{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T01:09:55Z","timestamp":1776820195065,"version":"3.51.2"},"reference-count":148,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme (FRGS)","award":["FRGS\/1\/2018\/ICT04\/UM\/02\/17"],"award-info":[{"award-number":["FRGS\/1\/2018\/ICT04\/UM\/02\/17"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various disciplines and domains, such as urban sensing, epidemiology, public transportation, data protection, and criminology. These digital traces provide significant spatiotemporal (geospatial and time-related) data, revealing people\u2019s mobility patterns as well as communication (incoming and outgoing calls) data, revealing people\u2019s social networks and interactions. Thus, service providers collect smartphone data by recording the details of every user activity or interaction (e.g., making a phone call, sending a text message, or accessing the internet) done using a smartphone and storing these details on their databases. This paper surveys different methods and approaches for assessing and predicting human communication behaviors and mobility patterns from mobile phone data and differentiates them in terms of their strengths and weaknesses. It also gives information about spatial, temporal, and call characteristics that have been extracted from mobile phone data and used to model how people communicate and move. We survey mobile phone data research published between 2013 and 2021 from eight main databases, namely, the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Based on our inclusion and exclusion criteria, 148 studies were selected.<\/jats:p>","DOI":"10.3390\/s23020908","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T02:57:33Z","timestamp":1673578653000},"page":"908","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Mobile Phone Data: A Survey of Techniques, Features, and Applications"],"prefix":"10.3390","volume":"23","author":[{"given":"Mohammed","family":"Okmi","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"},{"name":"Department of Information Technology and Security, Jazan University, Jazan 45142, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5865-1533","authenticated-orcid":false,"given":"Lip Yee","family":"Por","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tan Fong","family":"Ang","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0793-3308","authenticated-orcid":false,"given":"Chin Soon","family":"Ku","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1140\/epjds\/s13688-015-0046-0","article-title":"A survey of results on mobile phone datasets analysis","volume":"4","author":"Blondel","year":"2015","journal-title":"EPJ data science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/JAS.2020.1003120","article-title":"Urban sensing based on mobile phone data: Approaches, applications, and challenges","volume":"7","author":"Ghahramani","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/TIFS.2015.2510826","article-title":"SIIMCO: A forensic investigation tool for identifying the influential members of a criminal organization","volume":"11","author":"Taha","year":"2015","journal-title":"IEEE Trans. Inf. Secur."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102045","DOI":"10.1016\/j.ijinfomgt.2019.102045","article-title":"Leveraging deep learning and SNA approaches for smart city policing in the developing world","volume":"56","author":"Hassan","year":"2019","journal-title":"Int. J. Inf. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.apgeog.2017.06.007","article-title":"UK-based terrorists\u2019 antecedent behavior: A spatial and temporal analysis","volume":"86","author":"Griffiths","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15274","DOI":"10.1073\/pnas.0900282106","article-title":"Inferring friendship network structure by using mobile phone data","volume":"106","author":"Eagle","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15888","DOI":"10.1073\/pnas.1408439111","article-title":"Dynamic population mapping using mobile phone data","volume":"111","author":"Deville","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/1747423X.2017.1303546","article-title":"Improving land use inference by factorizing mobile phone call activity matrix","volume":"12","author":"Mao","year":"2017","journal-title":"J. Land Use Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1504\/IJAHUC.2014.065157","article-title":"2014. Consensus clustering for urban land use analysis using cell phone network data","volume":"17","author":"Soto","year":"2014","journal-title":"Int. J. Ad Hoc Ubiquitous Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jia, Y., Ge, Y., Ling, F., Guo, X., Wang, J., Wang, L., Chen, Y., and Li, X. (2018). Urban land use mapping by combining remote sensing imagery and mobile phone positioning data. Remote Sens., 10.","DOI":"10.3390\/rs10030446"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8956910","DOI":"10.1155\/2020\/8956910","article-title":"Recognition of functional areas based on call detail records and point of interest data","volume":"2020","author":"Yuan","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mao, H., Thakur, G., and Bhaduri, B. (2016, January 31). Exploiting mobile phone data for multi-category land use classification in Africa. Proceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, Burlingame, CA, USA.","DOI":"10.1145\/3007540.3007549"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"150449","DOI":"10.1098\/rsos.150449","article-title":"Comparing and modelling land use organization in cities","volume":"2","author":"Lenormand","year":"2015","journal-title":"R. Soc. Open Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.compenvurbsys.2016.08.007","article-title":"Land Use detection with cell phone data using topic models: Case Santiago, Chile","volume":"61","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1080\/13658816.2017.1287369","article-title":"Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation","volume":"31","author":"Tenkanen","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1111\/tgis.12323","article-title":"2018. Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records","volume":"22","author":"Liu","year":"2018","journal-title":"Trans. GIS"},{"key":"ref_18","first-page":"1","article-title":"Urban sensing using mobile phone network data: A survey of research","volume":"47","author":"Calabrese","year":"2014","journal-title":"Acm Comput. Surv. Csur"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.trc.2019.02.013","article-title":"Inferring dynamic origin-destination flows by transport mode using mobile phone data","volume":"101","author":"Bachir","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2769","DOI":"10.1068\/a130309p","article-title":"Evaluating the impact of land-use density and mix on spatiotemporal urban activity patterns: An exploratory study using mobile phone data","volume":"46","author":"Rietveld","year":"2014","journal-title":"Environ. Plan. A"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dong, Y., Pinelli, F., Gkoufas, Y., Nabi, Z., Calabrese, F., and Chawla, N.V. (2015, January 7\u201311). Inferring unusual crowd events from mobile phone call detail records. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Porto, Portugal.","DOI":"10.1007\/978-3-319-23525-7_29"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Furno, A., El Faouzi, N.E., Fiore, M., and Stanica, R. (2017, January 26\u201328). Fusing GPS probe and mobile phone data for enhanced land-use detection. Proceedings of the 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Naples, Italy.","DOI":"10.1109\/MTITS.2017.8005601"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s00779-019-01318-w","article-title":"Utilizing digital traces of mobile phones for understanding social dynamics in urban areas","volume":"24","author":"Zinman","year":"2020","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102384","DOI":"10.1016\/j.cities.2019.06.015","article-title":"Revealing the relationship of human convergence\u2013divergence patterns and land use: A case study on Shenzhen City, China","volume":"95","author":"Yang","year":"2019","journal-title":"Cities"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101914","DOI":"10.1016\/j.scs.2019.101914","article-title":"How urban land use influences commuting flows in Wuhan, Central China: A mobile phone signaling data perspective","volume":"53","author":"Liu","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Novovi\u0107, O., Brdar, S., Mesaro\u0161, M., Crnojevi\u0107, V., and Papadopoulos, A.N. (2020). Uncovering the relationship between human connectivity dynamics and land use. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9030140"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103223","DOI":"10.1016\/j.cities.2021.103223","article-title":"Ambient population and surveillance cameras: The guardianship role in street robbers\u2019 crime location choice","volume":"115","author":"Long","year":"2021","journal-title":"Cities"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jcrimjus.2016.03.002","article-title":"Exploring the impact of ambient population measures on London crime hotspots","volume":"46","author":"Malleson","year":"2016","journal-title":"J. Crim. Justice"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., and Pentland, A. (2014, January 12). Once upon a crime: Towards crime prediction from demographics and mobile data. Proceedings of the 16th international conference on multimodal interaction, Istanbul, Turkey.","DOI":"10.1145\/2663204.2663254"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1089\/big.2014.0054","article-title":"Moves on the street: Classifying crime hotspots using aggregated anonymized data on people dynamics","volume":"3","author":"Bogomolov","year":"2015","journal-title":"Big Data"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rummens, A., Snaphaan, T., Van de Weghe, N., Van den Poel, D., Pauwels, L.J., and Hardyns, W. (2021). Do mobile phone data provide a better denominator in crime rates and improve spatiotemporal predictions of crime?. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10060369"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Feng, J., Liu, L., Long, D., and Liao, W. (2019). An examination of spatial differences between migrant and native offenders in committing violent crimes in a large Chinese city. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030119"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, L., P\u00e1ez, A., Jiao, J., An, P., Lu, C., Mao, W., and Long, D. (2020). Ambient population and larceny-theft: A spatial analysis using mobile phone data. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9060342"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5733","DOI":"10.1016\/j.eswa.2014.03.024","article-title":"Detecting criminal organizations in mobile phone networks","volume":"41","author":"Ferrara","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1109\/TIFS.2016.2622226","article-title":"Using the spanning tree of a criminal network for identifying its leaders","volume":"12","author":"Taha","year":"2016","journal-title":"IEEE Trans. Inf. Secur."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1109\/TIFS.2018.2890811","article-title":"Shortlisting the influential members of criminal organizations and identifying their important communication channels","volume":"14","author":"Taha","year":"2019","journal-title":"IEEE Trans. Inf. Secur."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Taha, K., and Yoo, D. (2015, January 25\u201328). A system for analyzing criminal social networks. Proceedings of the 2015 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France.","DOI":"10.1145\/2808797.2808827"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fan, Y., Yang, T., Jiang, G., Zhu, L., and Peng, R. (2017). Identifying Criminals\u2019 Interactive Behavior and Social Relations through Data Mining on Call Detail Records, DEStech Transactions on Computer Science and Engineering (aiea).","DOI":"10.12783\/dtcse\/aiea2017\/14996"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s10207-017-0362-4","article-title":"Using targeted Bayesian network learning for suspect identification in communication networks","volume":"17","author":"Gruber","year":"2018","journal-title":"Int. J. Inf. Secur."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dileep, G.K., and Sajeev, G.P. (2021). A Graph Mining Approach to Detect Sandwich Calls. 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), IEEE.","DOI":"10.1109\/CONECCT52877.2021.9622627"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Traunmueller, M., Quattrone, G., and Capra, L. (2014, January 11\u201313). Mining mobile phone data to investigate urban crime theories at scale. Proceedings of the International Conference on Social Informatics, Barcelona, Spain.","DOI":"10.1007\/978-3-319-13734-6_29"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/s10940-019-09406-z","article-title":"Crime feeds on legal activities: Daily mobility flows help to explain thieves\u2019 target location choices","volume":"35","author":"Song","year":"2019","journal-title":"J. Quant. Criminol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1177\/0265813516672454","article-title":"New insights on relationships between street crimes and ambient population: Use of hourly population data estimated from mobile phone users\u2019 locations","volume":"45","author":"Hanaoka","year":"2018","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s10610-020-09452-5","article-title":"The \u2018exposed\u2019population, violent crime in public space and the night-time economy in Manchester, UK","volume":"27","author":"Haleem","year":"2020","journal-title":"Eur. J. Crim. Policy Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s10610-020-09456-1","article-title":"The influence of intra-daily activities and settings upon weekday violent crime in public spaces in Manchester, UK","volume":"27","author":"Lee","year":"2020","journal-title":"Eur. J. Crim. Policy Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.ins.2016.02.027","article-title":"Network structure and resilience of Mafia syndicates","volume":"351","author":"Agreste","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s13278-012-0060-1","article-title":"Forensic analysis of phone call networks","volume":"3","author":"Catanese","year":"2013","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.dcan.2019.10.005","article-title":"Development of multiple mobile networks call detailed records and its forensic analysis","volume":"5","author":"Abba","year":"2019","journal-title":"Digit. Commun. Netw."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Khan, E.S., Azmi, H., Ansari, F., and Dhalvelkar, S. (2018, January 5). Simple implementation of criminal investigation using call data records (CDRs) through big data technology. Proceedings of the 2018 International Conference on Smart City and Emerging Technology (ICSCET), Mumbai, India.","DOI":"10.1109\/ICSCET.2018.8537389"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kumar, M., Hanumanthappa, M., and Kumar, T.S. (2017, January 19\u201321). Crime investigation and criminal network analysis using archive call detail records. Proceedings of the 2016 Eighth International Conference on Advanced Computing (ICoAC), Chennai, India.","DOI":"10.1109\/ICoAC.2017.7951743"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Khan, S., Ansari, F., Dhalvelkar, H.A., and Computer, S. (2017, January 27\u201328). Criminal investigation using call data records (CDR) through big data technology. Proceedings of the 2017 International Conference on Nascent Technologies in Engineering (ICNTE), Navi Mumbai, India.","DOI":"10.1109\/ICNTE.2017.7947942"},{"key":"ref_52","unstructured":"Tseng, J.C., Tseng, H.C., Liu, C.W., Shih, C.C., Tseng, K.Y., Chou, C.Y., Yu, C.H., and Lu, F.S. (2013, January 25\u201327). A successful application of big data storage techniques implemented to criminal investigation for telecom. In Proceedings of the 2013 15th Asia-Pacific Network Operations and Management Symposium (APNOMS), Hiroshima, Japan."},{"key":"ref_53","unstructured":"Danya, B. (2019). Estimating Urban Mobility with Mobile Network Geolocation Data Mining. [PhD Thesis, Universit\u00e9 Paris-Saclay]."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"189","DOI":"10.3389\/fpubh.2015.00189","article-title":"Mobile network data for public health: Opportunities and challenges","volume":"3","author":"Oliver","year":"2015","journal-title":"Front. Public Health"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105821","DOI":"10.1016\/j.envint.2020.105821","article-title":"Who are more exposed to PM2. 5 pollution: A mobile phone data approach","volume":"143","author":"Guo","year":"2020","journal-title":"Environ. Int."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"eabc0764","DOI":"10.1126\/sciadv.abc0764","article-title":"Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle","volume":"6","author":"Oliver","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.1038\/s41467-020-18190-5","article-title":"The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology","volume":"11","author":"Grantz","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.puhe.2020.08.009","article-title":"Political partisanship and mobility restriction during the COVID-19 pandemic","volume":"187","author":"Hsiehchen","year":"2020","journal-title":"Public Health"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e417","DOI":"10.1016\/S2589-7500(20)30165-5","article-title":"Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: A modelling study using mobile phone data","volume":"2","author":"Zhou","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1016\/S1473-3099(20)30553-3","article-title":"Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study","volume":"20","author":"Badr","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"taz019","DOI":"10.1093\/jtm\/taz019","article-title":"Measuring mobility, disease connectivity and individual risk: A review of using mobile phone data and mHealth for travel medicine","volume":"26","author":"Lai","year":"2019","journal-title":"J. Travel Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"14","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_63","doi-asserted-by":"crossref","first-page":"105772","DOI":"10.1016\/j.envint.2020.105772","article-title":"Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data","volume":"141","author":"Yu","year":"2020","journal-title":"Environ. Int."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"102103","DOI":"10.1016\/j.apgeog.2019.102103","article-title":"Space-time personalized short message service (SMS) for infectious disease control\u2013Policies for precise public health","volume":"114","author":"Yin","year":"2020","journal-title":"Appl. Geogr."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e43481","DOI":"10.7554\/eLife.43481","article-title":"Mapping imported malaria in Bangladesh using parasite genetic and human mobility data","volume":"8","author":"Chang","year":"2019","journal-title":"Elife"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e43510","DOI":"10.7554\/eLife.43510","article-title":"Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa","volume":"8","author":"Tessema","year":"2019","journal-title":"Elife"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3897","DOI":"10.1038\/s41467-018-06290-2","article-title":"Estimating sources and sinks of malaria parasites in Madagascar","volume":"9","author":"Ihantamalala","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Sekimoto, Y., Sudo, A., Kashiyama, T., Seto, T., Hayashi, H., Asahara, A., Ishizuka, H., and Nishiyama, S. (2016, January 12\u201316). Real-time people movement estimation in large disasters from several kinds of mobile phone data. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, Heidelberg, Germany.","DOI":"10.1145\/2968219.2968421"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.trc.2019.02.008","article-title":"Transport mode detection based on mobile phone network data: A systematic review","volume":"101","author":"Huang","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1007\/s11116-018-9876-5","article-title":"Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data","volume":"46","author":"Zhong","year":"2019","journal-title":"Transportation"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1177\/0361198118774671","article-title":"Analyzing passenger travel demand related to the transportation hub inside a city area using mobile phone data","volume":"2672","author":"Zhong","year":"2018","journal-title":"Transp. Res. Rec."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1140\/epjds\/s13688-018-0177-1","article-title":"Inferring modes of transportation using mobile phone data","volume":"7","author":"Caro","year":"2018","journal-title":"EPJ Data Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"101348","DOI":"10.1016\/j.compenvurbsys.2019.101348","article-title":"Inferring fine-grained transport modes from mobile phone cellular signaling data","volume":"77","author":"Chin","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"100025","DOI":"10.1016\/j.eastsj.2020.100025","article-title":"Identification of various transport modes and rail transit behaviors from mobile CDR data: A case of Yangon City","volume":"6","author":"Lwin","year":"2020","journal-title":"Asian Transp. Stud."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.trc.2013.03.010","article-title":"Intelligent road traffic status detection system through cellular networks handover information: An exploratory study","volume":"32","author":"Demissie","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.trc.2014.01.002","article-title":"Development of origin\u2013destination matrices using mobile phone call data","volume":"40","author":"Iqbal","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1177\/2399808320924433","article-title":"Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework","volume":"47","author":"Yang","year":"2020","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.telpol.2014.04.001","article-title":"Data from mobile phone operators: A tool for smarter cities?","volume":"39","author":"Steenbruggen","year":"2015","journal-title":"Telecommun. Policy"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Phithakkitnukoon, S., Horanont, T., Lorenzo, G.D., Shibasaki, R., and Ratti, C. (2010). August. Activity-aware map: Identifying human daily activity pattern using mobile phone data. International Workshop on Human Behavior Understanding, Springer.","DOI":"10.1007\/978-3-642-14715-9_3"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1140\/epjds\/s13688-017-0108-6","article-title":"Inferring social influence in transport mode choice using mobile phone data","volume":"6","author":"Phithakkitnukoon","year":"2017","journal-title":"EPJ Data Sci."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Qu, Y., Gong, H., and Wang, P. (2015, January 15\u201318). Transportation mode split with mobile phone data. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain.","DOI":"10.1109\/ITSC.2015.56"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"20130789","DOI":"10.1098\/rsif.2013.0789","article-title":"The scaling of human interactions with city size","volume":"11","author":"Bettencourt","year":"2014","journal-title":"J. R. Soc. Interface"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.inffus.2020.05.009","article-title":"The four dimensions of social network analysis: An overview of research methods, applications, and software tools","volume":"63","author":"Camacho","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.ins.2019.05.082","article-title":"Predicting complex user behavior from CDR based social networks","volume":"500","author":"Doyle","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.osnem.2017.04.003","article-title":"2017. Urban communications and social interactions through the lens of mobile phone data","volume":"1","author":"Gaito","year":"2017","journal-title":"Online Soc. Netw. Media"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3934\/nhm.2015.10.87","article-title":"Characterizing ethnic interactions from human communication patterns in Ivory Coast","volume":"10","author":"Morales","year":"2015","journal-title":"Netw. Heterog. Media"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1111\/tgis.12755","article-title":"Analysis of urban agglomeration structure through spatial network and mobile phone data","volume":"25","author":"Liu","year":"2021","journal-title":"Trans. GIS"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1140\/epjds\/s13688-015-0053-1","article-title":"Quantifying socio-economic indicators in developing countries from mobile phone communication data: Applications to C\u00f4te d\u2019Ivoire","volume":"4","author":"Mao","year":"2015","journal-title":"EPJ Data Sci."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"3330","DOI":"10.1038\/s41467-018-05690-8","article-title":"Sequences of purchases in credit card data reveal lifestyles in urban populations","volume":"9","author":"Travizano","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"180749","DOI":"10.1098\/rsos.180749","article-title":"The time geography of segregation during working hours","volume":"5","author":"Dannemann","year":"2018","journal-title":"R. Soc. Open Sci."},{"key":"ref_91","unstructured":"Walsh, F., and Pozdnoukhov, A. (2011, January 12\u201315). Spatial structure and dynamics of urban communities. Proceedings of the 1st Workshop on Pervasive Urban Applications, San Francisco, CA, USA."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"41728","DOI":"10.1109\/ACCESS.2018.2859756","article-title":"Call detail records driven anomaly detection and traffic prediction in mobile cellular networks","volume":"6","author":"Sultan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Mededovic, E., Douros, V.G., and M\u00e4h\u00f6nen, P. (May, January 29). Node centrality metrics for hotspots analysis in telecom big data. Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, France.","DOI":"10.1109\/INFCOMW.2019.8845204"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/CC.2018.8357700","article-title":"Semi-supervised learning based big data-driven anomaly detection in mobile wireless networks","volume":"15","author":"Hussain","year":"2018","journal-title":"China Commun."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1140\/epjds\/s13688-015-0040-6","article-title":"High resolution population estimates from telecommunications data","volume":"4","author":"Douglass","year":"2015","journal-title":"EPJ Data Sci."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Kung, K.S., Greco, K., Sobolevsky, S., and Ratti, C. (2014). Exploring universal patterns in human home-work commuting from mobile phone data. PloS One, 9.","DOI":"10.1371\/journal.pone.0096180"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.engappai.2016.05.007","article-title":"Identifying user habits through data mining on call data records","volume":"54","author":"Bianchi","year":"2016","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s10610-020-09446-3","article-title":"Cell towers and the ambient population: A spatial analysis of disaggregated property crime","volume":"27","author":"Johnson","year":"2021","journal-title":"Eur. J. Crim. Policy Res."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1007\/s10796-020-10057-w","article-title":"Using mobile phone data for emergency management: A systematic literature review","volume":"22","author":"Wang","year":"2020","journal-title":"Inf. Syst. Front."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1080\/03036758.2019.1609052","article-title":"A survey on evolutionary machine learning","volume":"49","author":"Bi","year":"2019","journal-title":"J. R. Soc. New Zealand"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Liu, H., and Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Appl. Sci., 9.","DOI":"10.3390\/app9204396"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.future.2016.11.027","article-title":"A semi-supervised social relationships inferred model based on mobile phone data","volume":"76","author":"Yu","year":"2017","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.comcom.2018.03.012","article-title":"Enriching sparse mobility information in call detail records","volume":"122","author":"Chen","year":"2018","journal-title":"Comput. Commun."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s12243-020-00807-x","article-title":"Discovering locations and habits from human mobility data","volume":"75","author":"Andrade","year":"2020","journal-title":"Ann. Telecommun."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Zhang, G., Rui, X., Poslad, S., Song, X., Fan, Y., and Wu, B. (2020). A method for the estimation of finely-grained temporal spatial human population density distributions based on cell phone call detail records. Remote Sens., 12.","DOI":"10.3390\/rs12162572"},{"key":"ref_106","unstructured":"Gabrielli, L., Furletti, B., Giannotti, F., Nanni, M., and Rinzivillo, S. (2022, January 26\u201330). Use of mobile phone data to estimate visitors mobility flows. Proceedings of the International Conference on Software Engineering and Formal Methods, Berlin, Germany."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Gabrielli, L., Furletti, B., Trasarti, R., Giannotti, F., and Pedreschi, D. (November, January 29). City users\u2019 classification with mobile phone data. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA.","DOI":"10.1109\/BigData.2015.7363852"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Pinter, G., Mosavi, A., and Felde, I. (2020). Artificial intelligence for modeling real estate price using call detail records and hybrid machine learning approach. Entropy, 22.","DOI":"10.3390\/e22121421"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"101346","DOI":"10.1016\/j.compenvurbsys.2019.101346","article-title":"Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data","volume":"77","author":"Li","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Cecaj, A., Lippi, M., Mamei, M., and Zambonelli, F. (2020). Comparing deep learning and statistical methods in forecasting crowd distribution from aggregated mobile phone data. Appl. Sci., 10.","DOI":"10.3390\/app10186580"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"39747","DOI":"10.1109\/ACCESS.2020.2975029","article-title":"A Comparative Study on Contract Recommendation Model: Using Macao Mobile Phone Datasets","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"141034","DOI":"10.1016\/j.scitotenv.2020.141034","article-title":"Coupling mobile phone data with machine learning: How misclassification errors in ambient PM2. 5 exposure estimates are produced?","volume":"745","author":"Guo","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Murynets, I., Zabarankin, M., Jover, R.P., and Panagia, A. (May, January 27). Analysis and detection of SIMbox fraud in mobility networks. Proceedings of the IEEE INFOCOM 2014-IEEE Conference on Computer Communications, Toronto, ON, USA.","DOI":"10.1109\/INFOCOM.2014.6848087"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1140\/epjds\/s13688-017-0099-3","article-title":"Improving official statistics in emerging markets using machine learning and mobile phone data","volume":"6","author":"Jahani","year":"2017","journal-title":"EPJ Data Sci."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Sarraute, C., Blanc, P., and Burroni, J. (2014, January 17\u201320). A study of age and gender seen through mobile phone usage patterns in mexico. Proceedings of the 2014 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), Beijing, China.","DOI":"10.1109\/ASONAM.2014.6921683"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Chen, N.C., Xie, W., Welsch, R.E., Larson, K., and Xie, J. (2017, January 25\u201330). Comprehensive predictions of tourists\u2019 next visit location based on call detail records using machine learning and deep learning methods. Proceedings of the 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, USA.","DOI":"10.1109\/BigDataCongress.2017.10"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Zufiria, J., Pastor-Escuredo, D., \u00dabeda-Medina, L., Hernandez-Medina, M.A., Barriales-Valbuena, I., Morales, A.J., Jacques, D.C., Nkwambi, W., Diop, M.B., and Quinn, J. (2018). Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security. PloS ONE, 13.","DOI":"10.1371\/journal.pone.0195714"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Hughes, B., Bothe, S., Farooq, H., and Imran, A. (2019, January 18\u201321). Generative adversarial learning for machine learning empowered self organizing 5G networks. Proceedings of the 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA.","DOI":"10.1109\/ICCNC.2019.8685527"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"8853468","DOI":"10.1155\/2020\/8853468","article-title":"Automated Fraudulent Phone Call Recognition through Deep Learning","volume":"2020","author":"Xing","year":"2020","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJCINI.2020010101","article-title":"Deep convolutional neural networks for customer churn prediction analysis","volume":"14","author":"Chouiekh","year":"2020","journal-title":"Int. J. Cogn. Inform. Nat. Intell. (IJCINI)"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s40537-019-0191-6","article-title":"Customer churn prediction in telecom using machine learning in big data platform","volume":"6","author":"Ahmad","year":"2019","journal-title":"J. Big Data"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Br\u00e2ndu\u015foiu, I., Toderean, G., and Beleiu, H. (2016, January 9\u201311). Methods for churn prediction in the pre-paid mobile telecommunications industry. Proceedings of the 2016 International conference on communications (COMM), Bucharest, Romania.","DOI":"10.1109\/ICComm.2016.7528311"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s40537-020-00290-0","article-title":"2020. Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study","volume":"7","author":"Wassouf","year":"2020","journal-title":"J. Big Data"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40537-019-0180-9","article-title":"Predicting customer\u2019s gender and age depending on mobile phone data","volume":"6","author":"Jafar","year":"2019","journal-title":"J. Big Data"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"63","DOI":"10.25046\/aj050409","article-title":"Fraud Detection Call Detail Record Using Machine Learning in Telecommunications Company","volume":"5","author":"Jabbar","year":"2020","journal-title":"Adv. Sci. Technol. Eng. Syst. J."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Sultan, K., Ali, H., Ahmad, A., and Zhang, Z. (2019). Call details record analysis: A spatiotemporal exploration toward mobile traffic classification and optimization. Information, 10.","DOI":"10.3390\/info10060192"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"4986","DOI":"10.1109\/TII.2019.2953201","article-title":"Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks","volume":"16","author":"Hussain","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s40595-018-0116-x","article-title":"A hybrid mobile call fraud detection model using optimized fuzzy C-means clustering and group method of data handling-based network","volume":"5","author":"Subudhi","year":"2018","journal-title":"Vietnam J. Comput. Sci."},{"key":"ref_129","first-page":"1709","article-title":"Clustering of telecommunications user profiles for fraud detection and security enhancement in large corporate networks: A case study","volume":"9","author":"Hilas","year":"2015","journal-title":"Appl. Math. Inf. Sci."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1504\/IJSN.2016.075069","article-title":"Use of fuzzy clustering and support vector machine for detecting fraud in mobile telecommunication networks","volume":"11","author":"Subudhi","year":"2016","journal-title":"Int. J. Secur. Netw."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s11235-017-0310-7","article-title":"A churn prediction model for prepaid customers in telecom using fuzzy classifiers","volume":"66","author":"Azeem","year":"2017","journal-title":"Telecommun. Syst."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1109\/TMC.2017.2742953","article-title":"Clustering weekly patterns of human mobility through mobile phone data","volume":"17","author":"Thuillier","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1177\/0263775815608851","article-title":"No place to hide? The ethics and analytics of tracking mobility using mobile phone data","volume":"34","author":"Taylor","year":"2016","journal-title":"Environ. Plan. D Soc. Space"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2629668","article-title":"Anomaly detection from incomplete data","volume":"9","author":"Liu","year":"2014","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"150162","DOI":"10.1098\/rsos.150162","article-title":"Quantifying crowd size with mobile phone and Twitter data","volume":"2","author":"Botta","year":"2015","journal-title":"R. Soc. Open Sci."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Dobra, A., Williams, N.E., and Eagle, N. (2015). Spatiotemporal detection of unusual human population behavior using mobile phone data. PloS ONE, 10.","DOI":"10.1371\/journal.pone.0120449"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.eswa.2017.05.028","article-title":"Social network analytics for churn prediction in telco: Model building, evaluation and network architecture","volume":"85","author":"Bravo","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"6575","DOI":"10.1016\/j.eswa.2014.05.014","article-title":"Improved churn prediction in telecommunication industry by analyzing a large network","volume":"41","author":"Kim","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.asoc.2013.09.017","article-title":"Social network analysis for customer churn prediction","volume":"14","author":"Verbeke","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_140","unstructured":"Letouz\u00e9, E., Vinck, P., and Kammourieh, L. (2015). The Law, Politics and Ethics of Cell Phone Data Analytics, Data-Pop Alliance."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"180286","DOI":"10.1038\/sdata.2018.286","article-title":"On the privacy-conscientious use of mobile phone data","volume":"5","author":"Gambs","year":"2018","journal-title":"Sci. Data"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"e622","DOI":"10.1016\/S2589-7500(20)30193-X","article-title":"Measuring mobility to monitor travel and physical distancing interventions: A common framework for mobile phone data analysis","volume":"2","author":"Kishore","year":"2020","journal-title":"The Lancet Digital Health"},{"key":"ref_143","first-page":"1","article-title":"Location privacy-preserving mechanisms in location-based services: A comprehensive survey","volume":"54","author":"Jiang","year":"2021","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/COMST.2019.2944748","article-title":"Differential privacy techniques for cyber physical systems: A survey","volume":"22","author":"Hassan","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"101786","DOI":"10.1016\/j.datak.2019.101786","article-title":"PRIMULE: Privacy risk mitigation for user profiles","volume":"125","author":"Pratesi","year":"2020","journal-title":"Data Knowl. Eng."},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Gramaglia, M., Fiore, M., Tarable, A., and Banchs, A. (2017, January 1\u20134). Preserving mobile subscriber privacy in open datasets of spatiotemporal trajectories. Proceedings of the IEEE INFOCOM 2017-IEEE Conference on Computer Communications, Atlanta, GA, USA.","DOI":"10.1109\/INFOCOM.2017.8056979"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/TASE.2018.2795241","article-title":"2018. Mobile phone data analysis: A spatial exploration toward hotspot detection","volume":"16","author":"Ghahramani","year":"2018","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_148","unstructured":"Milusheva, S., Lewin, A., Gomez, T.B., Matekenya, D., and Reid, K. (July, January 28). Challenges and opportunities in accessing mobile phone data for COVID-19 response in developing countries. Proceedings of the COMPASS \u201921: ACM SIGCAS Conference on Computing and Sustainable Societie, online. Data & Policy."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/908\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:04:29Z","timestamp":1760119469000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/908"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,12]]},"references-count":148,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020908"],"URL":"https:\/\/doi.org\/10.3390\/s23020908","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,12]]}}}