Traffic Monitoring and Diagnosis with Multivariate Statistical Network Monitoring: A Case Study

2017 ◽  
Author(s):  
Jose Camacho ◽  
Pedro Garcia-Teodoro ◽  
Gabriel Macia-Fernandez
Keyword(s):  
Network Monitoring ◽  
Traffic Monitoring ◽  
Multivariate Statistical ◽  
Monitoring And Diagnosis

Multivariate statistical monitoring of buildings. Case study: Energy monitoring of a social housing building

2015 ◽  
Vol 103 ◽  
pp. 338-351 ◽  
Author(s):  
Llorenç Burgas ◽  
Joaquim Melendez ◽  
Joan Colomer ◽  
Joaquim Massana ◽  
Carles Pous
Keyword(s):  
Social Housing ◽  
Energy Monitoring ◽  
Multivariate Statistical ◽  
Statistical Monitoring

scholarly journals Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

2013 ◽  
Vol 1 (2) ◽  
pp. 957-1000 ◽  
Author(s):  
M. Fressard ◽  
Y. Thiery ◽  
O. Maquaire
Keyword(s):  
Landslide Susceptibility ◽  
Roc Curves ◽  
Statistical Modelling ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Statistical Accuracy ◽  
Multivariate Statistical ◽  
The Impact ◽  
Modelling Methods

Abstract. The objective of this paper is to assess the impact of the datasets quality for the landslide susceptibility mapping using multivariate statistical modelling methods at detailed scale. This research is conducted in the Pays d'Auge plateau (Normandy, France) with a scale objective of 1/10000, in order to fit the French guidelines on risk assessment. Five sets of data of increasing quality (considering accuracy, scale fitting, geomophological significance) and cost of acquisition are used to map the landslide susceptibility using logistic regression. The best maps obtained with each set of data are compared on the basis of different statistical accuracy indicators (ROC curves and relative error calculation), linear cross correlation and expert opinion. The results highlights that only high quality sets of data supplied with detailed geomorphological variables (i.e. field inventory and surficial formations maps) can predict a satisfying proportion of landslides on the study area.

scholarly journals Using Multivariate Statistical Methods to Analyze High-Quality Bicycle Path Service Systems: A Case Study of Popular Bicycle Paths in Taiwan

2020 ◽  
Vol 12 (17) ◽  
pp. 7185
Author(s):  
Shinn-Jou Lin ◽  
Guey-Shin Shyu ◽  
Wei-Ta Fang ◽  
Bai-You Cheng
Keyword(s):  
Explanatory Power ◽  
Value Added ◽  
Service Systems ◽  
Image Management ◽  
High Quality ◽  
Multivariate Statistical ◽  
The Individual ◽  
Quantitative Indicators

Taiwan has promoted bicycle tourism for nearly 20 years, and the bicycle paths it has constructed throughout the island are diverse in design. In the present study, an evaluation scale for bicycle path sightseeing potential was devised with a focus on the overall service quality of the paths; 30 popular bicycle paths were analyzed using a field survey, with expert consultation on quantitative indicators, and a qualitative analysis entailing interviews with people regarding the bicycle paths. A multivariate statistical analysis was performed on the quality of the service systems for these paths. The results revealed that the quality of these service systems is influenced by four principal components, namely, landscape attractiveness, image management, bicycle-specific paths, and accessibility, for a total explanatory power of 76.21%; the individual explanatory power of these components was 25.89%, 21.49%, 16.81%, and 12.03%, respectively. Bicycle path conditions, service maintenance, and cleanliness and bicycle specificity are required for future high-quality bicycle paths; diverse bicycle rental services and bicycle types, entrance visibility, and ecological introduction boards along paths are value-added factors to bicycle path quality.

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