Outlier Detection and Spatial Analysis Algorithms

TL;DR

This paper surveys various outlier detection methods specifically for spatial data, highlighting their importance in data mining applications like fraud detection, network security, and environmental monitoring.

Contribution

It provides a comprehensive overview of outlier detection techniques tailored for geospatial data, emphasizing their significance and application in diverse fields.

Findings

Outlier detection is crucial for accurate spatial data analysis.

Different methods are suited for various types of spatial outliers.

Proper handling of outliers can improve data quality and decision-making.

Abstract

Outlier detection is a significant area in data mining. It can be either used to pre-process the data prior to an analysis or post the processing phase (before visualization) depending on the effectiveness of the outlier and its importance. Outlier detection extends to several fields such as detection of credit card fraud, network intrusions, machine failure prediction, potential terrorist attacks, and so on. Outliers are those data points with characteristics considerably different. They deviate from the data set causing inconsistencies, noise and anomalies during analysis and result in modification of the original points However, a common misconception is that outliers have to be immediately eliminated or replaced from the data set. Such points could be considered useful if analyzed separately as they could be obtained from a separate mechanism entirely making it important to the research question. This study surveys the different methods of outlier detection for spatial analysis. Spatial data or geospatial data are those that exhibit geographic properties or attributes such as position or areas. An example would be weather data such as precipitation, temperature, wind velocity, and so on collected for a defined region.