Deconstruction of annoyance due to air pollution by multiple correspondence analyses.

Annoyance caused by air pollution is a matter of public health as it can cause stress and ill-health and affect quality of life, among other burdens. The aim of this study is to apply the multiple correspondence analyses (MCA) technique as a differential tooling to explore relationships between variables that can influence peoples' behaviour concerning annoyance caused by air pollution. Data were collected through a survey on air pollution, environmental issues and quality of life. Face-to-face survey studies were conducted in two industrialized urban areas (Vitoria in Brazil and Dunkirk in France). These two regions were chosen as their inhabitants often report feeling annoyed by air pollution, and both regions have similar industrial characteristics. The results showed a progressive correspondence between levels of annoyance and other active variables in the "air pollution" factor group: as the levels of annoyance increased, the levels of the other qualitative variables (importance of air quality, perceived exposure to industrial risk, assessment of air quality, perceived air pollution) also increased. Respondents who reported feeling annoyed by air pollution also thought that air quality was very important and were very concerned about exposure to industrial risks. Furthermore, they often assessed air quality as horrible, and they could frequently perceive air pollution by dust, odours and decreased visibility. The results also showed a statistically significant association between occurrence of allergies and high levels of annoyance.

Large-scale simulation of traffic flow using Markov model.

Modeling and simulating movement of vehicles in established transportation infrastructures, especially in large urban road networks is an important task. It helps in understanding and handling traffic problems, optimizing traffic regulations and adapting the traffic management in real time for unexpected disaster events. A mathematically rigorous stochastic model that can be used for traffic analysis was proposed earlier by other researchers which is based on an interplay between graph and Markov chain theories. This model provides a transition probability matrix which describes the traffic's dynamic with its unique stationary distribution of the vehicles on the road network. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution which can handle the traffic's dynamic together with the vehicles' distribution. In addition, the weighted least squares estimation method is applied for estimating this new parameter matrix using trajectory data. In a case study, we apply our method on the Taxi Trajectory Prediction dataset and road network data from the OpenStreetMap project, both available publicly. To test our approach, we have implemented the proposed model in software. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory and superior to the frequency based maximum likelihood method. In a real application, we have unfolded a stationary distribution on the map graph of Porto, based on the dataset. The approach described here combines techniques which, when used together to analyze traffic on large road networks, has not previously been reported.

Bias-corrected estimation of the Rudas-Clogg-Lindsay mixture index of fit.

Rudas, Clogg, and Lindsay (1994, J. R Stat Soc. Ser. B, 56, 623) introduced the so-called mixture index of fit, also known as pi-star (π*), for quantifying the goodness of fit of a model. It is the lowest proportion of 'contamination' which, if removed from the population or from the sample, makes the fit of the model perfect. The mixture index of fit has been widely used in psychometric studies. We show that the asymptotic confidence limits proposed by Rudas et al. (1994, J. R Stat Soc. Ser. B, 56, 623) as well as the jackknife confidence interval by Dayton (, Br. J. Math. Stat. Psychol., 56, 1) perform poorly, and propose a new bias-corrected point estimate, a bootstrap test and confidence limits for pi-star. The proposed confidence limits have coverage probability much closer to the nominal level than the other methods do. We illustrate the usefulness of the proposed method in practice by presenting some practical applications to log-linear models for contingency tables.

Promises and Pitfalls of Anchoring Vignettes in Health Survey Research.

Data harmonization is a topic of growing importance to demographers, who increasingly conduct domestic or international comparative research. Many self-reported survey items cannot be directly compared across demographic groups or countries because these groups differ in how they use subjective response categories. Anchoring vignettes, already appearing in numerous surveys worldwide, promise to overcome this problem. However, many anchoring vignettes have not been formally evaluated for adherence to the key measurement assumptions of vignette equivalence and response consistency. This article tests these assumptions in some of the most widely fielded anchoring vignettes in the world: the health vignettes in the World Health Organization (WHO) Study on Global AGEing and Adult Health (SAGE) and World Health Survey (WHS) (representing 10 countries; n = 52,388), as well as similar vignettes in the Health and Retirement Study (HRS) (n = 4,528). Findings are encouraging regarding adherence to response consistency, but reveal substantial violations of vignette equivalence both cross-nationally and across socioeconomic groups. That is, members of different sociocultural groups appear to interpret vignettes as depicting fundamentally different levels of health. The evaluated anchoring vignettes do not fulfill their promise of providing interpersonally comparable measures of health. Recommendations for improving future implementations of vignettes are discussed.