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The precision, accuracy and efficiency of geographic profiling predictions: a simple heuristic versus mathematical algorithms

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The precision, accuracy and efficiency of geographic profiling predictions: a simple heuristic versus mathematical algorithms. / Bennell, Craig; Emeno, K; Snook, Brent et al.
In: Crime Mapping: A Journal of Research and Practice, Vol. 1, No. 2, 2009, p. 65-84.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Bennell, C, Emeno, K, Snook, B, Taylor, P & Goodwill, A 2009, 'The precision, accuracy and efficiency of geographic profiling predictions: a simple heuristic versus mathematical algorithms', Crime Mapping: A Journal of Research and Practice, vol. 1, no. 2, pp. 65-84. <http://cacs.unlv.edu/CrimeMapping/Abstracts12.html>

APA

Vancouver

Bennell C, Emeno K, Snook B, Taylor P, Goodwill A. The precision, accuracy and efficiency of geographic profiling predictions: a simple heuristic versus mathematical algorithms. Crime Mapping: A Journal of Research and Practice. 2009;1(2):65-84.

Author

Bennell, Craig ; Emeno, K ; Snook, Brent et al. / The precision, accuracy and efficiency of geographic profiling predictions : a simple heuristic versus mathematical algorithms. In: Crime Mapping: A Journal of Research and Practice. 2009 ; Vol. 1, No. 2. pp. 65-84.

Bibtex

@article{628a1f250a534cf08f82397415714515,
title = "The precision, accuracy and efficiency of geographic profiling predictions: a simple heuristic versus mathematical algorithms",
abstract = "This study compared the precision, accuracy, and efficiency of geographic profiles made by students to those made by mathematical algorithms. After making predictions on 20 maps, each depicting a different offense series, nearly half of the sampled students were instructed that {"}the majority of offenders commit offenses close to home{"}. All of the students were then asked to make predictions on a different set of 20 maps. Seven different mathematical algorithms, several derived from a new Bayesian journey-to-crime estimation method, were also applied to the 40 maps. Results showed that informing students about the {"}distance decay heuristic{"} increased the precision of their predictions, but these predictions were not as accurate or efficient as those made by most of the algorithmic procedures. Implications of these results for the field of geographic profiling are discussed.",
author = "Craig Bennell and K Emeno and Brent Snook and Paul Taylor and Alasdair Goodwill",
year = "2009",
language = "English",
volume = "1",
pages = "65--84",
journal = "Crime Mapping: A Journal of Research and Practice",
issn = "2152-9876",
number = "2",

}

RIS

TY - JOUR

T1 - The precision, accuracy and efficiency of geographic profiling predictions

T2 - a simple heuristic versus mathematical algorithms

AU - Bennell, Craig

AU - Emeno, K

AU - Snook, Brent

AU - Taylor, Paul

AU - Goodwill, Alasdair

PY - 2009

Y1 - 2009

N2 - This study compared the precision, accuracy, and efficiency of geographic profiles made by students to those made by mathematical algorithms. After making predictions on 20 maps, each depicting a different offense series, nearly half of the sampled students were instructed that "the majority of offenders commit offenses close to home". All of the students were then asked to make predictions on a different set of 20 maps. Seven different mathematical algorithms, several derived from a new Bayesian journey-to-crime estimation method, were also applied to the 40 maps. Results showed that informing students about the "distance decay heuristic" increased the precision of their predictions, but these predictions were not as accurate or efficient as those made by most of the algorithmic procedures. Implications of these results for the field of geographic profiling are discussed.

AB - This study compared the precision, accuracy, and efficiency of geographic profiles made by students to those made by mathematical algorithms. After making predictions on 20 maps, each depicting a different offense series, nearly half of the sampled students were instructed that "the majority of offenders commit offenses close to home". All of the students were then asked to make predictions on a different set of 20 maps. Seven different mathematical algorithms, several derived from a new Bayesian journey-to-crime estimation method, were also applied to the 40 maps. Results showed that informing students about the "distance decay heuristic" increased the precision of their predictions, but these predictions were not as accurate or efficient as those made by most of the algorithmic procedures. Implications of these results for the field of geographic profiling are discussed.

M3 - Journal article

VL - 1

SP - 65

EP - 84

JO - Crime Mapping: A Journal of Research and Practice

JF - Crime Mapping: A Journal of Research and Practice

SN - 2152-9876

IS - 2

ER -