Studies that evaluate determinants of residential water demand typically use data from a single spatial scale. Although household‐scale data are preferred, especially when econometric models are used, researchers may be limited to aggregate data. There is little, if any, empirical analysis to assess whether spatial scale may lead to ecological fallacy problems in residential water use research. Using linear mixed‐effects models, we compare the results for the relationship of single‐family water use with its determinants using data from the household and census tract scales in the city of Phoenix. Model results between the household and census tract scale are similar suggesting the ecological fallacy may not be significant. Common significant determinants on these two spatial scales include household size, household income, house age, pool size, irrigable lot size, precipitation, and temperature. We also use city/town scale data from the Phoenix metropolitan area to parameterize the linear mixed‐effects model. The difference in the parameter estimates of those common variables compared to the first two scales indicates there is spatial heterogeneity in the relationship between single‐family water use and its determinants among cities and towns. The negative relationship between single‐family house density and residential water use suggests that residential water consumption could be reduced through coordination of land use planning and water demand management. 相似文献
Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.
Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).
Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.
Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy. 相似文献