Much of the motivation for the research questions investigated by social scientists arises from the uniquely social and interactive nature of human beings. Social science phenomena are uniquely interesting, many social scientists argue, because of the consequences for both individuals and societies that are produced by the interactions between family members, co-workers, fellow citizens, tribes, and nation-states. These same interactions uniquely predispose social science data toward spatial dependence. Shared concerns between social actors combine with spatial proximity to promote familiarity. In turn, this familiarity between social actors breeds both contempt and conflict and interaction and interdependence. And by extension, it produces similar behaviors (even when these behaviors are shared conflict) between spatially proximate actors – in short, it produces spatial dependence. Alternatively, as Galton's problem recognizes, even in those instances in which actors do not interact with each other, their spatial locations can induce spatial dependence because of shared attributes that influence human behavior.

The time has never been better for social scientists to model the spatial dependence that is predicted by our theories of human behavior and that exists in the data we employ. The past two decades have witnessed a unique confluence of advances in four areas: (1) the geocoding of social science data, (2) the computational capabilities of computers, (3) the development of diagnostics and estimators for spatial autocorrelation, and (4) the inclusion of routines for spatial diagnosis and estimation in both general and more spatially specialized software packages. This book has sought to demonstrate how social scientists can diagnose and model spatial dependence. In the process, it has examined a variety of spatial diagnostics and estimators. It has also explored some of the concerns that pose challenges for spatial analysis such as the modifiable areal unit problem (MAUP) and the boundary value problem.

Going forward, three particular concerns are particularly important for future developments in spatial analysis.