In light of recent local, national, and global events, spatial justice provides a potentially powerful lens by which to explore a multitude of spatial inequalities. For more than two decades, scholars have been espousing the power of this concept to help develop more equitable and just communities. However, defining spatial justice and developing a methodology for quantitatively analyzing it is complicated and no agreed upon metric for examining spatial justice has been developed. Instead, individual measures of spatial injustices have been studied. One such individual measure is economic mobility. Recent research on economic mobility has revealed the importance of local geography on upward mobility and may serve as an important keystone in developing a metric for multiple place-based issues of spatial inequality. This paper seeks to explore place-based variables within individual census tracts in an effort to understand their impact on economic mobility and potentially spatial justice. The methodology relies on machine learning techniques and the results show that the best performing model is able to predict economic mobility of a census tract based on its spatial variables with 86% accuracy. The availability and density of jobs, compactness of the area, and the presence of medical facilities and underground storage tanks have the greatest influence, whereas some of the influential features are positively while the others are negatively associated. In the end, this research will allow for comparative analysis between differing geographies and also identify leading variables in the overall quest for spatial justice.
Evidence-based instructional practices were incorporated in class, which gave immediate indication on student's problem solving skills and class participation information. This pedagogy showed positive results and broader acceptance by students in several semesters of intervention. Significant usage of mobile devices during class motivates the extension of this pedagogical approach of asynchronous problem solving using mobile devices. We believe that use of such devices in the classroom for solving interactive problems will enhance student's abilities to solve problems by using their preferred interaction mode. This paper presents the results of the evidence based pedagogy and development of a mobile classroom response system that extends this pedagogy to help student solve interactive problems in their mobile devices to improve their class engagement and problem solving skills.
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