A machine learning‐based analysis of 311 requests in the Miami‐Dade County
A machine learning‐based analysis of 311 requests in the Miami‐Dade County
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DOI：
10.1111/grow.12578
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发表时间：
2021-10
影响因子：
3.2
通讯作者：
Shaoming Cheng;S. Ganapati;G. Narasimhan;F. Yusuf
中科院分区：
文献类型：
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作者：
Shaoming Cheng;S. Ganapati;G. Narasimhan;F. Yusuf
本文阐述了机器学习算法在使用行政数据的地方政府预测分析中的应用。开发和测试的机器学习预测算法克服了传统普通最小二乘法的已知限制。这些限制包括但不限于强加的线性、以自变量作为假定原因和以因变量作为假定结果的假定因果关系、特征之间可能的高度多重共线性以及空间自相关。该研究将算法应用于迈阿密-戴德县的311个非紧急服务请求。该算法被应用到预测的311服务请求的量和社区特征影响跨人口普查区的体积。应用了四种常见的算法家族及其集合。它们是随机森林，支持向量机，lasso和弹性网络正则化广义线性模型，以及极端梯度提升。两种特征选择方法，即Boruta和fscaret，适用于识别显着的群落特征。结果表明，机器学习算法捕捉空间自相关和聚类。由fscaret算法生成的特征在预测311服务请求量时是吝啬的。
This paper illustrates the application of machine learning algorithms in predictive analytics for local governments using administrative data. The developed and tested machine learning predictive algorithm overcomes known limitations of the conventional ordinary least squares method. Such limitations include but not limited to imposed linearity, presumed causality with independent variables as presumed causes and dependent variables as presume result, likely high multicollinearity among features, and spatial autocorrelation. The study applies the algorithms to 311 non‐emergency service requests in the context of Miami‐Dade County. The algorithms are applied to predict the volume of 311 service requests and the community characteristics affecting the volume across Census tract neighborhoods. Four common families of algorithms and an ensemble of them are applied. They are random forest, support vector machines, lasso and elastic‐net regularized generalized linear models, and extreme gradient boosting. Two feature selection methods, namely Boruta and fscaret, are applied to identify the significant community characteristics. The results show that the machine learning algorithms capture spatial autocorrelation and clustering. The features generated by fscaret algorithms are parsimonious in predicting the 311 service request volume.