Transit proximity effects : Capital MetroRail and its impact on land prices in Austin, Texas

textSince its first operation in 2010, the 32-mile Austin Capital MetroRail has connected downtown Austin to the city of Leander with 9 stations operating in total. Currently, no research has investigated the impact of the Austin MetroRail on property values. This study fills the research gap by understanding its impacts on property values and other socio-economical influences. Hedonic models have been constructed to assess the effects of transit proximity on land price in this research. The model suggests Austin MetroRail has a positive impact on property values- for properties in the study area, every 100 feet further away from the train station, property values will decrease $13,068/acre. Another analysis of the rail only focused on 7 stations located in the city of Austin and suggests transit premium varies by different neighborhood. In high-income neighborhoods, transit proximity impact is positive; and in middle or low- income neighborhoods, it is negative. When stations were grouped into different study areas, models reveal transit proximity has different effects throughout the system. The research findings confirm the positive effect of Austin MetroRail although in some neighborhoods the effects vary and suggest a series of value-capture strategies to help finance future development.Community and Regional Plannin

Structural and electronic properties of ScnOm (n=1~3, m=1~2n) clusters: Theoretical study using screened hybrid density functional theory

The structural and electronic properties of small scandium oxide clusters ScnOm (n = 1 - 3, m = 1 - 2n) are systematically studied within the screened hybrid density functional theory. It is found that the ground states of these scandium oxide clusters can be obtained by the sequential oxidation of small "core" scandium clusters. The fragmentation analysis demonstrates that the ScO, Sc2O2, Sc2O3, Sc3O3, and Sc3O4 clusters are especially stable. Strong hybridizations between O-2p and Sc-3d orbitals are found to be the most significant character around the Fermi level. In comparison with standard density functional theory calculations, we find that the screened hybrid density functional theory can correct the wrong symmetries and yield more precise description for the localized 3d electronic states of scandium.Comment: 8 figure

First-order multivariate integer-valued autoregressive model with multivariate mixture distributions

The univariate integer-valued time series has been extensively studied, but literature on multivariate integer-valued time series models is quite limited and the complex correlation structure among the multivariate integer-valued time series is barely discussed. In this study, we proposed a first-order multivariate integer-valued autoregressive model to characterize the correlation among multivariate integer-valued time series with higher flexibility. Under the general conditions, we established the stationarity and ergodicity of the proposed model. With the proposed method, we discussed the models with multivariate Poisson-lognormal distribution and multivariate geometric-logitnormal distribution and the corresponding properties. The estimation method based on EM algorithm was developed for the model parameters and extensive simulation studies were performed to evaluate the effectiveness of proposed estimation method. Finally, a real crime data was analyzed to demonstrate the advantage of the proposed model with comparison to the other models

BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation

Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation