Multi-Scale Toolbox: An Automated ArcGIS Tool for Evaluating Pupil–Teacher Ratios in U.S. Public School Districts

Abstract

Due to the teacher shortage in the U.S., an automatic toolbox with secondary development based on the ArcPy package was created to explore the spatial imbalance of the pupil–teacher ratio. It consists of four tools (or toolsets) for multi-scale spatial visualization, a sensitivity analysis with a heat map, the ordinary least squares regression with spatial autocorrelation, and the random forest tree regression. This study demonstrated the application of the toolset in the evaluation of educational resource spatial misallocation. Firstly, multi-scale analysis results showed that, the loss of teachers was mainly distributed in Oregon, Nevada, Arizona, and California from the state level, while it focused on such counties as Terrebonne Parish, Concordia Parish, and Bienville Parish in Louisiana in the county level. Secondly, it was found through heatmap sensitivity analysis that pupil–teacher ratios were highly related to low levels of student support services staff, free lunch programs, and low levels of local education agency (LEA) administrators. Then, the OLS tool was used to automatically calculate the spatial weighted matrix, the Moran I, R2, and AICC indices, AdjR2, F-Stat, F-Prob, and the Wald statistic, which showed whether the model was significant or not. This was followed by random forest tree regression modeling, which found that the LEA administrative support staff and the totally free lunch number highly impacted pupil–teacher ratios. Besides, the designed tool provided ribbons for the Common Core of Data (CCD) to link to other data sources.

Keywords

• ArcPy secondary development
• multiple secular visualization
• sensitive analysis
• OLS
• random forest tree