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'''Rossmo's formula''' is a [[geographic profiling]] formula to predict where a serial criminal lives. The formula was developed and patented<ref>Rossmo, D. K. (1996). U.S. Patent No. 5,781,704. Washington, DC: U.S. Patent and Trademark Office.</ref> by [[criminologist]] [[Kim Rossmo]] and integrated into a specialized crime analysis software product called [http://www.ecricanada.com/rigel/ Rigel].  The Rigel product is developed by the software company [[Environmental Criminology Research Inc.]] (ECRI), which Rossmo co-founded.<ref>Rich, T. and Shively, M (2004, December).P. 14. A Methodology for Evaluating Geographic Profiling Software. U.S. Department of Justice, Retrieved from https://www.ncjrs.gov/pdffiles1/nij/grants/208993.pdf</ref>
 
==Formula==
Imagine a map with an overlaying grid of little squares named sectors. If this map is a [[raster image]] file on a computer, these sectors are pixels. A sector <math>S_{i,j}</math> is the square on row ''i'' and column ''j'', located at coordinates (''X<sub>i</sub>'',''Y<sub>j</sub>'').
The following function gives the probability <math>p_{i,j}</math> of the position of the serial criminal residing within a specific sector (or point) <math>(X_{i},Y_{j})</math>:<ref>{{cite journal
| first = Kim D.
| last = Rossmo
| title = Geographic profiling: target patterns of serial murderers
| page = 225
| year = 1995
| publisher = [[Simon Fraser University]]
| url = http://lib-ir.lib.sfu.ca/bitstream/1892/8121/1/b17675819.pdf
}}
</ref>
 
:<math>
p_{i,j} = k \sum_{n=1}^{(\mathrm{total\;crimes})}
\left [
 
\underbrace{
\frac{\phi_{ij}}{
(|X_i-x_n| + |Y_j-y_n|)^f
}
}_{ 1^{\mathrm{st}}\mathrm{\;term} }
+
 
\underbrace{
\frac{(1-\phi_{ij})(B^{g-f})}{
(2B - \mid X_i - x_n \mid - \mid Y_j-y_n \mid)^g
}
}_{ 2^{\mathrm{nd}}\mathrm{\;term} }
 
\right ],
 
</math>
where:
<math>
\phi_{ij} =
\begin{cases}
1, & \mathrm{\quad if\;} ( \mid X_i - x_n \mid + \mid Y_j - y_n \mid ) > B \quad \\
0, & \mathrm{\quad else}
\end{cases}
</math>
 
Here the summation is over past crimes located at coordinates (''x<sub>n</sub>'',''y<sub>n</sub>'').
<math>\phi_{ij}</math> is a [[characteristic function (probability theory)|characteristic function]] that returns 0 when a point <math>(X_{i},Y_{j})</math> is an element of the buffer zone B (the neighborhood of a criminal residence that is swept out by a radius of B from its center). <math>\phi_{ij}</math> allows ''p'' to switch between the two terms.
If a crime occurs within the buffer zone, then <math>\phi_{ij}=0</math> and, thus, the first term does not contribute to the overall result.
This is a prerogative for defining the first term in the case when the distance between a point (or pixel) becomes equal to zero.
When <math>\phi_{ij} = 1</math>, the 1st term is used to calculate <math>p_{i,j}</math>.
 
<math>\mid X_i - x_n \mid + \mid Y_j - y_n \mid</math>
is the [[Manhattan distance]] between a point <math>(X_{i},Y_{j})</math> and the ''n''-th crime site <math>(x_{n},y_{n})</math>.
 
 
 
<!-- hiding this until the math gets corrected
==Alternative Implementation==
<math>p_{i,j}</math> is not well suited for image processing because of the asymptotic behavior near the coordinates of a crime site.
 
Alternatively, Rossmo's function may use other [[distance decay]] functions instead of <math>\frac{1}{(\mathrm{Mathattan\;Distance})^f}</math>.
 
One method would be to use a probability distribution similar to the [[Gaussian Distribution]] as a distance decay function:
 
<math>
1^{\mathrm{st}}\mathrm{\;term}(x,y) =
\left\lfloor
(\mathrm{\#\;of\;colors}) \times \sum_{n=1}^{(\mathrm{total\;crimes})}
\frac{1}{\sqrt{e^{(\mid x - C_{n}(x) \mid^{2} + \mid y - C_{n}(y) \mid^{2})}}}
\right\rfloor
</math>
 
If implementing on a computer, the maximum value of p() matches the maximum value of a set of colors being used to create the n by m '''Jeopardy Surface''' matrix J. The elements of the matrix J may represent the pixel values of an image.
 
Where:
<math>
J =
\begin{bmatrix}
p(n,0)  & \cdots  & \;      & p(n,m) \\
\vdots  & \ddots  & \;      & \vdots \\
p(x,0)  & \cdots  & p(x,y)  & \vdots \\
\vdots  & \;      & \;      & \vdots \\
p(0,0)  & p(1,0)  & p(2,0)  & \cdots
\end{bmatrix}
</math>
 
-->
 
==Explanation==
The summation in the formula consists of two terms. The first term describes the idea of ''decreasing probability with increasing distance''. The second term deals with the concept of a ''buffer zone''. The variable <math>\phi</math> is used to put more weight on one of the two ideas. The variable <math>B</math> describes the radius of the buffer zone. The constant <math>k</math> is empirically determined.
 
The main idea of the formula is that the probability of crimes first increases as one moves through the buffer zone away from the ''hotzone'', but decreases afterwards. The variable <math>f</math> can be chosen so that it works best on data of past crimes. The same idea goes for the variable <math>g</math>.
 
The distance is calculated with the [[Taxicab geometry|Manhattan distance formula]].
 
==Applications==
The formula has been applied to fields other than forensics. Because of the buffer zone idea, the formula works well for studies concerning predatory animals such as white sharks.
<ref>{{cite journal
| author = R. A. Martin; D. K. Rossmo; N. Hammerschlag
| title = Hunting patterns and geographic profiling of white shark predation
| journal = Journal of Zoology
| volume = 279
| pages = 111–118
| year = 2009
| doi = 10.1111/j.1469-7998.2009.00586.x
| url = http://www.rjd.miami.edu/scientific-publications/pdf/Martin_Rossmo_Hammerschlag_2009_JZool.pdf
}}
</ref>
 
This formula and math behind it were used in crime detecting in the [[Pilot (Numb3rs)|Pilot]] episode of the  
TV-series [[Numb3rs]] and in the 100th episode of the same show, called "[[Disturbed (Numb3rs)|Disturbed]]".
 
<!-- hiding unused stuff
==Distance formula==
Let C be a set of coordinates of sectors of crimes.
:<math>
C = \{
(x_1, y_1), (x_2, y_2), \dots ,  (x_c, y_c)
\} \qquad c \in \mathbb{N}
</math>
<math>x \in C</math> is an element in a two-dimensional vector space.
The distance from a given sector ''s'' to all other sectors <math>e \in C_s</math> is calculated with the [[Taxicab geometry|Manhattan distance formula]]:
:<math>\sum_{i=1}^n |s-e_i| \qquad e_i \in C_s</math>
-->
 
==Notes==
<references />
(fr) http://www.siteduzero.com/tutoriel-3-422405-profilage-geographique.html
 
==References==
*{{cite book
| title = The numbers behind [[NUMB3RS]]: solving crime with mathematics
| last1 = Devlin
| first1 = Keith J.
| author2-link = Gary Lorden
| last2 = Lorden
| first2 = Gary
| isbn = 978-0-452-28857-7
| year = 2007
| publisher = Plumer
| edition = illustrated
| pages = 1–12
}}
 
*{{cite book
| title = Geographic profiling
| first = Kim D.
| last = Rossmo
| isbn = 978-0-8493-8129-4
| year = 2000
| publisher = CRC Press
| edition = illustrated
}}
 
[[Category:Offender profiling]]
[[Category:Criminology]]
[[Category:Crime mapping]]
[[Category:Spatial data analysis]]
[[Category:Forensic techniques]]

Revision as of 07:56, 23 August 2013

Rossmo's formula is a geographic profiling formula to predict where a serial criminal lives. The formula was developed and patented[1] by criminologist Kim Rossmo and integrated into a specialized crime analysis software product called Rigel. The Rigel product is developed by the software company Environmental Criminology Research Inc. (ECRI), which Rossmo co-founded.[2]


Formula

Imagine a map with an overlaying grid of little squares named sectors. If this map is a raster image file on a computer, these sectors are pixels. A sector Si,j is the square on row i and column j, located at coordinates (Xi,Yj). The following function gives the probability pi,j of the position of the serial criminal residing within a specific sector (or point) (Xi,Yj):[3]

pi,j=kn=1(totalcrimes)[ϕij(|Xixn|+|Yjyn|)f1stterm+(1ϕij)(Bgf)(2BXixnYjyn)g2ndterm],

where: ϕij={1,if(Xixn+Yjyn)>B0,else

Here the summation is over past crimes located at coordinates (xn,yn). ϕij is a characteristic function that returns 0 when a point (Xi,Yj) is an element of the buffer zone B (the neighborhood of a criminal residence that is swept out by a radius of B from its center). ϕij allows p to switch between the two terms. If a crime occurs within the buffer zone, then ϕij=0 and, thus, the first term does not contribute to the overall result. This is a prerogative for defining the first term in the case when the distance between a point (or pixel) becomes equal to zero. When ϕij=1, the 1st term is used to calculate pi,j.

Xixn+Yjyn is the Manhattan distance between a point (Xi,Yj) and the n-th crime site (xn,yn).



Explanation

The summation in the formula consists of two terms. The first term describes the idea of decreasing probability with increasing distance. The second term deals with the concept of a buffer zone. The variable ϕ is used to put more weight on one of the two ideas. The variable B describes the radius of the buffer zone. The constant k is empirically determined.

The main idea of the formula is that the probability of crimes first increases as one moves through the buffer zone away from the hotzone, but decreases afterwards. The variable f can be chosen so that it works best on data of past crimes. The same idea goes for the variable g.

The distance is calculated with the Manhattan distance formula.

Applications

The formula has been applied to fields other than forensics. Because of the buffer zone idea, the formula works well for studies concerning predatory animals such as white sharks. [4]

This formula and math behind it were used in crime detecting in the Pilot episode of the TV-series Numb3rs and in the 100th episode of the same show, called "Disturbed".


Notes

  1. Rossmo, D. K. (1996). U.S. Patent No. 5,781,704. Washington, DC: U.S. Patent and Trademark Office.
  2. Rich, T. and Shively, M (2004, December).P. 14. A Methodology for Evaluating Geographic Profiling Software. U.S. Department of Justice, Retrieved from https://www.ncjrs.gov/pdffiles1/nij/grants/208993.pdf
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(fr) http://www.siteduzero.com/tutoriel-3-422405-profilage-geographique.html

References

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