IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v4y2021i2d10.1007_s42001-021-00103-1.html
   My bibliography  Save this article

Anchored k-medoids: a novel adaptation of k-medoids further refined to measure long-term instability in the exposure to crime

Author

Listed:
  • Monsuru Adepeju

    (Manchester Metropolitan University)

  • Samuel Langton

    (Manchester Metropolitan University)

  • Jon Bannister

    (Manchester Metropolitan University)

Abstract

Longitudinal clustering techniques are widely deployed in computational social science to delineate groupings of subjects characterized by meaningful developmental trends. In criminology, such methods have been utilized to examine the extent to which micro places (such as streets) experience macro-level police-recorded crime trends in unison. This has largely been driven by a theoretical interest in the longitudinal stability of crime concentrations, a topic that has become particularly pertinent amidst a widespread decline in recorded crime. Recent studies have tended to rely on a generic implementation k-means to unpick this stability, with little consideration for its theoretical suitability. This study makes two methodological contributions. First, it demonstrates the application of k-medoids to study longitudinal crime concentrations, and second, it develops a novel ‘anchored k-medoids’ (ak-medoids), a bespoke clustering method specifically designed to meet the theoretical requirements of micro-place investigations into long-term stability. Using both simulated data and 15-years of police-recorded crime data from Birmingham, England, we compare the performances of k-medoids against ak-medoids. We find that both methods highlight instability in the exposure to crime over time, but the consistency and contribution of cluster solutions determined by ak-medoids provide insight overlooked by k-medoids, which is sensitive to short-term fluctuations and subject starting points. This has important implications for the theories said to explain longitudinal crime concentrations, and the law enforcement agencies seeking to offer an effective and equitable service to the public.

Suggested Citation

  • Monsuru Adepeju & Samuel Langton & Jon Bannister, 2021. "Anchored k-medoids: a novel adaptation of k-medoids further refined to measure long-term instability in the exposure to crime," Journal of Computational Social Science, Springer, vol. 4(2), pages 655-680, November.
    • Handle: RePEc:spr:jcsosc:v:4:y:2021:i:2:d:10.1007_s42001-021-00103-1
    DOI: 10.1007/s42001-021-00103-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-021-00103-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-021-00103-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Richard Harris & Martin Charlton & Chris Brunsdon, 2018. "Mapping the changing residential geography of White British secondary school children in England using visually balanced cartograms and hexograms," Journal of Maps, Taylor & Francis Journals, vol. 14(1), pages 65-72, January.
    2. Douglas Steinley & Michael J. Brusco, 2007. "Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques," Journal of Classification, Springer;The Classification Society, vol. 24(1), pages 99-121, June.
    3. Christophe Genolini & René Ecochard & Mamoun Benghezal & Tarak Driss & Sandrine Andrieu & Fabien Subtil, 2016. "kmlShape: An Efficient Method to Cluster Longitudinal Data (Time-Series) According to Their Shapes," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-24, June.
    4. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    5. Genolini, Christophe & Alacoque, Xavier & Sentenac, Mariane & Arnaud, Catherine, 2015. "kml and kml3d: R Packages to Cluster Longitudinal Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i04).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Langton, Samuel & Dixon, Anthony & Farrell, Graham, 2021. "Small area variation in crime effects of COVID-19 policies in England and Wales," Journal of Criminal Justice, Elsevier, vol. 75(C).
    2. Langton, Samuel & Dixon, Anthony & Farrell, Graham, 2021. "Small area variation in crime effects of COVID-19 policies in England and Wales," SocArXiv cw6a4, Center for Open Science.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 295-304, June.
    2. Ricardo Fraiman & Badih Ghattas & Marcela Svarc, 2013. "Interpretable clustering using unsupervised binary trees," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(2), pages 125-145, June.
    3. Aurora Torrente & Juan Romo, 2021. "Initializing k-means Clustering by Bootstrap and Data Depth," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 232-256, July.
    4. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    5. Michael Brusco & Douglas Steinley, 2015. "Affinity Propagation and Uncapacitated Facility Location Problems," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 443-480, October.
    6. Michael Brusco & Douglas Steinley, 2007. "A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 583-600, December.
    7. Michael C. Thrun & Alfred Ultsch, 2021. "Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 280-312, July.
    8. Niwan Wattanakitrungroj & Saranya Maneeroj & Chidchanok Lursinsap, 2017. "Versatile Hyper-Elliptic Clustering Approach for Streaming Data Based on One-Pass-Thrown-Away Learning," Journal of Classification, Springer;The Classification Society, vol. 34(1), pages 108-147, April.
    9. Krzanowski, Wojtek J. & Hand, David J., 2009. "A simple method for screening variables before clustering microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2747-2753, May.
    10. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(2), pages 295-304, June.
    11. Jerzy Korzeniewski, 2013. "Empirical Evaluation of OCLUS and GenRandomClust Algorithms of Generating Cluster Structures," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(3), pages 487-494, September.
    12. Joeri Hofmans & Eva Ceulemans & Douglas Steinley & Iven Mechelen, 2015. "On the Added Value of Bootstrap Analysis for K-Means Clustering," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 268-284, July.
    13. Jaehong Yu & Hua Zhong & Seoung Bum Kim, 2020. "An Ensemble Feature Ranking Algorithm for Clustering Analysis," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 462-489, July.
    14. Ekaterina Kovaleva & Boris Mirkin, 2015. "Bisecting K-Means and 1D Projection Divisive Clustering: A Unified Framework and Experimental Comparison," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 414-442, October.
    15. Mohammad Rezaei, 2020. "Improving a Centroid-Based Clustering by Using Suitable Centroids from Another Clustering," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 352-365, July.
    16. Javier Albert-Smet & Aurora Torrente & Juan Romo, 2023. "Band depth based initialization of K-means for functional data clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 463-484, June.
    17. Alicja Grześkowiak, 2016. "Assessment of Participation in Cultural Activities in Poland by Selected Multivariate Methods," European Journal of Social Sciences Education and Research Articles, Revistia Research and Publishing, vol. 3, January -.
    18. Yunpeng Zhao & Qing Pan & Chengan Du, 2019. "Logistic regression augmented community detection for network data with application in identifying autism‐related gene pathways," Biometrics, The International Biometric Society, vol. 75(1), pages 222-234, March.
    19. Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
    20. José E. Chacón, 2021. "Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 257-263, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:4:y:2021:i:2:d:10.1007_s42001-021-00103-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.