MULTICOLLINEARITY IN REGRESSION MODELS WITH MULTIPLE DISTANCE MEASURES*
在多中心城市中，多个距离变量可能高度相关导致空间多重共线性，本文证明通过精心选择观测的地理范围可以避免或减轻该问题。
ABSTRACT. In a polycentric urban context, several urban nodes may exert an influence over land rents. Where this is the case, regression analysis to explain land rents should employ distance variables corresponding to each of the urban nodes. However, these distance measures may be highly intercorrelated, thereby posing a problem of “spatial multicollinearity.” This paper demonstrates that problems arising from spatial multicollinearity can be avoided or substantially lessened by carefully selecting the geographic domain from which observations are drawn.