Using social media data to identify neighbourhood change

被引：0

作者：

Comber, Alexis ^{[1
,2
]}

Kieu, Minh ^{[3
]}

Bui, Quang-Thanh ^{[4
]}

Malleson, Nick ^{[1
,3
]}

机构：

[1] Univ Leeds, Sch Geog, Leeds, W Yorkshire, England

[2] Univ Leeds, Leeds Inst Data Analyt, Leeds, W Yorkshire, England

[3] Univ Auckland, Dept Civil & Environm Engn, Auckland, New Zealand

[4] VNU Univ Sci, Fac Geog, Hanoi, Vietnam

来源：

27TH AGILE CONFERENCE ON GEOGRAPHIC INFORMATION SCIENCE GEOGRAPHIC INFORMATION SCIENCE FOR A SUSTAINABLE FUTURE | 2024年 / 5卷

关键词：

social media data; gentrification; house price;

D O I：

10.5194/agile-giss-5-20-2024

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper explores the use social media data from Twitter to capture perceptions of neighbourhood characteristics, in relation to gentrification. It does this by defining a rudimentary lexicon of words associated with gentrification which was used to calculate gentrification scores for geo-located tweets. These were then interpolated to create the surfaces describing the spatial and temporal patterns of gentrification. There are a number of limitations to the methods used in this study, which are discussed and a number of related areas of future work are indicated.

引用

页数：6

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