All Limbs Lead to the Trunk

This poster describes the development of and the psychometric properties of the trunk scale that measures the voluntary motor ability in the thoracic and upper lumbar regions. The function of the trunk musculature has far reaching implications, particularly in persons with SCI, where postural control and voluntary movement are compromised to varying degrees. Precisely coordinated muscle actions must occur in the appropriate sequence, duration, and combination for the optimal movement function and maintenance of balance and posture during dynamic activities. Trunk mobility is required for nearly all mobility tasks, particularly transitional movements such as rolling, supine to sit, and sit to stand, as well as activities of daily living which involve upper extremity movements such as reaching. The muscles innervated by the thoracic and lumbar spine play key roles in body positioning and posture which are very important in conducting functional activities such as ambulation, reaching and activities of daily living (ADL)1. Poster presented at: ISCOS Annual Meeting in Dublin Ireland.https://jdc.jefferson.edu/rmposters/1004/thumbnail.jp

Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Filter (ESF) approach, to more accurately account for spatial autocorrelation and enhance prediction accuracy whilst improving explainability needed for mass appraisal exercises. By undertaking such an approach, the research demonstrates the application of this method can be easily adopted for property tax jurisdictions in a framework which is more interpretable, transparent and useable within mass appraisal given its simple and appealing approach. The findings reveal that the integration of the ESFs improves model explainability, prediction accuracy and spatial residual error compared to baseline classical regression and Elastic-net regularized regression architectures, whilst offering the necessary ‘front-facing’ and flexible structure for in-sample and out-of-sample assessment needed by the assessment community for valuing the unsold housing stock. In terms of policy and practice, the study demonstrates some important considerations for mass appraisal tax assessment and for the improvement of taxation assessment and the alleviation of horizontal and vertical inequity