scholarly journals Quantifying the Spatio-Temporal Process of Township Urbanization: A Large-Scale Data-Driven Approach

2019 ◽  
Vol 8 (9) ◽  
pp. 389
Author(s):  
Xinliang Liu ◽  
Yi Wang ◽  
Yong Li ◽  
Jinshui Wu
Keyword(s):  
Large Scale ◽  
Driving Forces ◽  
Data Driven ◽  
Population Migration ◽  
Urbanization Process ◽  
Large Scale Data ◽  
Temporal Process ◽  
Data Driven Approach ◽  
Spatio Temporal ◽  
Scale Data

The integrated recognition of spatio-temporal characteristics (e.g., speed, interaction with surrounding areas, and driving forces) of urbanization facilitates regional comprehensive development. In this study, a large-scale data-driven approach was formed for exploring the township urbanization process. The approach integrated logistic models to quantify urbanization speed, partial triadic analysis to reveal dynamic relationships between rural population migration and urbanization, and random forest analysis to identify the response of urbanization to spatial driving forces. A typical subtropical town was chosen to verify the approach by quantifying the spatio-temporal process of township urbanization from 1933 to 2012. The results showed that (i) urbanization speed was well reflected by the changes of time-course areas of urban cores fitted by a four-parameter logistic equation (R2 = 0.95–1.00, p < 0.001), and the relatively fast and steady developing periods were also successfully predicted, respectively; (ii) the spatio-temporal sprawl of urban cores and their interactions with the surrounding rural residential areas were well revealed and implied that the town experienced different historically aggregating and splitting trajectories; and (iii) the key drivers (township merger, elevation and distance to roads, as well as population migration) were identified in the spatial sprawl of urban cores. Our findings proved that a comprehensive approach is powerful for quantifying the spatio-temporal characteristics of the urbanization process at the township level and emphasized the importance of applying long-term historical data when researching the urbanization process.

A data-driven mechanism for large-scale data distribution

2016 ◽  
Author(s):  
Peichang Shi ◽  
Yiying Li ◽  
Bo Ding ◽  
Longquan Jiang ◽  
Hui Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Distribution ◽  
Data Driven ◽  
Large Scale Data ◽  
Scale Data

Real Men Don’t Say “Cute”

2016 ◽  
Vol 8 (3) ◽  
pp. 310-322 ◽  
Author(s):  
Jordan Carpenter ◽  
Daniel Preotiuc-Pietro ◽  
Lucie Flekova ◽  
Salvatore Giorgi ◽  
Courtney Hagan ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Groups ◽  
Education Level ◽  
Political Orientation ◽  
Data Driven ◽  
Social Categories ◽  
Verbal Expression ◽  
Large Scale Data ◽  
The Social ◽  
Scale Data

People associate certain behaviors with certain social groups. These stereotypical beliefs consist of both accurate and inaccurate associations. Using large-scale, data-driven methods with social media as a context, we isolate stereotypes by using verbal expression. Across four social categories—gender, age, education level, and political orientation—we identify words and phrases that lead people to incorrectly guess the social category of the writer. Although raters often correctly categorize authors, they overestimate the importance of some stereotype-congruent signal. Findings suggest that data-driven approaches might be a valuable and ecologically valid tool for identifying even subtle aspects of stereotypes and highlighting the facets that are exaggerated or misapplied.

scholarly journals Mask R-CNN with data augmentation for food detection and recognition

2020 ◽  
Author(s):  
Than Le
Keyword(s):  
Large Scale ◽  
Data Augmentation ◽  
State Of The Art ◽  
Projective Representation ◽  
Large Scale Data ◽  
Food Detection ◽  
Food Recognition ◽  
Data Driven Approach ◽  
Scale Data ◽  
Detection And Recognition

In this paper, we focus on simple data-driven approach to solve deep learning based on implementing the Mask R-CNN module by analyzing deeper manipulation of datasets. We firstly approach to affine transformation and projective representation to data augmentation analysis in order to increasing large-scale data manually based on the state-of-the-art in views of computer vision. Then we evaluate our method concretely by connection our datasets by visualization data and completely in testing to many methods to understand intelligent data analysis in object detection and segmentation by using more than 5000 image according to many similar objects. As far as, it illustrated efficiency of small applications such as food recognition, grasp and manipulation in robotics<br>

Large-Scale Data-Driven Traffic Sensor Health Monitoring

2021 ◽  
Author(s):  
Tongge Huang ◽  
Pranamesh Chakraborty ◽  
Anuj Sharma ◽  
Chinmay Hegde
Keyword(s):  
Health Monitoring ◽  
Large Scale ◽  
Data Driven ◽  
Large Scale Data ◽  
Scale Data ◽  
Sensor Health

Large-Scale Data-Driven Financial Risk Assessment

2019 ◽  
pp. 387-408 ◽  
Author(s):  
Wolfgang Breymann ◽  
Nils Bundi ◽  
Jonas Heitz ◽  
Johannes Micheler ◽  
Kurt Stockinger
Keyword(s):  
Risk Assessment ◽  
Financial Risk ◽  
Large Scale ◽  
Data Driven ◽  
Large Scale Data ◽  
Scale Data

Large-scale Data-driven Segmentation of Banking Customers

2020 ◽  
Author(s):  
Md Monir Hossain ◽  
Mark Sebestyen ◽  
Dhruv Mayank ◽  
Omid Ardakanian ◽  
Hamzeh Khazaei
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Large Scale Data ◽  
Scale Data

scholarly journals STANDARDS-BASED SERVICES FOR BIG SPATIO-TEMPORAL DATA

2016 ◽  
Vol XLI-B4 ◽  
pp. 691-699
Author(s):  
P. Baumann ◽  
V. Merticariu ◽  
A. Dumitru ◽  
D. Misev
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Point Clouds ◽  
Current Status ◽  
Temporal Data ◽  
Large Scale Data ◽  
Spatio Temporal ◽  
High Level ◽  
Immense Potential ◽  
Scale Data

With the unprecedented availability of continuously updated measured and generated data there is an immense potential for getting new and timely insights &ndash; yet, the value is not fully leveraged as of today. The quest is up for high-level service interfaces for dissecting datasets and rejoining them with other datasets &ndash; ultimately, to allow users to ask "any question, anytime, on any size" enabling them to "build their own product on the go". <br><br> With OGC Coverages, a concrete, interoperable data model has been established which unifies n-D spatio-temporal regular and irregular grids, point clouds, and meshes. The Web Coverage Service (WCS) suite provides versatile streamlined coverage functionality ranging from simple access to flexible spatio-temporal analytics. Flexibility and scalability of the WCS suite has been demonstrated in practice through massive services run by large-scale data centers. We present the current status in OGC Coverage data and service models, contrast them to related work, and describe a scalable implementation based on the rasdaman array engine.

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