{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T09:23:42Z","timestamp":1776158622356,"version":"3.50.1"},"reference-count":86,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.K. Economic and Social Research Council Consumer Data Research Centre (CDRC)","award":["ES\/L011840\/1"],"award-info":[{"award-number":["ES\/L011840\/1"]}]},{"name":"U.K. Economic and Social Research Council Consumer Data Research Centre (CDRC)","award":["ES\/V00445X\/1"],"award-info":[{"award-number":["ES\/V00445X\/1"]}]},{"name":"Economic and Social Research Council under the U.K. Research and Innovation open call on COVID-19","award":["ES\/L011840\/1"],"award-info":[{"award-number":["ES\/L011840\/1"]}]},{"name":"Economic and Social Research Council under the U.K. Research and Innovation open call on COVID-19","award":["ES\/V00445X\/1"],"award-info":[{"award-number":["ES\/V00445X\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>This study aimed to evaluate the relationships between different groups of explanatory variables (i.e., dynamic human activity variables, static variables of social disorganisation and crime generators, and combinations of both sets of variables) and property crime patterns across neighbourhood areas of London during the pandemic (from 2020 to 2021). Using the dynamic human activity variables sensed from mobile phone GPS big data sets, three types of \u2018Least Absolute Shrinkage and Selection Operator\u2019 (LASSO) regression models (i.e., static, dynamic, and static and dynamic) differentiated into explanatory variable groups were developed for seven types of property crime. Then, the geographically weighted regression (GWR) model was used to reveal the spatial associations between distinct explanatory variables and the specific type of crime. The findings demonstrated that human activity dynamics impose a substantially stronger influence on specific types of property crimes than other static variables. In terms of crime type, theft obtained particularly high relationships with dynamic human activity compared to other property crimes. Further analysis revealed important nuances in the spatial associations between property crimes and human activity across different contexts during the pandemic. The result provides support for crime risk prediction that considers the impact of dynamic human activity variables and their varying influences in distinct situations.<\/jats:p>","DOI":"10.3390\/ijgi12120488","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T03:48:41Z","timestamp":1701834521000},"page":"488","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Applying Dynamic Human Activity to Disentangle Property Crime Patterns in London during the Pandemic: An Empirical Analysis Using Geo-Tagged Big Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1763-6876","authenticated-orcid":false,"given":"Tongxin","family":"Chen","sequence":"first","affiliation":[{"name":"SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London WC1E 6BT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0965-492X","authenticated-orcid":false,"given":"Kate","family":"Bowers","sequence":"additional","affiliation":[{"name":"Department of Security and Crime Science, University College London, Tavistock Square, London WC1H 9EZ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5503-9813","authenticated-orcid":false,"given":"Tao","family":"Cheng","sequence":"additional","affiliation":[{"name":"SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London WC1E 6BT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,6]]},"reference":[{"key":"ref_1","first-page":"30","article-title":"Oxford COVID-19 government response tracker (OxCGRT)","volume":"8","author":"Hale","year":"2020","journal-title":"Last Updat."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"999521","DOI":"10.3389\/fpubh.2022.999521","article-title":"Human mobility variations in response to restriction policies during the COVID-19 pandemic: An analysis from the Virus Watch community cohort in England, UK","volume":"10","author":"Cheng","year":"2022","journal-title":"Front. Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1093\/police\/paaa037","article-title":"Changes in police calls for service during the early months of the 2020 coronavirus pandemic","volume":"14","author":"Ashby","year":"2020","journal-title":"Polic. J. Policy Pract."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40163-020-00117-6","article-title":"Initial evidence on the relationship between the coronavirus pandemic and crime in the United States","volume":"9","author":"Ashby","year":"2020","journal-title":"Crime Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1007\/s12103-020-09578-6","article-title":"Exploring the immediate effects of COVID-19 containment policies on crime: An empirical analysis of the short-term aftermath in Los Angeles","volume":"46","author":"Campedelli","year":"2021","journal-title":"Am. J. Crim. Justice"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40163-021-00142-z","article-title":"Six months in: Pandemic crime trends in England and Wales","volume":"10","author":"Langton","year":"2021","journal-title":"Crime Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101692","DOI":"10.1016\/j.jcrimjus.2020.101692","article-title":"Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis","volume":"68","author":"Mohler","year":"2020","journal-title":"J. Crim. Justice"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40163-020-00120-x","article-title":"Routine activity effects of the COVID-19 pandemic on burglary in Detroit, March, 2020","volume":"9","author":"Felson","year":"2020","journal-title":"Crime Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1038\/s41562-021-01139-z","article-title":"A global analysis of the impact of COVID-19 stay-at-home restrictions on crime","volume":"5","author":"Nivette","year":"2021","journal-title":"Nat. Hum. Behav."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s40163-021-00147-8","article-title":"The U-shaped crime recovery during COVID-19: Evidence from national crime rates in Mexico","volume":"10","year":"2021","journal-title":"Crime Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s40163-021-00162-9","article-title":"Offline crime bounces back to pre-COVID levels, cyber stays high: Interrupted time-series analysis in Northern Ireland","volume":"10","author":"Zeng","year":"2021","journal-title":"Crime Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1007\/s12103-021-09666-1","article-title":"Disentangling the impact of COVID-19: An interrupted time series analysis of crime in New York city","volume":"48","author":"Koppel","year":"2022","journal-title":"Am. J. Crim. Justice"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40163-021-00151-y","article-title":"Crime and COVID-19: Effect of changes in routine activities in Mexico City","volume":"10","year":"2021","journal-title":"Crime Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40163-020-00121-w","article-title":"Crime and coronavirus: Social distancing, lockdown, and the mobility elasticity of crime","volume":"9","author":"Halford","year":"2020","journal-title":"Crime Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"588","DOI":"10.2307\/2094589","article-title":"Social Change and Crime Rate Trends: A Routine Activity Approach","volume":"44","author":"Cohen","year":"1979","journal-title":"Am. Sociol. Rev."},{"key":"ref_16","unstructured":"Brantingham, P.J., and Brantingham, P.L. (1984). Patterns in Crime, Macmillan."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/BF01561001","article-title":"Human ecology and crime: A routine activity approach","volume":"8","author":"Felson","year":"1980","journal-title":"Hum. Ecol."},{"key":"ref_18","unstructured":"Felson, M. (2016). Environmental Criminology and Crime Analysis, Routledge."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Brantingham, P.J., and Brantingham, P.L. (2016). Environmental Criminology and Crime Analysis, Routledge.","DOI":"10.1002\/9781119011385.ch22"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/BF02242925","article-title":"Criminality of place: Crime generators and crime attractors","volume":"3","author":"Brantingham","year":"1995","journal-title":"Eur. J. Crim. Policy Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1126\/science.277.5328.918","article-title":"Neighborhoods and violent crime: A multilevel study of collective efficacy","volume":"277","author":"Sampson","year":"1997","journal-title":"Science"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1086\/210356","article-title":"Systematic social observation of public spaces: A new look at disorder in urban neighborhoods","volume":"105","author":"Sampson","year":"1999","journal-title":"Am. J. Sociol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0042098018795363","article-title":"Neighbourhood effects and beyond: Explaining the paradoxes of inequality in the changing American metropolis","volume":"56","author":"Sampson","year":"2019","journal-title":"Urban Stud."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s43762-022-00041-2","article-title":"Spatio-temporal stratified associations between urban human activities and crime patterns: A case study in San Francisco around the COVID-19 stay-at-home mandate","volume":"2","author":"Chen","year":"2022","journal-title":"Comput. Urban Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s40163-020-00131-8","article-title":"Disentangling community-level changes in crime trends during the COVID-19 pandemic in Chicago","volume":"9","author":"Campedelli","year":"2020","journal-title":"Crime Sci."},{"key":"ref_26","first-page":"106","article-title":"Crime and mobility during the COVID-19 lockdown: A preliminary empirical exploration","volume":"56","author":"Cheung","year":"2022","journal-title":"N. Z. Econ. Pap."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101996","DOI":"10.1016\/j.jcrimjus.2022.101996","article-title":"Household occupancy and burglary: A case study using COVID-19 restrictions","volume":"82","author":"Frith","year":"2022","journal-title":"J. Crim. Justice"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"393","DOI":"10.2307\/2579053","article-title":"Urban dynamics and ecological studies of delinquency","volume":"63","author":"Bursik","year":"1984","journal-title":"Soc. Forces"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1086\/229068","article-title":"Community structure and crime: Testing social-disorganization theory","volume":"94","author":"Sampson","year":"1989","journal-title":"Am. J. Sociol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s10940-018-9397-6","article-title":"Toward an integrated multilevel theory of crime at place: Routine activities, social disorganization, and the law of crime concentration","volume":"35","author":"Jones","year":"2019","journal-title":"J. Quant. Criminol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1111\/1745-9125.12070","article-title":"The law of crime concentration and the criminology of place","volume":"53","author":"Weisburd","year":"2015","journal-title":"Criminology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1111\/1745-9125.12116","article-title":"Crime diversity","volume":"54","author":"Brantingham","year":"2016","journal-title":"Criminology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"04015011","DOI":"10.1061\/(ASCE)NH.1527-6996.0000190","article-title":"Modeling the relationship between natural disasters and crime in the United States","volume":"17","author":"Prelog","year":"2016","journal-title":"Nat. Hazards Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.1080\/09640568.2015.1116981","article-title":"The importance of place in early disaster recovery: A case study of the 2013 Colorado floods","volume":"59","author":"Rumbach","year":"2016","journal-title":"J. Environ. Plan. Manag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1177\/0002716205285589","article-title":"Metaphors matter: Disaster myths, media frames, and their consequences in Hurricane Katrina","volume":"604","author":"Tierney","year":"2006","journal-title":"Ann. Am. Acad. Political Soc. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s11292-021-09495-6","article-title":"In a world called catastrophe: The impact of COVID-19 on neighbourhood level crime in Vancouver, Canada","volume":"19","author":"Andresen","year":"2023","journal-title":"J. Exp. Criminol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"101881","DOI":"10.1016\/j.jcrimjus.2022.101881","article-title":"Crime down in the Paris of the prairies: Spatial effects of COVID-19 and crime during lockdown in Saskatoon, Canada","volume":"78","author":"Hodgkinson","year":"2022","journal-title":"J. Crim. Justice"},{"key":"ref_38","unstructured":"MacEachren, A.M. (2017). Spatial Data Handling in Big Data Era, Springer."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2018.01.001","article-title":"Human mobility: Models and applications","volume":"734","author":"Barbosa","year":"2018","journal-title":"Phys. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jenkins, A., Croitoru, A., Crooks, A.T., and Stefanidis, A. (2016). Crowdsourcing a collective sense of place. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0152932"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1177\/23998083221075634","article-title":"Human mobility data and analysis for urban resilience: A systematic review","volume":"49","author":"Haraguchi","year":"2022","journal-title":"Environ. Plan. Urban Anal. City Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s10115-018-1186-x","article-title":"Analyzing large-scale human mobility data: A survey of machine learning methods and applications","volume":"58","author":"Toch","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1038\/nature04292","article-title":"The scaling laws of human travel","volume":"439","author":"Brockmann","year":"2006","journal-title":"Nature"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mir, D.J., Isaacman, S., C\u00e1ceres, R., Martonosi, M., and Wright, R.N. (2013, January 6\u20139). Dp-where: Differentially private modeling of human mobility. Proceedings of the 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA.","DOI":"10.1109\/BigData.2013.6691626"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2743025","article-title":"Trajectory data mining: An overview","volume":"6","author":"Zheng","year":"2015","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Alessandretti, L., Sapiezynski, P., Lehmann, S., and Baronchelli, A. (2017). Multi-scale spatio-temporal analysis of human mobility. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0171686"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhao, K., Tarkoma, S., Liu, S., and Vo, H. (2016, January 5\u20138). Urban human mobility data mining: An overview. Proceedings of the 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA.","DOI":"10.1109\/BigData.2016.7840811"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.trc.2010.12.003","article-title":"Smart card data use in public transit: A literature review","volume":"19","author":"Pelletier","year":"2011","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1080\/13658816.2019.1587616","article-title":"Estimating real-time high-street footfall from Wi-Fi probe requests","volume":"34","author":"Soundararaj","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., and Pentland, A. (2014, January 12\u201316). 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_51","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_52","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_53","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_54","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_55","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_56","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1177\/0003122420972323","article-title":"Triple disadvantage: Neighborhood networks of everyday urban mobility and violence in US cities","volume":"85","author":"Levy","year":"2020","journal-title":"Am. Sociol. Rev."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1140\/epjds\/s13688-018-0150-z","article-title":"Mining large-scale human mobility data for long-term crime prediction","volume":"7","author":"Kadar","year":"2018","journal-title":"EPJ Data Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1080\/15230406.2014.972456","article-title":"UK open source crime data: Accuracy and possibilities for research","volume":"42","author":"Tompson","year":"2015","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_59","first-page":"804","article-title":"Crime risk estimation with a commuter-harmonized ambient population","volume":"106","author":"Mburu","year":"2016","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1093\/bjc\/azt050","article-title":"Collective efficacy, deprivation and violence in London","volume":"53","author":"Sutherland","year":"2013","journal-title":"Br. J. Criminol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1086\/691261","article-title":"Ecological networks and neighborhood social organization","volume":"122","author":"Browning","year":"2017","journal-title":"Am. J. Sociol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"62","DOI":"10.2148\/benv.34.1.62","article-title":"Crime attractors, generators and detractors: Land use and urban crime opportunities","volume":"34","author":"Kinney","year":"2008","journal-title":"Built Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1177\/0022427810384135","article-title":"Robberies in Chicago: A block-level analysis of the influence of crime generators, crime attractors, and offender anchor points","volume":"48","author":"Bernasco","year":"2011","journal-title":"J. Res. Crime Delinq."},{"key":"ref_64","first-page":"1","article-title":"Analyzing and predicting spatial crime distribution using crowdsourced and open data","volume":"3","author":"Belesiotis","year":"2018","journal-title":"ACM Trans. Spat. Algorithms Syst. (TSAS)"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Redfern, J., Sidorov, K., Rosin, P.L., Corcoran, P., Moore, S.C., and Marshall, D. (2020). Association of violence with urban points of interest. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0239840"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Hariharan, R., and Toyama, K. (2004, January 20\u201323). Project Lachesis: Parsing and modeling location histories. Proceedings of the International Conference on Geographic Information Science, Adelphi, MD, USA.","DOI":"10.1007\/978-3-540-30231-5_8"},{"key":"ref_67","unstructured":"Pappalardo, L., Simini, F., Barlacchi, G., and Pellungrini, R. (2019). Scikit-mobility: A Python library for the analysis, generation and risk assessment of mobility data. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2390","DOI":"10.1109\/TKDE.2012.153","article-title":"T-finder: A recommender system for finding passengers and vacant taxis","volume":"25","author":"Yuan","year":"2012","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1177\/0022427812469114","article-title":"A stab in the dark? A research note on temporal patterns of street robbery","volume":"50","author":"Tompson","year":"2013","journal-title":"J. Res. Crime Delinq."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1111\/j.1467-9868.2011.00771.x","article-title":"Regression shrinkage and selection via the lasso: A retrospective","volume":"73","author":"Tibshirani","year":"2011","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10940-020-09490-6","article-title":"Reducing crime through environmental design: Evidence from a randomized experiment of street lighting in New York City","volume":"38","author":"Chalfin","year":"2022","journal-title":"J. Quant. Criminol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"123627","DOI":"10.1016\/j.physa.2019.123627","article-title":"Crime risk analysis through big data algorithm with urban metrics","volume":"545","author":"Wang","year":"2020","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1068\/a301905","article-title":"Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis","volume":"30","author":"Fotheringham","year":"1998","journal-title":"Environ. Plan. A"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10109-008-0073-5","article-title":"Comparing spatially varying coefficient models: A case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests","volume":"11","author":"Wheeler","year":"2009","journal-title":"J. Geogr. Syst."},{"key":"ref_78","unstructured":"Bernasco, W., and Elffers, H. (2010). Handbook of Quantitative Criminology, Springer."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"102072","DOI":"10.1016\/j.apgeog.2019.102072","article-title":"Mapping crime\u2013Hate crimes and hate groups in the USA: A spatial analysis with gridded data","volume":"111","author":"Jendryke","year":"2019","journal-title":"Appl. Geogr."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Chen, T., Zhu, D., Cheng, T., Gao, X., and Chen, H. (2023). Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0277913"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1111\/1745-9125.12204","article-title":"Neighborhood immigrant concentration and violent crime reporting to the police: A multilevel analysis of data from the National Crime Victimization Survey","volume":"57","author":"Xie","year":"2019","journal-title":"Criminology"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10610-014-9248-4","article-title":"Crime mapping on-line: Public perception of privacy issues","volume":"21","author":"Kounadi","year":"2015","journal-title":"Eur. J. Crim. Policy Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1177\/2043820613513392","article-title":"The quality of big (geo) data","volume":"3","author":"Goodchild","year":"2013","journal-title":"Dialogues Hum. Geogr."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"3498","DOI":"10.1177\/1461444818765154","article-title":"Mobile phones and inequality: Findings, trends, and future directions","volume":"20","author":"Marler","year":"2018","journal-title":"New Media Soc."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1177\/1477370819877753","article-title":"Environmental criminology in the big data era","volume":"18","author":"Snaphaan","year":"2021","journal-title":"Eur. J. Criminol."},{"key":"ref_86","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"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/12\/488\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:33:50Z","timestamp":1760132030000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/12\/488"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,6]]},"references-count":86,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["ijgi12120488"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12120488","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,6]]}}}