|제목||[학술세미나] [학과세미나] 5월 11일(금) 11시 학과세미나 안내|
|내용||[학과세미나] 5월 11일(금) 11시 학과세미나 안내
▪제목 : Accounting for and predicting the influence of spatial autocorrelation in modeling the distribution of natural resources
▪연사 : 김대현 (서울대 지리학과)
▪일시 : 2018년 5월 11일(금) AM 11:00 – 12:00
▪장소 : 25동 405호
Although numerous environmental and ecological modeling investigations have documented the effects and importance of spatial autocorrelation (SAC), little is known about how to predict the magnitude of those effects from the degree of SAC in the model variables. In this study, I quantified the SAC inherent in soil, landform, water quality, and anthropogenic variables of many widely divergent pedogeomorphological and hydrological systems around the world to examine general relationships between SAC and spatial regression model results. A suite of spatial regressions were performed by incorporating various spatial factors, such as filters, lag, and error, into non-spatial models as additional independent variables. Results indicated that incorporation of these spatial factors improved the performance of the non-spatial regressions—increases in R2 and decreases in both Akaike Information Criterion and residual SAC were observed. More remarkable was that the degree of improvement was strongly and linearly related (i.e., proportional) to the level of SAC inherently possessed by each dependent variable. These findings show that spatial modeling outcomes are sensitive to the degree of SAC possessed by a dependent variable of interest. Thus, the level of SAC present in a dependent variable can serve as a direct indicator for how much improvement a non-spatial model will undergo if that SAC is appropriately taken into account.
세미나 안내_180511_김대현.hwp [14.5KB]