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PEP 0003 Spatial Smoothing Module
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Author    Myunghwa Hwang <mhwang4@gmail.com>
          Luc Anselin <luc.anselin@asu.edu>
          Serge Rey <srey@asu.edu>
Status    Approved 1.0
Created   11-Feb-2010
Updated   
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Abstract
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Spatial smoothing techniques aim to adjust problems with applying simple 
normalization to rate computation. Geographic studies of disease widely 
adopt these techniques to better summarize spatial patterns of disease occurrences.
The smoothing module combines a number of previously developed and to-be-developed 
classes for carrying out spatial smoothing. It will include classes for 
the following techniques: mean and median based smoothing, nonparametric smoothing, 
and empirical Bayes smoothing.


Motivation
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Despite wide usage of spatial smoothing techniques in epidemiology, 
there are only few software libraries that include a range of different smoothing 
techniques at one place. 
Since spatial smoothing is a subtype of exploratory data analysis method, 
PySAL is the best place that host multiple smoothing techniques. 

The smoothing module will mainly implement the techniques reported in [Anselin2006].


Reference Implementation
========================

We suggest adding the module ``pysal.esda.smoothing`` which in turn would
encompass the following modules:

* locally weighted averages, locally weighted median, headbanging
* spatial rate smoothing
* excess risk, empricial Bayes smoothing, spatial empirical Bayes smoothing
* headbanging


References
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[Anselin2006] Anselin, L., N. Lozano, and J. Koschinsky (2006) Rate Transformations and Smoothing, GeoDa Center Research Report.

