Zoekresultaten

Local food and changes in tourist eating habits in a sun-and-sea destination: a segmentation approach

Purpose: Little is known about how tourists’ eating habits change between everyday life and holidays. This study aims to identify market segments based on changes in food consumption and experiences of a sun-and-sea destination’s local food. The authors evaluate to what extent tourists consume local food and assess the contribution of local food experiences to the tourists’ overall experience. Design/methodology/approach: The target population was all tourists visiting the Algarve in the Summer 2018 and included both domestic and international sun-and-sea tourists. A sample of 378 valid questionnaires was collected. Data analysis included descriptive analysis, statistical tests and cluster analysis. Findings: Cluster analysis identified three segments: non-foodies, selective foodies and local gastronomy foodies. Results indicate that tourists change their eating habits during holidays, eating significantly more seafood and fish and less legumes, meat, fast food and cereals and their derivatives. International and domestic sun-and-sea tourists reported that eating local food contributes significantly to their overall tourism experience. Practical implications: Sun-and-sea destinations should promote the offer of local dishes, especially those that include locally produced fish and seafood, to improve the tourist experience, differentiate the destination and increase sustainability. Originality/value: The authors address three identified research gaps: a posteriori segmentation based on tourists’ food consumption behaviour; measurement of changes in eating practices between home and in a sun-and-sea destination; and assessment of the role of food experiences to overall tourism experience of tourists visiting a sun-and-sea destination.

Head detection in stereo data for people counting and segmentation

In this paper we propose a head detection method using range data from a stereo camera. The method is based on a technique that has been introduced in the domain of voxel data. For application in stereo cameras, the technique is extended (1) to be applicable to stereo data, and (2) to be robust with regard to noise and variation in environmental settings. The method consists of foreground selection, head detection, and blob separation, and, to improve results in case of misdetections, incorporates a means for people tracking. It is tested in experiments with actual stereo data, gathered from three distinct real-life scenarios. Experimental results show that the proposed method performs well in terms of both precision and recall. In addition, the method was shown to perform well in highly crowded situations. From our results, we may conclude that the proposed method provides a strong basis for head detection in applications that utilise stereo cameras.

Millennials as potential creative tourists in South Africa: A CHAID approach to market segmentation

Creative tourism has recently emerged as an important area of tourism development, particularly in the Global North. In the Global South, studies of the profile of creative tourists and their motives for partaking in creative tourism are limited. This paper investigates creative tourism demand among South African millennials, analysing what motivates their participation and developing a descriptive consumer profile. CHAID analysis was used for segmentation, revealing a group with a high participation intention and a second group with a low probability of creative tourism participation. Creative tourism intentions were linked to knowledge acquisition, skills and escape motivations, and demographic characteristics including relationship status and gender. Respondents were more likely to participate in domestic rather than international creative tourism, indicating the potential for creative tourism development in South Africa. The findings could help managers and policymakers meet the needs of creative tourists, addressing shortfalls in product development, experience design and marketing.

Augmented Reality Brand Segmentation Model

Evaluation of a Fully Automatic Deep Learning-Based Method for the Measurement of Psoas Muscle Area

Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2.Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.

Segmenting incoming tourist in Amsterdam.

Bewerking van een onderzoek naar benefit segmentation van toeristen in Amsterdam. legt de relatie tussen nationaliteiten en benefits sought van toeristen in de hoofdstad. Aangeboden aan de VVV Amsterdam.