Eigenvector spatial filtering for continuous space

Abstract

Eigenvector spatial filtering (ESF) is a relatively new technique that considers spatial autocorrelation. It is a practical technique that can be easily implemented using standard statistical software packages and can be easily combined with other statistical methods such as general linear model, mixed effect model and so on, and so, applications of ESF is expanding more and more. However, ESF is restrictive in that it cannot consider continuity of space, and therefore, it cannot be applied to spatially continuous variables consistently. In this study, we extend ESF so as to consider the continuity of space. The extended method is practical as same as conventional ESF. To confirm the effectiveness of our method, our method, linear regression model, and kriging (a geostatistical method) are compared using a case study of land price modeling.