Objectives

Criminological scholars have long been interested in how macro-level characteristics of cities, counties, or metropolitan areas are related to levels of crime. The standard analytic approach in this literature aggregates constructs of interest, including crime rates, to the macro geographic units and estimates regression models, but this strategy ignores possible sub-city-level processes that occur simultaneously.

Methods

One solution uses multilevel data of crime in meso-level units within a large number of cities; however, such data is very difficult and time intensive to collect. We propose an alternative approach which utilizes insights from existing literature on meso-level processes along with meso-level socio-demographic measures in cities to impute crime data from the city to the smaller geographic units. This strategy allows researchers to estimate full multilevel models that estimate the effects of macro-level processes while controlling for sub-city-level factors.

Results

We demonstrate that the strategy works as expected on a sample of 91 cities with meso-level data, and also works well when estimating the multilevel model on a sample of cities different from the imputation model, or even in a different time period.

Conclusions

The results demonstrate that existing studies aggregated to macro units can yield considerably different (and therefore potentially problematic) results when failing to account for meso-level processes.

中文翻译：

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在使用城市级犯罪数据估计模型时考虑中观或微观影响：引入一种新的插补技术

目标

长期以来，犯罪学学者一直对城市、县或大都市区的宏观特征与犯罪水平之间的关系感兴趣。该文献中的标准分析方法将感兴趣的结构（包括犯罪率）聚合到宏观地理单位并估计回归模型，但该策略忽略了可能同时发生的次城市级过程。

方法

一种解决方案使用大量城市中级单位的多级犯罪数据；然而，收集此类数据非常困难且耗时。我们提出了一种替代方法，该方法利用现有文献中关于中观层次过程的见解以及城市中观层次的社会人口统计措施，将城市的犯罪数据归入较小的地理单位。这种策略允许研究人员估计完整的多层次模型，这些模型估计宏观层次过程的影响，同时控制子城市层次的因素。

结果

我们证明了该策略在具有中级数据的 91 个城市样本上按预期工作，并且在对与插补模型不同的城市样本或什至在不同时间段内估计多级模型时也能很好地工作。

结论

结果表明，当未能考虑中观层面的过程时，汇总到宏观单位的现有研究可能会产生相当不同（因此可能存在问题）的结果。