Abstract
ABSTRACT This paper investigates spatial effects in a generalized least squares regression model for small‐area social indicators. The significance of using spatial correlation in addition to an intermediate weighting (allowing for sampling and extraneous variation in the indicator) differs according to the index analyzed. It is greater for small‐area fertility (over 75S London wards) than for small‐area mortality. The model is extended in include serial as well as spatial correlation, then to spatial forecasting. The analyses show that the extent of spatial correlation can vary considerably according to (a) the specification of the form of spatial interaction, and (b) which independent variates are included. Further, spatial effects can be much reduced by including serial correlation.
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