The Vector Topological Data Model in the Geographical Information Systems - Term Paper Example

Name: Tutor: Title: The Vector Topological Data Model in GIS and how it underpins the widely used Spatial Analysis Techniques. Course: Date: Introduction Topology is an important model, particularly where the vector data model is applied to analyze spatial geographical data. It is a mathematical method commonly used to define the spatial relationships that enables most of the Geographical Information Systems (GIS) to structure data-based on in regard to the geographical principles of feature adjacency and connectivity. Therefore, topological data model is increasingly used in that software that implements a complete range of operations carried out on the vector representations. It has been reported that the topological model comprises of network relationships in addition to the coordinate measurements (Chrisman 1997). It is the nature of the topological data structure that determines the manner which and where the points and lines connect. This implies that the order of points and lines connectivity defines the shape of either an arc or a polygon, indicating that without the topologic data structure, manipulation and analysis of the data capability of a GIS would not be achievable. A polygon in a topological model, therefore, is identified by a series of arcs that start and end at nodes, which occur where arcs meet. Each arc is identified by a group of coordinate pairs which start and end with node. GIS software, therefore, stores the topological definitions of geographical data in three different tables to represent the different features such as point, line, and area. On the one hand, the node table captures and stores information concerning the nodes and arcs which are connected to it. On the other hand, the arc table stores topological information about the arcs, inclusive of the start and end nodes as well as the polygons on both the right and left of each arc. It is apparent that the polygon table represents the arcs that structure up each polygon as illustrated below (Chrisman 1997). One of the significant examples of how the Geographic Information Systems (GIS) can be utilized to model the real world events is its capability to model networks. Since there are several networks in the geographical data, for instance, water courses and the road networks, network analysis enhanced with topological modeling can be used to analyze the possible flow around such networks. It is useful particularly in flood analysis commonly referred to as route finding. However, it can only work out if the data is modeled using the correct network topology (DeMers 1997). GIS are conventionally identified as systems that collect, store, manipulate and disseminate geographical information that is tied into certain set of some locations on the earth’s surface, inclusive of those directly adjacent such as the sub-surface, oceans and environment. Spatial has become a commonly used synonym in geographical analysis of data.  Similarly, the term ‘geospatial’ is gaining popularity which also illustrates the trend towards the convergence of various spatial technologies such as aerial and remote sensing, context-aware computing as well as the Global Positioning Systems (Bailey & Gatrell 1995). It is relevant to argue that GIS was developed based on the three principal roots which include the need for geographical data analysis and reporting tools, the computerization of map production, introduction of landscape architecture and to promote environmentally sensitive planning. Spatial data requires topological component so as to completely describe the relationships between the existing geographic entities. This comprise of the connections between lines of a network which may include rivers as well as the geometric correlations between polygons within the vector theme. A number of large GIS systems provide appropriate mechanisms to store and manage all geographical data either ‘in-house’ or simply by creating links with some external data sources, particularly where a polygon on a given GIS layer is linked to certain event record within the conventional database (Connolly &Lake 2006). In the vector data modeling, the spatial database stores geometric description information of the theme in discussion, while a raster representation which are regularly spaced samples of a subject are stored. Therefore, vector data is similar to data within the CAD package in which each element in a layer is represented in form of some geometric entity, for instance, point, line, or polygon. It is apparent that the process of creating the vector data is comparable to drawing with digitizing tablet or illustrating objects on a computer screen which proves to be time consuming and more expensive. However, it is more advantageous because it provides a compact data-storage format that allows for scalable presentation. Being based on geometric objects, it is straightforward to link these to text-based records. Vector representation of geographical data permits easy quantification of areas as well as certain analytical methods, including the network analysis. For example, Ordnance Survey Landline mapping (OSLM) collected at the base scales of 1:1,250, 1:2,500 and 1:10,000 is a good example of quantified urban, rural, upland or moorland areas respectively as representation of vector data containing distinctive layers (Gillings, Mattingly & van Dalen 1999). Typical vector applications, therefore, include the spatially referenced database applications, such as the location maps, sites, monuments and work of arts. This involves mapping applications as well as managing networks such as roads, utilities and terrain analysis by use of a network of triangles commonly known as a Triangulated Irregular Network (TIN) elevation models. Many themes can be clearly represented by either by use of the vector or raster data models. For example, Terrain can be illustrated either by a vector model, TIN or a raster altitude matrix where each cell contains the altitude at that particular location. The selection of representation pattern depends on a range of key factors such as the capability of GIS or the software, availability of the source data as well as the intended objectives of the data (Lock 2000). Furthermore, in a vector model, objects or conditions within the real world are identified by the points and lines that clearly define their boundaries to appear as if they are drawn on a map. Figure: SMR overlay showing same Designed Landscape as an archaeological site Therefore, with the vector representation, most of the course features are illustrated through a series of points that, if they can be joined with straight lines based on the graphic representation of their features. Such points are encoded themselves with paired numbers creating their X, Y coordinates within a real world of map projection. The non-spatial attributes of the identified features are, therefore, stored enabled by a predictable database management system. The association between the spatial data files and attribute data file may be used as a simple identifier number to uniquely show each feature in a map (Unwin 1996). Vector model is further subdivided into three major sub-categories to include spaghetti model, topological model and the triangular irregular network (TIN). However, the topological model which represents a sub-division in form of nodes, faces, lines and bodies is richer than spaghetti node. As a result, an increasing number of efficient implementation of geographical operations which require some topological information, for instance, the efficiency of an organized spatial data, spatial query and analysis, spatial is reasoning as well as consistency test. Therefore, it is important to pay some spatial attendee to the 3D topological data model. The vector data model is the first model used to indicate geographical space referred to as vector, allows geographers to give specific spatial locatitions in a more explicit manner (Gillings, Mattingly & van Dalen 1999). Therefore, the vector data structure represents locations dimensionally as it would be identified on a map. This means that the vector data model is useful in providing the precise positioning of various features in space. Depending on the analytical geometry, the vector model creates a complex representation from simply primitive objects to make dimensions such as points, lines and areas. Advantages of using the Vector topology data model is appropriate for spatial representation of compact data structure, accurate graphics, updating and editing is enhanced. Some of the disadvantages of its application include complex data structure, overlaying combinations which becomes more difficult to implement, it is not suitable for digital images, plotting prove to be expensive and time consuming particularly with area fills and the technology used is more expensive (DeMers 1997). The concept of error and uncertainty has been the subject in the modern techniques of the spatial information science that trigger GIS. Unwin (1995) argued that although different types and sources of both error and uncertainty within the geographical data as well as their processing exists, in essence the problem is not technical, instead it arises from the evident inability of the applied GIS technology to adequately represent the how the really world is in a manner that informs much of geographical theory. Unwin added that it is the previous problems of geographical description, thus much of the contemporary social theoretical critique of GIS are based on it (Unwin 1995). The identification as well as measurement of spatial clustering of the geographical variable has become a focal issue within the positive and exploratory analysis of spatial data. Fischer (1999) examined that various efforts have been made as a part of the broader attempt to present spatial data as general statistics by considering that regular statistical assumptions rarely hold for spatial data. For instance, various data points within the geographically located data sets do not operate independent from each other because of the spatial auto-correlation or spatial dependence. Therefore, spatial distributions in general display important local variations from the available discrete spatial regimes in a given area of study (Fischer 1999). Based on the spatial turn of spatial analysis, Unwin (1995) argument on the development of a bi-variate spatial association measure is that whereas the uni-variate relationship measures focus more on the spatial clustering of various observations regarding a single variable, the relationship between two variables is captured by a bivariate spatial association measure. However, topological correlation among the observations must be considered. A numeric vector identified with n data points with unique values can result in n different permutations, each with a distinct order of the numeric vector may generate different patterns with different levels of univariate spatial clustering (Unwin 1995). The role of GIS in visualization, exploration and modeling of Public and Environmental Health data Geographical Information System (GIS) plays a major role in all aspects of health research, ranging from description to exploration of the spatial variation of various diseases and illness, planning and delivery of health services (Gatrell & Senior 1999). For example, geographical visualization and exploratory data analysis is a topological technique commonly applied within the health research contexts to model the health outcomes based on the environmental covariates. Vector-based GIS is used to investigate how blood flows within the micro-vascular networks and the associated problems at more predictable geographical scales. This implies that various methods as well as techniques of the spatial data analysis enabled with GIS can clarify a wide variety of the health problems, ranging from microscopic to global scales (Roth & Kiani 1999). Exploratory visualization of the health data within the spatial setting involves the integration of various tools designed to map in a more imaginative manner, the spatially-referenced data as well as analyze that particular data to detect patterns, identify clusters and isolate outliers (Haining 1998). The integration of exploratory and visualization of the health data within a spatial setting by use of GIS is enhanced through application of new tools that were developed in the dynamic graphics works originating from Bell Labs in the USA. With this technique, plots in a single window are linked to others so that the interactive selection of sub-set data in a single view, for instance, a histogram causes automatic selection of the corresponding sub-set within the other views which may include scatter-plots and geographic maps (Gatrell 1999). Conclusion A number of large GIS systems provide appropriate mechanisms to store and manage all geographical data either ‘in-house’ or as links created with some external data sources. Topology is an essential model particularly where the vector data model is used to analyze spatial geographical data. Therefore, without the topologic data structure, manipulation and analysis of the data capability of a GIS would not be achievable. Spatial data requires topological component so as to completely describe the relationships between the existing geographic entities. Vector representation of geographical data permits easy quantification of areas as well as certain analytical methods, including the network analysis. Typical vector applications include the spatially referenced database applications, such as the location maps, sites, monuments and work of arts. The identification and measurement of spatial clustering of the geographical variable has become a crucial issue within the positive and exploratory analysis of spatial data. Based on the analytical geometry, the vector model creates complex geographical data representations from simply primitive objects to make dimensions such as points, lines and areas. The vector data structure represents locations dimensionally as it would be identified on a map, and thus useful in the provision of precise positioning of various features in space. Various data points in geographically located data sets do not operate independent from each other because of the spatial auto-correlation or spatial dependence. Visualization and exploratory data analysis is a topological technique commonly used in the health research contexts to model the health outcomes based on the environmental variables. 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(1998) Spatial statistics and the analysis of health data, pp. 29-47 in Gatrell, A.C. and Löytönen, M. (eds) GIS and Health, Taylor and Francis, London. Lock, G. (ed) (2000) Beyond the Map: Archaeology and spatial technologies. Nato Science Series, Series A: Life Sciences – Vol. 321. Oxford: IOS Press. Roth, N.M., Kiani, M.F. (1999) A 'geographic information systems' based technique for the study of micro-vascular networks, Ann Biomed Eng, 27, 42-7. Unwin DJ., (1995) Geographic Information Systems and the problem of 'error and uncertainty' Progress in human geography 19, 549-558. Unwin, D., (1996) Integration through overlay analysis. In: M. Fischer, H.j. Scholten and D. Unwin, eds, Spatial analytical perspectives On GlS. London: Taylor &. Francis, 1996, 129-138. Read More