English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Understanding the behaviors of both locals and tourists is essential for good city planning, especially in tourism-dependent cities. This study aimed to explore the disparities between the two groups on the basis of their geotagged photos taken in Tokyo during the last decade (2009–2019). The photos were collected from the photosharing platform Flickr. Locals and tourists were then identified. Next, a transfer-learning-based convolutional neural network model was developed to multi-label photos into eight general categories reflecting the major frequented activities/locations, including nature, amusement, and culture. Additional information was assigned to these records, including distances to various nearest points of interest. Qualitative and quantitative methods were used to investigate the differences between locals and tourists. Results showed that tourists have a strong preference for amusement while locals are attracted to nature. In contrast to tourists who are not followed by job obligations, locals’ photos are mostly taken during the weekends. Given their familiarity with the area, locals tend to cover a wider spatial extent compared to tourists who are concentrated near the Yamanote railway loop line connecting most of the tourist attractions.

Exploring the Differences Between Tourists and Locals in Urban Settings Through Multi-labeled Geotagged Photos: The Case of Tokyo