Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Frey's Scenario F simulation mentioned in account of the Democratic Party's tribulations

U-M Poverty Solutions funds nine projects

Dynarski says NY's Excelsior Scholarship Program could crowd out low-income and minority students

More News

Highlights

Workshops on EndNote, NIH reporting, and publication altmetrics, Jan 26 through Feb 7, ISR

2017 PAA Annual Meeting, April 27-29, Chicago

NIH funding opportunity: Etiology of Health Disparities and Health Advantages among Immigrant Populations (R01 and R21), open Jan 2017

Russell Sage 2017 Summer Institute in Computational Social Science, June 18-July 1. Application deadline Feb 17.

More Highlights

Next Brown Bag

Mon, Jan 23, 2017 at noon:
Decline of cash assistance and child well-being, Luke Shaefer

Local spatial modeling of white-tailed deer distribution

Publication Abstract

Shi, H., E.J. Laurent, J. LeBouton, L. Racevskis, K.R. Hall, M. Donovan, M.B. Doepker, M.B. Walters, F. Lupi, and Jianguo Liu. 2006. "Local spatial modeling of white-tailed deer distribution." Ecological Modelling, 190(1-2): 171-189.

Complex spatial heterogeneity of ecological systems is difficult to capture and interpret using global models alone. For this reason, recent attention has been paid to local spatial modeling techniques. We used one local modeling approach, geographically weighted regression (GWR), to investigate the effects of local spatial heterogeneity on multivariate relationships of white-tailed deer distribution using land cover patch metrics and climate factors. The results of these analyses quantify differences in the contributions of model parameters to estimates of deer density over space. A GWR model with local kernel bandwidth was compared to a GWR model with global kernel bandwidth and an ordinary least-squares regression (OLS) model with the same parameters to evaluate their relative abilities in modeling deer distributions. The results indicated that the GWR models predicted deer density better than the traditional ordinary least-squares model and also provided useful information regarding local environmental processes affecting deer distribution. GWR model comparisons showed that the local kernel bandwidth GWR model was more realistic than the global kernel bandwidth GWR model, as the latter exaggerated local spatial variation. The parameter estimates and model statistics (e.g., model R-2) of the GWR models were mapped using geographic information systems (GIS) to illustrate local spatial variation in the regression relationship and to identify causes of large-scale model misspecifications and low estimation efficiencies.

DOI:10.1016/j.ecolmodel.2005.04.007 (Full Text)

Browse | Search : All Pubs | Next