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Open AccessArticle

Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data

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Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
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Department of Physical Geography and Geoinformatics, University of Szeged, 6722 Szeged, Hungary
3
Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(9), 378; https://doi.org/10.3390/ijgi7090378
Received: 17 August 2018 / Revised: 11 September 2018 / Accepted: 11 September 2018 / Published: 14 September 2018
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3–4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors’ spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations. View Full-Text
Keywords: urban parks; urban green areas; spatial analysis; GIS; sentiment analysis; temporal analysis; livability; social media analysis; accessibility analysis; urban planning urban parks; urban green areas; spatial analysis; GIS; sentiment analysis; temporal analysis; livability; social media analysis; accessibility analysis; urban planning
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Kovacs-Györi, A.; Ristea, A.; Kolcsar, R.; Resch, B.; Crivellari, A.; Blaschke, T. Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data. ISPRS Int. J. Geo-Inf. 2018, 7, 378.

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