Spatial autocorrelation is a very general statistical property of spatial variables, it indicates correlation of a variable with itself through space. Spatial association rule mining, discovery of interesting, meaningful rules in spatial databases, ignores autocorrelation of spatial data, or just generalizes the spatial data into attribute data currently. In most of the ways on spatial association rules mining, they transferred the spatial relations into non-spatial relations by virtue of spatial analysis. This means the separation of spatial autocorrelation from spatial association rule mining. In order to study the relations between spatial autocorrelation and spatial association rule mining, in this paper, the spatial association rules were mined by developed Apriori algorithm. Then, spatial autocorrelation analysis was implemented in the same spatial data set. A basic assumption of many spatial association rules mining is lacking for a priori information about spatial attributes. The two dimensional spatial autocorrelation results were used as priori knowledge in spatial association rules mining in this paper. The experimental data is about the amount of the hay fever (disease caused by pollen allergic rhinitis) patients and its factors, including temperature, precipitation and vegetation types of each county in the United Kingdom in 2000. The obtained frequent itemsets and the spatial association rules prove that factors have stronger correlation with hay fever (correlation coefficient is lager) appear with hay fever simultaneously more frequently in the spatial database, which confirms the existence of the effects that spatial autocorrelation has on spatial association rule mining. The analysis results not only point out the relation between spatial autocorrelation and spatial association rule mining, but also provide priori knowledge in the process of spatial association rule mining, making the mining process more targeted. Besides, without calculating the Cartesian in developed Apriori algorithm, spatial autocorrelation analysis can get the correlation coefficients efficiently, making the mining process more effectively. Further work would focus on how to evaluate the effects of the spatial autocorrelation on spatial association rules mining, how to find out the candidate frequent spatial itemsets from the results of spatial autocorrelation analysis in practical application.