Typologies of Urban Cyclists: Review of Market Segmentation Methods for Planning Practice

2017 ◽  
Vol 2662 (1) ◽  
pp. 125-133 ◽  
Author(s):  
Rosa Félix ◽  
Filipe Moura ◽  
Kelly J. Clifton
Keyword(s):  
Multivariate Analysis ◽  
Market Segmentation ◽  
Infrastructure Development ◽  
Urban Mobility ◽  
Rule Based ◽  
Analysis Techniques ◽  
Trip Purpose ◽  
Segmentation Methods ◽  
Urban Cycling ◽  
Different Types

Following global guidelines, several cities are investing in urban cycling. Cities are in different stages of cycling development and have adopted different approaches and packages of policies that are likely to be most effective at each stage. Urban cycling plans include investment in infrastructure, promotion, and education supporting the adoption of active modes for urban mobility. Some investments aim to meet current cyclists’ needs and others those of potential bicycle adopters. With respect to urban cyclists, several studies propose typologies, usually related to frequency, trip purpose, or motivation. This paper compares a set of cyclist typologies and the corresponding categorization methods and reviews 20 studies that considered different cyclists’ profiles. Most studies relied on expert judgment approaches or rule-based decisions; five considered multivariate analysis techniques for clustering groups, on the basis of data from surveys. Despite the variety of group categorizations, commonalities were found in most cases and divided cyclists into three main types: current cyclists (typically more proficient riders), potential cyclists (willing but not convinced), and noncyclists (unaware of or unwilling to shift to cycling). Dividing the population of potential cyclists into different typologies can better inform the different stages of planning for cycling infrastructure development by targeting more accurately the needs and requirements of different types of users. This is a key element in the management of a cycling network and cycling infrastructure, which are intended to be built on the basis of effective solutions and decisions to achieve desirable bicycle modal shares of regular trips.

Market Segmentation of Online Reviews: A Network Analysis Approach

2022 ◽  
pp. 147078532110590
Author(s):  
Hui-Ju Wang
Keyword(s):  
Network Analysis ◽  
Market Segmentation ◽  
Online Reviews ◽  
Analysis Approach ◽  
Online Review ◽  
Analysis Techniques ◽  
The Core ◽  
Segmentation Methods ◽  
Traditional Market ◽  
Complex Features

With the popularity of online reviews, brand managers have opportunities to segment their markets according to the reviews of their products or services by customers. Nonetheless, it has been suggested that traditional market segmentation methods are ineffective at analyzing online review data due to the complex features and large amount of this type of data; specifically, traditional methods fail to take into account the networked nature of interactive relationships among reviewers and brands across online review websites. Accordingly, this study proposes a network analysis approach for the market segmentation of online reviews. Collecting samples from Yelp via web scraping, this study demonstrates how network analysis techniques can be utilized to segment online reviewers through a four-step process. The results reveal the core and peripheral market segments, as well as the bridge segment in the core. The study contributes to offering marketing researchers and managers a new network structure analysis approach for the market segmentation of online reviews.

BUSINESS FAILURE PREDICTION USING ROUGH SETS: A COMPARISON WITH MULTIVARIATE ANALYSIS TECHNIQUES

1999 ◽  
Vol 04 (01) ◽  
Author(s):  
C. Zopounidis ◽  
M. Doumpos ◽  
R. Slowinski ◽  
R. Susmaga ◽  
A. I. Dimitras
Keyword(s):  
Multivariate Analysis ◽  
Rough Sets ◽  
Failure Prediction ◽  
Business Failure ◽  
Analysis Techniques

MODEL-FREE FUNCTIONAL MRI ANALYSIS USING TOPOGRAPHIC INDEPENDENT COMPONENT ANALYSIS

2004 ◽  
Vol 14 (04) ◽  
pp. 217-228 ◽  
Author(s):  
ANKE MEYER-BÄSE ◽  
OLIVER LANGE ◽  
AXEL WISMÜLLER ◽  
HELGE RITTER
Keyword(s):  
Independent Component Analysis ◽  
Slight Modification ◽  
Component Analysis ◽  
Independent Component ◽  
Self Organizing Map ◽  
Analysis Techniques ◽  
Model Free ◽  
Temporal Domain ◽  
Different Types ◽  
Fmri Analysis

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.

scholarly journals Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Suk-Ju Hong ◽  
Shin-Joung Rho ◽  
Ah-Yeong Lee ◽  
Heesoo Park ◽  
Jinshi Cui ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Seed Oil ◽  
Prediction Models ◽  
Oxidation Process ◽  
Analysis Techniques ◽  
Peroxide Values ◽  
Perilla Seed

Near-infrared spectroscopy and multivariate analysis techniques were employed to nondestructively evaluate the rancidity of perilla seed oil by developing prediction models for the acid and peroxide values. The acid, peroxide value, and transmittance spectra of perilla seed oil stored in two different environments for 96 and 144 h were obtained and used to develop prediction models for different storage conditions and time periods. Preprocessing methods were applied to the transmittance spectra of perilla seed oil, and multivariate analysis techniques, such as principal component regression (PCR), partial least squares regression (PLSR), and artificial neural network (ANN) modeling, were employed to develop the models. Titration analysis shows that the free fatty acids in an oil oxidation process were more affected by relative humidity than temperature, whereas peroxides in an oil oxidation process were more significantly affected by temperature than relative humidity for the two different environments in this study. Also, the prediction results of ANN models for both acid and peroxide values were the highest among the developed models. These results suggest that the proposed near-infrared spectroscopy technique with multivariate analysis can be used for the nondestructive evaluation of the rancidity of perilla seed oil, especially the acid and peroxide values.

scholarly journals Quantification of vascular networks in photoacoustic mesoscopy

2021 ◽  
Author(s):  
Emma L Brown ◽  
Thierry L Lefebvre ◽  
Paul W Sweeney ◽  
Bernadette Stolz ◽  
Janek Gröhl ◽  
...  
Keyword(s):  
Blood Volume ◽  
Network Structure ◽  
In Silico ◽  
Ex Vivo ◽  
Image Filtering ◽  
Vascular Networks ◽  
Ground Truth Data ◽  
Rule Based ◽  
Segmentation Methods

Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature and has the potential to assess prognosis and therapeutic response. Currently, evaluating vasculature using mesoscopic PAI involves visual or semi-quantitative 2D measurements, which fail to capture 3D vessel network complexity, and lack robust ground truths for assessment of segmentation accuracy. Here, we developed an in silico, phantom, in vivo, and ex vivo-validated end-to-end framework to quantify 3D vascular networks captured using mesoscopic PAI. We applied our framework to evaluate the capacity of rule-based and machine learning-based segmentation methods, with or without vesselness image filtering, to preserve blood volume and network structure by employing topological data analysis. We first assessed segmentation performance against ground truth data of in silico synthetic vasculatures and a photoacoustic string phantom. Our results indicate that learning-based segmentation best preserves vessel diameter and blood volume at depth, while rule-based segmentation with vesselness image filtering accurately preserved network structure in superficial vessels. Next, we applied our framework to breast cancer patient-derived xenografts (PDXs), with corresponding ex vivo immunohistochemistry. We demonstrated that the above segmentation methods can reliably delineate the vasculature of 2 breast PDX models from mesoscopic PA images. Our results underscore the importance of evaluating the choice of segmentation method when applying mesoscopic PAI as a tool to evaluate vascular networks in vivo.

scholarly journals Analysis of chicken and turkey meat consumption by segmentation of Spanish consumers using food-related lifestyle

2021 ◽  
Vol 19 (1) ◽  
pp. e0101 ◽  
Author(s):  
Amparo Baviera-Puig ◽  
Luis Montero-Vicente ◽  
Carmen Escribá-Pérez ◽  
Juan Buitrago-Vera
Keyword(s):  
Statistical Analysis ◽  
Market Segmentation ◽  
Consumer Behaviour ◽  
Telephone Survey ◽  
Meat Consumption ◽  
Marketing Mix ◽  
Analysis Techniques ◽  
Turkey Meat ◽  
Segmentation Strategy ◽  
Shopping Location

Aim of study: Commercially, chicken meat has a similar positioning to turkey meat, as both are healthy and low-fat meats. For this reason, we proposed analysing consumer behaviour with respect to each of these meats based on market segmentation.Area of study: Spain.Material and methods: We carried out a telephone survey with an error of ± 4.0% at a confidence level of 95.5%, using the food-related lifestyle (FRL) instrument as part of the questionnaire. The statistical analysis techniques employed were different depending on the objective pursued: univariate, bivariate and multivariate analysis.Main results: Five segments were obtained: “Manager cook” (24.5%), “Healthy cook” (20.8%), “Concerned with food, but not cooks” (22%), “Total detachment” (11.9%) and “Rational shopper with little interest in cuisine” (20.8%). Notwithstanding the similar positioning of chicken and turkey meats, there are significant differences in purchasing and consumption habits between FRL segments. Specifically, there were significant differences in the frequency of purchase, the usual shopping location, purchasing criteria and preparation methods.Research highlights: Knowing the profile of these segments allows us to adapt the marketing mix (product, place, price and promotion) to each one. This is very useful for the companies due to the wide demand they face. First, they can choose the FRL segments to target and, second, they can define an appropriate marketing strategy according to these segments. In this way, market segmentation strategy based on food-related lifestyles may ensure companies a greater likelihood of success in the market.

Acoustic Feature Analysis for Hypernasality Detection in Children

2008 ◽  
pp. 16-22
Author(s):  
Genaro Daza ◽  
Luis Gonzalo Sánchez ◽  
Franklin A. Sepúlveda ◽  
Castellanos D. Germán
Keyword(s):  
Feature Selection ◽  
Multivariate Analysis ◽  
Latent Variable ◽  
Search Algorithms ◽  
Feature Analysis ◽  
Automatic Identification ◽  
Acoustic Features ◽  
Acoustic Feature ◽  
Analysis Techniques ◽  
Heuristic Search Algorithms

The present work analyzes the statistical effectiveness of different acoustic features in the automatic identification of hypernasality. Acoustic features reflect part of information contained in perceptual analysis; in part, due to their estimation is derived directly or indirectly from the vocal cords behavior. Consequently, it is convenient to apply multivariate analysis techniques in determining the effectiveness of voice features. The effectiveness is studied by using multivariate analysis techniques that are meant for feature extraction and feature selection, as well (latent variable models, heuristic search algorithms).

scholarly journals Redewiedergabe – Schritte zur automatischen Erkennung

2019 ◽  
Vol 47 (1) ◽  
pp. 216-248
Author(s):  
Annelen Brunner
Keyword(s):  
Machine Learning ◽  
Automatic Detection ◽  
Learning Approach ◽  
Manual Annotation ◽  
Narrative Texts ◽  
Rule Based ◽  
Machine Learning Approach ◽  
Different Types ◽  
Short Narrative ◽  
Over Time

Abstract This contribution presents a quantitative approach to speech, thought and writing representation (ST&WR) and steps towards its automatic detection. Automatic detection is necessary for studying ST&WR in a large number of texts and thus identifying developments in form and usage over time and in different types of texts. The contribution summarizes results of a pilot study: First, it describes the manual annotation of a corpus of short narrative texts in relation to linguistic descriptions of ST&WR. Then, two different techniques of automatic detection – a rule-based and a machine learning approach – are described and compared. Evaluation of the results shows success with automatic detection, especially for direct and indirect ST&WR.

scholarly journals Forest Ecology beyond the Growing Season: Surveys of Fallen Leaves & Learning about Ecological Interactions

2020 ◽  
Vol 82 (4) ◽  
pp. 241-246
Author(s):  
Evan Lampert
Keyword(s):  
Forest Ecology ◽  
Mechanical Damage ◽  
Growing Season ◽  
Literacy Skills ◽  
Quantitative Literacy ◽  
Ecological Interactions ◽  
Analysis Techniques ◽  
Animal Activity ◽  
Different Types ◽  
Fallen Leaves

Forests are excellent “outdoor classrooms” for active learning in ecology and evolution; however, in many areas trees have no leaves or visible animal activity for much of the year. Fallen leaves may preserve evidence of interactions such as herbivory and infectious diseases, although these can be difficult to differentiate from mechanical damage and decomposition in older fallen leaves. I developed an exercise in which students collect fallen leaves and observe several different types of damage to the leaves. I provide images and descriptions of different types of damage and practices to differentiate them. In addition, I provide a list of questions that can be answered by collecting fallen leaves and observations of damage. My students gained valuable quantitative literacy skills by entering data into an online worksheet and performing various calculations and data analysis techniques. This exercise provides many benefits and can be an engaging addition to a high school's or college's outdoor curriculum outside of the growing season.

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