NDLI: Localizing general models based on local indices of spatial association

Content Provider	Springer Nature Link
Author	Räty, Minna Kangas, Annika
Copyright Year	2006
Abstract	A general regression model for large areas may have poor statistical properties for smaller sub-regions. In this study, we test the local indicators of spatial association (LISA) in the selection of localization areas of a general regression model. We present four different LISAs: Moran’s I i , Geary’s c i , G i , and G i . These indices show if there is a cluster of similar values in the data (Moran’s I i and Geary’s c i ) or if there is a cluster of positive or negative values (G i and G i ). The material is NFI9 (9th National Forest Inventory) sample tree (Pinus sylvestris) data from Southern Finland. LISAs were calculated from the residuals of a form height regression model, which was fitted to the original data. We detected statistically significant clustering of similar values with both global indices Moran’s I and Geary’s c. This means that local indices may show statistically significant clustering of similar values only because the surrounding of an observation happens to have high values. Therefore we use G i -index in selection of sub-areas. We tested the localization in three sub-areas: (1) one where the G i -index was positive, (2) one where the index was negative, and (3) one where the index was both positive and negative and zero. In particular, localization removed the local bias of the global model. The effect of localization on variances was minor. The effect of localization on residuals in Areas 1 and 2 correspond to a level correction of the global model. The G i *-index (and G i -index) seem to be useful for selecting localization areas, even though there is still need for future studies.
Starting Page	279
Ending Page	289
Page Count	11
File Format	PDF
ISSN	16124669
Journal	Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch
Volume Number	126
Issue Number	2
e-ISSN	16124677
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2007-04-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Regression Localization Spatial association Local indicators of spatial association LISA Forestry Plant Sciences Plant Ecology
Content Type	Text
Resource Type	Article
Subject	Plant Science Forestry

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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