Effective Methods of Semantic Analysis in Spatial Contexts

Abstract

With the growing spread of spatial data, exploratory analysis has gained a considerable amount of attention. Particularly in the fields of Information Retrieval and Data Mining, the integration of data points helps uncover interesting patterns not always visible to the naked eye. Social networks often link entities that share places and activities; marketing tools target users based on behavior and preferences; and medical technology combines symptoms to categorize diseases. Many of the current approaches in this field of research depend on semantic analysis, which is good for inferencing and decision making. From a functional point of view, objects can be investigated from a spatial and temporal perspectives. The former attempts to verify how proximity makes the objects related; the latter adds a measure of coherence by enforcing time ordering. This type of spatio-temporal reasoning examines several aspects of semantic analysis and their characteristics: shared relationships among objects, matches versus mismatches of values, distances among parents and children, and bruteforce comparison of attributes. Most of these approaches suffer from the pitfalls of disparate data, often missing true relationships, failing to deal with inexact vocabularies, ignoring missing values, and poorly handling multiple attributes. In addition, the vast majority does not consider the spatio-temporal aspects of the data. This research studies semantic techniques of data analysis in spatial contexts. The proposed solutions represent different methods on how to relate spatial entities or sequences of entities. They are able to identify relationships that are not explicitly written down. Major contributions of this research include (1) a framework that computes a numerical entity similarity, denoted a semantic footprint, composed of spatial, dimensional, and ontological facets; (2) a semantic approach that translates categorical data into a numerical score, which permits ranking and ordering; (3) an extensive study of GML as a representative spatial structure of how semantic analysis methods are influenced by its approaches to storage, querying, and parsing; (4) a method to find spatial regions of high entity density based on a clustering coefficient; (5) a ranking strategy based on connectivity strength which differentiates important relationships from less relevant ones; (6) a distance measure between entity sequences that quantifies the most related streams of information; (7) three distance-based measures (one probabilistic, one based on spatial influence, and one that is spatiological) that quantifies the interactions among entities and events; (8) a spatio-temporal method to compute the coherence of a data sequence.