Assessing cycling-friendly environments for children: are micro-environmental factors equally important across different street settings?

BACKGROUND: As physical activity levels decrease as children age, sustainable and accessible forms of physical activity are needed from a young age. Transportation cycling is one such physical activity and has been associated with many benefits. The aims of the study were to identify whether manipulating micro-environmental factors (e.g. speed limis, evenness of cycle path) within a photographed street influences the perceived supportiveness for transportation cycling; and whether changing these micro-environmental factors has the same effect across different street settings. METHODS: We recruited 305 fifth and sixth grade children and their parents from twelve randomly selected primary schools in Flanders, Belgium. They completed a web-based questionnaire including 12 choice-based conjoint tasks, in which they had to choose between two possible routes depicted on manipulated photographs, which the child would cycle along. The routes differed in four attributes: general street setting (enclosed, half open, open), evenness of cycle path (very uneven, moderately uneven, even), speed limit (70 km/h, 50 km/h, 30 km/h) and degree of separation between a cycle path and motorised traffic (no separation, curb, hedge). Hierarchical Bayes analyses revealed the relative importance of each micro-environmental attribute across the three street settings. RESULTS: For each attribute, children and their parents chose routes that had the best alternative (i.e. open street setting, even cycle path, 30 km/h, a hedge separating the cycle path from motorised traffic). The evenness of the cycle path and lower speed limit had the largest effect for the children, while the degree of separation and lower speed limit had the largest effect for their parents. Interactions between micro-scale and macro-scale factors revealed differences in the magnitude but not direction of their effects on route choice. The results held across the different kinds of street settings tested. CONCLUSIONS: Improving micro-scale attributes may increase the supportiveness of a street for children's transportation cycling. We call for on-site research to test effects of changes in micro-environmental attributes on transportation cycling among children.

High breakdown bundle adjustment

Identifying the parameters of a model such that it best fits an observed set of data points is fundamental to the majority of problems in computer vision. This task is particularly demanding when portions of the data has been corrupted by gross outliers, measurements that are not explained by the assumed distributions. In this paper we present a novel method that uses the Least Quantile of Squares (LQS) estimator, a well known but computationally demanding high-breakdown estimator with several appealing theoretical properties. The proposed method is a meta-algorithm, based on the well established principles of proximal splitting, that allows for the use of LQS estimators while still retaining computational efficiency. Implementing the method is straight-forward as the majority of the resulting sub-problems can be solved using existing standard bundle-adjustment packages. Preliminary experiments on synthetic and real image data demonstrate the impressive practical performance of our method as compared to existing robust estimators used in computer vision.

Estimating cointegrated panels with common factors and the forward rate unbiasedness hypothesis

This article proposes a bias-adjusted estimator for use in cointegrated panel regressions when the errors are cross-sectionally correlated through an unknown common factor structure. The asymptotic distribution of the new estimator is derived and is examined in small samples using Monte Carlo simulations. For the estimation of the number of factors, several information-based criteria are considered. The simulation results suggest that the new estimator performs well in comparison to existing ones. In our empirical application, we provide new evidence suggesting that the forward rate unbiasedness hypothesis cannot be rejected. © The Author 2007.

Practitioners' corner: data dependent endogeneity correction in cointegrated panels

This paper examines the small-sample performance of several information based criteria that can be employed to facilitate data dependent endogeneity correction in estimation of cointegrated panel regressions. The Monte Carlo evidence suggests that the criteria generally perform well but that there are differences of practical importance. In particular, the evidence suggests that, although the estimators of the cointegration vectors generally perform well, the criterion with best small-sample performance also leads to the best performing estimator. © Blackwell Publishing Ltd, 2005.

Quality-based SQL: specifying information quality in relational database queries

A Structured Query Language extension uses an estimator module to evaluate quality profiles that rate the accuracy and completeness of query results. Users receive information that matches their defined quality constraints and better serves their data needs.

Creating cycling-friendly environments for children: which micro-scale factors are most important? An experimental study using manipulated photographs

BACKGROUND: Increasing participation in transportation cycling represents a useful strategy for increasing children's physical activity levels. Knowledge on how to design environments to encourage adoption and maintenance of transportation cycling is limited and relies mainly on observational studies. The current study experimentally investigates the relative importance of micro-scale environmental factors for children's transportation cycling, as these micro-scale factors are easier to change within an existing neighborhood compared to macro-scale environmental factors (i.e. connectivity, land-use mix, …). METHODS: Researchers recruited children and their parents (n = 1232) via 45 randomly selected schools across Flanders and completed an online questionnaire which consisted of 1) demographic questions; and 2) a choice-based conjoint (CBC) task. During this task, participants chose between two photographs which we had experimentally manipulated in seven micro-scale environmental factors: type of cycle path; evenness of cycle path; traffic speed; traffic density; presence of speed bumps; environmental maintenance; and vegetation. Participants indicated which route they preferred to (let their child) cycle along. To find the relative importance of these micro-scale environmental factors, we conducted Hierarchical Bayes analyses. RESULTS: Type of cycle path emerged as the most important factor by far among both children and their parents, followed by traffic density and maintenance, and evenness of the cycle path among children. Among parents, speed limits and maintenance emerged as second most important, followed by evenness of the cycle path, and traffic density. CONCLUSION: Findings indicate that improvements in micro-scale environmental factors might be effective for increasing children's transportation cycling, since they increase the perceived supportiveness of the physical environment for transportation cycling. Investments in creating a clearly designated space for the young cyclist, separated from motorized traffic, appears to be the most effective way to increase perceived supportiveness. Future research should confirm our laboratory findings with experimental on-site research.

The role of middle class in democratic diffusion

The modernization hypothesis and the democratic domino theory have been at the forefront in explaining the democratization around the globe. This paper empirically investigates the ‘middle class-driven modernization’ hypothesis and the ‘middle class-driven democratic domino’ effect in a panel of 145 countries over the period 1985 to 2013. Using several middle class measures and a dynamic panel estimator, we show that the ‘middle class-driven modernization’ hypothesis finds strong empirical support in the sample of developing countries excluding Eastern Europe and Central Asia, while the ‘middle class-driven democratic domino’ effect finds support in the sample of developing countries excluding East Asia and the Pacific

Streaming variational inference for dirichlet process mixtures

Bayesian nonparametric models are theoretically suitable to learn streaming data due to their complexity relaxation to the volume of observed data. However, most of the existing variational inference algorithms are not applicable to streaming applications since they re-quire truncation on variational distributions. In this paper, we present two truncation-free variational algorithms, one for mix-membership inference called TFVB (truncation-free variational Bayes), and the other for hard clustering inference called TFME (truncation-free maximization expectation). With these algorithms, we further developed a streaming learning framework for the popular Dirichlet process mixture (DPM) models. Our ex-periments demonstrate the usefulness of our framework in both synthetic and real-world data.

Prediction interval-based control of nonlinear systems using neural networks

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.