{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T09:57:05Z","timestamp":1780999025913,"version":"3.54.1"},"reference-count":61,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,6]],"date-time":"2017-01-06T00:00:00Z","timestamp":1483660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41231171"],"award-info":[{"award-number":["41231171"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371420"],"award-info":[{"award-number":["41371420"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371377"],"award-info":[{"award-number":["41371377"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41301511"],"award-info":[{"award-number":["41301511"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"innovative research funding of Wuhan University","award":["2042015KF0167"],"award-info":[{"award-number":["2042015KF0167"]}]},{"name":"Arts and Sciences Excellence Professorship and the Alvin and Sally Beaman Professorship at the University of Tennessee"},{"name":"International Science-technology Cooperation Project of Guangdong Province","award":["2014A050503053"],"award-info":[{"award-number":["2014A050503053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The advent of big data has aided understanding of the driving forces of human mobility, which is beneficial for many fields, such as mobility prediction, urban planning, and traffic management. However, the data sources used in many studies, such as mobile phone location and geo-tagged social media data, are sparsely sampled in the temporal scale. An individual\u2019s records can be distributed over a few hours a day, or a week, or over just a few hours a month. Thus, the representativeness of sparse mobile phone location data in characterizing human mobility requires analysis before using data to derive human mobility patterns. This paper investigates this important issue through an approach that uses subscriber mobile phone location data collected by a major carrier in Shenzhen, China. A dataset of over 5 million mobile phone subscribers that covers 24 h a day is used as a benchmark to test the representativeness of mobile phone location data on human mobility indicators, such as total travel distance, movement entropy, and radius of gyration. This study divides this dataset by hour, using 2- to 23-h segments to evaluate the representativeness due to the availability of mobile phone location data. The results show that different numbers of hourly segments affect estimations of human mobility indicators and can cause overestimations or underestimations from the individual perspective. On average, the total travel distance and movement entropy tend to be underestimated. The underestimation coefficient results for estimation of total travel distance are approximately linear, declining as the number of time segments increases, and the underestimation coefficient results for estimating movement entropy decline logarithmically as the time segments increase, whereas the radius of gyration tends to be more ambiguous due to the loss of isolated locations. This paper suggests that researchers should carefully interpret results derived from this type of sparse data in the era of big data.<\/jats:p>","DOI":"10.3390\/ijgi6010007","type":"journal-article","created":{"date-parts":[[2017,1,6]],"date-time":"2017-01-06T10:08:12Z","timestamp":1483697292000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators"],"prefix":"10.3390","volume":"6","author":[{"given":"Shiwei","family":"Lu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1651-878X","authenticated-orcid":false,"given":"Zhixiang","family":"Fang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xirui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Information Center of Urban Planning, Land &amp; Real Estate of Shenzhen Municipality, 8007 Hongli West Road, Shenzhen 518040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shih-Lung","family":"Shaw","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"},{"name":"Department of Geography, University of Tennessee, Knoxville, TN 37996, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Yin","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Road, Shenzhen 518005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2507-4757","authenticated-orcid":false,"given":"Xiping","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1038\/nature04292","article-title":"The scaling laws of human travel","volume":"439","author":"Brockmann","year":"2006","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1038\/nature06958","article-title":"Understanding individual human mobility patterns","volume":"453","author":"Hidalgo","year":"2008","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., and Mascolo, C. (2011). A tale of many cities: Universal patterns in human urban mobility. PLoS ONE.","DOI":"10.1371\/annotation\/ca85bf7a-7922-47d5-8bfb-bcdf25af8c72"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/nature10856","article-title":"A universal model for mobility and migration patterns","volume":"484","author":"Simini","year":"2012","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1068\/b32047","article-title":"Mobile landscapes: Using location data from cell phones for urban analysis","volume":"33","author":"Ratti","year":"2006","journal-title":"Environ. Plan. B Plan. Des."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1111\/tgis.12042","article-title":"Discovering spatial interaction communities from mobile phone data","volume":"17","author":"Gao","year":"2013","journal-title":"Trans. GIS"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1080\/13658816.2014.913794","article-title":"A new insight into land use classification based on aggregated mobile phone data","volume":"28","author":"Pei","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1109\/TITS.2012.2189006","article-title":"Traffic flow estimation models using cellular phone data","volume":"13","author":"Caceres","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.ejor.2013.02.044","article-title":"Estimating freeway traffic measures from mobile phone location data","volume":"229","author":"Gao","year":"2013","journal-title":"Eur. J. Oper. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12942-016-0042-z","article-title":"Dynamic assessment of exposure to air pollution using mobile phone data","volume":"15","author":"Dewulf","year":"2016","journal-title":"Int. J. Health Geogr."},{"key":"ref_11","first-page":"1066","article-title":"Next Big Thing in Big Data: The Security of the ICT Supply Chain","volume":"10","author":"Lu","year":"2013","journal-title":"Int. Conf. Soc. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.trc.2013.11.003","article-title":"Understanding monthly variability in human activity spaces: A twelve-month study using mobile phone call detail records","volume":"38","author":"Ahas","year":"2014","journal-title":"Trans. Res. Part C: Emerg. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Sui, Z., Kang, C., and Gao, Y. (2014). Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS ONE.","DOI":"10.1371\/journal.pone.0086026"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhong, C., Batty, M., Manley, E., Wang, J., Wang, Z., Chen, F., and Schmitt, G. (2016). Variability in regularity: Mining temporal mobility patterns in London, Singapore and Beijing using smart-card data. PLoS ONE.","DOI":"10.1371\/journal.pone.0149222"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lenormand, M., Louail, T., Cant\u00faros, O.G., Picornell, M., Herranz, R., Arias, J.M., Barthelemy, M., Miguel, M.S., and Ramasco, J.J. (2014). Corrigendum: Influence of sociodemographic characteristics on human mobility. Sci. Rep.","DOI":"10.1038\/srep12188"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Li, Q., Chen, Y., Xie, X., and Ma, W.Y. (2008, January 21\u201324). Understanding mobility based on GPS data. Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea.","DOI":"10.1145\/1409635.1409677"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gallotti, R., Bazzani, A., Rambaldi, S., and Barthelemy, M. (2016). A stochastic model of randomly accelerated walkers for human mobility. Nat. Commun.","DOI":"10.1038\/ncomms12600"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wind, D.K., Sapiezynski, P., Furman, M.A., and Lehmann, S. (2016). Inferring Stop-Locations from WiFi. PLoS ONE.","DOI":"10.1371\/journal.pone.0149105"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wesolowski, A., Eagle, N., Noor, A.M., Snow, R.W., and Buckee, C.O. (2011). Heterogeneous mobile phone ownership and usage patterns in Kenya. PLoS ONE.","DOI":"10.1371\/journal.pone.0035319"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wesolowski, A., Eagle, N., Noor, A.M., Snow, R.W., and Buckee, C.O. The impact of biases in mobile phone ownership on estimates of human mobility. J. R. Soc. Interface, 2013.","DOI":"10.1098\/rsif.2012.0986"},{"key":"ref_21","unstructured":"Mislove, A., Lehmann, S., Ahn, Y.Y., Onnela, J.P., and Rosenquist, J.N. (2011, January 17\u201321). Understanding the demography of Twitter users. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain."},{"key":"ref_22","unstructured":"Hecht, B., and Stephens, M. (2014, January 2\u20134). A tale of cities: Urban biases in volunteered geographic information. Proceedings of the Eighth International AAAI conference on Weblogs and Social Media, Ann Arbor, MI, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.jtrangeo.2012.03.018","article-title":"Spatiotemporal analysis of critical transportation links based on time geographic concepts: A case study of critical bridges in Wuhan, China","volume":"23","author":"Fang","year":"2012","journal-title":"J. Trans. Geogr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/2398356.2398375","article-title":"Human mobility characterization from cellular network data","volume":"56","author":"Becker","year":"2013","journal-title":"Commun. ACM"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1145\/2412096.2412101","article-title":"Are call detail records biased for sampling human mobility?","volume":"16","author":"Ranjan","year":"2012","journal-title":"ACM Sigmobile Mob. Comput. Commun. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1080\/13658816.2015.1137298","article-title":"Understanding the bias of call detail records in human mobility research","volume":"30","author":"Zhao","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sagarra, O., Szell, M., Santi, P., D\u00edaz-Guilera, A., and Ratti, C. (2015). Supersampling and network reconstruction of urban mobility. PLoS ONE.","DOI":"10.1371\/journal.pone.0134508"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s11116-015-9597-y","article-title":"Understanding aggregate human mobility patterns using passive mobile phone location data: A home-based approach","volume":"42","author":"Xu","year":"2015","journal-title":"Transportation"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1126\/science.1223467","article-title":"Quantifying the impact of human mobility on malaria","volume":"338","author":"Wesolowski","year":"2012","journal-title":"Science"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.trc.2012.09.009","article-title":"Understanding individual mobility patterns from urban sensing data: A mobile phone trace example","volume":"26","author":"Calabrese","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kung, K.S., Greco, K., Sobolevsky, S., and Ratti, C. (2013). Exploring universal patterns in human home-work commuting from mobile phone data. PLoS ONE.","DOI":"10.1371\/journal.pone.0096180"},{"key":"ref_32","first-page":"1","article-title":"Inferring individual daily activities from mobile phone traces: A Boston example","volume":"43","author":"Diao","year":"2015","journal-title":"Environ. Plan. B Plan. Des."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, X., Fang, Z., Xu, Y., Shaw, S.L., Zhao, Z., Yin, L., Zhang, T., and Lin, Y. (2016). Understanding spatiotemporal patterns of human convergence and divergence using mobile phone location data. ISPRS Int. J. Geo-Inf.","DOI":"10.3390\/ijgi5100177"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xu, Y., Shaw, S.L., Fang, Z., and Ling, Y. (2016). Estimating potential demand of bicycle trips from mobile phone data\u2014An anchor-point based approach. ISPRS Int. J. Geo-Inf.","DOI":"10.3390\/ijgi5080131"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/MPRV.2011.41","article-title":"Estimating origin-destination flows using opportunistically collected mobile phone location data from one million users in Boston metropolitan area","volume":"10","author":"Calabrese","year":"2011","journal-title":"IEEE Pervasive Comput."},{"key":"ref_36","first-page":"489","article-title":"Another tale of two cities: Understanding human activity space using actively tracked cellphone location data","volume":"106","author":"Xu","year":"2016","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.compenvurbsys.2011.07.003","article-title":"Correlating mobile phone usage and travel behavior\u2014A case study of Harbin, china","volume":"36","author":"Yuan","year":"2012","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1038\/nphys1760","article-title":"Modelling the scaling properties of human mobility","volume":"6","author":"Song","year":"2010","journal-title":"Nat. Phys."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1126\/science.1177170","article-title":"Limits of predictability in human mobility","volume":"327","author":"Song","year":"2010","journal-title":"Science"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gallotti, R., Bazzani, A., Degli Esposti, M., and Rambaldi, S. (2013). Entropic measures of individual mobility patterns. J. Stat. Mech. Theory Exp.","DOI":"10.1088\/1742-5468\/2013\/10\/P10022"},{"key":"ref_41","unstructured":"Cuttone, A., Lehmann, S., and Gonz\u00e1lez, M.C. (2016). Understanding predictability and exploration in human mobility. e-Print: arXiv."},{"key":"ref_42","first-page":"42","article-title":"Privacy in the age of big data: A time for big decisions","volume":"20","author":"Tene","year":"2012","journal-title":"Stanf. Law Rev. Online"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Smith, M., Szongott, C., Henne, B., and Von Voigt, G. (2012). Big data privacy issues in public social media. IEEE Int. Conf. Digit. Ecosyst. Technol.","DOI":"10.1109\/DEST.2012.6227909"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yin, L., Wang, Q., Shaw, S.L., Fang, Z., Hu, J., Tao, Y., and Wang, W. (2015). Re-identification risk versus data utility for aggregated mobility research using mobile phone location data. PLoS ONE.","DOI":"10.1371\/journal.pone.0140589"},{"key":"ref_45","first-page":"3","article-title":"How good is volunteered geographical information? A comparative study of Openstreetmap and ordnance survey datasets","volume":"93","author":"Haklay","year":"2010","journal-title":"Environ. Plan. B Plan. Des."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.spasta.2012.03.002","article-title":"Assuring the quality of volunteered geographic information","volume":"1","author":"Goodchild","year":"2012","journal-title":"Spat. Stat."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Fu, K., and Chau, M. (2013). Reality check for the Chinese microblog space: A random sampling approach. PLoS ONE.","DOI":"10.1371\/journal.pone.0058356"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1179\/000870410X12911304958827","article-title":"How many volunteers does it take to map an area well? The validity of Linus\u2019 law to volunteered geographic information","volume":"47","author":"Haklay","year":"2013","journal-title":"Cartogr. J."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Arai, A., Fan, Z., Matekenya, D., and Shibasaki, R. (2016). Comparative perspective of human behavior patterns to uncover ownership bias among mobile phone users. ISPRS Int. J. Geo-Inf.","DOI":"10.3390\/ijgi5060085"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3141\/2086-01","article-title":"Evaluation of cell phone traffic data in Minnesota","volume":"11","author":"Liu","year":"2008","journal-title":"Transp. Res. Rec."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, B.Y., Shi, C., Zhang, J., Lam, W.H., Li, Q., and Xiang, S. (2016). Most reliable path-finding algorithm for maximizing on-time arrival probability. Transp. B Transp. Dyn.","DOI":"10.1080\/21680566.2016.1169953"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1177\/2043820613513392","article-title":"The quality of big (geo) data","volume":"3","author":"Goodchild","year":"2013","journal-title":"Dialogues Hum. Geogr."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Goodchild, M.F., and Gopal, S. (1989). Accuracy of Spatial Databases, Taylor and Francis.","DOI":"10.1201\/b12612"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Goodchild, M. (2002). Uncertainty in Geographic Information, CRC Press.","DOI":"10.4324\/9780203471326"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.sste.2012.02.002","article-title":"A research agenda: Does geocoding positional error matter in health GIS studies?","volume":"3","author":"Jacquez","year":"2012","journal-title":"Spat. Spatio-Temporal Epidemiol."},{"key":"ref_56","first-page":"1199","article-title":"Quantitative analysis of the effects of spatial scales on intra-urban human mobility","volume":"41","author":"Lu","year":"2016","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/1536616.1536632","article-title":"The pathologies of big data","volume":"52","author":"Jacobs","year":"2009","journal-title":"Commun. ACM"},{"key":"ref_58","unstructured":"Shenzhen Statistical Yearbook 2012, Available online: http:\/\/www.sztj.gov.cn\/nj2012\/indexeh.htm."},{"key":"ref_59","unstructured":"Whitepaper of Urban Planning, Land & Resources Commission of Shenzhen Municipality 2015, Available online: http:\/\/www.szfdc.gov.cn\/xxgk\/ghjh\/td\/201508\/t20150813_108651.html."},{"key":"ref_60","unstructured":"Fotheringham, A.S., and O\u2019Kelly, M.E. (1989). Spatial Interaction Models: Formulations and Applications, Kluwer Academic Publishers."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1093\/oxfordjournals.aje.a113284","article-title":"A review of goodness of fit statistics for use in the development of logistic regression models","volume":"116","author":"Lemeshow","year":"1982","journal-title":"Am. J. Epidemiol."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/1\/7\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:25:37Z","timestamp":1760207137000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/1\/7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1,6]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,1]]}},"alternative-id":["ijgi6010007"],"URL":"https:\/\/doi.org\/10.3390\/ijgi6010007","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1,6]]}}}