Out of Ferguson: Misdemeanors, Municipal Courts, Tax Distribution and Constitutional Limitations

The matter of police and municipal courts as revenue producers became increasingly prominent following Michael Brown’s death from a police shooting. This article considers the use of misdemeanors, especially traffic violations, for the purpose of collecting substantial portions of the annual operating budgets in municipalities in St. Louis County, Missouri. The article argues that the revenue raising function of traffic offenses has displaced their public safety and traffic regulation functions. The change in function from public safety to revenue suggests that the governing laws are no longer valid as exercise of policing power but must be reenacted under the taxing power in order to remain valid. Constitutional tax limitations in Missouri, however, prohibit the increase of existing or enactment of new taxes without an affirmative vote of the electorate. Municipalities have circumvented the constitutional taxing limitations by using laws enacted under policing powers in violation of the constitution. The police and the municipal courts enforcing traffic laws have produced a racially discriminatory and regressive local tax system that violates the tax limitations of the Missouri constitution

Out of Ferguson: Misdemeanors, Municipal Courts, Tax Distribution and Constitutional Limitations

The matter of police and municipal courts as revenue producers became increasingly prominent following Michael Brown’s death from a police shooting. This article considers the use of misdemeanors, especially traffic violations, for the purpose of collecting substantial portions of the annual operating budgets in municipalities in St. Louis County, Missouri. The article argues that the revenue raising function of traffic offenses has displaced their public safety and traffic regulation functions. The change in function from public safety to revenue suggests that the governing laws are no longer valid as exercise of policing power but must be reenacted under the taxing power in order to remain valid. Constitutional tax limitations in Missouri, however, prohibit the increase of existing or enactment of new taxes without an affirmative vote of the electorate. Municipalities have circumvented the constitutional taxing limitations by using laws enacted under policing powers in violation of the constitution. The police and the municipal courts enforcing traffic laws have produced a racially discriminatory and regressive local tax system that violates the tax limitations of the Missouri constitution

Examining the Local Spatial Variability of Robberies in Saint Louis Using a Multi-Scale Methodology

The current study spatially examines the local variability of robbery rates in the City of Saint Louis, Missouri using both census tract and block group data disaggregated and standardized to the 250- and 500-m raster grid spatial scale. The Spatial Lag Model (SLM) indicated measures of race and stability as globally influencing robbery rates. To explore these relationships further, Geographically Weighted Regression (GWR) was used to determine the local spatial variability. We found that the standardized census tract data appeared to be more powerful in the models, while standardized block group data were more precise. Similarly, the 250-m grid offered greater accuracy, while the 500-m grid was more robust. The GWR models explained the local varying spatial relationships between race and stability and robbery rates in St. Louis better than the global models. The local models indicated that social characteristics occurring at higher-order geographies may influence robbery rates in St. Louis

A Cluster-Based Machine Learning Ensemble Approach for Geospatial Data: Estimation of Health Insurance Status in Missouri

Mainstream machine learning approaches to predictive analytics consistently prove their ability to perform well using a variety of datasets, although the task of identifying an optimally-performing machine learning approach for any given dataset becomes much less intuitive. Methods such as ensemble and transformation modeling have been developed to improve upon individual base learners and datasets with large degrees of variance. Despite the increased generalizability and flexibility of ensemble approaches, the cost often involves sacrificing inference for predictive ability. This paper introduces an alternative approach to ensemble modeling, combining the predictive ability of an ensemble framework with localized model construction through the incorporation of cluster analysis as a pre-processing technique. The workflow not only outperforms independent base learners and comparative ensemble methods, but also preserves local inferential capability by manipulating cluster parameters and maintaining interpretable relative importance values and non-transformed coefficients for the overall consideration of variable importance. This paper demonstrates the ensemble technique on a dataset to estimate rates of health insurance coverage across the state of Missouri, where the cluster pre-processing assists in understanding both local and global variable importance and interactions when predicting high concentration areas of low health insurance coverage based on demographic, socioeconomic, and geospatial variables

Correction: Dawson, T.; et al. A Spatial Analysis of the Relationship between Vegetation and Poverty. Int. J. Geo-Inf. 2018, 7, 83

The authors wish to make the following corrections to their paper[...