DOI QR코드

DOI QR Code

Urban Vitality Assessment Using Spatial Big Data and Nighttime Light Satellite Image: A Case Study of Daegu

공간 빅데이터와 야간 위성영상을 활용한 도시 활력 평가: 대구시를 사례로

  • 정시윤 (경북대학교 대학원 지리학과) ;
  • 전병운 (경북대학교 지리학과)
  • Received : 2020.12.06
  • Accepted : 2020.12.17
  • Published : 2020.12.31

Abstract

This study evaluated the urban vitality of Daegu metropolitan city in 2018 using emerging geographic data such as spatial big data, Wi-Fi AP(access points) and nighttime light satellite image. The emerging geographic data were used in this research to quantify human activities in the city more directly at various spatial and temporal scales. Three spatial big data such as mobile phone data, credit card data and public transport smart card data were employed to reflect social, economic and mobility aspects of urban vitality while public Wi-Fi AP and nighttime light satellite image were included to consider virtual and physical aspects of the urban vitality. With PCA (Principal Component Analysis), five indicators were integrated and transformed to the urban vitality index at census output area by temporal slots. Results show that five clusters with high urban vitality were identified around downtown Daegu, Daegu bank intersection and Beomeo intersection, Seongseo, Dongdaegu station and Chilgok 3 district. Further, the results unveil that the urban vitality index was varied over the same urban space by temporal slots. This study provides the possibility for the integrated use of spatial big data, Wi-Fi AP and nighttime light satellite image as proxy for measuring urban vitality.

본 연구는 공간 빅데이터, 공공 Wi-Fi AP와 야간 위성영상과 같은 새로운 지리 데이터를 활용하여 2018년 대구광역시의 도시 활력을 평가하였다. 새로운 지리 데이터는 다양한 시공간 스케일에서 도시민의 활동을 보다 직접적으로 파악하기 위하여 본 연구에서 사용되었다. 이동전화 데이터, 대중교통 스마트카드 데이터, 신용카드 데이터와 같은 세 가지 공간 빅데이터가 도시 활력의 사회적, 경제적 및 모빌리티 측면을 반영하기 위하여 사용되었다. 반면에, 공중 Wi-Fi AP와 야간 위성영상은 도시 활력의 가상적 및 물리적 측면을 고려하기 위하여 사용되었다. 다섯 개의 도시활력 지표들은 주성분 분석을 통해 통합되어 네 개의 시간대에서 집계구별 도시 활력 지수로 변환 되었다. 연구 결과에 의하면, 높은 도시 활력을 가진 다섯 개의 클러스터가 대구 도심, 대구은행 네거리와 범어역 네거리, 성서, 동대구역, 칠곡 3지구 주변에서 확인되었다. 또한, 도시 활력 지수는 같은 도시 공간상에서도 시간대별로 변한다는 것이 밝혀졌다. 본 연구는 도시 활력을 측정하기 위한 대리변수로 공간 빅데이터, 공공 Wi-Fi AP, 야간 위성영상을 통합하여 활용할 수 있는 가능성을 제시한다.

Keywords

References

  1. Abernathy, D. 2016. Using Geodata and Geolocation in the Social Science: Mapping Our Connected World, Sage Publications Ltd., London, UK. 327pp.
  2. Ahas, R., A. Aasa, Y. Yuan, M. Raubal, Z. Smoreda, Y. Liu, C. Ziemlicki, M. Tiru and M. Zook. 2015. Everyday space-time geographies: Using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn. International Journal of Geographical Information Science 29(11):2017-2039. https://doi.org/10.1080/13658816.2015.1063151
  3. He, Q., W. He, Y. Song, J. Wu, C. Yin and Y. Mou. 2018. The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical'big data'. Land Use Policy 78:726-738. https://doi.org/10.1016/j.landusepol.2018.07.020
  4. Hong, I.H. 2010. Spatial distribution and utilization feature of Wi-Fi. Journal of the Korean Urban Geographical Society 10(1):55-64.
  5. Jacobs, J. 1961. The Death and Life of Great American Cities. Vintage, New York, NY, USA. 458pp.
  6. Jeon, S.E. and D.B. Shin. 2018. A study on the agent based infection prediction model using space big data: focusing on MERS-CoV incident in Seoul. Journal of the Korean Association of Geographic Information Studies 21(2):94-106. https://doi.org/10.11108/KAGIS.2018.21.2.094
  7. Jin, X., Y. Long, W. Sun, Y. Lu, X. Yang and J. Tang. 2017. Evaluating cities' vitality and identifying ghost cities in China with emerging geographic data. Cities 63:98-109. https://doi.org/10.1016/j.cities.2017.01.002
  8. Jun, B.W. 2006a. A pixel-based assessment of urban quality of life. Journal of the Korean Association of Geographic Information Studies 9(3):146-155.
  9. Jun, B.W. 2006b. Urban quality of life assessment using satellite image and socioeconomic data in GIS. Korean Journal of Remote Sensing 22(5):325-335.
  10. Kim, D.H. and D. Kim. 2018. Development and application of dynamic visualization model for spatial big data. Journal of the Korean Association of Geographic Information Studies 21(1):57-70 https://doi.org/10.11108/KAGIS.2018.21.1.057
  11. Kim, M.H. 2020. The population estimation of metropolitan cities and provinces in South Korea using land use land cover and nighttime light satellite imagery. Journal of the Korean Association of Professional Geographers 54(3):243-254.
  12. Kim, Y.R. 2018. Seoul's Wi-Fi hotspots: Wi-Fi access points as an indicator of urban vitality. Computers, Environment and Urban Systems 72:13-24. https://doi.org/10.1016/j.compenvurbsys.2018.06.004
  13. Kim, Y.R. 2020. Data-driven approach to characterize urban vitality: How spatiotemporal context dynamically defines Seoul's nighttime. International Journal of Geographical Information Science 34(6):1235-1256. https://doi.org/10.1080/13658816.2019.1694680
  14. Kwan, M.P. 2012a. How GIS can help address the uncertain geographic context problem in social science research. Annals of GIS 18(4):245-255. https://doi.org/10.1080/19475683.2012.727867
  15. Kwan, M.P. 2012b. The uncertain geographic context problem. Annals of the Association of American Geographers 102(5):958-968. https://doi.org/10.1080/00045608.2012.687349
  16. Liu, S., L. Zhang and Y. Long. 2019. Urban vitality area identification and pattern analysis from the perspective of time and space fusion. Sustainability 11(15): 4032. https://doi.org/10.3390/su11154032
  17. Lu, S., Y. Huang, C. Shi and X. Yang. 2019. Exploring the association between urban form and neighborhood vibrancy: A case study of Chengdu, China. ISPRS International Journal of Geo-Information 8:165. https://doi.org/10.3390/ijgi8040165
  18. Sulis, P., E. Manle, C. Zhong and M. Batty, M. 2018. Using mobility data as proxy for measuring urban vitality. Journal of Spatial Information Science 16:137-162.
  19. Sung, H. and S. Lee. 2015. Residential built environment and walking activity: Empirical evidence of Jane Jacobs' urban vitality. Transportation Research Part D: Transport Environment 41:318-329. https://doi.org/10.1016/j.trd.2015.09.009
  20. Yang, J.H. and M.H. Kim. 2018. The estimation of electric power consumption for both metropolitan cities and provinces in the Republic of Korea using SNPP VIIRS DNB satellite imagery. Journal of the Korean Association of Professional Geographers 52(3):415-424.
  21. Ye, Y. and A. Van Nes. 2013. Measuring urban maturation processes in Dutch and Chinese new towns: Combining street network configuration with building density and degree of land use diversification through GIS. Journal of Spatial Syntax 4:18-37.
  22. Yue, W., C. Yang, Z. Qun and L. Yong. 2019. Spatial explicit assessment of urban vitality using multi-source data: A case of Shanghai, China. Sustainability 11(3):638. https://doi.org/10.3390/su11030638
  23. Zeng, C., Y. Song, Q. He and F. Shen. 2018. Spatially explicit assessment on urban vitality: Case studies in Chicago and Wuhan. Sustainable Cities and Society 40:296-306. https://doi.org/10.1016/j.scs.2018.04.021
  24. Zhu, L., D.H. Cho, C.W. Jeon and S.Y. Lee. 2016. Evaluating methods for extracting built-up area using NPP-VIIRS nighttime light data and local spatial statistics. Journal of the Korean Urban Geographical Society 19(3):145-163. https://doi.org/10.21189/JKUGS.19.3.11