Predictive Crime Mapping: Arbitrary Grids or Street Networks?
Predictive Crime Mapping: Arbitrary Grids or Street Networks?
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DOI：
10.1007/s10940-016-9321-x
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发表时间：
2016-09
影响因子：
3.6
通讯作者：
G. Rosser;Toby P Davies;K. Bowers;Shane D. Johnson;T. Cheng
中科院分区：
文献类型：
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作者：
G. Rosser;Toby P Davies;K. Bowers;Shane D. Johnson;T. Cheng
数十年的实证研究表明，犯罪集中在一系列空间尺度上，包括街道段。此外，特定地理单元的集群程度保持明显的稳定和一致；Weisburd（犯罪学53:133-157,2015）最近将这一发现称为“犯罪集中在地方的法律”。这些发现表明，未来的犯罪地点至少在某种程度上是可以预测的。迄今为止，预测下一次犯罪最有可能发生的地方的方法要么集中在区域层面上，要么集中在基于网格的预测上。据我们所知，没有一项研究开发并测试了在街道层面预测未来犯罪风险的方法的准确性。这是令人惊讶的，因为正是在这个级别的地方发生了许多犯罪，并部署了警务资源。方法：利用英国一个大型城市警察辖区的财产犯罪数据，我们引入并校准了一个基于网络的前瞻性犯罪地图版本[例如Bowers等人[Br J criminal . 44:641-658, 2004]，并将其性能与基于网格的替代方案进行比较。我们还研究了如何将预测准确性的度量转换到网络环境中，并展示了如何量化和测试两种情况之间的性能差异。研究结果表明，校准后的基于网络的模型在预测准确性方面大大优于基于网格的替代方案，例如，在覆盖率为5%的情况下，识别出的犯罪增加了约20%。准确度的提高在所有被测试的覆盖率水平（从1%到10%）上都具有高度统计学意义。本研究表明，至少对于财产犯罪，基于网络的犯罪预测方法可能优于基于网格的替代方法，因此应该用于业务警务。更复杂的模型变化是可能的，应该在未来的研究中开发和测试。
ObjectivesDecades of empirical research demonstrate that crime is concentrated at a range of spatial scales, including street segments. Further, the degree of clustering at particular geographic units remains noticeably stable and consistent; a finding that Weisburd (Criminology 53:133–157, 2015) has recently termed the ‘law of crime concentration at places’. Such findings suggest that the future locations of crime should—to some extent at least—be predictable. To date, methods of forecasting where crime is most likely to next occur have focused either on area-level or grid-based predictions. No studies of which we are aware have developed and tested the accuracy of methods for predicting the future risk of crime at the street segment level. This is surprising given that it is at this level of place that many crimes are committed and policing resources are deployed.MethodsUsing data for property crimes for a large UK metropolitan police force area, we introduce and calibrate a network-based version of prospective crime mapping [e.g. Bowers et al. (Br J Criminol 44:641–658, 2004)], and compare its performance against grid-based alternatives. We also examine how measures of predictive accuracy can be translated to the network context, and show how differences in performance between the two cases can be quantified and tested.ResultsFindings demonstrate that the calibrated network-based model substantially outperforms a grid-based alternative in terms of predictive accuracy, with, for example, approximately 20 % more crime identified at a coverage level of 5 %. The improvement in accuracy is highly statistically significant at all coverage levels tested (from 1 to 10 %).ConclusionsThis study suggests that, for property crime at least, network-based methods of crime forecasting are likely to outperform grid-based alternatives, and hence should be used in operational policing. More sophisticated variations of the model tested are possible and should be developed and tested in future research.