ANALYSIS OF COUNT DATA USING POISSON REGRESSION*

In geographical research the data of interest are often in the form of counts. Standard regression analysis is inappropriate for such data, but if certain assumptions are met, a form of regression based on the Poisson distribution can be used. This paper illustrates the use of Poisson regression in the computer package GLIM with an example from historical geography. Apprentice migration to Edinburgh is regressed on a combination of categorical, count, and continuous explanatory variables.     

REVIEWS OF GEOGRAPHIC SOFTWARE

CHAOS CLlM 3.77 LP88 and TPR088 MAPEDIT PRODESIGN II RANDMAP SPATIAL AUTOCORRELATION SIMULATION GAME . Daniel A. Griffith STATA THINKTANK NOTEBOOK II     

Generalizing Poisson Regression: Including Apriori Information Using the Method of Offsets*

In a recent note in The Professional Geographer, Lovett and Flowerdew (1989) introduce the analysis of count data using Poisson regression. Concern here rests with generalizing their discussion to show how a priori information may be included into models that incorporate dependent variables enumerated as counts. These models can be calibrated using the OFFSET command in GLIM. The paper concludes with a brief discussion of the applicability of the method of offsets to other problems in spatial analysis.