Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

Devkota, Bidur; Miyazaki, Hiroyuki; Witayangkurn, Apichon; Kim, Sohee Minsun

doi:10.3390/su11174718

Cited by 42 publications

(19 citation statements)

References 63 publications

(105 reference statements)

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“…Tourists in Yogyakarta City stay on average for two days, so they need to determine the places to visit. This behaviour can produce specific patterns that can be recognised through social media (Devkota et al, 2019). For this study, geotagged Flickr and Twitter data were analysed using a clustering algorithm to determine the centre of activity.…”

Section: Clusterization Methodsmentioning

confidence: 99%

Revealing Tourist Hotspots in Yogyakarta City Based on Social Media Data Clustering

Wibowo¹,

Santosa²,

Susilo³

et al. 2021

GTG

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Cities have a common characteristic in the form of land utilisation, which is dominated by built-up areas. Tourism is an essential aspect of city development because it can involve the identity of the city. Historical buildings, landmarks, shopping centres and museums are generally interesting places for tourists to visit. Yogyakarta, the research area, is synonymous as a city of culture and of students. Knowledge of the spatial clustering patterns of tourists can be one of the references for urban development. Social media data were used in the study as an alternative to direct data collection, which requires considerable resources. Flickr and Twitter were used as proxies to dete rmine the distribution of tourists, and the DBSCAN and HDBSCAN clustering algorithms were used to determine the centres of tourist activity. Furthermore, Flickr data were analysed temporally to determine the impact of the COVID-19 pandemic on tourism in Yogyakarta City. The clustering of social media data results shows that there are several city hotspots, besides the already well-known tourist attractions. Apart from city landmarks, several other tourist hotspots were revealed through the clustering process, such as accommodation, shopping centres, entertainment venues and souvenir shops, which also support tourism activities. The impact of COVID-19 on tourism in Yogyakarta City can be reflected through the number of uploaded photos by tourists on Flickr, which has decreased since March 2020.

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Section: Clusterization Methodsmentioning

confidence: 99%

Revealing Tourist Hotspots in Yogyakarta City Based on Social Media Data Clustering

Wibowo¹,

Santosa²,

Susilo³

et al. 2021

GTG

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“…Some research studies focus on the specific analysis of tourist flows, mostly focusing on exploiting GPS traces [ 11 , 12 ], and social media sensing [ 57 – 59 ].…”

Section: Related Workmentioning

confidence: 99%

Sensing and making sense of tourism flows and urban data to foster sustainability awareness: a real-world experience

Prandi

Nisi²,

Ribeiro³

et al. 2021

J Big Data

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Tourism is one of the world’s largest industries fundamentally arising from mobility as a form of capital. In destination islands that have a delicate ecosystem to maintain, this source of income can become problematic in terms of sustainability. A difficulty in making people aware of this issue is also represented by the fact that such sustainability-related issues (and their causes) are often not “visible” to citizens. To foster awareness about the relationship between sustainability and tourism in well-known destinations, we design a platform that engages users at two levels of participation: i. at the IoT and sensors level, in order to let them becoming providers of big data, deploying and enlarging the pervasive infrastructure; ii. at the (big) data visualization level, with the aim of engaging them in making sense of large volumes of data related to sustainability. This paper presents the design and implementation of a real-world experience where a low-cost collaborative platform made it possible to sense and visualize tourist flows and urban data into a rich interactive map-based visualization, open to the local communities. We deployed our case study in the Madeira archipelago, engaging locals and visitors of the island in two exploratory studies focused on measuring the impact of providing users with meaningful representations of tourism flows and related unperceivable aspects that affect the environmental sustainability. Analysing the findings of the two studies, we discuss the potentiality of using such a system to make sense of big data, fostering awareness about sustainability issues, and we point to future open challenges about citizens’ participation in sensing and making sense of big data.

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“…In many cases of a two-dimensional dataset, this can be kept at the default value of minPts = 4 [28], while in cases of large and high-dimensional datasets it can be set up minPts = 2*dim [33]. In some studies, a single absolute value is not suitable, so they have set it up based on a percentage of the data point ownership [34], using a heuristic approach based on the size of the dataset [35] or perform its value estimation using an objective function [36]. In general, larger values of minPts are considered more robust to noise and produce more significant clusters.…”

Section: Clustering Analysismentioning

confidence: 99%

“…The ε hyperparameter that represents the maximum distance of the search radius must be set up with the smallest possible value. This hyperparameter has also been tuned in many studies using the k-NN distance (i.e., 25 to 550 m) or considering the application domain and knowledge of the study area [33,36,37].…”

Section: Clustering Analysismentioning

confidence: 99%

Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour

Rodríguez-Echeverría

Šemanjski

Gheluwe

et al. 2020

IJGI

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Knowledge about the spots where tourist activity is undertaken, including which segments from the tourist market visit them, is valuable information for tourist service managers. Nowadays, crowdsourced smartphones applications are used as part of tourist surveys looking for knowledge about the tourist in all phases of their journey. However, the representativeness of this type of source, or how to validate the outcomes, are part of the issues that still need to be solved. In this research, a method to discover hotspots using clustering techniques and give to these hotspots a data-driven interpretation is proposed. The representativeness of the dataset and the validation of the results against existing statistics is assessed. The method was evaluated using 124,725 trips, which have been gathered by 1505 devices. The results show that the proposed approach successfully detects hotspots related with the most common activities developed by overnight tourists and repeat visitors in the region under study.

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Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

Cited by 42 publications

References 63 publications

Revealing Tourist Hotspots in Yogyakarta City Based on Social Media Data Clustering

Revealing Tourist Hotspots in Yogyakarta City Based on Social Media Data Clustering

Sensing and making sense of tourism flows and urban data to foster sustainability awareness: a real-world experience

Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour

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