DOI: 10.57760/sciencedb.35670
title: Geotagged Social Media Data and Environmental Features of Urban Attractions in Beijing, 2023
title: Geotagged Social Media Data and Environmental Features of Urban Attractions in Beijing, 2023

description: This dataset provides a detailed examination of urban attractions within Beijing, using geotagged social media data and various environmental attributes to analyze visitor experiences and attraction dynamics. The dataset is structured around four key components:Shapefile of the Study Area: Represents the 215 urban attractions identified based on geotagged Sina Weibo posts, where each location recorded more than 100 posts within a 500-meter buffer. This shapefile delineates the boundaries of each attraction. Specifically, the ‘Type’ field indicates the attraction category, with ‘A’ representing natural landscapes, ‘B’ representing cultural-tourism complexes, and ‘C’ representing historic sites.Explanatory Variables: The explanatory variables are categorized into three dimensions: Landscape Environment, Built Environment, and Social Environment. First, the Landscape Environment dimension comprises 13 landscape characteristic variables derived from the SinoLC-1 land use dataset. Second, the Built Environment dimension utilizes 2023 AutoNavi Point of Interest (POI) data and OpenStreetMap data to quantify the number of food outlets, shops, and parking facilities, the distance to the nearest subway stations and bus stops, and the road network density within a 500-meter buffer. Finally, the Social Environment dimension consists of a single variable: crowdedness. This variable represents the visitor volume within each attraction, derived from China Unicom’s mobile signaling records.Weibo Textual Data within Attractions: This dataset encompasses the text content of geotagged Weibo posts within the boundaries of each attraction, providing qualitative insights into visitor experiences and perceptions.Together, these datasets offer a comprehensive foundation for understanding urban touristic dynamics in Beijing, supporting analyses of visitor behavior, attraction popularity, and environmental interactions.
description: This dataset provides a detailed examination of urban attractions within Beijing, using geotagged social media data and various environmental attributes to analyze visitor experiences and attraction dynamics. The dataset is structured around four key components:Shapefile of the Study Area: Represents the 215 urban attractions identified based on geotagged Sina Weibo posts, where each location recorded more than 100 posts within a 500-meter buffer. This shapefile delineates the boundaries of each attraction. Specifically, the ‘Type’ field indicates the attraction category, with ‘A’ representing natural landscapes, ‘B’ representing cultural-tourism complexes, and ‘C’ representing historic sites.Explanatory Variables: The explanatory variables are categorized into three dimensions: Landscape Environment, Built Environment, and Social Environment. First, the Landscape Environment dimension comprises 13 landscape characteristic variables derived from the SinoLC-1 land use dataset. Second, the Built Environment dimension utilizes 2023 AutoNavi Point of Interest (POI) data and OpenStreetMap data to quantify the number of food outlets, shops, and parking facilities, the distance to the nearest subway stations and bus stops, and the road network density within a 500-meter buffer. Finally, the Social Environment dimension consists of a single variable: crowdedness. This variable represents the visitor volume within each attraction, derived from China Unicom’s mobile signaling records.Weibo Textual Data within Attractions: This dataset encompasses the text content of geotagged Weibo posts within the boundaries of each attraction, providing qualitative insights into visitor experiences and perceptions.Together, these datasets offer a comprehensive foundation for understanding urban touristic dynamics in Beijing, supporting analyses of visitor behavior, attraction popularity, and environmental interactions.