{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T16:49:36Z","timestamp":1785602976290,"version":"3.56.0"},"reference-count":137,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial\u2013temporal patterns of crime, and ambient population measures have a significant impact on crime rates.<\/jats:p>","DOI":"10.3390\/s23094350","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T02:02:23Z","timestamp":1682647343000},"page":"4350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Systematic Review of Mobile Phone Data in Crime Applications: A Coherent Taxonomy Based on Data Types and Analysis Perspectives, Challenges, and Future Research Directions"],"prefix":"10.3390","volume":"23","author":[{"given":"Mohammed","family":"Okmi","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia"},{"name":"Department of Information Technology and Security, Jazan University, Jazan 45142, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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, Universiti Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tan Fong","family":"Ang","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5838-8912","authenticated-orcid":false,"given":"Ward","family":"Al-Hussein","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,28]]},"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 Sci."},{"key":"ref_2","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_3","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_4","doi-asserted-by":"crossref","unstructured":"Okmi, M., Por, L.Y., Ang, T.F., and Ku, C.S. (2023). Mobile Phone Data: A Survey of Techniques, Features, and Applications. Sensors, 23.","DOI":"10.3390\/s23020908"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s13278-016-0351-z","article-title":"Influence of social relations on human mobility and sociality: A study of social ties in a cellular network","volume":"6","author":"Phithakkitnukoon","year":"2016","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"102973","DOI":"10.1016\/j.annals.2020.102973","article-title":"Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data","volume":"84","author":"Park","year":"2020","journal-title":"Ann. Tour. Res."},{"key":"ref_8","first-page":"1420","article-title":"Tourism geography through the lens of time use: A computational framework using fine-grained mobile phone data","volume":"111","author":"Xu","year":"2021","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_9","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_10","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_11","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_12","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_13","doi-asserted-by":"crossref","unstructured":"Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., and Pentland, A. (2014, January 12\u201316). November. 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_14","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_15","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2019.04.017","article-title":"Social sensing from street-level imagery: A case study in learning spatio-temporal urban mobility patterns","volume":"153","author":"Zhang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1057\/s41599-019-0242-9","article-title":"Exploring the use of mobile phone data for national migration statistics","volume":"5","author":"Lai","year":"2019","journal-title":"Palgrave Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"164746","DOI":"10.1109\/ACCESS.2019.2952911","article-title":"Inferring and modeling migration flows using mobile phone network data","volume":"7","author":"Hankaew","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","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_20","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_21","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_22","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_23","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_24","doi-asserted-by":"crossref","first-page":"5943","DOI":"10.1038\/s41598-021-81873-6","article-title":"Countrywide population movement monitoring using mobile devices generated (big) data during the COVID-19 crisis","volume":"11","author":"Szocska","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Willberg, E., J\u00e4rv, O., V\u00e4is\u00e4nen, T., and Toivonen, T. (2021). Escaping from Cities during the COVID-19 Crisis: Using Mobile Phone Data to Trace Mobility in Finland. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10020103"},{"key":"ref_26","first-page":"73","article-title":"Impacts of the Covid-19 pandemic in inner areas. Remote work and near-home tourism through mobile phone data in Piacenza Apennine","volume":"2","author":"Lanza","year":"2022","journal-title":"TEMA"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sakamanee, P., Phithakkitnukoon, S., Smoreda, Z., and Ratti, C. (2020). Methods for inferring route choice of commuting trip from mobile phone network data. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9050306"},{"key":"ref_28","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_29","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_30","first-page":"1527723","article-title":"Social physics: Uncovering human behaviour from communication","volume":"4","author":"Bhattacharya","year":"2019","journal-title":"Adv. Phys. X"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.infsof.2008.09.009","article-title":"Systematic literature reviews in software engineering\u2013a systematic literature review","volume":"51","author":"Kitchenham","year":"2009","journal-title":"Inf. Softw. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1016\/j.jclinepi.2009.06.006","article-title":"The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration","volume":"62","author":"Liberati","year":"2009","journal-title":"J. Clin. Epidemiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5276","DOI":"10.1038\/srep05276","article-title":"From mobile phone data to the spatial structure of cities","volume":"4","author":"Louail","year":"2014","journal-title":"Sci. Rep."},{"key":"ref_35","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_36","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_37","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_38","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_39","unstructured":"Gabrielli, L., Furletti, B., Giannotti, F., Nanni, M., and Rinzivillo, S. (2015). Proceedings of the International Conference on Software Engineering and Formal Methods, York, UK, 7\u201311 September 2015, Springer."},{"key":"ref_40","unstructured":"Gabrielli, L., Furletti, B., Trasarti, R., Giannotti, F., and Pedreschi, D. (2015). Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, 29 October\u20131 November 2015, IEEE."},{"key":"ref_41","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_42","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_43","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_44","doi-asserted-by":"crossref","unstructured":"Taha, K., and Yoo, P.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_45","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_46","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/19475683.2014.992372","article-title":"Human mobility patterns in different communities: A mobile phone data-based social network approach","volume":"21","author":"Shi","year":"2015","journal-title":"Ann. GIS"},{"key":"ref_47","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_48","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_49","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_50","doi-asserted-by":"crossref","unstructured":"Arfaoui, S., Belmekki, A., and Mezrioui, A. (2018, January 2\u20134). Privacy increase on telecommunication processes. Proceedings of the 2018 International Conference on Advanced Communication Technologies and Networking (CommNet), Marrakech, Morocco.","DOI":"10.1109\/COMMNET.2018.8360266"},{"key":"ref_51","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_52","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_53","doi-asserted-by":"crossref","unstructured":"Liu, L., Peng, Z., Wu, H., Jiao, H., and Yu, Y. (2018). Exploring urban spatial feature with dasymetric mapping based on mobile phone data and LUR-2SFCAe method. Sustainability, 10.","DOI":"10.3390\/su10072432"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Salat, H., Smoreda, Z., and Schl\u00e4pfer, M. (2020). A method to estimate population densities and electricity consumption from mobile phone data in developing countries. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0235224"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Peng, Z., Wang, R., Liu, L., and Wu, H. (2020). Fine-Scale Dasymetric Population Mapping with Mobile Phone and Building Use Data Based on Grid Voronoi Method. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9060344"},{"key":"ref_56","first-page":"109","article-title":"Estimating the residential population from mobile phone data, an initial exploration","volume":"505","author":"Sakarovitch","year":"2018","journal-title":"Econ. Stat."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhang, G., Rui, X., Poslad, S., Song, X., Fan, Y., and Ma, Z. (2019). Large-scale, fine-grained, spatial, and temporal analysis, and prediction of mobile phone users\u2019 distributions based upon a convolution long short-term model. Sensors, 19.","DOI":"10.3390\/s19092156"},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1111\/tgis.12323","article-title":"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_60","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_61","doi-asserted-by":"crossref","first-page":"101263","DOI":"10.1016\/j.pmcj.2020.101263","article-title":"Towards a methodological framework for estimating present population density from mobile network operator data","volume":"68","author":"Ricciato","year":"2020","journal-title":"Pervasive Mob. Comput."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.pmcj.2016.04.009","article-title":"Beyond the \u201csingle-operator, CDR-only\u201d paradigm: An interoperable framework for mobile phone network data analyses and population density estimation","volume":"35","author":"Ricciato","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Shi, Y., Yang, J., and Shen, P. (2020). Revealing the correlation between population density and the spatial distribution of urban public service facilities with mobile phone data. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9010038"},{"key":"ref_64","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_65","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_66","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_67","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_68","doi-asserted-by":"crossref","unstructured":"Tongsinoot, L., and Muangsin, V. (2017, January 18\u201320). Exploring home and work locations in a city from mobile phone data. Proceedings of the 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), Bangkok, Thailand.","DOI":"10.1109\/HPCC-SmartCity-DSS.2017.16"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"935","DOI":"10.2478\/jos-2018-0046","article-title":"Assessing the Quality of Home Detection from Mobile Phone Data for Official Statistics","volume":"34","author":"Vanhoof","year":"2018","journal-title":"J. Off. Stat."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhao, Z., and Lu, S. (2016). Exploring Spatial-Temporal Patterns of Urban Human Mobility Hotspots. Sustainability, 8.","DOI":"10.3390\/su8070674"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/TASE.2018.2795241","article-title":"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_72","doi-asserted-by":"crossref","unstructured":"Truic\u0103, C.O., Novovi\u0107, O., Brdar, S., and Papadopoulos, A.N. (2018, January 3\u20136). Community detection in who-calls-whom social networks. Proceedings of the International Conference on Big Data Analytics and Knowledge Discovery, Regensburg, Germany.","DOI":"10.1007\/978-3-319-98539-8_2"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.neucom.2015.09.070","article-title":"Mining community and inferring friendship in mobile social networks","volume":"174","author":"Xu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Lind, A., Hadachi, A., Piksarv, P., and Batrashev, O. (2017, January 6\u20138). Spatio-temporal mobility analysis for community detection in the mobile networks using CDR data. Proceedings of the 2017 9th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Munich, Germany.","DOI":"10.1109\/ICUMT.2017.8255177"},{"key":"ref_75","first-page":"246","article-title":"Crowd estimation at a social event using call data records","volume":"28","author":"Sumathi","year":"2018","journal-title":"Int. J. Bus. Inf. Syst."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Filipowska, A., Mucha, M., Perkowski, B., Szczekocka, E., and Gromada, J. (2015, January 17\u201319). Towards social telco applications based on the user behaviour and relations between users. Proceedings of the 2015 18th International Conference on Intelligence in Next Generation Networks, Paris, France.","DOI":"10.1109\/ICIN.2015.7073813"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1140\/epjds\/s13688-018-0174-4","article-title":"Social network differences of chronotypes identified from mobile phone data","volume":"7","author":"Aledavood","year":"2018","journal-title":"EPJ Data Sci."},{"key":"ref_78","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_79","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.osnem.2017.04.003","article-title":"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_80","doi-asserted-by":"crossref","first-page":"7047","DOI":"10.1073\/pnas.1525443113","article-title":"Scaling identity connects human mobility and social interactions","volume":"113","author":"Deville","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_81","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_82","doi-asserted-by":"crossref","unstructured":"Chemello, N. (2016, January 12\u201314). Correlating CDR with other data sources. Proceedings of the 2016 IEEE International Conference on Cybercrime and Computer Forensic (ICCCF), Vancouver, BC, Canada.","DOI":"10.1109\/ICCCF.2016.7740425"},{"key":"ref_83","unstructured":"Kumar, M., Hanumanthappa, M., and Kumar, T.S. (2017). Proceedings of the 2016 Eighth International Conference on Advanced Computing (ICoAC), Chennai, India, 19\u201321 January 2017, IEEE."},{"key":"ref_84","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_85","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), Vashi, India.","DOI":"10.1109\/ICNTE.2017.7947942"},{"key":"ref_86","unstructured":"Hoyos, I., Esposito, B., and Nunez-del-Prado, M. (2018). Proceedings of the Annual International Symposium on Information Management and Big Data, Lima, Peru, 4\u20136 September 2018, Springer."},{"key":"ref_87","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_88","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.diin.2019.03.004","article-title":"CaseNote: Mobile phone call data obfuscation & techniques for call correlation","volume":"29","author":"Marshall","year":"2019","journal-title":"Digit. Investig."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.scijus.2022.03.011","article-title":"Investigating the uses of mobile phone evidence in China criminal proceedings","volume":"62","author":"Zhang","year":"2022","journal-title":"Sci. Justice"},{"key":"ref_90","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_91","doi-asserted-by":"crossref","unstructured":"Cavallaro, L., Ficara, A., De Meo, P., Fiumara, G., Catanese, S., Bagdasar, O., Song, W., and Liotta, A. (2020). Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0236476"},{"key":"ref_92","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_93","unstructured":"Andrea, C., Lehmann, S., and Larsen, J.E. (2014, January 13\u201317). Inferring human mobility from sparse low accuracy mobile sensing data. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, Seattle, DC, USA."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Ficara, A., Cavallaro, L., Curreri, F., Fiumara, G., De Meo, P., Bagdasar, O., Song, W., and Liotta, A. (2021). Criminal networks analysis in missing data scenarios through graph distances. PLoS ONE, 16.","DOI":"10.21428\/cb6ab371.0255af76"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Dileep, G.K., and Sajeev, G.P. (2021, January 8\u201310). A Graph Mining Approach to Detect Sandwich Calls. Proceedings of the 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT52877.2021.9622627"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1140\/epjds\/s13688-022-00366-2","article-title":"Enhancing short-term crime prediction with human mobility flows and deep learning architectures","volume":"11","author":"Wu","year":"2022","journal-title":"EPJ Data Sci."},{"key":"ref_97","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_98","doi-asserted-by":"crossref","unstructured":"Long, D., and Liu, L. (2021). Do Migrant and Native Robbers Target Different Places?. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10110771"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.future.2022.07.020","article-title":"BTG: A Bridge to Graph machine learning in telecommunications fraud detection","volume":"137","author":"Hu","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_100","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_101","doi-asserted-by":"crossref","unstructured":"Chu, G., Wang, J., Qi, Q., Sun, H., Tao, S., Yang, H., Liao, J., and Han, Z. (2022). Exploiting Spatial-Temporal Behavior Patterns for Fraud Detection in Telecom Networks. IEEE Trans. Dependable Secur. Comput., 1\u201313.","DOI":"10.1109\/TDSC.2022.3228797"},{"key":"ref_102","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_103","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_104","doi-asserted-by":"crossref","unstructured":"Kilinc, H.H. (2022, January 14\u201316). Anomaly Pattern Analysis Based on Machine Learning on Real Telecommunication Data. Proceedings of the 2022 7th International Conference on Computer Science and Engineering (UBMK), Diyarbakir, Turkey.","DOI":"10.1109\/UBMK55850.2022.9919564"},{"key":"ref_105","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_106","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_107","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_108","doi-asserted-by":"crossref","unstructured":"Arcolezi, H.H., Couchot, J.-F., Al Bouna, B., and Xiao, X. (2022). Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates. Digit. Commun. Netw., in press.","DOI":"10.1016\/j.dcan.2022.07.003"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3451178","article-title":"GLOVE: Towards privacy-preserving publishing of record-level-truthful mobile phone trajectories","volume":"2","author":"Gramaglia","year":"2021","journal-title":"ACM\/IMS Trans. Data Sci. (TDS)"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"e8","DOI":"10.1017\/dap.2021.9","article-title":"On the use of data from multiple mobile network operators in Europe to fight COVID-19","volume":"3","author":"Vespe","year":"2021","journal-title":"Data Policy"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.geoforum.2016.07.019","article-title":"Evidence and future potential of mobile phone data for disease disaster management","volume":"75","author":"Cinnamon","year":"2016","journal-title":"Geoforum"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1080\/26939169.2022.2089411","article-title":"Data Science Ethos Lifecycle: Interplay of ethical thinking and data science practice","volume":"30","author":"Tanweer","year":"2022","journal-title":"J. Stat. Data Sci. Educ."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s13347-022-00503-9","article-title":"Government surveillance, privacy, and legitimacy","volume":"35","author":"Peter","year":"2022","journal-title":"Philos. Technol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1109\/TMC.2014.2352253","article-title":"Privacy and quality preserving multimedia data aggregation for participatory sensing systems","volume":"14","author":"Qiu","year":"2014","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_115","unstructured":"Jin, H., Su, L., Ding, B., Nahrstedt, K., and Borisov, N. (2016). 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), IEEE."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"2842","DOI":"10.1109\/TMC.2018.2884945","article-title":"Dynamic participant selection for large-scale mobile crowd sensing","volume":"18","author":"Li","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Guo, B., Yu, Z., Zhou, X., and Zhang, D. (2014, January 24\u201328). From participatory sensing to mobile crowd sensing. Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), Budapest, Hungary.","DOI":"10.1109\/PerComW.2014.6815273"},{"key":"ref_118","first-page":"1831","article-title":"ilocus: Incentivizing vehicle mobility to optimize sensing distribution in crowd sensing","volume":"19","author":"Xu","year":"2019","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1109\/COMST.2019.2914030","article-title":"A survey on mobile crowdsensing systems: Challenges, solutions, and opportunities","volume":"21","author":"Capponi","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/JIOT.2016.2560768","article-title":"Security, privacy, and incentive provision for mobile crowd sensing systems","volume":"3","author":"Gisdakis","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s10707-013-0193-z","article-title":"User-side adaptive protection of location privacy in participatory sensing","volume":"18","author":"Agir","year":"2014","journal-title":"GeoInformatica"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Jin, W., Xiao, M., Li, M., and Guo, L. (May, January 29). If you do not care about it, sell it: Trading location privacy in mobile crowd sensing. Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications, Paris, France.","DOI":"10.1109\/INFOCOM.2019.8737457"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Chen, Z., Gul, O.M., and Kantarci, B. (2023). Practical Byzantine Fault Tolerance-based Robustness for Mobile Crowdsensing. Distrib. Ledger Technol. Res. Pract.","DOI":"10.1145\/3580392"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Chen, S., and Li, Z. (2022, January 19\u201321). Research on Enterprise Innovation Behavior Based on the Regression Analysis Under Big Data Technology. Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022), Hulunbuir, China.","DOI":"10.2991\/978-94-6463-064-0_68"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"874820","DOI":"10.3389\/fpsyg.2022.874820","article-title":"Investment Behavior Related to Automated Machines and Biased Technical Change: Based on Evidence from Listed Manufacturing Companies in China","volume":"13","author":"Jiang","year":"2022","journal-title":"Front. Psychol."},{"key":"ref_126","first-page":"123","article-title":"Next generation sequencing and health technology assessment in autism spectrum disorder","volume":"24","author":"Ungar","year":"2015","journal-title":"J. Can. Acad. Child Adolesc. Psychiatry"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10544-012-9734-8","article-title":"Microfluidics and cancer: Are we there yet?","volume":"15","author":"Zhang","year":"2013","journal-title":"Biomed. Microdevices"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"31925","DOI":"10.1364\/OE.463923","article-title":"Dual-function photonic spin Hall effect sensor for high-precision refractive index sensing and graphene layer detection","volume":"30","author":"Liu","year":"2020","journal-title":"Opt. Express"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"6065","DOI":"10.1364\/OL.476048","article-title":"High sensitivity multitasking non-reciprocity sensor using the photonic spin Hall effect","volume":"47","author":"Sui","year":"2022","journal-title":"Opt. Lett."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Wang, S., Tian, Y., Liu, X., and Foley, M. (2020). How Farmers Make Investment Decisions: Evidence from a Farmer Survey in China. Sustainability, 12.","DOI":"10.3390\/su12010247"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Kuang, Y., Shi, X., and Dong, C. (2018). Sustainable investment in a supply chain in the big data era: An information updating approach. Sustainability, 10.","DOI":"10.3390\/su10020403"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"5206","DOI":"10.1080\/00207543.2018.1427900","article-title":"Investments in big data analytics and firm performance: An empirical investigation of direct and mediating effects","volume":"56","author":"Raguseo","year":"2018","journal-title":"Int. J. Prod. Res."},{"key":"ref_133","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_134","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_135","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_136","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1111\/tgis.12285","article-title":"How friends share urban space: An exploratory spatiotemporal analysis using mobile phone data","volume":"21","author":"Xu","year":"2017","journal-title":"Trans. GIS"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Hoteit, S., Chen, G., Viana, A., and Fiore, M. (2016, January 3\u20137). Filling the gaps: On the completion of sparse call detail records for mobility analysis. Proceedings of the Eleventh ACM Workshop on Challenged Networks, New York, NY, USA.","DOI":"10.1145\/2979683.2979685"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4350\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:25:15Z","timestamp":1760124315000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4350"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,28]]},"references-count":137,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094350"],"URL":"https:\/\/doi.org\/10.3390\/s23094350","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,28]]}}}