Anchored k-medoids: a novel adaptation of k-medoids further refined to measure long-term instability in the exposure to crime
Anchored k-medoids: a novel adaptation of k-medoids further refined to measure long-term instability in the exposure to crime
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DOI：
10.1007/s42001-021-00103-1
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发表时间：
2021-11-01
影响因子：
3.2
通讯作者：
Bannister, Jon
中科院分区：
文献类型：
--
作者：
Adepeju, Monsuru;Langton, Samuel;Bannister, Jon
纵向聚类技术在计算社会科学中被广泛应用，以描绘具有有意义的发展趋势的主题分组。在犯罪学中，这种方法被用来检查微观场所(如街道)在多大程度上经历了宏观层面上警方记录的犯罪趋势。这在很大程度上是由对犯罪集中度纵向稳定性的理论兴趣推动的，在有记录的犯罪普遍下降的情况下，这一主题变得特别相关。最近的研究倾向于依赖于通用的实现k-Means来取消这种稳定性，很少考虑其理论适用性。本研究在方法论上有两点贡献。首先，它展示了k-medoid在纵向犯罪集中度研究中的应用，其次，它开发了一种新的“锚定k-medoids”(AK-medoids)，这是一种专门设计的定制聚类方法，旨在满足微观场所调查对长期稳定性的理论要求。使用模拟数据和英国伯明翰警方记录的15年犯罪数据，我们比较了k-medoid和AK-medoid的性能。我们发现，这两种方法都强调了犯罪暴露随时间的不稳定性，但由AK-medoid确定的簇解的一致性和贡献提供了k-medoid忽略的洞察，k-medoid对短期波动和受试者起点敏感。这对于解释纵向犯罪集中的理论以及执法机构寻求为公众提供有效和公平的服务具有重要意义。
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.