If you have an idea for an article of no more than 3,000 words presenting an applied spatial data analysis method or technique, please send a one-paragraph abstract to for review. Researchers increasingly use these new techniques to enhance their understanding of urban patterns but often do not have access to short demonstration articles for applied guidance. Through this department of Cityscape, the Office of Policy Development and Research introduces readers to the use of emerging spatial data analysis methods or techniques for measuring geographic relationships in research data. SpAM SpAM (Spatial Analysis and Methods) presents short articles on the use of spatial statistical techniques for housing or urban development research. A different picture emerges when the full set of metrics is considered, and especially when we focus on four key metrics with the best symmetry properties. To illustrate our findings we compare the performance of 5 ML-based AVMs and find, that the most popular metrics in the AVM literature can generate misleading results. We also show how popular existing metrics can be altered so that they adhere to these conditions. While none of the commonly used metrics satisfy both conditions, we propose a number of new metrics that do.
Here we collect the most commonly used metrics from the AVM literature and elsewhere, and evaluate them with respect to two symmetry conditions: symmetry with respect to prediction error rates and symmetry with respect to the treatment of actual and predicted values. While there is consensus in the literature that cross-validation (CV) should be used for model selection in this context, the question of which performance metrics to use is generally neglected. Automated Valuation Models (AVMs) based on Machine Learning (ML) algorithms are widely used for the prediction of house prices.
0 kommentar(er)
