Widespread use of spatial databases[24] is leading to an increasing interest in mining interesting and useful but implicit spatial patterns[14, 17, 10, 22]. Efficient tools for extracting information from geo-spatial data, the focus of this work, are crucial to organizations which make decisions based on large spatial data sets. These organizations are spread across many domains including ecolo...

Spatial locations can be extracted from language statistics, based on the idea that nearby locations are mentioned in similar linguistic contexts, akin to Tobler’s first law of geography. However, the performance of language-based estimates is inferior to human estimates, raising questions about whether human spatial representations can actually be informed by such (inferior) statistics. We sho...

Most spatial data in GIS are not independent, they have high autocorrelation. For example, temperatures of nearby locations are often related. Most of the spatial association rule mining algorithms derived from the attribute association rule mining algorithms which assume that spatial data is independent. In these situations, the rules or knowledge derived from spatial mining will be wrong. It ...

An evaluation of spatial patterns and a clustering play an important role among methods of spatial statistics. However, traditional clustering techniques are seldom suitable for analyses of spatial data and patterns because they usually do not count on spatial relations and qualities of objects. This paper aims to introduce usage of methods of spatial clustering estimation, which are based main...

In disaster insurance and reinsurance, GIS has been used to visualize and manage geospatial data and to help vulnerability and risk analysis for years. However, hazard insurance is a multidisciplinary issue that involves complex factors and uncertainty. GIS, if used alone, has limited functionality due to poor incorporation of intelligence and spatial statistics. The Spatial Decision Support Sy...

Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographic information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfac...

Significant research effort has been focusing on using GIS for advanced spatial statistics, modeling, and simulation. This paper argues that even though GIS have great potential to facilitate sophisticated spatial modeling and spatial statistics, the simple but important theme of combining spatial information with statistical analysis has not received enough attention and should not be neglecte...

Studying the temporal and spatial evolution trends in earthquakes an area is beneficial for determining earthquake risk of so that local governments can make correct decisions disaster prevention reduction. In this paper, we propose a new method analyzing based on magnitude 3.0 or above from 1980 to 2019 California Nevada. The experiment’s results show (1) frequency events 4.5 present relativel...