Geographical Information Systems (GIS

A geographic information system is a computer-based tool for mapping and analyzing geographic phenomenon that exist, and events that occur, on Earth. GIS technology integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps. These abilities distinguish GIS from other information systems and make it valuable to a wide range of public and private enterprises for explaining events, predicting outcomes, and planning strategies. Map making and geographic analysis are not new, but a GIS performs these tasks faster and with more sophistication than do traditional manual methods.

2020

GIS stands for Geographic Information Systems and it is defined from the perspective of tool, technology, service and science. Data collection method includes data capture which constitute primary and secondary data. Map projections are a systematic rendering of points from the curved earth surface onto a flat map surface. The world is seen as discrete objects which represent vector data and as a continuous surface which reflects raster data. Vector uses points, lines, polygons and raster use a cell or grid structure. Geoprocessing is any GIS operation used to manipulate data such as clip or buffer. Automation is the ability to perform repetitive tasks. Geodatabase is a database designed to store, query and manipulate geographic information and spatial data. Data quality is the degree of data excellency that satisfy the given objective, it can be categorized as precision and accuracy. Metadata is information about data, it documents the who, what when, where, how, and why of a data resource.

1991

Geographic Information System is virtually used in every field. It's comprised of hardware, software, data, humans, and a set of organizational protocols. These components must be well integrated for effective use of GIS to help people reach a common goal. GIS technology applies geographic science with tools for understanding and collaboration. It’s used in every aspect of our daily lives and it is difficult to count its uses as it is used by different institutions and hundreds of thousands of organizations for many different purposes and to make maps that communicate, perform analysis, share information and solve complex problems around the world since it allows the analyzation of environmental, demographic, and topographic data. This is changing the way the world works. GIS is a tool used by individuals and organizations, schools, governments, and businesses seeking innovative ways to solve their problems. It's considered as a decision-making tool which uses spatial information. It supports the study of natural and man-made phenomena. Words can't explain how his tool is so very important in our lives so that the intention of this book is to lay the principle basics foundation of GIS and to define some of the common terminologies which GIS users using.

2009

An analysis of critical gaps in current geographic information systems (GIS) that impede their use for spatial decision support. • A research agenda adapting expert system and other related technologies to fill these gaps. • Discussion of emerging technologies for supporting spatial decision makers. Geographic Information System (GIS) technology provides powerful software tools for analyzing maps, charts, and other spatial data. It is differentiated from traditional Data Base Management Systems (DBMS) and Computer Aided Design (CAD) systems. This differentiation is a result of its unique ability to manipulate existing spatial data and "synthesize" new spatial entities that are explicit representations of spatial relations previously only implicit in the database. Chapter 1 gives an overview of GIS technology. GISs equipped with powerful tools for spatial analysis have been available for two decades, yet they still lack ways of recording the most basic data quality information and viewing the most fundamental data dependencies and lineage relationships between maps stored in their databases. This is because GISs are poor in capabilities for handling information about spatial analysis and the new information that results from it. This results in communication and coordination problems between GIS staff members and subsequently a significant drop in productivity. Chapter 2 details a series of critical gaps resulting from these problems. Mr. Jack Kawashirna of SCE believed that intelligent computer systems could play a number of roles required to fill the gaps resulting in lost productivity. In his vision he saw increased levels of automation leading to higher GIS productivity resulting from a sharing of responsibility between human operators and Intelligent Assistant computer systems. The research agenda presented in Chapter 3 focuses on applying artificial intelligence techniques of expert and related systems to create Intelligent Assistant systems to support the GIS user with capabilities for coping with the critical 'gaps. Six principal types of support roles are proposed for Intelligent Assistants. These range from providing "no assistance" in using the GIS (the current situation), to "complete assistance" where the needs of decision makers are met automatically without intervention by a human GIS user. In order to design intelligent computer systems to play the different roles existing between the extremes it will be necessary to take advantage of emerging expert systems, knowledge acquisition, and case-based reasoning technologies. These technologies are discussed in Chapter 4. 1. GEOGRAPHIC INFORMATION SYSTEMS A Geographic Information System (GIS) is computer software designed to collect, store, retrieve, manipulate, and display cartographic features defined as points, lines, or areas (Tomlinson et al. 1976; Marble 1984; Clarke 1986; and Dueker 1987). The GIS's unique ability to manipulate existing data and "synthesize" new spatial entities separates it from other information processing technologies (Burrough 1986; Cowen 1988). This is based on software functions for transforming and combining cartographic data acquired from maps of differing spatial coordinate and projection systems. Once the geographic features are placed in common geographic coordinate and projection systems they can be organized in the GIS database. GIS data is typically organized in one of three ways: (1) Least Common Geographical Unit (LCGU), (2) objects, and (3) layers. Least Common Geographical Units, also known as Integrated Terrain Units (ITUs) combine and integrate all spatial data into a single spatial data record containing all feature and attribute classes (Chrisman 1975). Object-oriented organizations integrate individual geographic features and their attributes into semantically defined objects with inheritable properties (Kjerne and Dueker 1986; Charlwood et al. 1987; Herring 1987; Gahegan and Roberts 1988; Egenhofer and Frank 1989). LCGU and object-oriented approaches represent a very small minority of systems'. However, they are finding favor among their users. Layer-based systems represent the most widely used and understood GIS. Ibis is because the organization of data fe atures into thematic layers closely corresponds with the way geographic data has been handled for centuries by cartographers. Layer-based GIS databases allow the users to visualize the cartographic database as a series of registered map separations. Spatial relationships between relevant geographic entities can be analyzed, in such systems, with the help of neighborhood and overlay functions (Chrisman and Niemann 1985; Kjerne and Dueker 1986; Aronson 1987; Bracken and Webster 1989). Examples of layer-based GIS include Environmental Science Research Institute's vector-based ARC/INFO, Tomlin's raster-based Map Analysis Package (Tomlin 1980), and Intera Tydac's quadtree based SPANS. In any of the above organizational approaches, geographic features have characteristics that exist in three dimensions: (1) the spatial dimension captures the location of the phenomena, (2) the aspatial dimension captures descriptive attributes about the phenomena, and (3) the temporal dimension captures the time and duration of the phenomena (Berry 1964). The Spatial Dimension Vector and tessellation models offer two alternative successful approaches for storing and processing spatial data in GIS (Peucker and Chrisman 1975; Peuquet 1984; Maffini 1987). Vector approaches represent graphical aspects of geographic features as points, lines, and polygons with strings of coordinate X,Y pairs. The evolution of vector data models has witnessed an increase in the amount of spatial information explicitly represented in the point, line, and polygon data structures. This evolution has moved from a simple spaghetti data structure, where no information about the entities is encoded in the data structure, to the entity-by-entity, point dictionary, and chain-code structures encoding single polygons without regard for neighboring spaces. To address the concern for neighborhoods the topological vector data structure was introduced with its evolution from GBF/DIME , to POLVRT structures. In contrast to vector data models, tessellation models organize point, line, and polygonal entities within a mosaic of geometric tiles. Each tile representing a homogenous region in space characterized by the absence or presence of some geographical entity. 'Me geometry of the tile may be regular squares, triangles, or hexagons which may be nested in hierarchies subdividing large non-homogeneous areas (e.g. quad-trees). Of the three, square (raster and grid cell) tessellations are the most popular spatial data organization in non-vector GIS. Peuquet (1984) discusses the strengths and weaknesses of the vector and raster models. She concludes that "neither type of data model is intrinsically a better representation of space. The representational and algorithmic advantages of each are data and application dependent, even though both theoretically have the capability to accommodate any type of data or procedure." AspatiaI Dimension Spatial data are linked with their aspatial attributes to describe relevant characteristics of geographic features. Separating aspatial attributes from the graphic representation permits retrieval and display of geographic features meeting a combination of spatial and thematic query criteria. Aspatial characteristics such as type, material, names, etc. can be stored in either the hierarchical, network, or relational data models. The hierarchical model organizes data in a tree structure. It is made of links connecting nodes in branches. Each node represents an entity and consists of collections of descriptive attributes. The oldest data base systems (DBMSs) are based on hierarchical data organization. Examples include, IBM's IMS, Intel's System 2000, and Informatic's Mark IV. Hierarchical systems are built on rigid tree structures that are not easily reorganized and promote redundant encoding and storage of attributes describing features stored in different nodes within different branches of the tree. They do, however, provide for rapid partitioning of data sets and retrieval of stored data. The network data model, in contrast, organizes attribute data as nodes interconnected by a network of directed labelled links. Examples of commercial network DBMS's include Cullinane's IDMS, Cincom's TOTAL, Honeywell's IDS/II, UNIVAC's 1100, and DEC's DBMS-20. Network-based systems are more powerful than hierarchical systems in modelling real world data relationships without redundancy. This power, however, is balanced by navigation and update complexity resulting in a detailed understanding of the complex web of nodes and pointers as a prerequisite of data retrieval. The relational data model uses tables to organize attributes of entities and relationships between geographic features. 'Me use of tables to organize feature attributes makes the relational data model easy to understand focusing attention on the database's information content. The hierarchical and network models, in contrast, force users to pay attention to the physical storage of data in order to access its information content. The relational model is also preferable in that it supports database normalization to remove redundant information. In addition, the relational model is based on a solid mathematical theory of relations not present for the other data models. Power associated with ease of understanding, database normalization, and mathematical basis is offset by the high cost in computational overhead that threatens to slow down the processing of large databases. Most commercial GIS's however use a relational DBMS to provide their users with flexible organizational and query capabilities. Examples of relational DBMSs used with GIS are Henco's INFO, Relational Technologies' INGRES, and Oracle Corporation's ORACLE. entail...