Spatial Clustering Methods

Introduction: Spatial data clustering identifies clusters, or densely populated regions, according to some distance measurement in a large, multidimensional data set, since cluster analysis usually considers spatial data clustering in examples and applications.

Spatial Classification and Spatial Trend Analysis

Spatial classification analyzes spatial objects to derive classification schemes in relevance to certain spatial properties, such as the neighborhood of a district, highway, or river.

Example: Spatial classification. Suppose that you would like to classify regions in a province into rich versus poor according to the average family income. In doing so, you would like to identify the important spatial-related factors that determine a region’s classification.

Many properties are associated with spatial objects, such as hosting a university, containing interstate highways, being near a lake or ocean, and so on. These properties can be used for relevance analysis and to find interesting classification schemes. Such classification schemes may be represented in the form of decision trees or rules, for example, as described in Chapter 6.

Spatial trend analysis deals with another issue: the detection of changes and trends along a spatial dimension. Typically, trend analysis detects changes with time, such as the changes of temporal patterns in time-series data. Spatial trend analysis replaces time with space and studies the trend of non spatial or spatial data changing with space. For example, we may observe the trend of changes in economic situation when moving away from the center of a city, or the trend of changes of the climate or vegetation with the increasing distance from an ocean. For such analyses, regression and correlation analysis methods are often applied by utilization of spatial data structures and spatial access methods.

There are also many applications where patterns are changing with both space and time. For example, traffic flows on highways and in cities are both time and space related. Weather patterns are also closely related to both time and space. Although there have been a few interesting studies on spatial classification and spatial trend analysis, the investigation of spatiotemporal data mining is still in its early stage. More methods and applications of spatial classification and trend analysis, especially those associated with time, need to be explored.

Mining Raster Databases

Spatial database systems usually handle vector data that consist of points, lines, polygons (regions), and their compositions, such as networks or partitions. Typical examples of such data include maps, design graphs, and 3-D representations of the arrangement of the chains of protein molecules. However, a huge amount of space-related data is in digital raster (image) forms, such as satellite images, remote sensing data, and computer tomography. It is important to explore data mining in raster or image databases. Methods for mining raster and image data are examined in the following section regarding the mining of multimedia data.