Spatial Data Models An abstraction of the real world which incorporates only those
properties thought to be relevant to the application at hand,
define specific groups of entities, and their attributes and the
relationships between these entities. A data model is independent of a computer system.
Vector Data model
All geographical data models are methods for recording geographic features’ spatial locations in a database. The use of vectors (directional lines) to represent a geographic feature is referred to as vector storage.
Geographic information systems (GIS’s) have three basic vector types: points, lines, and polygons “Points, Lines, and Polygons”. Points are one-dimensional objects with only one pair of coordinates. Buildings, wells, power poles, sample locations, and other solitary, distinct characteristics are often represented by points. The sole quality that points have is that they are located.
The node and the vertex are two more forms of point characteristics. A point is a single-feature object, whereas a node is a topological junction that represents a shared X, Y coordinate pair between intersecting lines and/or polygons. Each bend along a line or polygon feature that is not the junction of lines or polygons is referred to as a vertex.
Points can be joined spatially to create more complex features. Lines are one-dimensional features made up of many points that are intentionally connected. Roads, streams, faults, boundaries, and other linear characteristics are represented by lines. The property of length exists in lines. Chains, edges, segments, and arcs are terms used to describe lines that connect two nodes directly.
Polygons are two-dimensional features formed by many lines looping back on themselves to form a “closed” feature. The first coordinate pair (point) on the first line segment is the same as the last coordinate pair on the last line segment in the case of polygons. City boundaries, geologic formations, lakes, soil associations, vegetation communities, and other characteristics are represented by polygons.
Vector Data Models Structures
The spaghetti data model
Vector data models can be organized in a variety of ways. Here, we’ll look at two of the most prevalent data structures. The spaghetti data model is the most basic vector data structure (Dangermond 1982). J. “A Classification of Frequently Used Software Components in Geographic Information Systems.” 70–91 in Proceedings of the US-Australia Workshop on Computer-Based Geographic Information Systems Design and Implementation. Honolulu, Honolulu, Honolulu, Honolulu, Hon Each point, line, and/or polygon feature in the spaghetti model is represented as a string of X, Y coordinate pairs with no inherent structure (or as a single X, Y coordinate pair in the case of a vector picture with a single point)
While the location designations associated with each line or strand of spaghetti, spatial linkages are assumed by their placement rather than explicitly stored within the spaghetti model. This results in a lack of topological data, which is problematic if the user wants to make measurements or perform analysis. If any advanced analytical techniques are used on vector files constructed in this way, the processing requirements are extremely high. Nonetheless, because this topological information is unnecessary for plotting and printing, the spaghetti data model’s basic structure enables for rapid reproduction of maps and graphics.
Topological data model
The topological data model, in contrast to the spaghetti data model, is distinguished by the presence of topological information inside the dataset, as the name implies. Topology is a set of principles that specifies how surrounding points, lines, and polygons share geometry and models their interactions. Consider the case of two adjacent polygons. The shared boundary of two surrounding polygons in the spaghetti model is defined as two independent, identical lines. Because topology is included in the data model, a single line can be used to represent the shared border, with an explicit reference indicating which side of the line belongs to which polygon. When shapes are bent, stretched, or subjected to comparable geometric transformations, topology is concerned with retaining spatial features.
Benefits of a Vector Data Model
Without generalization, data can be represented in its original resolution and shape.
Most data, such as hard copy maps, is in vector form, thus no data translation is necessary.
Due to the accuracy and precision of points, lines, and polygons across the regularly spaced grid cells of the raster model, vector data models tend to be better approximations of reality than raster data models. As a result, vector data tends to be more attractive to the eye than raster data.
The flexibility to change the scale of observation and analysis is also improved with vector data. Because each coordinate pair associated with a point, line, or polygon represents an infinitesimally exact location (albeit limited by the number of significant digits and/or data acquisition methodologies), zooming deep into a vector image has no effect on the view of a vector graphic in the same way that it does on a raster graphic.
Because vector data has a more compact data structure than raster data, file sizes are often substantially smaller. Despite the fact that the ability of modern computers has reduced the need of maintaining tiny file sizes, vector data often requires a fraction of the computer storage space required by raster data.
The vector model’s inherent topology is the final advantage of vector data. When employing a vector model, this topological information simplifies spatial analysis (error detection, network analysis, proximity analysis, and spatial transformation).
The vector data model, on the other hand, has two major drawbacks. To begin with, the data structure is far more complicated than a simple raster data model. Because each vertex’s location must be saved.
Vector data model disadvantages
• Each vertex’s location must be stored explicitly.
• Vector data must be transformed into a raster format for optimal analysis.
• Algorithms for manipulating and analytical functions are difficult to understand.
It’s also possible that it’ll take a long time to process. This frequently restricts the options available.
Huge data sets, such as a large number of features, require special capabilities.
• Continuous data, such as elevation information, is ineffective.
depicted as a vector In most cases, significant data generalization is required.
For these data layers, interpolation or extrapolation is required.
• It’s unable to do spatial analysis and filtering within polygons.
The Raster Data Model
A widely used technique of storing geographic data is the raster data model. The most popular approach is a grid-like structure that retains values at regularly spaced intervals over the raster’s length. Rasters are best for storing continuous data like temperature and elevation readings, but they can also store discrete and categorical data like land use. A raster’s resolution is measured in linear units (e.g., meters) or angular units (e.g., one arc second), and it specifies the extent of the grid cell on one side. High (or fine) resolution rasters have a smaller grid size and more grid cells than low (or coarse) resolution rasters, hence they take up more memory to store.
Rasters are widely employed to represent continuous data because they can store values more efficiently than an equivalent vector or point-based lattice system at the point densities necessary. This is because, rather than being explicitly saved, coordinates are stored implicitly as a location in a data table (as coordinates). Rasters, on the other hand, are frequently employed to depict categorical (e.g., land use) or discrete data. In certain circumstances, the area corresponding to the raster’s cell may be combined with more than one pixel type.
The benefits of using a raster data model
• Each cell’s geographic location is deduced from its position in the cell matrix. As a result, no geographic coordinates are maintained other than an origin point, such as the bottom left corner.
• Data analysis is usually simple to program and rapid to execute due to the nature of the data storage technique.
• Raster maps, such as one attribute maps, are well-suited to mathematical modeling and quantitative analysis because to their inherent nature.
• Discrete data, such as forestry stands, is accommodated just as well as continuous data, such as elevation data, and the integration of the two data types is facilitated.
• Raster-based output devices, such as electrostatic plotters and graphic terminals, are extremely compatible with grid-cell systems.
The raster data model has a number of drawbacks in Spatial data models
• The resolution at which the data is represented is determined by the cell size.
• Depending on the cell resolution, it’s especially challenging to accurately represent linear characteristics. As a result, establishing network connections is challenging.
• Processing linked attribute data can be time-consuming if there is a lot of it. Raster maps can only show one attribute or characteristic for a given area. Because the majority of the incoming data is vector, it must be converted to raster. In addition to increasing processing needs, data integrity concerns may arise as a result of generalization and the selection of an unsuitable cell size.
• The majority of grid-cell system output maps do not meet high-quality cartographic requirements.