scholarly journals A framework for inserting visually-supported inferences into geographical analysis workflow: application to road safety research

2022 ◽  
Author(s):  
Roger Beecham ◽  
Robin Lovelace
Keyword(s):  
Data Analysis ◽  
Medical Research ◽  
Road Safety ◽  
Categorical Variables ◽  
Complex Data ◽  
Geographical Analysis ◽  
Policy Interventions ◽  
Safety Research ◽  
Analysis Workflow ◽  
Road Crash

Road safety research is a data-rich field with large social impacts. Like in medical research, the ambition is to build knowledge around risk factors that can save lives. Unlike medical research, road safety research generates empirical findings from messy observational datasets. Records of road crashes contain numerous intersecting categorical variables, dominating patterns that are complicated by confounding and, when conditioning on data to make inferences net of this, observed effects that are subject to uncertainty due to diminishing sample sizes. We demonstrate how visual data analysis approaches can inject rigour into exploratory analysis of such datasets. A framework is presented whereby graphics are used to expose, model and evaluate spatial patterns in observational data, as well as protect against false discovery. The framework is supported through an applied data analysis of national crash patterns recorded in STATS19, the main source of road crash information in Great Britain. Our framework moves beyond typical depictions of exploratory data analysis and helps navigate complex data analysis decision spaces typical in modern geographical analysis settings, generating data-driven outputs that support effective policy interventions and public debate.

scholarly journals Examining the relationship between road safety outcomes and the built environment in Bogotá, Colombia

2020 ◽  
Vol 31 (3) ◽  
pp. 33-47 ◽  
Author(s):  
Erik Vergel-Tovar ◽  
Segundo López ◽  
Natalia Lleras ◽  
Darío Hidalgo ◽  
Maryfely Rincón ◽  
...  
Keyword(s):  
Data Analysis ◽  
Built Environment ◽  
Road Safety ◽  
Quantitative Data ◽  
Transit System ◽  
Crash Data ◽  
Road Users ◽  
Quantitative Data Analysis ◽  
Road Crash ◽  
The Relationship

The study of the relationship between the built environment and road safety suggests that density and urban design features may be associated with traffic incidents. In this study, quantitative data analysis using generalized ordinal logit models, and linear and log-linear regressions was conducted to estimate the influence of the built environment on road safety in Bogotá, focusing on road crash outcomes by estimating the influence of built environment attributes on fatalities and injured victims. The analysis was performed using georeferenced road crash data from 2012 to 2016 provided by Bogotá’s Department of Mobility. The quantitative data analysis focused on arterial roads, considering crash severity and types of road users involved, as well as Bus Rapid Transit System corridors. This analysis was complemented with on-site interviews. The results suggest that the presence of pedestrian bridges is positively associated with the number of road crashes for all road users. Other urban variables such as density and distance to intersections showed significant correlations with safety.

Model of artificial neural network for complex data analysis in slag glass-ceramic

2004 ◽  
Vol 95 (2) ◽  
pp. 97-101 ◽  
Author(s):  
Hongyuan Sun ◽  
Qiye Wen ◽  
Peixin Zhang ◽  
Jianhong Liu ◽  
Qianling Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Glass Ceramic ◽  
Complex Data ◽  
Slag Glass ◽  
Artificial Neural

scholarly journals A Comprehensive Survey on Local Differential Privacy

2020 ◽  
Vol 2020 ◽  
pp. 1-29 ◽  
Author(s):  
Xingxing Xiong ◽  
Shubo Liu ◽  
Dan Li ◽  
Zhaohui Cai ◽  
Xiaoguang Niu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Learning ◽  
Data Privacy ◽  
Statistical Estimation ◽  
Differential Privacy ◽  
Future Research ◽  
Reference Source ◽  
Complex Data ◽  
Comprehensive Survey

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

scholarly journals Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines

2017 ◽  
Author(s):  
Baekdoo Kim ◽  
Thahmina Ali ◽  
Carlos Lijeron ◽  
Enis Afgan ◽  
Konstantinos Krampis
Keyword(s):  
Data Analysis ◽  
Cloud Service ◽  
Application Programming Interface ◽  
Single Step ◽  
Easy Access ◽  
Complex Data ◽  
The Galaxy ◽  
Ngs Data Analysis ◽  
Single Data ◽  
Ngs Data

ABSTRACTBackgroundProcessing of Next-Generation Sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized post-analysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers, towards seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform.FindingsWe present an approach for abstracting the complex data operations of multi-step, bioinformatics pipelines for NGS data analysis. As examples, we have deployed two pipelines for RNAseq and CHIPseq, pre-configured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines is as simple as running a single bioinformatics tool. This is achieved through a “meta-script” that automatically starts the Bio-Docklets, and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface (API). The pipelne output is post-processed using the Visual Omics Explorer (VOE) framework, providing interactive data visualizations that users can access through a web browser.ConclusionsThe goal of our approach is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts, on any computing environment whether a laboratory workstation, university computer cluster, or a cloud service provider,. Besides end-users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

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