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

PSC In The News

RSS Feed icon

Frey says crime alone can't explain why so many black Chicagoans are headed south

Smock cited in amicus brief for Supreme Court case on citizenship rights for foreign-born children of unwed parents

Levy, Buchmueller and colleagues examine Medicaid expansion's impact on ER visits

More News

Highlights

MiCDA Research Fellowship - applications due July 21, 2017

U-M awarded $58 million to develop ideas for preventing and treating health problems

Bailey, Eisenberg , and Fomby promoted at PSC

Former PSC trainee Eric Chyn wins PAA's Dorothy S. Thomas Award for best paper

More Highlights

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