Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

Patterns of acculturative stress: Continuity, discontinuity, and adjustment among Latino youth

This study evaluated inter- and intra-individual changes in acculturation, acculturative stress, and adaptation experiences, as well as their associations with adjustment outcomes among a group of Latino adolescents in South Florida. Specifically, the current study investigated the incidence, changes, and effects of stressors that arise from acculturation experiences (e.g., related to culture, discrimination, language difficulties) among Latino youth by employing a person-centered approach and a longitudinal research design. Four separate groups of analyses were conducted to investigate (a) within-group differences in levels of reported acculturative stress, (b) patterns of continuity and discontinuity in levels of acculturative stress across time, (c) adjustment outcomes associated with distinct patterns of acculturative stress within each measurement occasion, and (d) predictive relations between longitudinal acculturative stress trajectories in early adolescence and psychosocial adjustment outcomes in young adulthood. ^ Results from the multivariate analyses indicated great within group heterogeneity in acculturative stress among Latino youth during early adolescence, as well as significant continuity and discontinuity in the patterns of shifts among acculturative stress profiles between contiguous measurement occasions. Within each developmental period, membership in acculturative stress clusters was significantly and differentially associated with multiple adjustment outcomes, suggesting that maladaptive outcomes are more likely to occur among Latino adolescents experiencing high levels of psychological distress across multiple acculturative domains. In general, Latino youth acculturation is best understood as multi-dimensional, to be variable across time, and to be fluid and responsive to multiple factors and influences. Implications for preventive strategies are discussed with regard to acculturation and developmental psychology research literatures. ^

Sequential Logistic Regression: A Method to Reveal Subtlety in Self-Efficacy

This paper uses self-efficacy to predict the success of women in introductory physics. We show how sequential logistic regression demonstrates the predictive ability of self-efficacy, and reveals variations with type of physics course. Also discussed are the sources of self-efficacy that have the largest impact on predictive ability.

On the developmental continuity /discontinuity of early adolescent patterns of racial identity salience: Relations with later identities and psychosocial adjustment

Given the significant amount of attention placed upon race within our society, racial identity long has been nominated as a meaningful influence upon human development (Cross, 1971; Sellers et al., 1998). Scholars investigating aspects of racial identity have largely pursued one of two lines of research: (a) describing factors and processes that contribute to the development of racial identities, or (b) empirically documenting associations between particular racial identities and key adjustment outcomes. However, few studies have integrated these two approaches to simultaneously evaluate developmental and related adjustment aspects of racial identity among minority youth. Consequently, relations between early racial identity developmental processes and correlated adjustment outcomes remain ambiguous. Even less is known regarding the direction and function of these relationships during adolescence. To address this gap, the present study examined key multivariate associations between (a) distinct profiles of racial identity salience and (b) adjustment outcomes within a community sample of African-American youth. Specifically, a person-centered analytic approach (i.e., cluster analysis) was employed to conduct a secondary analysis of two archived databases containing longitudinal data measuring levels of racial identity salience and indices of psychosocial adjustment among youth at four different measurement occasions.^ Four separate groups of analyses were conducted to investigate (a) the existence of within-group differences in levels of racial identity salience, (b) shifts among distinct racial identity types between contiguous times of measurement, (c) adjustment correlates of racial identity types at each time of measurement, and (d) predictive relations between racial identity clusters and adjustment outcomes, respectively. Results indicated significant heterogeneity in patterns of racial identity salience among these African-American youth as well as significant discontinuity in the patterns of shifts among identity profiles between contiguous measurement occasions. In addition, within developmental stages, levels of racial identity salience were associated with several adjustment outcomes, suggesting the protective value of high levels of endorsement or internalization of racial identity among the sampled youth. Collectively, these results illustrated the significance of racial identity salience as a meaningful developmental construct in the lives of African-American adolescents, the implications of which are discussed for racial identity and practice-related research literatures. ^

Comparison of Some Improved Estimators for Linear Regression Model under Different Conditions

Multiple linear regression model plays a key role in statistical inference and it has extensive applications in business, environmental, physical and social sciences. Multicollinearity has been a considerable problem in multiple regression analysis. When the regressor variables are multicollinear, it becomes difficult to make precise statistical inferences about the regression coefficients. There are some statistical methods that can be used, which are discussed in this thesis are ridge regression, Liu, two parameter biased and LASSO estimators. Firstly, an analytical comparison on the basis of risk was made among ridge, Liu and LASSO estimators under orthonormal regression model. I found that LASSO dominates least squares, ridge and Liu estimators over a significant portion of the parameter space for large dimension. Secondly, a simulation study was conducted to compare performance of ridge, Liu and two parameter biased estimator by their mean squared error criterion. I found that two parameter biased estimator performs better than its corresponding ridge regression estimator. Overall, Liu estimator performs better than both ridge and two parameter biased estimator.

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.