Dynamic heterogeneity: a framework to promote ecological integration and hypothesis generation in urban systems

Urban areas are understood to be extraordinarily spatially heterogeneous. Spatial heterogeneity, and its causes, consequences, and changes, are central to ecological science. The social sciences and urban design and planning professions also include spatial heterogeneity as a key concern. However, urban ecology, as a pursuit that integrates across these disciplines, lacks a theoretical framework that synthesizes the diverse and important aspects of heterogeneity. This paper presents the concept of dynamic heterogeneity as a tool to explore how social and ecological heterogeneities interact and how they together act as both an outcome of past interactions and a driver future heterogeneity and system functions. To accomplish this goal, we relate heterogeneity to the fundamental concept of the human ecosystem. The human ecosystem concept identifies key processes that require operationalized models of dynamic heterogeneity in three process realms: the flow of materials, the assembly of urban ecosystem biota, and the locational choices humans make concerning land. We exemplify a specific dynamic model of heterogeneity in each of these realms, and indicate a range of complementary statistical approaches to integrate the drivers and outcomes of dynamic heterogeneity across the three realms. We synthesize a hierarchical framework for a theory of dynamic urban heterogeneity, noting its complementarity to other major urban theories and general model approaches. We hypothesize that human actions and structures amplify the dynamics of heterogeneity in urban systems.     

Legitimate concerns about industry involvement in gambling research: response to Delfabbro and King (2017)

This paper provides a response to the commentary by Delfabbro and King (2017), which describes major criticisms of articles situated in a ‘political economy’ conceptual framework. The current paper argues that these criticisms obfuscate legitimate concerns about industry involvement in gambling research, and disregard important evidence about activities of hazardous-consumption industries which provide grounds for strong concern about commercial practices of the gambling industry. Although many aspects of the commentary by Delfabbro and King (2017) are contentious, the current paper summarizes conceptual and empirical literature which supports concern about (a) agency and collaboration in practices of the gambling industry, which make assertions of equivalence with a conspiracy theory both inappropriate and misleading, and (b) the role of research on problem gambling behaviours and pathologies in supporting agendas that may undermine public health. This indicates the need for the scientific community to take seriously its role in improving awareness of the hazards of commercial involvement in gambling research, and thus helping researchers to make informed decisions about these risks and their own manner of engagement with the gambling industry.     

Investment model predictions of workplace ostracism on K–12 teachers’ commitment to their schools and the profession of teaching

The purpose of this study was to determine how the ostracism of K-12 teachers influences their commitment to their schools and commitment to the teaching profession. The investment model was used to situate ostracism as a predictor of teacher commitment. Participants were 200 full-time K-12 teachers who completed a survey assessing their experiences with ostracism at work and investment model variables (i.e., investments, quality of alternatives, satisfaction, and commitment). Results confirmed investment model predictions with teachers’ investments, quality of alternatives, and satisfaction predicting their commitment to their schools and profession. Results of mediation models also demonstrated that controlling for teachers’ investments and quality of alternatives, ostracism predicted commitment indirectly through its effect on satisfaction. This study revealed that teacher commitment is explained by investment model predictions, but after controlling for those predictions, is further explained by ostracism from teacher colleagues.     

Merging data from multiple sources: pretest and shrinkage perspectives

In this article, we have developed asymptotic theory for the simultaneous estimation of the k means of arbitrary populations under the common mean hypothesis and further assuming that corresponding population variances are unknown and unequal. The unrestricted estimator, the Graybill-Deal-type restricted estimator, the preliminary test, and the Stein-type shrinkage estimators are suggested. A large sample test statistic is also proposed as a pretest for testing the common mean hypothesis. Under the sequence of local alternatives and squared error loss, we have compared the asymptotic properties of the estimators by means of asymptotic distributional quadratic bias and risk. Comprehensive Monte-Carlo simulation experiments were conducted to study the relative risk performance of the estimators with reference to the unrestricted estimator in finite samples. Two real-data examples are also furnished to illustrate the application of the suggested estimation strategies.     

Getting to Where You Want to Go: Designing Information Systems and Using Your Data Through the Application of Analytics and Informatics

ABSTRACT

This article, based on the closing session from the 2017 NC Serials Conference, describes a systems perspective where data serve as both real-time and summative information and knowledge about how an organization is performing, what services and resources its users are using and interacting with, and areas for which the organization is doing well or needs improvement. The author posits that data are most useful when the context is informed by clear goals that provide a direction to where the organization is and wants to be. Applied examples are discussed for several environments, including an academic department, school of education, regional libraries, and public libraries.     

Efficient arrangements of two-level orthogonal arrays in two and four blocks

When orthogonal arrays are used in practical applications, it is often difficult to perform all the designed runs of the experiment under homogeneous conditions. The arrangement of factorial runs into blocks is usually an action taken to overcome such obstacles. However, an arbitrary configuration might lead to spurious analysis results. In this work, the nice properties of two-level orthogonal arrays are taken into consideration and an effective method for arranging experimental runs into two and four blocks of the same size is proposed. This method is based on the so-called J-characteristics of the corresponding array. General theoretical results are given for studying up to four experimental factors in two blocks, as well as for studying up to three experimental factors in four blocks. Finally, we provide best blocking arrangements when the number of the factors of interest is larger, by exploiting the known lists of non-isomorphic orthogonal arrays with two levels and various run sizes.     

Estimation and diagnostic for skew-normal partially linear models

Partially linear models (PLMs) are an important tool in modelling economic and biometric data and are considered as a flexible generalization of the linear model by including a nonparametric component of some covariate into the linear predictor. Usually, the error component is assumed to follow a normal distribution. However, the theory and application (through simulation or experimentation) often generate a great amount of data sets that are skewed. The objective of this paper is to extend the PLMs allowing the errors to follow a skew-normal distribution [A. Azzalini, A class of distributions which includes the normal ones, Scand. J. Statist. 12 (1985), pp. 171–178], increasing the flexibility of the model. In particular, we develop the expectation-maximization (EM) algorithm for linear regression models and diagnostic analysis via local influence as well as generalized leverage, following [H. Zhu and S. Lee, Local influence for incomplete-data models, J. R. Stat. Soc. Ser. B 63 (2001), pp. 111–126]. A simulation study is also conducted to evaluate the efficiency of the EM algorithm. Finally, a suitable transformation is applied in a data set on ragweed pollen concentration in order to fit PLMs under asymmetric distributions. An illustrative comparison is performed between normal and skew-normal errors.     

Statistical inference for the Gompertz distribution based on Type-II progressively hybrid censored data

In this article, the statistical inference for the Gompertz distribution based on Type-II progressively hybrid censored data is discussed. The estimation of the parameters for Gompertz distribution is obtained using maximum likelihood method (MLE) and Bayesian method under three different loss functions. We also proved the existence and uniqueness of the MLE. The one-sample Bayesian prediction intervals are obtained. The work is done for different values of the parameters. We apply the Monto Carlo simulation to compare the proposed methods, also an example is discussed to construct the Prediction intervals.     

The effect of different data structures,sample sizes on model fit measures

In this study, our aim was to investigate the changes of different data structures and different sample sizes on the structural equation modeling and the influence of these factors on the model fit measures. Examining the created structural equation modeling under different data structures and sample sizes, the evaluation of model fit measures were performed with a simulation study. As a result of the simulation study, optimization and negative variance estimation problems have been encountered depending on the sample size and changing correlations. It was observed that these problems disappeared either by increasing the sample size or the correlations between the variables in factor. For upcoming studies, the choice of RMSEA and IFI model fit measures can be suggested in all sample sizes and the correlation values for data sets are ensured the multivariate normal distribution assumption.