, such as cameras, scanners or LIDAR attached to mobile platforms such as airplanes or satellites.

Currently, most digital data comes from the interpretation of aerial photographs. To do this, workstations are used that directly digitize geographical elements through stereoscopic pairs of digital photographs.

These systems allow data to be captured in two and three dimensions, with elevations measured directly from a stereoscopic pair according to the principles of photogrammetry.

Satellite remote sensing provides another important source of spatial data. In this case, satellites use different sensors to measure the reflectance of parts of the electromagnetic spectrum, or radio waves that are sent from an active sensor such as radar. Remote sensing collects raster data that can be processed using different bands to determine classes and objects of interest, such as different land covers.

When data is captured, the user must consider whether it should be captured with relative accuracy or absolute precision. This decision is important since it not only influences the interpretation of the information, but also the cost of its capture.

Likewise, Mobile Mapping, also known as mobile mapping, is a technique that allows the collection of point clouds, geolocated 360° images and geographic data, all through technologies incorporated in a vehicle or mobile platform to tour and inventory the different elements that make up urban environments (public lighting management, sanitation and drinking water, urban signage, etc.).

In addition to capturing and entering spatial data, attribute data is also entered into a GIS.

During cartography digitization processes, it is common for involuntary topological errors (dangles, , , , , , etc.) to occur in the vector data and they must be corrected. After entering the data into a GIS, it will normally require further editing or processing to eliminate the aforementioned errors. A "topological correction" must be made before they can be used in some advanced analyzes and, for example, in a road network the lines must be connected to nodes at intersections.

Raster-vector data conversion

GIS can carry out data restructuring to transform it into different formats. For example, it is possible to convert a satellite image to a map of vector elements by generating lines around cells with the same classification, determining their spatial relationship, such as proximity or inclusion.

Unassisted vectorization of raster images using advanced algorithms is a technique that has been developed since the late 1960s. To achieve this, contrast improvement, false color images, as well as the design of filters are used through the implementation of two-dimensional Fourier transforms.

The reverse process of converting vector data to a data structure based on a raster matrix is ​​called rasterization.

Since digital data is collected and stored in both vector and raster forms, a GIS must be able to convert geographic data from one storage structure to another.

Projections, coordinate systems and reprojection

Before analyzing the data in the GIS, the cartography must all be in the same projection and coordinate systems. To do this, it is often necessary to reproject the information layers before integrating them into the geographic information system.

The Earth can be represented cartographically by several mathematical models, each of which can provide a different set of coordinates (e.g. latitude, longitude "Longitude (cartography)", altitude) for any given point on its surface. The simplest model is to assume that the Earth is a perfect sphere. As more measurements of the planet have been accumulated, models of the geoid have become more sophisticated and more precise. In fact, some of these are applied to different regions of the Earth to provide greater precision (for example, the European Terrestrial Reference System 1989 - ETRS89 - works well in Europe but not in North America).

Projection is a fundamental component when creating a map. A mathematical projection is the way to transfer information from a model of the Earth, which represents a curved surface in three dimensions, to another two-dimensional model such as paper or a computer screen. To do this, different cartographic projections are used depending on the type of map you want to create, since there are certain projections that are better adapted to some specific uses than others. For example, a projection that accurately represents the shape of the continents distorts their relative sizes.

Since much of the information in a GIS comes from existing cartography, a geographic information system uses computer processing power to transform digital information, obtained from sources with different projections or different coordinate systems, to a common projection and coordinate system. In the case of images (orthophotos, satellite images, etc.) this process is called rectification.

Spatial analysis using GIS

Given the wide range of spatial analysis techniques that have been developed over the last half century, any summary or review can only cover the topic in a limited depth. This is a rapidly changing field and GIS software packages increasingly include analysis tools, either in standard versions or as optional extensions to it. In many cases such tools are provided by the original software providers, while in other cases implementations of these new functionalities have been developed and are provided by third parties. Additionally, many products offer software development kits (SDKs), programming languages, scripting languages, etc. for the development of own analysis tools or other functions.

A GIS can recognize and analyze the spatial relationships that exist in stored geographic information. These topological relationships allow for complex spatial modeling and analysis. Thus, for example, the GIS can discern the cadastral parcel or parcels that are crossed by a high voltage line, or know which group of lines form a certain road.

In short, we can say that in the field of geographic information systems, topology is understood as the spatial relationships between the different graphic elements (node/point topology, network/arc/line topology, polygon topology) and their position on the map (proximity, inclusion, connectivity and neighborhood). These relationships, which for humans may be obvious to the naked eye, must be established by the software using a language and rules of mathematical geometry.

To carry out analyzes in which it is necessary to have topological consistency of the elements of the database, it is usually necessary to previously carry out validation and topological correction of the graphic information. To achieve this, there are GIS tools that facilitate the rectification of common errors automatically or semi-automatically.

Networks

A GIS intended for the calculation of optimal routes for emergency services is capable of determining the shortest path between two points taking into account both directions and directions of circulation and prohibited directions, etc. avoiding impassable areas. A GIS for the management of a water supply network would be able to determine, for example, how many subscribers would be affected by the interruption of service at a certain point in the network.

A geographic information system can simulate flows along a linear network. Values ​​such as slope, speed limit, service levels, etc. can be incorporated into the model in order to obtain greater accuracy. The use of GIS for network modeling is commonly used in transportation planning, hydrological planning or linear infrastructure management.

Map Overlay

Combining multiple sets of spatial data (points, lines, or polygons) can create another new set of vector data. Visually it would be similar to stacking several maps of the same region. These overlays are similar to the mathematical overlays of the Venn diagram. A join of overlay layers combines the geographic features and attribute tables from all of them into a new layer. In the case of an intersection of layers, this would define the area in which both overlap, and the result maintains the set of attributes for each of the regions. In the case of a symmetrical difference superposition, a resulting area is defined that includes the total surface of both layers except for the intersection zone.

In the analysis of raster data, the superposition of the data set is carried out through a process known as map algebra), through the application of simple mathematical methods that allow the values ​​of each raster matrix to be combined. In map algebra it is possible to weight certain coverages that assign the degree of importance of various factors in a geographic phenomenon.

Automated cartography

Both digital cartography and geographic information systems encode spatial relationships in structured formal representations. GIS are used in the creation of digital cartography as tools that allow an automated or semi-automated map-making process called automated cartography to be carried out.

In practice this would be a subset of GIS that would be equivalent to the final map composition phase, given that in most cases not all geographic information systems software have this functionality.

The resulting final cartographic product can be in both digital and printed formats. The combined use in certain GIS of powerful spatial analysis techniques together with a professional cartographic representation of the data means that high-quality maps can be created in a short period of time. The main difficulty in automated cartography is using a single data set to produce several products according to different types of scales "Scale (cartography)"), a technique known as generalization.

Geostatistics

Geostatistics analyzes spatial patterns in order to achieve predictions from specific spatial data. It is a way of viewing the statistical properties of spatial data. Unlike common statistical applications, geostatistics uses graph theory and algebraic matrices to reduce the number of parameters in the data. After that, the analysis of the data associated with geographical entity would be carried out secondly.

When phenomena are measured, 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 in the ocean, etc.), constant or dynamic degree of accuracy is always lost in the measurement. This loss of precision is determined from the scale and distribution of the data collected. GIS has tools that help carry out these analyses, highlighting the generation of spatial interpolation models.

Geocoding

Geocoding is the process of assigning geographic coordinates (latitude-longitude) to map points (addresses, points of interest, etc.). One of the most common uses is the georeferencing of postal addresses. This requires a base cartography on which to reference the geographic codes. This base layer can be, for example, a plot of street axes with street names and police numbers. The specific addresses that you want to georeference on the map, which usually come from tabulated tables, are positioned by interpolation or estimation. The GIS then locates the point in the street axes layer that is closest to reality according to the geocoding algorithms it uses.

Geocoding can also be done with more precise real data (for example, cadastral mapping). In this case, the result of the geographical coding will conform to a greater extent to that carried out, prevailing over the interpolation method.

In the case of reverse geocoding the process would be the other way around. An estimated street address with its portal number would be assigned to certain x,y coordinates. For example, a user could click "Click (computing)") on a layer that represents the road axes of a city and would obtain information about the postal address with the police number of a building. This portal number is calculated in an estimated manner by the GIS through interpolation from numbers already budgeted. If the user clicks on the midpoint of a segment that starts at portal 1 and ends with 100, the value returned for the selected location will be close to 50. Keep in mind that reverse geocoding does not return actual addresses, but only estimates of what should exist based on already known data.

Contenido

La información geográfica puede ser consultada, transferida, transformada, superpuesta, procesada y mostradas utilizando numerosas aplicaciones de software. Dentro de la industria empresas comerciales como ESRI, Intergraph, MapInfo"), Bentley Systems, Autodesk o Smallworld") son algunas de las compañías más importantes, con mucha experiencia en el ámbito de geoprocesamiento y que ofrecen aplicaciones propietarias en este campo. Por otro lado el software libre ha entrado con fuerza en la última década en el sector, captando una importante masa de usuarios y desarrolladores y siendo una opción cada vez más elegida por empresas y administraciones públicas. Bajo el paraguas de la fundación OSGeo se agrupan muchos de los mejores y más relevantes proyectos de software libre de este tipo existentes hoy en día.

El manejo de este tipo de sistemas son llevados a cabo generalmente por profesionales de diversos campos del conocimiento con experiencia en sistemas de información geográfica (cartografía, geografía, topografía, etc.), ya que el uso de estas herramientas requiere un aprendizaje previo que necesita de conocer las bases metodológicas sobre las que se fundamentan. Aunque existen herramientas gratuitas para ver información geográfica, el acceso del público en general a los geodatos está dominado por los recursos en línea, como Google Earth y otros basados en tecnología web mapping.

Originalmente hasta finales de los 90, cuando los datos del SIG se localizaban principalmente en grandes ordenadores y se utilizan para mantener registros internos, el software era un producto independiente. Sin embargo con el cada vez mayor acceso a Internet/Intranet y a la demanda de datos geográficos distribuidos, el software SIG ha cambiado gradualmente su perspectiva hacia la distribución de datos a través de redes. Los SIG que en la actualidad se comercializan son combinaciones de varias aplicaciones interoperables y APIs.

Hoy por hoy dentro del software SIG se distingue a menudo siete grandes tipos de programas informáticos:.

GIS software comparison

The following table includes only the most relevant GIS systems today, which stand out for their popularity and widespread use in industry and research.

Muchas disciplinas y especializaciones se han beneficiado de la tecnología subyacente en los SIG. El activo mercado de los sistemas de información geográfica se ha traducido en una reducción de costes y mejoras continuas en los componentes de hardware y software de los sistemas. Esto ha provocado que el uso de esta tecnología haya sido asimilada por universidades, gobiernos, empresas e instituciones que lo han aplicado a sectores como los bienes raíces, la salud pública, la criminología, la defensa nacional, el desarrollo sostenible, los recursos naturales, la arqueología, la ordenación del territorio, el urbanismo, el transporte, la sociología, el turismo sostenible e inteligente o la logística entre otros.

En la actualidad los SIG están teniendo una fuerte implantación en los llamados Servicios Basados en la Localización (LBS) debido al abaratamiento y masificación de la tecnología GPS integrada en dispositivos móviles de consumo (teléfonos móviles, PDAs, ordenadores portátiles). Los LBS permiten a los dispositivos móviles con GPS mostrar su ubicación respecto a puntos de interés fijos (restaurantes, gasolineras, cajeros, hidrantes, etc. más cercanos), móviles (amigos, hijos, autobuses, coches de policía) o para transmitir su posición a un servidor central para su visualización u otro tipo de tratamiento.

Otra de las líneas a destacar dentro de la rama de especialización de análisis de datos espaciales es el auge de las modelizaciones cartográficas. Gracias a ellas podemos modelizar y evaluar tanto aspectos o escenarios actuales como los futuribles con base en variables que les concretemos. Es una gran herramienta de predicción y evaluación y nos permiten evaluar casuísticas que reflejen el comportamiento por ejemplo de las zonas con mayor vulnerabilidad frente a determinados riesgos; la distribución potencial de especies o la dispersión de contaminantes por la atmósfera.[19]​.

Cartography in web environments

On the other hand, the world of GIS has seen in recent years an explosion of applications designed to display and edit cartography in web environments such as Google Maps, Bing Maps or OpenStreetMap among others. These websites give the public access to enormous amounts of geographic data. Some of them use software that, through an API, allows users to create custom applications. These services typically offer street maps, aerial or satellite imagery, geocoding, gazetteer searches, or routing functionality.

The development of the Internet and communication networks, as well as the emergence of OGC standards that facilitate the interoperability of spatial data, has promoted web mapping technology, with the emergence of numerous applications that allow the publication of geographic information on the web. In fact, this type of web mapping services based on map servers that are accessed through the browser itself have begun to adopt the most common characteristics in traditional GIS, which has caused the line that separates both types of software to become increasingly blurred.

The third dimension

The systems currently on the market are basically based on two-dimensional data management and analysis, with the limitations that this entails. There are hybrid systems halfway between 2D and 3D that have capabilities, mainly visualization, called two and a half dimensions (2.5D) or false 3D.

However, today more and more advanced applications are required with functionalities capable of managing complex data sets as they are perceived in the real world by the user, that is, in three dimensions. This environment provides a much better understanding of geospatial phenomena and patterns "Pattern (geography)"), whether on a small or large scale, for example in urban planning, geology, mining, supply network management, etc.[20]​.

The difficulties faced by a completely 3D GIS are great and range from the management of 3D geometries and their topology to their visualization in a simple way, through the analysis and geoprocessing of the information.

Currently the Open Geospatial Consortium is working on how to address the combination of the different types of modeling resulting from the different GIS, CIM), CAD and BIM technologies in the most comprehensive way possible. The interoperability of these data formats and models constitutes the first step towards the creation of intelligent 3D models at different scales.[21]​.

Semantics and GIS

The tools and technologies emerging from the W3C Semantic Web Activity are proving useful for data integration problems in information systems. Likewise, these technologies have been proposed as a means to facilitate interoperability and data reuse between GIS applications[22]​

[23]​ and also to allow new analysis mechanisms.[24]​

In short, the incorporation of certain artificial intelligence that provides these systems with new machine learning functionalities, such as selective information retrieval, statistical analysis, automatic generalization of maps or automatic interpretation of geospatial images.[25]​.

Ontologies are a key component of this semantic approach, as they facilitate machine readability of concepts and relationships in a given domain. This in turn allows GIS to focus on the meaning of the data rather than its syntax or structure. For example, we can reason that a land cover type classified as hardwood deciduous forests is a detailed data set of a layer over forest-type land covers with a less detailed classification, which could help a GIS to automatically merge both data sets into a more general land cover classification layer.

But the future development of GIS with the inclusion of semantics in management would not only allow the generalization or coflation of geospatial data with a certain similarity, but, for example, would facilitate the automated or semi-assisted generation of a task traditionally considered tedious and unrewarding, such as the creation of metadata for the different layers of geographic information.[26]​.

Very deep and exhaustive ontologies have been developed in areas related to the use of GIS, such as the Hydrology Ontology developed by the Ordnance Survey in the United Kingdom, the FAO geopolitical ontology,[27]​ the OWL ontologies hydroOntology and GML Ontology and the SWEET ontologies carried out by NASA's Jet Propulsion Laboratory.

Temporary GIS

Temporal GIS incorporates the three spatial dimensions (X, Y and Z) also adding time in a 4D representation that more closely resembles reality. Temporality in GIS collects the dynamic processes of the represented elements. For example, let's imagine the possibilities that a geographic information system would offer that allows us to slow down and accelerate the time of the geomorphological processes that are modeled in it and analyze the different morphogenetic sequences of a given land relief; or to model the urban development of a given area over a given period.[21]​.

Within raster file management, the time factor also plays an important role. For example when visualizing changes on the earth's surface. With the availability of satellite images for free from platforms such as Land Viewer, it is possible to have a wide repertoire of satellite images through which to make timelapses and see the evolution of information over time.

GIS and Spatial Data Infrastructures (SDI)

The exponential growth of Geographic Information Systems, their tools and the ease of access to them, has produced an undesirable effect on government agencies, which is the excessive dispersion and divergence of information, as well as the poor standardization of data. To solve this problem and achieve unified, quality, standardized, sustainable and publicly accessible information, so-called local and regional Spatial Data Infrastructures (SDI) have been developed. Through these, the aim is to obtain a convergence of efforts on the management of public information, as well as that corresponding to research organizations.

Educational GIS

At the end of the century, GIS began to be recognized as tools that promoted learning, mainly through research, constructivism and problem-based learning. The benefits of GIS seem focused on developing the so-called spatial thinking"), but there is not enough bibliography or statistical data to show the specific scope of the use of GIS in education around the world, although in those countries where the curriculum mentions them, its expansion has been faster.[28]​.

GIS seems to provide many advantages in the teaching of Geography because they allow a truthful analysis based on real geographical data and also pose many research questions by teachers and students in the classrooms, as well as contribute to the improvement of learning by developing spatial and geographical thinking and, in many cases, the motivation of the students.[29]​.

GIS and technologies

Technology has evolved hand in hand with Geographic Information Systems, with GIS often being an additional complement to the geographic information itself. An example of this is found with the arrival of tools such as Augmented Reality or the incorporation of drones into our lives. The management of Augmented Reality has allowed the understanding of GIS and has been an additional tool in the management of the territory in addition to an educational tool. As an example we find Sand Box. Other technologies such as unmanned aerial vehicles, or drones, have made it possible to map the territory, obtaining high-resolution images that can be taken at any time without the need to fly in small planes or use satellites.

The analysis of the metric properties of GIS reveals that they lead to erroneous assessments, evaluations and conclusions. These errors have their origin when GIS consider the concept of enlargement, zoom or zoom as synonymous with scale. This error leads to an impossible precision at a scale of 1:5,000 (plan creation scale) to: georeferencing, distance measurement, the surface of enclosures and graphic representation. In the Agricultural Parcels GIS (SIGPAC) and the Cadastre GIS (SIGCA), it is possible to visualize at a pseudoscale (magnification) 1:400, obtain UTM coordinates and make measurements with precision of one centimeter, on a plan and an orthophotography at a 1:5,000 scale. All this is incorrect, it lacks technical rigor.

We call it pseudoscale because:.

The origin of the error may be in the digital (virtual) methodology of the GIS without analog and distant references (the monitor and orthophotography), which has not been tested (calibrated).

Everything is very easy and intuitive, it does not require technical knowledge. We come to think that everything can be done from the monitor and with precision of one cm, better than with a topographic survey. In conclusion, current GIS needs a review that includes: reporting on the scale, adjusting the significant figures, giving different treatment to the elements of the symbology when we make enlargements according to it,..., until then, be extremely cautious with numerical data and visual information (when we superimpose the plan on orthophotography).[30]​.