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[ 그림 1] 2013, Tilottama Ghosh, et al. (GDP와야간광의상관관계 ) 미국, 중국, 멕시코, 인도를같은그룹으로조사하였다. - 2 -
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값 특정 값 값 특정 값 - 18 -
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1( 순위 ) New York( 도시명 ) 146,800,014( 중심성 ) 64( 활동성 ) 2 London 145,041,900 64 3 Tokyo 116,843,871 64 4 Los Angeles 90,096,603 29 5 Paris 87,553,220 64 6 Dallas 81,572,839 58 7 Washington 73,560,155 35 8 Frankfurt 69,770,131 58 9 Houston 66,215,892 22 10 Las Vegas 61,885,752 3 11 Hong Kong 61,512,579 44 12 Denver 57,067,699 58 13 Shanghai 55,079,434 37 14 Osaka 52,130,267 64 15 Madrid 51,609,300 31 16 San Francisco 51,556,747 62 17 Bangkok 51,493,779 52 18 Seoul 51,177,372 64-22 -
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도시명 중심성 활동성 Hong Kong 61512579 44 Shanghai 55079433 37 Beijing 50070493 18 Shenzhen 17869097 51 Chengdu 14344607 10 Hangzhou 9721206 9 Chongqing 7579973 2 Shenyang 6678566 10 Nanjing 6163758 18 Fuzhou 5133767 6 Chiang Mai 4304847 2 Zhengzhou 4006258 17 Wenzhou 3889897 7 Nanchang 2998107 7 Changchun 2969062 7 Tianjin 3179473 21 Jingdezhen 179891 3-25 -
대륙 국가명 도시명 중심성 활동성 순위 Asia Japan Tokyo 116843871.3 64 1 Asia Japan Osaka 52130267.12 64 1 Asia Japan Nagoya 16044335.72 64 1 Asia Philippines Manila 18083591.33 64 1 Asia Republic of Korea Seoul 51177372.33 64 1 Asia Japan Tsushima 330188.2807 64 1 Asia Japan Fukuoka 34322139.38 63 2 Asia Japan Tokushima 3277746.303 63 2 Asia Chinese Taipei Kaohsiung 8938950.427 63 2 Asia Japan Okinawa 18512963.72 63 2 Asia Macao Macau 5719483.323 63 2 Asia Philippines Cebu 3239765.33 62 3 Asia Chinese Taipei Taipei 40505715.83 61 4 Asia Japan Okayama 3288657.721 61 4 Asia India Chandigarh 122811.5072 60 5 Asia Russian Federation Chita 57465.53545 60 5 Asia Singapore Singapore 46889090.7 60 5 Asia Japan Hiroshima 7545520.331 59 6 Asia Viet Nam Haiphong 66309.76931 59 6 Asia Japan Saga 651936.0298 58 7 Asia Japan Tottori 942710.5251 57 8 Asia Myanmar Yangon 2295985.082 55 9 Asia Japan Kumamoto 7044622.502 55 9 Asia Indonesia Yogyakarta 1738521.486 54 10 Asia Indonesia Surabaya 5473517.494 54 10 Asia Republic of Korea Busan 10541687.01 53 11 Asia India Delhi 15799479.96 53 11 Asia Japan Toyama 3201753.215 52 12 Asia Thailand Bangkok 51493779.41 52 12 Asia Japan Aomori 2785402.893 52 12 Asia China Shenzhen 17869097.64 51 13 Asia Japan Niigata 1822877.279 50 14-26 -
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아시아 중심성 활동성 중심성 1.0000 활동성 0.4774 1.0000 Number of objects : 344 Spearman's rho : 0.6238 Prob > t : 0.0000 p-value : 0.0000 유럽 중심성 활동성 중심성 1.0000 활동성 0.3423 1.0000 Number of objects : 278 Spearman's rho : 0.5081 Prob > t : 0.0000 p-value : 0.0000 북미 중심성 활동성 중심성 1.0000 활동성 0.2594 1.0000 Number of objects : 350 Spearman's rho : 0.2499 Prob > t : 0.0000 p-value : 0.0000 남미 중심성 활동성 중심성 1.0000 활동성 0.4410 1.0000 Number of objects : 122 Spearman's rho : 0.5141 Prob > t : 0.0000 p-value : 0.0000 중동 중심성 활동성 중심성 1.0000 활동성 0.6195 1.0000 Number of objects : 42 Spearman's rho : 0.5894 Prob > t : 0.0000 p-value : 0.0000 중미 중심성 활동성 중심성 1.0000 활동성 1.0000 Number of objects : 11 Spearman's rho : 0.2055 Prob > t : 0.5444 p-value : X ( 신뢰성없음 ) 캐리비안 중심성 활동성 중심성 1.0000 활동성 0.3785 1.0000 Number of objects : 41 Spearman's rho : 0.3869 Prob > t : 0.0125 p-value : 0.0147 호주 중심성 활동성 중심성 1.0000 활동성 0.3192 1.0000 Number of objects : 53 Spearman's rho : 0.4003 Prob > t : 0.0030 p-value : 0.0198 아프리카 중심성 활동성 중심성 1.0000 활동성 0.4080 1.0000 Number of objects : 137 Spearman's rho : 0.7077 Prob > t : 0.000 p-value : 0.000-28 -
Sydney, Melbourne, Rio de Janeiro, Sao Paulo, Quebec, Prague, Paris, Saint-Denis, London, Osaka, Tokyo, Manila, Columbia, Tel Aviv, Adelaide, Alexandria, Vancouver, Nagoya, Brunswic, New York, Auburn, Glasgow, Norfolk, Philadelphia, Addis Ababa, Perth, Beirut - 29 -
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국내논문 김상우외.(2005), DMSP-OLS 영상에서관측한동해야간조업어선의분포특성, J. Kor. Fish, Soc, 38(5): 323-330. 김민호 (2014), 위성영상을활용한국내빛공해변화에관한연구, 한국지도학회지, 14(1): 49-59. 정해영 (2015), 세계도시의위계와수렴현상, 서울대학교환경대학원석사학위논문 해외논문 山中裕則, 外.(1993), DMSP 全球夜間画像の作成と夜の光分布に関する地理学的考察, 日本リモートセンシング学会誌, 13(4): 1~14. Bin Gao et al.(2015), Dynamics of Urbanization Levels in China from 1992 to 2012 Perspective from DMSP/OLS Nighttime Light Data, Remote Sensing, 7(2): 1721-1735. Benjamin T. Tuttle et al.(2014), Aladdin s Magic Lamp Active Target Calibration of the DMSP OLS, Remote Sensing, 6(12): 12708-12722. Brian Min, Kwawu Mensan Gaba,(2014), Tracking Electrification in Vietnam Using Nighttime Lights, Remote Sensing, 6(10): 9511-9529. Christopher c. M. Kyba et al.(2015), High-Resolution Imagery of Earth at Night New Sources, Opportunities and Challenges, Remote Sensing, 7(1): 1-23. Changyong Cao, Yan Bai,(2014), Quantitative Analysis of VIIRS DNB Nightlight Point Source for Light Power Estimation and Stability Monitoring, Remote Sensing, 6(12): 11915-11935. Christopher D. Elvidge et al.(2014), National Trends in Satellite-Observed Lighting 1992-2012, Global Urban Monitoring and Assessment, 7(2): 97-119. Christopher D. Elvidge et al.(2011), National Trends in Satellite Observed Lighting: 1992-2009, Remote Sensing, (3): 1-17. Christopher D. Elvidge et al.(2009), A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data, Energies, 2(3): 595-622. Christopher D. Elvidge et al.(1997), Mapping City Lights With Nighttime Data - 33 -
from the DMSP Operational Linescan System, Photogrammetric Engineering & Remote Sensing, 63(6): 727-734. Feng-Chi Hsu et al.(2015), DMSP-OLS Radiance Calibrated Nighttime Lights Time Series with Intercalibration, Remote Sensing, 7(2): 1855-1876. Hanwei Liang et al.(2014), Modeling In-Use Steel Stock in China s Buildings and Civil Engineering Infrastructure Using Time-Series of DMSP-OLS Nighttime Lights, Remote Sensing, 6(6): 4780-4800. Junfu Fan et al.(2014), Comparative Estimation of Urban Development in China s Cities Using Socioeconomic and DMSP-OLS Night Light Data, Remote Sensing, 6(8): 7840-7856. J. Y. Tsao et al.(2010), Solid-state lighting: an energy-economics perspective, Journal of Physics D: Applied Physics, 43(35): 1-18. J. Vernon Henderson et al.(2009), Measuring Economic Growth from Outer Space (15199), NBER, p.270. Kaifang Shi et al.(2014), Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the GDP and EPC, Remote Sensing, 6(2): 1705-1724. Li Zhang et al.(2015), Estimating Land Development Time Lags in China Using DMSP-OLS Nighttime Light Image, Remote Sensing, 7(1): 882-904. Lin Ma et al.(2014), Evaluating Saturation Correction Methods for DMSP OLS, Remote Sensing, 6(10): 9853-9872. Naizhuo Zhao et al.(2015), Correcting Incompatible DN Values and Geometric Errors in Nighttime Lights Time-Series Images, Geoscience and Remote Sensing, IEEE Transactions on, 53(4): 2039-2049. Pengpeng Han et al.(2014), Monitoring Trends in Light Pollution in China Based on Nighttime Satellite Imagery, Remote Sensing, 6(6), pp.5541-5558. Qingxu Huang et al.(2014), Application of DMSP-OLS Nighttime Light Images_A Meta-Analysis and a Systematic Literature Review, Remote Sensing, 6(8): 6844-6866. Qingling Zhang, et al.(2013), The Vegetation Adjusted NTL Urban Index: A new - 34 -
approach to reduce saturation and increase variation in nighttime luminosity, Remote Sensing of Environment, 129: 32-41. Qingling Zhang, Karen C,Seto.(2011), Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data, Remote Sensing of Environment, 115: 2320-2329. Rafael Wiemker.(1995), Das Farbkonstanzproblem in der multispektralen Fernerkundung, Universit at Hamburg: Institut fur Experimentalphysik. Ruifang Hao et al.(2015), Integrating Multiple Source Data to Enhance Variation and Weaken the Blooming Effect of DMSP-OLS Light, Remote Sensing, 7(2): 1422-1440. Tao Xu et al.(2014), Characterizing Spatio-Temporal Dynamics of Urbanization in China Using Time Series of DMSP-OLS Night Light Data, Remote Sensing, 6(8): 7708-7731. Tilottama Ghosh, et al.(2013), Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being, Sustainability, 5(12): 4988-5019. William C. Straka, et al.(2015), Utilization of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) DayNight Band for Arctic Ship Tracking and Fisheries Management, Remote Sensing, 7(1): 971-989. Wenze Yue et al.(2014), Estimation of GDP Using Multi-Sensor remote sensing data: A Case Study in Zhejiang Province, East China, Remote Sensing, 6(8): 7260-7275. Yuke Zhou et al.(2015), Nighttime Light Derived Assessment of Regional Inequality of Socioeconomic Development in China, Remote Sensing, 7(2): 1242-1262. Yang-Woon Lee et al.(2014), Satellite-based assessment of large-scale land cover change in Asian arid regions in the period of 2001-2009, Environ Earth Sci, 71(9): 3935-3944. Yuyu Zhou et al.(2014), A Cluster-based method to map urban area from DMSP/OLS nightlights, Remote Sensing of Environment, 147: 173-185. - 35 -
웹사이트 A Review of the Universe, Metabolic Rate and Kleiber s Law, https://universe-review.ca/r10-35-metabolic.htm Euromonitor International, Top 100 City Destinations Ranking - Jan 27th 2015, http://blog.euromonitor.com/2015/01/top-100-city-destinations-ranking.html Baruch College, Top 100 World Urban Areas - Ranked by Population 2015*, https://www.baruch.cuny.edu/nycdata/world_cities/largest_cities-world.htm National Centers for Environmental Information, DMSP-OLS Nighttime Lights, http://www.ngdc.noaa.gov/eog/dmsp/downloadv4composites.html Redrise, Lumen / Lux Calculator and their definitions, http://www.ledrise.com/shop_content.php?coid=19l - 36 -
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Average Visible, Stable Lights, & Cloud Free Coverages Year/ Sat. F10 F12 F14 F15 F16 F18 1992 F101992 ------- ------- ------- ------- ------- 1993 F101993 ------- ------- ------- ------- ------- 1994 F101994 F121994 ------- ------- ------- ------- 1995 ------- F121995 ------- ------- ------- ------- 1996 ------- F121996 ------- ------- ------- ------- 1997 ------- F121997 F141997 ------- ------- ------- 1998 ------- F121998 F141998 ------- ------- ------- 1999 ------- F121999 F141999 ------- ------- ------- 2000 ------- ------- F142000 F152000 ------- ------- 2001 ------- ------- F142001 F152001 ------- ------- 2002 ------- ------- F142002 F152002 ------- ------- 2003 ------- ------- F142003 F152003 ------- ------- 2004 ------- ------- ------- F152004 F162004 ------- 2005 ------- ------- ------- F152005 F162005 ------- 2006 ------- ------- ------- F152006 F162006 ------- 2007 ------- ------- ------- F152007 F162007 ------- 2008 ------- ------- ------- ------- F162008 ------- 2009 ------- ------- ------- ------- F162009 ------- 2010 ------- ------- ------- ------- ------- F182010 2011 ------- ------- ------- ------- ------- F182011 2012 ------- ------- ------- ------- ------- F182012 2013 ------- ------- ------- ------- ------- F182013-38 -
대륙 국가 도시 중심성 활동성 Africa Egypt Alexandria 598712.8 64.1108 Africa Egypt Alexandria 461552 64.1108 Africa Ethiopia Addis Ababa 1822840 63.933 Africa Egypt Asyut 60971.86 63.9252 Africa Ghana Accra 1471067 56.1137 Africa Congo Brazzaville 475307 54.8556 Africa Senegal Saint Louis 5831.186 54.5145 Africa South Africa Durban 3204452 54.0066 Africa Egypt Luxor 908429.2 53.941 Africa South Africa Johannesburg 16531620 53.7501 Africa Tunisia Tunis 4581735 51.8577 Africa Morocco Rabat 312073.8 49.5752 Africa Sudan Khartoum 1339942 46.2321 Africa Egypt Aswan 554944.2 45.3131 Africa Madagascar Antananarivo 967613.6 42.8938 Africa Ethiopia Mekele 92073.36 40.6203 Africa Mali Bamako 740205.4 39.0494 Africa Zimbabwe Harare 685198.2 36.9861 Africa Zambia Lusaka 500810.1 35.9783 Africa Mauritius Mauritius 2781838 35.5149 Africa United Republic of Tanzania Koblenz 294163.8 32.7606 Africa Madagascar Sainte-Marie 28022.04 30.667 Africa Morocco Casablanca 6249574 30.5672 Africa Zimbabwe Bulawayo 36017.86 29.2668 Africa Kenya Nairobi 4662060 25.2199 Africa South Africa Kimberley 109217 23.7024 Africa Mozambique Maputo 586103.9 23.6148 Africa Senegal Dakar 2177153 22.222 Africa Kenya Mombasa 602834.1 21.4736-39 -
Comparative Analysis of Centrality and Activity in Global Cities Using DMSP-OLS Night Light Data Jiyong Song Dept. of Environmental Planning Graduate School of Environmental Studies Seoul National University