RESUMEN
Since the accessory dwelling unit (ADU) has emerged as a policy alternative to increase housing stock and provide affordable options for areas impacted by housing shortages, many local governments recently adopted ADU policies that promote the construction of ADUs. Taking the City of Los Angeles as the study area, this paper examines how the city's ADU ordinance impacts the relationship of the characteristics of the properties and neighborhoods with ADU development by constructing multilevel logistic regression models. The outputs of the models suggest that the ordinance contributes to diversifying the types and locations of the properties and neighborhoods where ADUs are built. The influence of the property characteristics associated with ADU development before the implementation of the ordinance significantly diminished after the ordinance. The outputs also indicate that the ordinance probably attracted ADU developments in the areas with higher accessibility to bus transit. These findings will help planners take appropriate actions and policies that support ADU developments.
RESUMEN
The current study introduces the flexible approach of mixture components to model the spatiotemporal interaction for ranking of hazardous sites and compares the model performance with the conventional methods. In case of predictive accuracy based on in-sample errors (posterior deviance), the Mixture-5 demonstrated superior performance in majority of the cases, indicating the advantage of mixture approach to accurately predict crash counts. LPML (log pseudo marginal likelihood) was also calculated as a cross-validation measure based on out-of-sample errors and this criterion also established the dominance of Mixture-5, further reinforcing the superiority of the mixture approach from different perspectives. The site ranking evaluation results demonstrated the advantages of adopting the mixture approach. In terms of total rank difference (TRD) results, there were several discrepancies between the two approaches, suggesting that two approaches designate unsafe sites differently. Another site ranking criterion, site consistency test (SCT), was employed to explore the difference in identification of unsafe sites based on two datasets: estimated crash count (traditional) and the spatial variability across time. The advantage of mixture models to act as a complimentary approach for site ranking was revealed by the spatial variability SCT results. The method consistency test (MCT) results also indicate the advantages of mixture models over the Base one. These findings suggested that mixture approach may prove helpful in the network screening step of safety management process to identify sites which may turn unsafe in the future and escape the detection from traditional methods.