{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T02:08:25Z","timestamp":1776218905256,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,6]],"date-time":"2019-09-06T00:00:00Z","timestamp":1567728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFC1503003"],"award-info":[{"award-number":["2017YFC1503003"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2017YFB0503605"],"award-info":[{"award-number":["2017YFB0503605"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The daily nighttime lights (NTL) and the amount of location-service requests (NLR) data have been widely used as a proxy for measures of disaster-induced power outages and geo-tagged human activity dynamics. However, the association between the two datasets is not well understood. In this study, we investigated how the NTL signals and geo-tagged human activities changed in response to Typhoon Mangkhut. The confusion matrix is constructed to quantify the changes of the NLR in response to Typhoon Mangkhut, as well as the changes of the NTL signals at the grid level. Geographically-weighted regression and quantile regression were used to examine the associations between the changes of the NTL and the NLR at both grid and county levels. The quantile regressions were also used to quantify the relationships between the dimmed NTL signals and the change of the NLR in disaster damage estimates at the county level. Results show that the percent of the grids with anomalous human activities is significantly correlated with the nearby air pressure and wind speed. Geo-tagged human activities varied in response to the evolution of Mangkhut with significant areal differentiation. Over 69.3% of the grids with significant human activity change is also characterized by declined NTL brightness, which is closely associated with abnormal human activities. Significant log-linear and moderate positive correlations were found between the changes of the NTL and NLR at both the grid and county levels, as well as between the county-level changes of NLR\/NTL and the damage estimates. This study shows the geo-tagged human activities are closely associated with the changes of the daily NTL signals in response to Typhoon Mangkhut. The two datasets are complimentary in sensing the typhoon-induced losses and damages.<\/jats:p>","DOI":"10.3390\/rs11182091","type":"journal-article","created":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T03:14:41Z","timestamp":1567998881000},"page":"2091","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut"],"prefix":"10.3390","volume":"11","author":[{"given":"Zhang","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yunyan","family":"Du","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jiawei","family":"Yi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Fuyuan","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Earth, Atmospheric, and Geographic Information Sciences, Western Illinois University, Macomb, IL 61455, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4362-9330","authenticated-orcid":false,"given":"Ting","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5311-8761","authenticated-orcid":false,"given":"Tao","family":"Pei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1126\/science.aad8728","article-title":"Global trends in satellite-based emergency mapping","volume":"353","author":"Voigt","year":"2016","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Novellino, A., Jordan, C., Ager, G., Bateson, L., Fleming, C., and Confuorto, P. (2019). Remote Sensing for Natural or Man-Made Disasters and Environmental Changes. Geological Disaster Monitoring Based on Sensor Networks, Springer Natural Hazards.","DOI":"10.1007\/978-981-13-0992-2_3"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.21523\/gcj1.19030101","article-title":"Landslide Hazard Zonation and Slope Instability Assessment using Optical and InSAR Data: A Case Study from Gidole Town and its Surrounding Areas, Southern Ethiopia","volume":"3","author":"Mengistu","year":"2019","journal-title":"Remote Sens. Land"},{"key":"ref_4","first-page":"1005","article-title":"Remote sensing of floods and flood-prone areas: An overview","volume":"31","author":"Klemas","year":"2014","journal-title":"J. Coast. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.1109\/JPROC.2012.2196404","article-title":"Remote sensing and earthquake damage assessment: Experiences, limits, and perspectives","volume":"100","author":"Gamba","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.rse.2018.03.017","article-title":"NASA\u2019s Black Marble nighttime lights product suite","volume":"210","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1080\/2150704X.2014.900205","article-title":"Night-time lights time series of tsunami damage, recovery, and economic metrics in Sumatra, Indonesia","volume":"5","author":"Gillespie","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6844","DOI":"10.3390\/rs6086844","article-title":"Application of DMSP\/OLS nighttime light images: A meta-analysis and a systematic literature review","volume":"6","author":"Huang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, X., Zhan, C., Tao, J., and Li, L. (2018). Long-term monitoring of the impacts of disaster on human activity using dmsp\/ols nighttime light data: A case study of the 2008 Wenchuan, china earthquake. Remote Sens., 10.","DOI":"10.3390\/rs10040588"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/LGRS.2013.2262258","article-title":"Detecting light outages after severe storms using the S-NPP\/VIIRS day\/night band radiances","volume":"10","author":"Cao","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cole, T., Wanik, D., Molthan, A., Rom\u00e1n, M., and Griffin, R. (2017). Synergistic use of nighttime satellite data, electric utility infrastructure, and ambient population to improve power outage detections in urban areas. Remote Sens., 9.","DOI":"10.3390\/rs9030286"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Z., Rom\u00e1n, M., Sun, Q., Molthan, A., Schultz, L., and Kalb, V. (2018). Monitoring disaster-related power outages using NASA black marble nighttime light product. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 1853\u20131856.","DOI":"10.5194\/isprs-archives-XLII-3-1853-2018"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhao, X., Yu, B., Liu, Y., Yao, S., Lian, T., Chen, L., Yang, C., Chen, Z., and Wu, J. (2018). NPP-VIIRS DNB daily data in natural disaster assessment: Evidence from selected case studies. Remote Sens., 10.","DOI":"10.3390\/rs10101526"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mohamadi, B., Chen, S., and Liu, J. (2019). Evacuation Priority Method in Tsunami Hazard Based on DMSP\/OLS Population Mapping in the Pearl River Estuary, China. ISPRS Int. J. Geoinf., 8.","DOI":"10.3390\/ijgi8030137"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.1109\/JPROC.2017.2684460","article-title":"Social media: New perspectives to improve remote sensing for emergency response","volume":"105","author":"Li","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1080\/00045608.2015.1018773","article-title":"Social sensing: A new approach to understanding our socioeconomic environments","volume":"105","author":"Liu","year":"2015","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_17","first-page":"274","article-title":"Algorithmic geographies: Big data, algorithmic uncertainty, and the production of geographic knowledge","volume":"106","author":"Kwan","year":"2016","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Van Hentenryck, P., Fowler, J., and Cebrian, M. (2016). Rapid assessment of disaster damage using social media activity. Sci. Adv., 2.","DOI":"10.1126\/sciadv.1500779"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Thomas, D.S. (2018). The Role of Geographic Information Science & Technology in Disaster Management. Handbook of Disaster Research, Springer.","DOI":"10.1007\/978-3-319-63254-4_16"},{"key":"ref_20","first-page":"72","article-title":"Nowcasting events from the social web with statistical learning","volume":"3","author":"Lampos","year":"2012","journal-title":"Acm Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/978-3-319-71565-0_10","article-title":"Real-time Earthquake Intensity Estimation Using Streaming Data Analysis of Social and Physical Sensors","volume":"Volume I","author":"Kropivnitskaya","year":"2018","journal-title":"Earthquakes and Multi-Hazards around the Pacific Rim"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3141","DOI":"10.1038\/srep03141","article-title":"Quantifying the digital traces of Hurricane Sandy on Flickr","volume":"3","author":"Preis","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lu, X.S., Zhou, M., and Qi, L. (2017, January 5\u20138). Analyzing temporal-spatial evolution of rare events by using social media data. Proceedings of the 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC), Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8123031"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1140\/epjds\/s13688-019-0196-6","article-title":"Quantifying human mobility resilience to extreme events using geo-located social media data","volume":"8","author":"Roy","year":"2019","journal-title":"EPJ Data Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mart\u00edn, Y., Li, Z., and Cutter, S.L. (2017). Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0181701"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hultquist, C., Simpson, M., Cervone, G., and Huang, Q. (2015, January 3). Using nightlight remote sensing imagery and twitter data to study power outages. Proceedings of the 1st ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management, Seattle, WA, USA.","DOI":"10.1145\/2835596.2835601"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Thakuriah, P., Tilahun, N., and Zellner, M. (2017). Using Social Media and Satellite Data for Damage Assessment in Urban Areas During Emergencies. Seeing Cities through Big Data: Research, Methods and Applications in Urban Informatics, Springer Geography.","DOI":"10.1007\/978-3-319-40902-3"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1080\/01431161.2015.1117684","article-title":"Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study","volume":"37","author":"Cervone","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.cageo.2017.10.010","article-title":"Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring","volume":"111","author":"Abe","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1111\/jfr3.12154","article-title":"Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework","volume":"10","author":"Smith","year":"2015","journal-title":"J. Flood Risk Manag."},{"key":"ref_31","first-page":"1","article-title":"Social media meets big urban data: A case study of urban waterlogging analysis","volume":"3264587","author":"Zhang","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s11069-017-2755-0","article-title":"Rapid flood inundation mapping using social media, remote sensing and topographic data","volume":"87","author":"Rosser","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1080\/01431161.2017.1400193","article-title":"Enhancing the temporal resolution of satellite-based flood extent generation using crowdsourced data for disaster monitoring","volume":"39","author":"Panteras","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"251","DOI":"10.5194\/isprsannals-I-4-251-2012","article-title":"The use of LIDAR and volunteered geographic information to map flood extents and inundation","volume":"1","author":"McDougall","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/19475683.2018.1450787","article-title":"A near real-time flood-mapping approach by integrating social media and post-event satellite imagery","volume":"24","author":"Huang","year":"2018","journal-title":"Ann. GIS"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"735","DOI":"10.5194\/nhess-17-735-2017","article-title":"Probabilistic flood extent estimates from social media flood observations","volume":"17","author":"Brouwer","year":"2017","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/17538947.2015.1040474","article-title":"Mapping floods due to Hurricane Sandy using NPP VIIRS and ATMS data and geotagged Flickr imagery","volume":"9","author":"Sun","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1080\/15230406.2016.1271356","article-title":"A novel approach to leveraging social media for rapid flood mapping: A case study of the 2015 South Carolina floods","volume":"45","author":"Li","year":"2018","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.scitotenv.2019.04.088","article-title":"Spatiotemporal patterns and characteristics of remotely sensed region heat islands during the rapid urbanization (1995\u20132015) of Southern China","volume":"674","author":"Yu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s10291-019-0838-y","article-title":"Application of GNSS interferometric reflectometry for detecting storm surges","volume":"23","author":"Peng","year":"2019","journal-title":"GPS Solut."},{"key":"ref_41","unstructured":"(2018, November 03). National Meteorological Center of China Meteorological Administration. Available online: http:\/\/typhoon. nmc.cn\/web.html."},{"key":"ref_42","unstructured":"(2019, November 03). NOAA Comprehensive Large Array-Data Stewardship System (CLASS), Available online: https:\/\/ www.bou.class.noaa.gov\/saa\/products\/welcome."},{"key":"ref_43","unstructured":"(2019, November 03). NASA EARTHDATA powered by the Earth Observing System Data and Information System (EOSDIS), Available online: https:\/\/earthdata.nasa.gov\/."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2316","DOI":"10.1109\/TGRS.2009.2012696","article-title":"A dynamic lunar spectral irradiance data set for NPOESS\/VIIRS day\/night band nighttime environmental applications","volume":"47","author":"Miller","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","unstructured":"(2018, August 03). Tencent\u2019s location-aware data portal. Available online: http:\/\/heat.qq.com."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/2150704X.2018.1530484","article-title":"Quantitative responses of satellite-derived night-time light signals to urban depopulation during Chinese New Year","volume":"10","author":"Ma","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ma, T. (2018). Multi-Level Relationships between Satellite-Derived Nighttime Lighting Signals and Social Media\u2013Derived Human Population Dynamics. Remote Sens., 10.","DOI":"10.3390\/rs10071128"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, Z., Du, Y., Yi, J., Liang, F., Ma, T., and Pei, T. (2019). Quantitative estimates of collective geo-tagged human activities in response to typhoon Hato using location-aware big data. Int. J. Digit. Earth, 1\u201321.","DOI":"10.1080\/17538947.2019.1645894"},{"key":"ref_49","unstructured":"(2018, November 03). National Hurricane Center, Available online: https:\/\/www.nhc.noaa.gov\/gis."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1175\/2009WAF2222286.1","article-title":"A new method for estimating tropical cyclone wind speed probabilities","volume":"24","author":"DeMaria","year":"2009","journal-title":"Weather Forecast."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1175\/WAF-D-12-00116.1","article-title":"Improvements to the operational tropical cyclone wind speed probability model","volume":"28","author":"DeMaria","year":"2013","journal-title":"Weather Forecast."},{"key":"ref_52","unstructured":"(2018, November 03). National Oceanic and Atmospheric Administration (NOAA), Available online: https:\/\/gis.ncdc.noaa.gov\/ maps \/ncei\/cdo\/hourly."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1142\/S1793536910000422","article-title":"Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method","volume":"2","author":"Yeh","year":"2010","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble empirical mode decomposition: A noise-assisted data analysis method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1111\/j.1538-4632.1996.tb00936.x","article-title":"Geographically weighted regression: A method for exploring spatial nonstationarity","volume":"28","author":"Brunsdon","year":"1996","journal-title":"Geogr. Anal."},{"key":"ref_56","unstructured":"(2018, November 03). Fast EEMD Package. Available online: https:\/\/in.ncu.edu.tw\/~ncu34951\/research1.htm."},{"key":"ref_57","unstructured":"The R Development Core Team (2013). Version 2.6.2; R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v007.i02","article-title":"strucchange. An R package for testing for structural change in linear regression models","volume":"7","author":"Zeileis","year":"2002","journal-title":"J. Stat. Softw."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Qi, L., Li, J., Wang, Y., and Gao, X. (2019). Urban Observation: Integration of Remote Sensing and Social Media Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.","DOI":"10.1109\/JSTARS.2019.2908515"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2017.01.005","article-title":"Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics","volume":"192","author":"Bennett","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2091\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:17:23Z","timestamp":1760188643000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,6]]},"references-count":60,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["rs11182091"],"URL":"https:\/\/doi.org\/10.3390\/rs11182091","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,6]]}}}