Filed in GIS Data by Caitlin Dempsey on May 1, 2012
GIS data can be separated into two categories: spatially referenced data which is represented by vector and raster forms (including imagery) and attribute tables which is represented in tabular format. Within the spatial referenced data group, the GIS data can be further classified into two different types: vector and raster. Most GIS software applications mainly focus on the usage and manipulation of vector geodatabases with added components to work with raster-based geodatabases.
Vector data is split into three types: polygon, line (or arc) and point data. Polygons are used to represent areas such as the boundary of a city (on a large scale map), lake, or forest. Polygon features are two dimensional and therefore can be used to measure the area and perimeter of a geographic feature. Polygon features are most commonly distinguished using either a thematic mapping symbology (color schemes), patterns, or in the case of numeric gradation, a color gradation scheme could be used.
In this view of a polygon based dataset, frequency of fire in an area is depicted showing a graduate color symbology.
Line (or arc) data is used to represent linear features. Common examples would be rivers, trails, and streets. Line features only have one dimension and therefore can only be used to measure length. Line features have a starting and ending point. Common examples would be road centerlines and hydrology. Symbology most commonly used to distinguish arc features from one another are line types (solid lines versus dashed lines) and combinations using colors and line thicknesses. In the example below roads are distinguished from the stream network by designating the roads as a solid black line and the hydrology a dashed blue line.
Streams are shown as dashed blue lines and roads as solid black lines in this example.
Point data is most commonly used to represent nonadjacent features and to represent discrete data points. Points have zero dimensions, therefore you can measure neither length or area with this dataset. Examples would be schools, points of interest, and in the example below, bridge and culvert locations. Point features are also used to represent abstract points. For instance, point locations could represent city locations or place names.
GIS point data showing the location of bridges and culverts.
Both line and point feature data represent polygon data at a much smaller scale. They help reduce clutter by simplifying data locations. As the features are zoomed in, the point location of a school is more realistically represented by a series of building footprints showing the physical location of the campus. Line features of a street centerline file only represent the physical location of the street. If a higher degree of spatial resolution is needed, a street curbwidth file would be used to show the width of the road as well as any features such as medians and right-of-ways (or sidewalks).
Raster data (also known as grid data) represents the fourth type of feature: surfaces. Raster data is cell-based and this data category also includes aerial and satellite imagery. There are two types of raster data: continuous and discrete. An example of discrete raster data is population density. Continuous data examples are temperature and elevation measurements. There are also three types of raster datasets: thematic data, spectral data, and pictures (imagery).
Digital Elevation Model (DEM) showing elevation.
This example of a thematic raster dataset is called a Digital Elevation Model (DEM). Each cell presents a 30m pixel size with an elevation value assigned to that cell. The area shown is the Topanga Watershed in California and gives the viewer and understand of the topography of the region.
This image shows a portion of Topanga, California taken from a USGS DOQ.
Each cell contains one value representing the dominate value of that cell. Raster datasets are intrinsic to most spatial analysis. Data analysis such as extracting slope and aspect from Digital Elevation Models occurs with raster datasets. Spatial hydrology modeling such as extracting watersheds and flow lines also uses a raster-based system. Spectral data presents aerial or satellite imagery which is then often used to derive vegetation geologic information by classifying the spectral signatures of each type of feature.
Raster data showing vegetation classification. The vegetation data was derived from NDVI classification of a satellite image.
What results from the effect of converting spatial data location information into a cell based raster format is called stairstepping. The name derives from the image of exactly that, the square cells along the borders of different value types look like a staircase viewed from the side.
Unlike vector data, raster data is formed by each cell receiving the value of the feature that dominates the cell. The stairstepping look comes from the transition of the cells from one value to another. In the image above the dark green cell represents chamise vegetation. This means that the dominate feature in that cell area was chamise vegetation. Other features such as developed land, water or other vegetation types may be present on the ground in that area. As the feature in the cell becomes more dominantly urban, the cell is attributed the value for developed land, hence the pink shading.