Characterizing visitor engagement behavior at large-scale events: Activity sequence clustering and ranking using GPS tracking data

Abkarian, Hoseb; Tahlyan, Divyakant; Mahmassani, Hani S.; Smilowitz, Karen

doi:10.1016/j.tourman.2021.104421

Cited by 9 publications

(7 citation statements)

References 27 publications

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“…In addition, studies were conducted with a GPS-based tracking method to understand the spatial and temporal patterns of visitors in three national parks (D’Antonio et al 2010), and Sumter National Forest, and Cascade Mountains, all in the United States (Hallo et al 2012), exploiting the reduction in data collection time gained by collecting high-resolution data via GPS loggers. In recent research, Abkarian et al (2022) used GPS data tracking event participants at the AirVenture event to identify the spatial-temporal activity patterns. A machine learning algorithm was used to classify participants into groups based on their activity sequences.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Tourist Spatial and Temporal Patterns Using GPS and Sequence Alignment Method

Choe

Lee

Choi

et al. 2022

Journal of Travel Research

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This study identified the spatial and temporal patterns of national park visitors by combining global positioning system (GPS) technologies and surveys using the sequence alignment method (SAM) based on the concept of space–time path of time geography. Data were collected from a sample of 344 GPS tracks and questionnaires obtained from Gyeryongsan National Park in South Korea. A complex random stratified sampling method was employed to represent a national sample based on mobile positioning data. The result of the SAM analysis revealed that the derived visitor typologies constituted three groups from 83 unique paths extracted from 341 sequences. Significant intergroup differences were found for the paths based on average travel distance, average visit duration, and average travel speed. The main theoretical contribution of this study is in expanding the application of the theory by providing methodological improvements using SAM and GPS analysis of the theoretical basis of time geography.

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Section: Literature Reviewmentioning

confidence: 99%

Identifying Tourist Spatial and Temporal Patterns Using GPS and Sequence Alignment Method

Choe

Lee

Choi

et al. 2022

Journal of Travel Research

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“…Di negara maju, peta kota selalu diperbaharui secara rutin, misalnya setiap dua tahunan, dengan tujuan agar peta tersebut selalu menyajikan informasi yang aktual (Chen et al, 2021;Fang et al, 2021;Guo et al, 2021). Negara yang sedang berkembang, revisi terhadap peta kota belum intensif seperti yang dilaksanakan di negara maju (Abkarian et al, 2022;Ashour et al, 2021;Bada et al, 2021).…”

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“…Peta kota merupakan peta tematik berupa peta dua dimensi yang menonjolkan informasi tematik berupa jaringan jalan, perkantoran pemerintah, kawasan perdagangan, kawasan industri, kawasan pendidikan dan informasi lainnya yang dibutuhkan oleh masyarakat (Abkarian et al, 2022;Ashour et al, 2021;Bada et al, 2021) (Bjørnskov et al, 2021;Boakye et al, 2021;.…”

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“…Tingkat ketelitian peta untuk penataan ruang wilayah ditentukan berdasarkan pada skala minimal yang diperlukan untuk merekonstruksi informasi pada peta di muka bumi (Bjørnskov et al, 2021;Boakye et al, 2021;. Kesalahan yang disebabkan oleh alat-alat penggambaran seperti ketebalan pensil gambar, kesalahan pada penyimpangan penempatan mistar, penggaris dan lain-lain sedapat mungkin diusahakan agar besarnya tidak melebihi 0.2 mm (Abkarian et al, 2022;Ashour et al, 2021;Bada et al, 2021).…”

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“…Berdasarkan gambar 5, menunjukkan peta batas administrasi seluas 36,90 Ha, dan gambar 6 seluas 39,04 Ha. Terdapat perbedaan seluas 2,14 Ha dikarenakan pengambilan data tidak mengikuti patok yang ada di lapangan (Abkarian et al, 2022;Ashour et al, 2021;Bada et al, 2021). Berdasarkan gambar 7 menunjukkan Peta jaringan jalan terbagi menjadi 4 bagian: Dengan lebar jalan: Jalur merah ± 1 m, jalur kuning ± 2 m, jalur hijau ± 4 m, dan jalur hijau tua ± 8 m. Warna diperuntukkan untuk menunjukkan tingkat kesulitan dalam evakuasi bencana.…”

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Pemetaan Wilayah Kelurahan Karang Mumus Kota Samarinda Menggunakan Autodesk Map

2022

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Pesatnya pertumbuhan fisik kota Samarinda mempengaruhi struktur kota dengan adanya ruas jalan baru yang belum tergambar dalam peta. Perlu adanya peta jaringan jalan yang akurat untuk menggambar hal tersebut diperlukan sebuah alat sederhana berupa Global Positioning System (GPS). Tujuan penelitian adalah pembuatan peta batas wilayah administrasi Kelurahan Karang Mumus dan jaringan jalan. Metode penelitian yang digunakan tumpang susun data peta dasar Badan Informasi Geospatial dengan data survei GPS Garmin 76CSx di lapangan, dilakukan dua tahapan yaitu, data sekunder berupa peta wilayah kota Samarinda BAPPEDA Kota. Data primer dilakukan pengambilan titik koordinat peta batas wilayah administrasi dan jaringan jalan Kelurahan Karang Mumus. Dan melakukan proses tumpang susun semua data jaringan ruas jalan dari GPS ditransfer ke komputer kemudian diproses dengan Map Source dan Autodesk Map 2004. Selanjutnya proses drawing dilakukan mulai dari kalibrasi GPS, batas administrasi, dan jaringan jalan diolah dengan menggunakan Microsoft Excel sebagai crosscheck koordinat di lapangan. Data di ekspor ke program Autodesk Map di overlay dengan peta dasar dan citra Quick bird. Hasil penelitian menunjukkan peta wilayah administrasi Kelurahan Karang Mumus dari BAPPEDA Kota Samarinda berbeda dengan hasil survei lapangan dan tumpang susun. Di sebabkan adanya perbedaan peta wilayah administrasi dengan titik patok koordinat di lapangan Kelurahan Karang Mumus.

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Machine learning applied to tourism: A systematic review

Núñez,

Gómez‐Pulido,

Ramírez

2024

WIREs Data Min & Knowl

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The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry

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Characterizing visitor engagement behavior at large-scale events: Activity sequence clustering and ranking using GPS tracking data

Cited by 9 publications

References 27 publications

Identifying Tourist Spatial and Temporal Patterns Using GPS and Sequence Alignment Method

Identifying Tourist Spatial and Temporal Patterns Using GPS and Sequence Alignment Method

Pemetaan Wilayah Kelurahan Karang Mumus Kota Samarinda Menggunakan Autodesk Map

Machine learning applied to tourism: A systematic review

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