GIS Spatial Data Structure
Spatial Data structure refers to the form in which Spatial Data is stored and managed in the computer. In GIS, there are two commonly used Spatial Data structures: Vector Data structure and Raster Data structure. Vector Data structure is a kind of data organization that uses points, lines, surfaces and their combinations in geometry to represent the spatial distribution of geographical entities. Raster Data structure is the simplest and most direct Spatial Data structure, which divides the earth's surface into uniform and closely adjacent grid arrays, each grid is defined by rows and columns as a pixel or pixel, and the position of each pixel is determined by row and column numbers. The non-Geometric properties characteristics (such as elevation, temperature, and so on) of the figure or phenomenon at this location are represented by the values in the cell.
Raster Data can be a digital aerial photograph, a Satellite Image, a digital elevation model, a digital orthophoto, or a scanned map. Raster Data is mostly used in the study of regional problems such as natural resources, environment, agriculture and forestry in a large range and small scale. The most common Vector Data includes point data, line data and surface data, which are mostly used in urban zoning or detailed planning, land management, utility management, etc.
Difference between Vector Data and Raster Data
Raster Data can represent both discrete and continuous geographic entities, while Vector Data can represent continuous geographic entities. In comparison, it is very suitable for spatial continuous data, such as elevation, temperature, meteorology, environment, etc. A comparison of the vector and Raster Data structures is shown in the following table:
| Advantage | Shortcoming | |
| Vector Data | Compact structure and low redundancy; | The data structure is complex, each definition is not convenient for data standardization and normalization, and the data exchange is difficult; |
| Facilitating the description of lines or boundaries; | Difficulty in polygon overlay analysis; | |
| Facilitating network and index analysis, providing effective topological coding, and being more effective for operations requiring topological information; | Poor ability to express spatial variability; | |
| The graphic display has good quality and high precision. | The technical requirements of software and hardware are high, and the cost of display and drawing is high. | |
| Raster Data | Imple structure and easy data exchange; | Difficult to express topology; |
| Overlay analysis and geographic phenomenon simulation are easy; | Because of the large amount of graphic data and the uncompact data structure, compression technology is needed to solve this problem; | |
| Facilitating the application and analysis of remote sensing data and facilitating image processing; | Difficulty in Projection Transformation; | |
| The output is fast and the cost is low; | Graphic quality is low, graphic output is not beautiful, lines are jagged, need to increase the number of grids to overcome, but will increase the number of data files. |