Browse > Article
http://dx.doi.org/10.22640/lxsiri.2016.46.2.269

A Study on the Spatial Patterns of Tweet Data for Urban Areas by Time - A Case of Busan City -  

Ku, Cha Yong (Geography, Sangmyung University)
Publication Information
Journal of Cadastre & Land InformatiX / v.46, no.2, 2016 , pp. 269-281 More about this Journal
Abstract
The process of spatial big data, such as social media, is being paid more attention in the field of spatial information in recent years. This study, as an example of spatial big data analysis, analyzed the spatial and temporal distribution of Tweet data based on the location and time information. In addition, the characteristics of its spatial pattern by times were identified. Tweet data in Busan city are collected, processed, and analyzed to identify the characteristics of the temporal and spatial pattern. Then, the results of Tweet data analysis were compared with the characteristics of the land type. This study found that spatial pattern of tweeting in the city was associated with given time periods such as daytime and nighttime in both weekdays and weekends. The spatial distribution patterns of individual time periods were compared with the characteristics of the land for the spatially concentrated area. The results of this study showed that tweeted data would be related to different spatial distribution depending on the time, which potentially reflects the daily pattern and characteristics of the land type of urban area to some extent. This study presented the possible incorporation of social media data, e. g. Tweet data, into the field of spatial information. It is expected that there will be more advantage to use a variety of social media data in areas such as land planning and urban planning.
Keywords
Spatial big data; Social media; Spatial pattern by time; Tweet data;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Malleson N, Andersen MA. 2015. The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science. 42(2): 112-121.   DOI
2 Miller HJ. 2004. Activities in space and time. in Hensher DA, Button KJ, Haynes KE, Stopher P.(eds) Handbook of Transport Geography and Spatial Systems. Elsevier. Lodon.
3 Steiger E, Westerholt R, Resch B, Zipf A. 2015. Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data. Computers, Environment and Urban System. 54: 255-265.   DOI
4 Sui D, Goodchild M. 2011. The convergence of GIS and social media: challenges for GIScience. International Journal of Geographic Information Science. 25(11):1737-1748.   DOI
5 Xu C, Wong D W, Y ang C. 2013. Evaluating the "geographical awareness" of individuals: an exploratory analysis of twitter data. Cartography and Geographic Information Science. 40(2):103-115.   DOI
6 강애띠, 강영옥. 2015. 타임라인 데이터를 이용한 트위터 사용자의 거주 지역 유추방법. 한국공간정보학회지. 23(2): 69-81. Kang AT, Kang YO. 2015. Location inference of Twitter users using timeline data. Journal of Korea Spatial Information Society. 23(2): 69-81.
7 구자용. 2015. 공간정보 빅 데이터의 지도화와 공간적 분포 특성에 관한 연구 : 서울시 지역의 트윗 데이터를 사례로. 국토지리학회지. 49(3): 349-360. Ku CY. 2015. The study on them apping and spatial distribution analysis for spatial big data: the case study on the twit data of Seoul area. The Geographical Journal of Korea. 49(3): 349-360.
8 김대종. 2014. 정부 3.0을 위한 공간 빅 데이터 구축 및 활용방안. 국토. p. 42-51. Kim DJ. 2014. Building and Application of spatial big data for government 3.0. Planning and Policy. p. 42-51.
9 박재희, 강영옥. 2014. 트윗을 이용한 서울시 주거환경 만족의 공간적 특성 분석-도시정책지표 보완을 위한 활용방안 모색. 한국도시지리학회지. 17(1): 43-56. Park JH, Kang YO. 2014. An analysis of spatial characteristics of residential satisfaction in Seoul using Tweet data: an applicability of Tweet data for complementing urban policy indicators. Joural of the Korean Urban Geographical Society 17(1): 43-56.
10 박우진, 유기윤. 2015. 위치기반 소셜 미디어 데이터의 텍스트 마이닝 기반 공간적 클러스터링 분석 연구. 한국지형공간정보학회지. 23(2): 89-96. Park WJ, Yu KW. 2015. Spatial clustering anaysis based oon text mining of location-based social media data. Journal of Korean Society of Geospatial Information Science. 23(2): 89-96.   DOI
11 신정엽. 2014. 정보 격차의 맥락에서 트윗 데이터의 이론적 고찰과 실증적 공간 탐색: 미국 킹 카운티를 사례로. 한국지도학회지. 14(2): 89-106. Shin JY. 2014. Theoretical review and quantitative spatial exploration of Tweet data in the context of digital divice: case of King county, US. Journal of Korean Cartographic Association. 14(2): 89-106.
12 Kim MG, Kang YO, Lee JY, Koh JH. 2016. Inferring tweet location inference for twitter mining. Spatial Information Research. 24(4): 421-435.   DOI
13 안종욱, 이미숙, 신동빈. 2013. 공간 빅 데이터 개념 및 체계 구축방안 연구. 한국공간정보학회지. 21(5): 43-51. Ahn JW, Yi MS, Shin DB. 2013. Study for spatial big data concept and system building. Journal of Korea Spatial Information Society. 21(5): 43-51.
14 이광국, 조승구. 2007. 부산시 상업기능의 도시공간적 분포변화 특성. Journal of the Korean Data Analysis Society. 9(3): 1443-1456. Lee KK, Jo SK. 2007. A Study on characteristics of the periodical change by the urban spatial location of commercial function in Busan. Journal of the Korean Data Analysis Society. 9(3): 1443-1456.
15 조영임. 2013. 빅 데이터의 이해와 주요 이슈들. 한국지역정보학회지. 16(3): 43-65. Cho YI. 2013. Understanding and main issues of big data. Joural of Korean Association for Regional Information Society. 16(3): 43-65.
16 홍일영. 2015. 국내 지오트윗의 공간분포. 한국지도학회지. 15(2): 93-101. Hong IY. 2015. Spatial distribution of Korean Geotweets. Journal of Korean Cartographic Association. 15(2): 93-101.
17 홍일영. 2016. 국내 위치기반 소셜 네트워크 데이터의 공간분포. 한국지도학회지. 16(2): 95-104. Hong IY. 2016. Spatial distribution of LBSN data in Korea. Journal of Korean Cartographic Association. 16(2): 95-104.
18 Lee R, Wakamiya S, Sumiya K. 2013. Urban area characterization based on crowd behavioral lifelogs over Twitter. Personal Ubiquitous Computing. 17:605-620.   DOI
19 Li L, Goodchild M, Xu B. 2013. Spatial, temporal and socioeconomic patterns in the use of Twitter and Flickr. Cartography and Geographic Information Science. 40(2): 61-77.   DOI