TY  - JOUR
AU  - Goodwill, Alasdair M.
AU  - Allen, Jared C.
AU  - Kolarević, Dag
PY  - 2014
UR  - http://jakov.kpu.edu.rs/handle/123456789/567
AB  - This paper investigates thematic classification of homicides for the purpose of behavioural investigative analysis (e.g. offender profiling). Previous research has predominantly used smallest space analysis (SSA) to conceptualise and classify offences into thematic groups based on crime scene behaviour data. This paper introduces a combined approach utilising multiple correspondence analysis (MCA), cluster analysis (CA), and discriminant function analysis (DFA) to define and differentiate crime scenes into expressive or instrumental and impersonal or personal crimes. MCA is used to derive the latent structural dimensions in the crime data and produce quantitative scores for each offence along these dimensions. Two-step CA was then utilised to classify offences. Offence dimensional scores were then used to predict cluster membership under DFA, producing cluster centroids corresponding to MCA dimensions. Centroids were plotted on the MCA correspondence map to simultaneously conceptualise crime classification and the latent structure of the Serbian crime data. Classification of offences based on MCA dimensional scores were 91.5% accurate. This MCA-CA-DFA approach may reduce some of the more subjective aspects of SSA methodology used in classification, whilst producing a product more amenable to objective and cumulative review. Implications for offender profiling research utilising SSA and this approach are discussed.
PB  - Wiley-Blackwell, Hoboken
T2  - Journal of investigative psychology and offender profiling
T1  - Improvement of Thematic Classification in Offender Profiling: Classifying Serbian Homicides Using Multiple Correspondence, Cluster, and Discriminant Function Analyses
VL  - 11
IS  - 3
SP  - 221
EP  - 236
DO  - 10.1002/jip.1416
ER  - 

@article{
author = "Goodwill, Alasdair M. and Allen, Jared C. and Kolarević, Dag",
year = "2014",
abstract = "This paper investigates thematic classification of homicides for the purpose of behavioural investigative analysis (e.g. offender profiling). Previous research has predominantly used smallest space analysis (SSA) to conceptualise and classify offences into thematic groups based on crime scene behaviour data. This paper introduces a combined approach utilising multiple correspondence analysis (MCA), cluster analysis (CA), and discriminant function analysis (DFA) to define and differentiate crime scenes into expressive or instrumental and impersonal or personal crimes. MCA is used to derive the latent structural dimensions in the crime data and produce quantitative scores for each offence along these dimensions. Two-step CA was then utilised to classify offences. Offence dimensional scores were then used to predict cluster membership under DFA, producing cluster centroids corresponding to MCA dimensions. Centroids were plotted on the MCA correspondence map to simultaneously conceptualise crime classification and the latent structure of the Serbian crime data. Classification of offences based on MCA dimensional scores were 91.5% accurate. This MCA-CA-DFA approach may reduce some of the more subjective aspects of SSA methodology used in classification, whilst producing a product more amenable to objective and cumulative review. Implications for offender profiling research utilising SSA and this approach are discussed.",
publisher = "Wiley-Blackwell, Hoboken",
journal = "Journal of investigative psychology and offender profiling",
title = "Improvement of Thematic Classification in Offender Profiling: Classifying Serbian Homicides Using Multiple Correspondence, Cluster, and Discriminant Function Analyses",
volume = "11",
number = "3",
pages = "221-236",
doi = "10.1002/jip.1416"
}

Goodwill, A. M., Allen, J. C.,& Kolarević, D.. (2014). Improvement of Thematic Classification in Offender Profiling: Classifying Serbian Homicides Using Multiple Correspondence, Cluster, and Discriminant Function Analyses. in Journal of investigative psychology and offender profiling
Wiley-Blackwell, Hoboken., 11(3), 221-236.
https://doi.org/10.1002/jip.1416

Goodwill AM, Allen JC, Kolarević D. Improvement of Thematic Classification in Offender Profiling: Classifying Serbian Homicides Using Multiple Correspondence, Cluster, and Discriminant Function Analyses. in Journal of investigative psychology and offender profiling. 2014;11(3):221-236.
doi:10.1002/jip.1416 .

Goodwill, Alasdair M., Allen, Jared C., Kolarević, Dag, "Improvement of Thematic Classification in Offender Profiling: Classifying Serbian Homicides Using Multiple Correspondence, Cluster, and Discriminant Function Analyses" in Journal of investigative psychology and offender profiling, 11, no. 3 (2014):221-236,
https://doi.org/10.1002/jip.1416 . .