Journal of Korean Society for Geospatial Information Science (대한공간정보학회지)

Korea Spatial Information Society (대한공간정보학회)
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Analyzing Spatial Correlation between Location-Based Social Media Data and Real Estates Price Index through Rasterization

격자기반 분석을 통한 위치기반 소셜 미디어 데이터와 부동산 가격지수 간의 공간적 상관성 분석 연구

  • Park, Woo Jin (Center of Environmental Remediation and Risk Assessment, Seoul National University) ;
  • Eo, Seung Won (Department of Civil & Environmental Engineering, Seoul National University) ;
  • Yu, Ki Yun (Department of Civil & Environmental Engineering, Seoul National University)
  • 박우진 (서울대학교 환경정화기술 및 위해성평가 연구센터) ;
  • 어승원 (서울대학교 대학원 건설환경공학부) ;
  • 유기윤 (서울대학교 건설환경공학부)
  • Received : 2015.01.12
  • Accepted : 2015.03.09
  • Published : 2015.03.31
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Abstract

In this study, the spatial relevance between the regional housing price data and the spatial distribution of the location-based social media data is explored. The spatial analysis with rasterization was applied to this study, because the both data have a different form to analyze. The geo-tagged Twitter data had been collected for a month and the regional housing price index about sales and lease were used. The spatial range of both data includes Seoul and the some parts of the metropolitan area. 2,000m grid was constructed to consider the different spatial measure between two data, and they were combined into the constructed grids. The Hotspot Analysis was operated using the combined dataset to see the comparison of spatial distribution, and the bivariate spatial correlation coefficients between two data were measured for the quantitative analysis. The result of this study shows that Seocho-gu area is detected as a common hotspot of tweet and housing sales price index data. though the spatial relevance is not detected between tweet and housing lease price index data.

본 연구에서는 위치기반 소셜 미디어 데이터의 공간적 분포가 지역별 부동산 지수와 어떠한 공간적 관련성을 가지는지에 대해 알아보고자 한다. 두 데이터는 상이한 자료 형식을 가지고 있어, 이를 보완할 수 있는 방법론으로 본 연구에서는 격자 기반의 공간분석 방법을 적용하였다. 대상 데이터로는 2013년 8월 한 달간의 지오태그된 트윗 데이터와 행정구역별 주택가격지수(매매, 전세)를 이용하였으며, 공간적 범위는 서울과 수도권 일부를 포함하도록 하였다. 두 데이터 간의 상이한 공간적 단위를 고려하여 2,000m 단위의 격자망을 구성하고 이에 맞게 두 데이터를 격자 데이터 형태로 변환하였다. 변환된 두 데이터에 대하여 Hot spot 분석을 실시하여 공간적 분포를 시각적으로 비교하였으며, 공간시차를 고려한 이변량 공간적 상관계수를 측정함으로써 정량적 분석을 실시하였다. 시각적, 정량적 분석 결과, 서초구 지역이 트윗 데이터와 주택매매가격지수 데이터에서 공통적인 Hotspot 지역으로 탐색되었으나 주택전세가격지수 데이터와는 뚜렷한 공간적 상관성이 탐색되지 않았다.

Keywords

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