A common problem with spatial economic concentration measures (e.g. Gini, Herfindhal, entropy and Ellison-Glaeser indices) is accounting for the position of regions in space. While they purport to measure spatial clustering, these statistics are confined to calculations within individual areal units. They are insensitive to the proximity of regions - to neighboring effects. Clearly, economic clusters may cross the boundaries of the regions. Yet with current measures, any industrial agglomeration that traverses boundaries will be chopped into two or more pieces. Activity in adjacent spatial units is treated in exactly the same way as activity in far-flung, non-adjacent areas. This paper shows how some popular measures of spatial concentration relying on areal data can be modified to account for neighboring effects and spatial autocorrelation. With a U.S. application, we also show that the new instruments we propose are useful and easy to implement.