Detection of Hotspots on Spatial Data Using Principal Component Analysis

• 발행기관: 한국자료분석학회
• 저자명: Koji Kurihara Fumio Ishioka 문승호(Sungho Moon)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.8 No.2, 447~458쪽, 전체 12쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2006.04.30

영문 초록

Echelon analysis(Myers et al., 1997) is a method to investigate the phase-structure of spatial data systematically and objectively. This method is also useful to prospect the areas of interest in regional monitoring of a surface variable. The spatial scan statistic(Kulldorff, 1997) is a method of detection and inference for the zones of significantly high and low rates based on the likelihood ratio. These zones are called hotspots. Kurihara and Hong(2002) detected the hotspots area for geospatial lattice data based on the echelon analysis. This method is valid for the detection of univariate lattice data. With our approach, we can detect hotspots area for multivariate lattice data. The present paper takes an approach to echelon based on the spatial scan statistics and data reduction method such as principal component analysis(PCA). We apply this method to epidemiological data concerning some causes of death. We will attempt to investigate whether there exist the regional differences in mortality ratio for each cause or not, and if there are regional difference in each cause, we find which area is high mortality ratio or low mortality ratio.

목차

1. Introduction

2. Echelon Analysis

3. Spatial Scan Statistics

4. Detection of Hotspots on Multivariate Spatial Data Using PCA

5. Structure Analysis for Multivariate Spatial Temporal Data

6. Concluding Remarks

References

키워드

hotspots echelon analysis principal component analysis spatial scan statistics