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

PSC In The News

RSS Feed icon

Yang comments on importance of migrant remittances to future of recipient families

Frey says America's black population is changing with recent immigration

Bailey and Danziger's War on Poverty book reviewed in NY Review of Books

Highlights

Cheng wins ASA Outstanding Graduate Student Paper Award

Hicken wins 2015 UROP Outstanding Research Mentor Award

U-M ranked #1 in Sociology of Population by USN&WR's "Best Graduate Schools"

PAA 2015 Annual Meeting: Preliminary program and list of UM participants

Next Brown Bag

Mon, May 18
Lois Verbrugge, Disability Experience & Measurement

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