Centroids, Clusters and Crime: Anchoring the Geographic Profiles of ...

Team # 7507 Page 15
tiveness multiplier serving as a representation of our model’s utility in a general case of serial
crime.
6 Two Schemes for Spatial Prediction
As discussed in the Background, the problem of predicting next crime location is not well
documented. The most common approach to analyzing a criminal crime sequence is to predict
a home location. The research literature on journey-to-crime research for violent serial crimes
strongly suggests that serial crime is patterned around a criminals home, workplace, or other
place of daily activity[15, 8, 4, 6, 17]. This has lead researchers to spend most of their resources
developing and evaluating methods of finding this crime center point. The centroid is then
investigated as an anchor point in the criminal’s daily activity. For the majority of research,
this anchor point is the serial criminal’s home. This method has been tested on large sets of
data and was found to reduce the necessary search area by a factor of ten.
Our schemes will use the strength of an anchor point finding algorithm to predict likely
locations of future crimes. The motivation is straightforward: if we know the location of an
anchor point and the pattern of crime location from this anchor point, we can generate an area
of future likely hood by projecting from the anchor point back to the crime points. It seems
that this conceptualization of patterning is more accurate than a direct forecasting scheme that
ignores anchor point behavior.
We develop two schemes with the base assumption of the existence of anchor point patterning.
The first scheme assumes only a single anchor point. The second scheme assumes
multiple anchor points. These related schemes can then be used in combination to provide an
analysis of likely future crime locations.
7 Single Anchor Point: Centroid Method
Figure 2 is the algorithm used to predict likely crime locations using a single anchor point.
Because of the use of centroid to calculate the single anchor, we call this the centroid method
(although centroids will be used in both methods). Description of the algorithm and discussion
of its results are presented. The green blocks in the diagram represent extensions of the model
which will be discussed.