1,721,013 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Analysis of Association Between Demographic, Socioeconomic, and Built Environment Factors and Pedestrian Safety Using Traditional and AI Approaches
No full textPedestrian safety is a critical concern, particularly in urbanized areas where increasing population densities and heavy reliance on motorized transportation elevate risks for vulnerable road users who travel on foot. Creating safe, walkable environments is not only a public health priority but also vital for sustainable urban development. To better understand pedestrian crash risks, this dissertation explores the relationship between built-environment and socio-economic factors and their influence on crash risks at the Census Block Groups (CBGs) level in Austin, San Antonio, and Dallas. Given the spatial nature of CBGs, the spatial distribution of pedestrian crash risks within urban areas, such as Austin, is also examined to understand how built-environment and socio-economic factors contribute to this variability. Additionally, the study identifies distinct individual-scale pedestrian crash clustering patterns by severity using data from California.
The dissertation is organized around three major studies, each addressing a specific research question: What demographic, socioeconomic, and built environment factors are associated with pedestrian safety? What are the spatial variations in pedestrian crash risks at the census block group level in urban areas? Are individual-level pedestrian crashes spatially clustered? Specifically, the first study investigates the impact of socio-economic, built-environment, transit, and trip characteristics on pedestrian crashes in the Texas cities of Austin, San Antonio, and Dallas. It seeks to identify key factors influencing pedestrian crash risks across CBGs and evaluates the effectiveness of machine learning models, specifically SHapley Additive exPlanations (SHAP), in explaining how these factors affect Equivalent Property Damage Only (EPDO) rates. The findings reveal that auto-oriented network density is consistently associated with higher pedestrian crash risks, while pedestrian-oriented network density and sidewalk coverage generally have negative associations. Transit frequency and socio-economic factors, such as the percentage of zero-car households, also significantly impact pedestrian safety. The study underscores the need for targeted interventions in disadvantaged CBGs with higher levels of zero-car households, more mixed land use, and denser transit networks, but lower percentages of high-wage workers and certain community services.
The second study examines the spatial variability of pedestrian crash risks within urban areas, focusing on Austin using Multiscale Geographically Weighted Regression (MGWR). The analysis reveals that higher percentages of two-plus-car households are associated with lower crash risks, possibly due to reduced pedestrian exposure. Conversely, areas with higher employment rate and household entropy, auto network density, and higher transit frequency exhibit greater crash risks, likely driven by increased interactions between pedestrians and vehicles in more densely populated areas, transit-oriented environments.
The third study identifies patterns in pedestrian crash severity using a clustering framework enhanced by explainable artificial intelligence (AI) techniques. This analysis, based on data from California, uncovers distinct patterns in crash severity by examining factors associated with fatal, injury, and non-injury crashes. It also explores how societal and demographic factors differ in their association with varying levels of crash severity, highlighting the disparities between underserved and more resilient communities. The most impactful factors in fatal crashes include pedestrian sobriety impairment, lighting conditions, and macro-scale traffic fatalities. In less severe crashes, broader societal and demographic influences are more prominent. The dissertation may provide insight for policymakers seeking to improve pedestrian traffic safety.Geography and Environmental Studie
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Artificial Intelligence and Spatial Modeling to Estimate Traffic Volume Measures on Local Roadways
No full textThis study explores the integration of artificial intelligence (AI) and spatial modeling techniques to estimate Annual Average Daily Traffic (AADT) on local roadways, which are often data-scarce yet crucial for transportation planning and infrastructure development. Traditional traffic monitoring methods, such as permanent traffic count stations and short-term manual counts, are cost-prohibitive and fail to capture the variability and complexity of traffic flow on low-volume roads. To address this gap, the research develops and compares two modeling frameworks: a non-spatial Random Forest (RF) model and an enhanced spatial RF model. Using the comprehensive Statewide Traffic Monitoring Program (STMP) and Smart Location Database (SLD) dataset from Texas that incorporates socioeconomic, land use, environmental, and transportation accessibility variables, the study applies advanced machine learning methods to capture nonlinear relationships and interaction effects. The spatial RF model, augmented with geospatial diagnostics and cross-validation, demonstrates superior predictive performance over both the non-spatial RF and conventional Geographically Weighted Regression (GWR) models. Key predictors influencing traffic volume include regional centrality, transit ridership, and employment-residential balance. The results reveal complex, context-dependent relationships, emphasizing the importance of spatial heterogeneity and urban form in shaping traffic demand. The findings contribute valuable insights to data-driven traffic estimation on local roads, with practical implications for sustainable transportation planning, emission control, and equitable infrastructure investments. The study concludes by identifying model limitations and proposing future directions for improving the integration of dynamic temporal data and enhancing the interpretability of AI-based traffic models.Engineerin
Overbank Soil Erosion Model Validity for Plastic Riverbed Soils
No full textCritical shear stress is the hydraulic stress at which soil erosion initiates. An estimate of critical shear stress is needed to predict bridge scour; however, there is no unifying equation for predicting the critical shear stress of plastic soils based on soil properties. Therefore, the current design approach by most State Departments of Transportation is to use an in-house empirical equation, an overly conservative minimum critical shear stress based on an assumed soil property, or by direct measurement. For example, the Kansas Department of Transportation (KDOT) developed an empirical critical shear stress model in 2021 by analyzing 13 soil parameters based on 70 soil samples collected from overbanks. Critical shear stress is a function of the soil's physical, chemical, and biological properties, and these properties are dynamically linked. For this research, ten riverbeds with soils with plasticity were identified and sampled from Kansas. One sample from each site was tested in an erosion function apparatus to determine the critical shear stress. An additional sample was collected and used for measuring the same 13 soil parameters used to develop the previous KDOT model, as well as organic content. When the new data from the ten riverbed samples were combined with the original KDOT model data, the power of the KDOT model was found satisfactory. Furthermore, this study established a new model boundary limit, which improved the power of the overall KDOT model. Therefore, it is recommended that the existing overbank model be used for all riverbed sediments to predict critical shear stress and, ultimately, scour for bridge design in Kansas.Engineerin
Geo-Informed Deep Learning for Spatial Downscaling of Solute Transport in Heterogeneous Porous Media
No full textResolving solute transport in heterogeneous porous media is a complex phenomena as it encounters a data sparsity challenge when investigated experimentally and a computational cost challenge when simulated numerically. This work proposes a unique two-stage deep learning architecture comprising a dual-branch autoencoder and a Geo-informed super-resolution generative adversarial network (Gi-SRGAN) to address this dual challenge. The dual-branch autoencoder addresses the issue of sparsity by constructing a continuous, but coarse representation of concentration and pressure profiles from a sparse, discontinuous profile with up to 85% missing data points. The Gi-SRGAN is then employed to generate a finer representation of field variables from the outputs generated by the dual-branch autoencoder (i.e., downscaling). We train and test our framework using five solute transport cases with varying levels of heterogeneity and compare the results with standalone methods, namely the vanilla autoencoder and vanilla SRGAN in addition to ground truth profiles generated by finite element method (FEM). The comparison are performed based on several statistical metrics such as absolute point error (APE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS). The first four cases are used for training and evaluation, while the last case is utilized for blind testing to determine the generalization capability of the framework. Our results show that the dual-branch autoencoder outperforms the vanilla autoencoder, and the Gi-SRGAN outperforms the SRGAN during both the training and evaluation phases. Moreover, the proposed framework can successfully construct the fine representation of concentration profiles, compared to FEM, using coarse representation of pressure, concentration, and domain’s permeability fields. When tested on a blind test case, the dual-branch autoencoder and Gi-SRGAN exhibit superior performance compared to their counterparts in terms of evaluation metrics, while also accurately estimating the ground truth collectively.Engineerin
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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