Introduction
In order to maintain privacy, social, economic and health data are published based on geographic boundaries. However, agencies collect and report these spatial data using the boundaries and/or areas that benefit their mandate (see Figure 1). This results in the misalignment of boundaries when data from different agencies are used to model relationships. Despite this, for effective policy and proper planning, this disparity must be accounted for in any such analysis.
Methodology
The method uses a joint semiparametric (parametric + nonparametric methods) to modelthe processes (i.e. variables) of interest(see Figure 2).
Results
This method improves predictive performance when compared to using other methods.
Conclusion
The semiparametric method:
Relevance and Potential Application
This method increases the ability of researchers to examine relationship between variables which usually require the reconciliation of misaligned boundaries. Some examples include: the relationship between voting patterns and expenditure on education or health services; the incidence of cancer and temperature and many others.