Hierarchical Data Structure Research Articles

The construction of flood loss ratio function in cities lacking loss data based on dynamic proportional substitution and hierarchical Bayesian model

A reliable flood loss assessment in city is the basis of efficient flood risk management. However, it is difficult to fit empirical functions with typical case data for most of the world's arid or semi-arid cities that lack or even have no historical flood loss data. Under the background of frequent global extreme weather events and high-speed urbanization, urban flood loss assessment has become increasingly important to support urban flood risk management. At present, few researches have focused on the modeling of Flood Loss Ratio Function (FLRF ) for cities lacking loss data. Therefore, based on the improved analogy principle, a Dynamic Proportional Substitution (DPS) method in this paper was proposed to transfer large sample data from multiple quoted cities into a sample matrix of loss ratios in the study city to address the lack of data. Then, according to the hierarchical data structure characteristics of the loss ratio matrix, a Hierarchical Bayesian Model (HBM) of FLRF with unknown parameters was constructed based on the prior distribution of parameters . Finally, taking the city lacking data—Zhengzhou, China as the study area, the water depth – loss ratio functions of four land types (industry, commerce, residence, and public utilities) were calculated. The results showed that: 1. The combined feature schemes achieved dynamic selectivity; 2. The parameters gradually converged after a certain number of iterations; 3. The error of FLRF solved by HBM was less than that of the least square method (LSM); 4. The DPS-HBM model had good performance in transplantation verification. This study is expected to provide a reference for the establishment of flood loss functions in cities lacking data.

Bayesian hierarchical model for safety signal detection in multiple clinical trials

Clinical safety signal detection is of great importance in establishing the safety profile of new drugs and biologics during drug development. Bayesian hierarchical meta-analysis has proven to be a very effective method of identifying potential safety signals by considering the hierarchical structure of clinical safety data from multiple randomized clinical trials conducted under an Investigational New Drug (IND) application or Biological License Application (BLA). This type of model can integrate information across studies, for instance by grouping related adverse events using the MedDRA system-organ-class (SOC) and preferred terms (PT). It therefore improves the precision of parameter estimates compared to models that do not consider the hierarchical structure of the safety data. We propose to extend an existing four-stage Bayesian hierarchical model and consider the exposure adjusted incidence rate, assuming the number of adverse events (AEs) follows a Poisson distribution. The proposed model is applied to a real-world example, using data from three randomized clinical trials of a neuroscience drug and examine in three simulation studies motivated by real-world examples. Comparison is made between the proposed method and other existing methods. The simulation results indicate that our proposed model outperforms other two candidate models in terms of power and false detection rate.

Finding the bird’s wings: Dimensions of factional conflict on Twitter

Intra-party politics has long been neglected due to lacking data sources. While we have a good understanding of the dynamics of ideological competition between parties, we know less about how individuals or groups inside parties influence policy, leadership selection and coalition bargaining. These questions can only be answered if we can place individual politicians and sub-party groups like factions on the same dimensions as in inter-party competition. This task has been notoriously difficult, as most existing measures either work on the party level, or are in other ways determined by the party agenda. Social media is a new data source that allows analyzing positions of individual politicians in party-centered systems, as it is subject to limited party control. I apply canonical correspondence analysis to account for hierarchical data structures and estimate multidimensional positions of the Twitter accounts of 498 Members of the German Bundestag based on more than 800,000 tweets since 2017. To test the effect of intra-party actors on their relative ideological placement, I coded the faction membership of 247 Twitter users in the Bundestag. I show that Twitter text reproduces party positions and dimensions. Members of factions are more likely to represent their faction’s positions, both on the cultural and the economic dimension.

Determinants of student achievement at lower secondary schools in rural Cambodia

This study examines the effects of student-, family-, and school-related factors on student achievement at lower secondary schools in rural Cambodia. To investigate the determinants of student achievement, a survey study was conducted in four rural districts, including 20 lower secondary schools for investigation. A total of 517 seventh graders were randomly selected for the study. Questionnaires and achievement tests were utilized as tools for data collection. Due to the nested and hierarchical structure of data, a two-level hierarchical linear model was employed to assess what factors may have affected student achievement. The results indicated that absenteeism and private-tutoring attendance significantly affected student achievement. The negative effect of absenteeism has highlighted the critical roles of parents, teachers, and related educational stakeholders to ensure that students come to school on a regular basis. Despite the positive effect on achievement, private tutoring has greatly disadvantaged Cambodian students from financially disadvantaged backgrounds. In contrast to some existing findings, significant effects were not found among family and school factors. The findings make up the empirical evidence needed for the development of improved education policy and practice in Cambodia.

Suicidal behavior in problematic substance uses in South Gondar zone, Northwest Ethiopia: a cross-sectional survey

BackgroundSuicidal behavior has a significant contribution to the global burden of disease that affects individuals, families and communities at different age groups. Sadly, up to 75% of suicides in the world occur in low-and- middle income countries which have no adequate resource to prevent it. The aim of this study was to assess suicidal behavior and associated factors among community residents with problematic substance use in South Gondar zone, northwest Ethiopia.MethodsCommunity based cross-sectional survey was conducted by using a suicidal behavior revised questionnaire from January 15 to March 15, 2019. A total of 4035 participants were screened for problematic substance use by using multi stage cluster sampling and 846 participants were positive for problematic substance use then asked for suicidal behavior. Multiple logistic regression analyses used to see adjusted odd rations (AOR). Multilevel binary logistic regression was used to account for the hierarchical structure of the two-level data within individual and districts level.ResultsThe prevalence of suicidal behavior over the last 12 months in problematic substance uses was found to be 41.4% with 95% of confidence interval (CI) (38.2–44.9). Perceived stigma, [AOR = 1.605, 95% CI (1.16–2.23)], family history of suicide [AOR = 3.22, 95% CI (1.46–7.10)], physical illness [AOR = 2.45 95% CI (1.157–3.84)], rural resident [AOR = 1.74, 95% CI (1.16–2.62)], depression [AOR = 4.44, 95% CI (3.15–6.27)] and living alone (AOR = 1.61, 95% CI (1.16–2.24) were risks factors for suicidal behavior.ConclusionSuicidal behavior in problematic substance uses found to be high. Health workers should pay attention to decrease suicidal behavior and to control amendable factors.

Occupation Coding of Job Titles: Iterative Development of an Automated Coding Algorithm for the Canadian National Occupation Classification (ACA-NOC).

BackgroundIn many research studies, the identification of social determinants is an important activity, in particular, information about occupations is frequently added to existing patient data. Such information is usually solicited during interviews with open-ended questions such as “What is your job?” and “What industry sector do you work in?” Before being able to use this information for further analysis, the responses need to be categorized using a coding system, such as the Canadian National Occupational Classification (NOC). Manual coding is the usual method, which is a time-consuming and error-prone activity, suitable for automation.ObjectiveThis study aims to facilitate automated coding by introducing a rigorous algorithm that will be able to identify the NOC (2016) codes using only job title and industry information as input. Using manually coded data sets, we sought to benchmark and iteratively improve the performance of the algorithm.MethodsWe developed the ACA-NOC algorithm based on the NOC (2016), which allowed users to match NOC codes with job and industry titles. We employed several different search strategies in the ACA-NOC algorithm to find the best match, including exact search, minor exact search, like search, near (same order) search, near (different order) search, any search, and weak match search. In addition, a filtering step based on the hierarchical structure of the NOC data was applied to the algorithm to select the best matching codes.ResultsThe ACA-NOC was applied to over 500 manually coded job and industry titles. The accuracy rate at the four-digit NOC code level was 58.7% (332/566) and improved when broader job categories were considered (65.0% at the three-digit NOC code level, 72.3% at the two-digit NOC code level, and 81.6% at the one-digit NOC code level).ConclusionsThe ACA-NOC is a rigorous algorithm for automatically coding the Canadian NOC system and has been evaluated using real-world data. It allows researchers to code moderate-sized data sets with occupation in a timely and cost-efficient manner such that further analytics are possible. Initial assessments indicate that it has state-of-the-art performance and is readily extensible upon further benchmarking on larger data sets.

A painless automatic hp-adaptive strategy for elliptic problems

In this work, we introduce a novel hp-adaptive strategy. The main goal is to minimize the complexity and implementational efforts hence increasing the robustness of the algorithm while keeping close to optimal numerical results. We employ a multi-level hierarchical data structure imposing Dirichlet nodes to manage the so-called hanging nodes. The hp-adaptive strategy is based on performing quasi-optimal unrefinements. Taking advantage of the hierarchical structure of the basis functions both in terms of the element size h and the polynomial order of approximation p, we mark those with the lowest contributions to the energy of the solution and remove them. This straightforward unrefinement strategy does not need from a fine grid or complex data structures, making the algorithm flexible to many practical situations and existing implementations. On the other side, we also identify some limitations of the proposed strategy, namely: (a) data structures only support isotropic h-refinements (although p-anisotropic refinements are enabled), (b) we assume certain quasi-orthogonality properties of the basis functions in the energy norm, and (c) in this work, we restrict to symmetric and positive definite problems. We illustrate these and other advantages and limitations of the proposed hp-adaptive strategy with several one-, two- and three-dimensional Poisson examples.

Statistical Methods in Transdisciplinary Educational Research

A central task of educational research is to examine common issues of teaching and learning in all subjects taught at school. At the same time, the focus is on identifying and investigating unique subject-specific aspects on the one hand and transdisciplinary, generalizable effects on the other. This poses various methodological challenges for educational researchers, including in particular the aggregation and evaluation of already published study effects, hierarchical data structures, measurement errors, and comprehensive data sets with a large number of potentially relevant variables. In order to adequately deal with these challenges, this paper presents the core concepts of four methodological approaches that are suitable for the analysis of transdisciplinary research questions: meta-analysis, multilevel models, latent multilevel structural equation models, and machine learning methods. Each of these approaches is briefly illustrated with an example inspired by the interdisciplinary research project FALKE (subject-specific teacher competencies in explaining). The data and analysis code used are available online at https://osf.io/5sn9j/?view_only=17fe02d99f9643a08c110c4b1d369294. Finally, the described methods are compared, and some application hints are given.

CityJSON in QGIS: Development of an open-source plugin.

When QGIS 3.0 was released in 2018, it added support for 3D visualisation. At the same time, CityJSON has been developing as an easy‐to‐use JavaScript Object Notation (JSON) encoding for 3D city models using the CityGML 2.0 data model. Together, this opened the possibility to support semantic 3D city models in the popular open‐source GIS software for the first time. In order to add support for 3D city models in QGIS, we have developed a plugin that enables CityJSON datasets to be loaded. The plugin parses a CityJSON file and analyses its tree structure to identify all city objects. Then, the geometry and attributes of every city object are transformed into QGIS features and divided into layers according to user preferences. CityJSON parsing was proven to be straightforward and consistent when tested against several open datasets. One of the biggest challenges we faced, though, was mapping CityJSON’s hierarchical data structure to the relational model of QGIS. We undertook this issue by providing various methods on how geometries from the model are loaded as QGIS features. We intend to use the plugin for educational purposes in our university and we believe it can be proven a worthy tool for researchers and practitioners.

Cross Level Analysis: The Role of Knowledge Management Mediates the Effect of Organizational Culture on Job Performance

This study aims to examine the influence of organizational culture on performance through knowledge management. The sample in this study amounted to 259 lecturers at private universities in the city of Bandung. Two-level hierarchical linear modeling (HLM) is used to analyze hierarchical structure data in this study, namely at the lecturer level as an individual and at the study program as a group level. The results showed that organizational culture had a significant positive effect on knowledge management, knowledge management had a significant positive impact on performance, and organizational culture had a positive and significant impact on performance directly or indirectly.

Matrix decomposition for modeling lesion development processes in multiple sclerosis.

Our main goal is to study and quantify the evolution of multiple sclerosis lesions observed longitudinally over many years in multi-sequence structural magnetic resonance imaging (sMRI). To achieve that, we propose a class of functional models for capturing the temporal dynamics and spatial distribution of the voxel-specific intensity trajectories in all sMRI sequences. To accommodate the hierarchical data structure (observations nested within voxels, which are nested within lesions, which, in turn, are nested within study participants), we use structured functional principal component analysis. We propose and evaluate the finite sample properties of hypothesis tests of therapeutic intervention effects on lesion evolution while accounting for the multilevel structure of the data. Using this novel testing strategy, we found statistically significant differences in lesion evolution between treatment groups.

A Method for Generating Variable-Scale Maps for Small Displays

With the rapid development of the internet and information technology, visualization techniques for mobile and interactive web maps have developed different requirements. Small screens make it difficult to simultaneously present information details and the surrounding context. Aiming at this problem, this paper proposes a novel variable-scale method that can allow users to properly specify the size, shape, and number of the focus area(s). Our method first establishes a hierarchical data structure for representing geographic data and then the client-side can request and represent the information according to only the operational command input by users. Experimental results show that this method can realize the variable-scale representation of real geographic data on a single screen. It can effectively solve the contradiction between a small-screen display and a large quantity of information.

Integration of new information in memory: new insights from a complementary learning systems perspective.

According to complementary learning systems theory, integrating new memories into the neocortex of the brain without interfering with what is already known depends on a gradual learning process, interleaving new items with previously learned items. However, empirical studies show that information consistent with prior knowledge can sometimes be integrated very quickly. We use artificial neural networks with properties like those we attribute to the neocortex to develop an understanding of the role of consistency with prior knowledge in putatively neocortex-like learning systems, providing new insights into when integration will be fast or slow and how integration might be made more efficient when the items to be learned are hierarchically structured. The work relies on deep linear networks that capture the qualitative aspects of the learning dynamics of the more complex nonlinear networks used in previous work. The time course of learning in these networks can be linked to the hierarchical structure in the training data, captured mathematically as a set of dimensions that correspond to the branches in the hierarchy. In this context, a new item to be learned can be characterized as having aspects that project onto previously known dimensions, and others that require adding a new branch/dimension. The projection onto the known dimensions can be learned rapidly without interleaving, but learning the new dimension requires gradual interleaved learning. When a new item only overlaps with items within one branch of a hierarchy, interleaving can focus on the previously known items within this branch, resulting in faster integration with less interleaving overall. The discussion considers how the brain might exploit these facts to make learning more efficient and highlights predictions about what aspects of new information might be hard or easy to learn. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Adaptive Distance-Based Band Hierarchy (ADBH) for Effective Hyperspectral Band Selection

Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still lacking. The lack of proper understanding of the HSI data structure has resulted in the inconsistency in the outcome of UBS. Besides, most of the UBS methods are either relying on complicated measurements or rather noise sensitive, which hinder the efficiency of the determined band subset. In this article, an adaptive distance-based band hierarchy (ADBH) clustering framework is proposed for UBS in HSI, which can help to avoid the noisy bands while reflecting the hierarchical data structure of HSI. With a tree hierarchy-based framework, we can acquire any number of band subset. By introducing a novel adaptive distance into the hierarchy, the similarity between bands and band groups can be computed straightforward while reducing the effect of noisy bands. Experiments on four datasets acquired from two HSI systems have fully validated the superiority of the proposed framework.

The bhsdtr package: a general-purpose method of Bayesian inference for signal detection theory models.

We describe a novel method of Bayesian inference for hierarchical or non-hierarchical equal variance normal signal detection theory models with one or more criteria. The method is implemented as an open-source R package that uses the state-of-the-art Stan platform for sampling from posterior distributions. Our method can accommodate binary responses as well as additional ratings and an arbitrary number of nested or crossed random grouping factors. The SDT parameters can be regressed on additional predictors within the same model via intermediate unconstrained parameters, and the model can be extended by using automatically generated human-readable Stan code as a template. In the paper, we explain how our method improves on other similar available methods, give an overview of the package, demonstrate its use by providing a real-study data analysis walk-through, and show that the model successfully recovers known parameter values when fitted to simulated data. We also demonstrate that ignoring a hierarchical data structure may lead to severely biased estimates when fitting signal detection theory models.

Assessing the state of the art in Discrete Global Grid Systems: OGC criteria and present functionality

The continuous growth of available geospatial data requires new methods for its integration, analysis, and visualization to be explored and implemented in software available to the geospatial community. Discrete Global Grid Systems (DGGS) are an emerging method for spatial data handling in the digital earth framework. DGGS are hierarchical data structures for discretizing the Earth’s surface that have seen considerable theoretical development over the last two decades. In this paper, four software implementations are reviewed, dggridR, H3, OpenEAGGR, and S2, to explore their potential applications in data modelling and GIS, as well as their performance. These software implementations were also evaluated against the recently published Open Geospatial Consortium (OGC) abstract specification. The results indicate great potential and versatility for utilizing such systems in geospatial analysis, if basic methods for converting and handling spatial features are further developed. The performance of these systems is shown to be highly scalable and operational with datasets of various sizes. Yet, it is demonstrated that the current software implementations generally fall short of fulfilling all of the OGC requirements or it was not possible to confirm their compliance. The assessment here identified that further enhancements, endorsement of OGC criteria, and their explicit acknowledgment within official documentation remain key research needs for the evaluated software packages. Further work developing operational DGGS that solve real world problems may promote greater community adoption and integration of DGGS data structures into commonly used geospatial platforms.

A Multilevel Structural Equation Model for the Interrelationships Between Multiple Latent Dimensions of Childhood Socio-Economic Circumstances, Partnership Transitions and Mid-Life Health

SummaryWe propose a multilevel structural equation model to investigate the interrelationships between childhood socio-economic circumstances, partnership formation and stability, and mid-life health, using data from the 1958 British birth cohort. The structural equation model comprises latent class models that characterize the patterns of change in four dimensions of childhood socio-economic circumstances and a joint regression model that relates these categorical latent variables to partnership transitions in adulthood and mid-life health, while allowing for informative dropout. The model can be extended to handle multiple outcomes of mixed types and at different levels in a hierarchical data structure.

Multiple Imputation of Binary Multilevel Missing not at Random Data

SummaryWe introduce a selection model-based multilevel imputation approach to be used within the fully conditional specification framework for multiple imputation. Concretely, we apply a censored bivariate probit model to describe binary variables assumed to be missing not at random. The first equation of the model defines the regression model for the missing data mechanism. The second equation specifies the regression model of the variable to be imputed. The non-random selection of the binary data is mapped by correlations between the error terms of the two regression models. Hierarchical data structures are modelled by random intercepts in both equations. To fit the novel imputation model we use maximum likelihood and adaptive Gauss–Hermite quadrature. A comprehensive simulation study shows the overall performance of the approach. We test its usefulness for empirical research by applying it to a common problem in social scientific research: the emergence of educational aspirations. Our software is designed to be used in the R package mice.

Depicting More Information in Enriched Squarified Treemaps with Layered Glyphs

The Treemap is one of the most relevant information visualization (InfoVis) techniques to support the analysis of large hierarchical data structures or data clusters. Despite that, Treemap still presents some challenges for data representation, such as the few options for visual data mappings and the inability to represent zero and negative values. Additionally, visualizing high dimensional data requires many hierarchies, which can impair data visualization. Thus, this paper proposes to add layered glyphs to Treemap’s items to mitigate these issues. Layered glyphs are composed of N partially visible layers, and each layer maps one data dimension to a visual variable. Since the area of the upper layers is always smaller than the bottom ones, the layers can be stacked to compose a multidimensional glyph. To validate this proposal, we conducted a user study to compare three scenarios of visual data mappings for Treemaps: only Glyphs (G), Glyphs and Hierarchy (GH), and only Hierarchy (H). Thirty-six volunteers with a background in InfoVis techniques, organized into three groups of twelve (one group per scenario), performed 8 InfoVis tasks using only one of the proposed scenarios. The results point that scenario GH presented the best accuracy while having a task-solving time similar to scenario H, which suggests that representing more data in Treemaps with layered glyphs enriched the Treemap visualization capabilities without impairing the data readability.

A Semi-Hierarchical Confirmatory Factor Model for Speeded Data

ABSTRACT This paper presents the semi-hierarchical model for confirmatory factor analysis of speeded data. The semi-hierarchical model is achieved by integrating information from the second level of the hierarchical structure of speeded data into the customary confirmatory factor model. The second level of speeded data originates from subsets of participants responding on the basis of different sources. In the semi-hierarchical model, the contributions of the latent variables are modified to reflect characteristics of these subsets. Furthermore, the results of a simulation study comparing the semi-hierarchical model adapted to speeded data and the customary confirmatory factor model are reported. Whereas all the models yielded good model fit according to RMSEA and SRMR, differences between the models were signified by CFI and TLI. The semi-hierarchical model led to the better model fit in data showing noticeable speededness.