# opgam {DCluster}

### Description

Scan an area with Openshaw's Geographical Analysis Machine to look for clusters.

*opgam* is the main function, while *gam.intern* is called from there.

### Usage

opgam(data, thegrid=NULL, radius=Inf, step=NULL, alpha, iscluster=opgam.iscluster.default, set.idxorder=TRUE, ...) opgam.intern(point, data, rr, set.idxorder, iscluster, alpha, ...)

### Arguments

.

- data
- A dataframe with the data, as described in
*DCluster*manual page. - thegrid
- A two-columns matrix containing the points of the grid to be used. If it is null, a rectangular grid of step
*step*is built. - radius
- The radius of the circles used in the computations.
- step
- The step of the grid.
- alpha
- Significance level of the tests performed.
- iscluster
- Function used to decide whether the current circle is a possible cluster or not. It must have the same arguments and return the same object than
*gam.iscluster.default* - set.idxorder
- Whether an index for the ordering by distance to the center of the current ball is calculated or not.
- point
- Point where the curent ball is centred.
- rr
- rr=radius*radius .
- ...
- Aditional arguments to be passed to
*iscluster*.

### Details

The *Geographical Analysis Machine* was developed by Openshaw et al. to perform geographical studies of the relationship between different types of cancer and their proximity to nuclear plants.

In this method, a grid of a fixed step is built along the study region, and small balls of a given radius are created at each point of the grid. Local observed and expected number of cases and population are calculated and a function is used to assess whether the current ball is a cluster or not. For more information about this function see *opgam.iscluster.default*, which is the default function used.

If the obverved number of cases excess a critical value, which is calculated by a function passed as an argument, then that circle is marked as a possible cluster. At the end, all possible clusters are drawn on a map. Clusters may be easily identified then.

Notice that we have follow a pretty flexible approach, since user-implemented functions can be used to detect clusters, such as those related to ovedispersion (Pearson's Chi square statistic, Potthoff-Whittinghill's statistic) or autocorrelation (Moran's I statistic and Geary's c statistic), or a bootstrap procedure, although it is not recommended because it can be VERY slow.

### Values

A dataframe with five columns:

- x
- Easting coordinate of the center of the cluster.
- y
- Northing coordinate of the center of the cluster.
- statistic
- Value of the statistic computed.
- cluster
- Is it a cluster (according to the criteria used)? It should be always TRUE.
- pvalue
- Significance of the cluster.

### References

Openshaw, S. and Charlton, M. and Wymer, C. and Craft, A. W. (1987). A mark I geographical analysis machine for the automated analysis of point data sets. International Journal of Geographical Information Systems 1, 335-358.

Waller, Lance A. and Turnbull, Bruce W. and Clarck, Larry C. and Nasca, Philip (1994). Spatial Pattern Analyses to Detect Rare Disease Clusters. In 'Case Studies in Biometry'. Chapter 1, 3-23.

### See Also

DCluster, opgam.iscluster.default

### Examples

library(spdep) data(nc.sids) sids<-data.frame(Observed=nc.sids$SID74) sids<-cbind(sids, Expected=nc.sids$BIR74*sum(nc.sids$SID74)/sum(nc.sids$BIR74)) sids<-cbind(sids, x=nc.sids$x, y=nc.sids$y) #GAM using the centroids of the areas in data sidsgam<-opgam(data=sids, radius=30, step=10, alpha=.002) #Plot centroids plot(sids$x, sids$y, xlab="Easting", ylab="Northing") #Plot points marked as clusters points(sidsgam$x, sidsgam$y, col="red", pch="*")

Documentation reproduced from package DCluster, version 0.2-6. License: GPL (>= 2)