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Title: A machine learning‐based analysis of 311 requests in the Miami‐Dade County
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.  more » « less
Award ID(s):
1924154
PAR ID:
10344744
Author(s) / Creator(s):
; ; ;
Editor(s):
Carruthers, John; Duncan, Natasha; He, Canfei; Zhu, Shengjun
Date Published:
Journal Name:
Growth and Change
Volume:
0
ISSN:
0017-4815
Page Range / eLocation ID:
1-19
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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