In the case of raster graphics with coarse spatial resolution, the data values associated with specific locations are not necessarily explicit in the raster data model. An effective pixel resolution will take both the map scale and the minimum mapping unit of the other GIS data into consideration. A raster model with pixels representing 10 m by 10 m (or 100 square meters) in the real world would be said to have a spatial resolution of 10 m a raster model with pixels measuring 1 km by 1 km (1 square kilometer) in the real world would be said to have a spatial resolution of 1 km and so forth.Ĭare must be taken when determining the resolution of a raster because using an overly coarse pixel resolution will cause a loss of information, whereas using overly fine pixel resolution will result in significant increases in file size and computer processing requirements during display and/or analysis. Specifically, resolution is determined by measuring one side of the square pixel. The area covered by each pixel determines the spatial resolution of the raster model from which it is derived. Therefore, the more area covered per pixel, the less accurate the associated data values. The raster model will average all values within a given pixel to yield a single value. The advance of computer technology has made this second methodology increasingly feasible as large datasets are no longer constrained by computer storage issues as they were previously. Alternatively, the raster graphic can reference a database management system wherein open-ended attribute tables can be used to associate multiple data values to each pixel. The data type for that cell value can be either integer or floating-point (Section 5.1). Each cell in a raster carries a single value, which represents the characteristic of the spatial phenomenon at a location denoted by its row and column. Typically, a single data value will be assigned to each grid locale.
RASTER AND VECTOR DATA MODEL IN GIS SERIES
Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD.īecause of the reliance on a uniform series of square pixels, the raster data model is referred to as a grid-based system. Figure 4.3 Common Raster Graphics Used in GIS Applications: Aerial Photograph (left) and USGS DEM (right) These squares are typically reformed into rectangles of various dimensions if the data model is transformed from one projection to another (e.g., from State Plane coordinates to UTM coordinates). Accordingly, the vast majority of available raster GIS data are built on the square pixel (Figure 4.3). Although pixels may be triangles, hexagons, or even octagons, square pixels represent the simplest geometric form with which to work. These pixels are used as building blocks for creating points, lines, areas, networks, and surfaces (Figure 2.6 illustrates how a land parcel can be converted to a raster representation). The raster data model consists of rows and columns of equally sized pixels interconnected to form a planar surface. If you are as generous as the author, you may indeed think of your raster dataset creations as sublime works of art. The neoimpressionist artist, Georges Seurat, developed a painting technique referred to as “pointillism” in the 1880s, which similarly relies on the amassing of small, monochromatic “dots” of ink that combine to form a larger image (Figure 4.2). Notably, the foundation of this technology predates computers and digital cameras by nearly a century. Figure 4.1 Digital Picture with Zoomed Inset Showing Pixilation of Raster Imageįurthermore, all liquid crystal display (LCD) computer monitors are based on raster technology as they are composed of a set number of rows and columns of pixels. The ubiquitous JPEG, BMP, and TIFF file formats (among others) are based on the raster data model (see Figure 4.1). Most likely, you are already very familiar with this data model if you have any experience with digital photographs. The raster data model is widely used in applications ranging far beyond geographic information systems (GISs). The objective of this section is to understand how raster data models are implemented in GIS applications.
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