{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:32:32Z","timestamp":1742920352548,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030705718"},{"type":"electronic","value":"9783030705725"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-70572-5_17","type":"book-chapter","created":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T07:03:53Z","timestamp":1614755033000},"page":"269-286","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Policing Using Deep Learning: A Community Policing Practical Case Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3028-6180","authenticated-orcid":false,"given":"Omowunmi","family":"Isafiade","sequence":"first","affiliation":[]},{"given":"Brian","family":"Ndingindwayo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0774-5251","authenticated-orcid":false,"given":"Antoine","family":"Bagula","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"17_CR1","unstructured":"South African Government: Crime report. In South African police service annual crime report 2017\/2018. https:\/\/www.gov.za\/sites\/default\/files\/gcis_document\/201809\/crime-stats201718.pdf. Accessed June 2020"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Isafiade, O., Bagula, A.: Fostering smart city development in developing nations: a crime series data analytics approach. In: Proceedings of the ITU-Kaleidoscope: Challenges for a Data-Driven Society, pp. 89\u201395. IEEE, Nanjing, China (2017)","DOI":"10.23919\/ITU-WT.2017.8246992"},{"key":"17_CR3","doi-asserted-by":"publisher","unstructured":"Du Plessis, A., Louw, A.: Crime prevention in South Africa: 10 Years After. Can. J. Criminol. Crim. Justice\/La Revue canadienne de criminologie et de justice p\u00e9nale 47(2), 1\u201320 (2005). https:\/\/doi.org\/10.3138\/cjccj.47.2.427","DOI":"10.3138\/cjccj.47.2.427"},{"issue":"1","key":"17_CR4","first-page":"3","volume":"3","author":"South African Police Service","year":"2014","unstructured":"South African Police Service: Together squeezing crime to zero. J. Strateg. Plan 3(1), 3\u201321 (2014)","journal-title":"J. Strateg. Plan"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Isafiade, O., Bagula, A.: CitiSafe: adaptive spatial pattern knowledge using Fp-growth algorithm for crime situation recognition. In: Proceedings of the IEEE International Conference on Ubiquitous Intelligence and Computing (UIC-ATC), pp. 551\u2013556, December 2013","DOI":"10.1109\/UIC-ATC.2013.72"},{"key":"17_CR6","unstructured":"National Development Plan 2030: Our Future - make it work, pp. 1\u201370. https:\/\/www.gov.za\/sites\/default\/files\/Executive%20Summary-NDP%202030%20-%20Our%20future%20-%20make%20it%20work.pdf. Accessed June 2020"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Mohler, G.O., et al.: Randomized controlled field trials of predictive policing. J. Am. Stat. Assoc. 110(512) (2015)","DOI":"10.1080\/01621459.2015.1077710"},{"key":"17_CR8","unstructured":"MeMeZa crime prevention and community mobilisation project: Memeza shout crime prevention Diepsloot pilot results summary. http:\/\/memeza.co.za\/wp-content\/uploads\/2016\/05\/Memeza-Diepsloot-Pilot-Report-2015.pdf. Accessed September 2018"},{"key":"17_CR9","doi-asserted-by":"publisher","unstructured":"Wilson, J.M., Weis, A.: Police staffing allocation and managing workload demand: a critical assessment of existing practices. J. Policing 8, 1\u201313 (2014). https:\/\/doi.org\/10.1093\/police\/pau00. Advance Access-Oxford University Press","DOI":"10.1093\/police\/pau00"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Isafiade, O., Bagula, A.: Data mining trends and applications in criminal science and investigations, pp. 1\u2013386. IGI Global, USA (2016)","DOI":"10.4018\/978-1-5225-0463-4"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Kang, H.W., Kang, H.B.: Prediction of crime occurrence from multi-modal data using deep learning. J. PLOS-ONE 12(4), 1\u201319 (2017)","DOI":"10.1371\/journal.pone.0176244"},{"key":"17_CR12","unstructured":"Mookiah, L., Eberle, W., Siraj, A.: Survey of crime analysis and prediction. In: The Twenty-Eighth International Flairs Conference, pp. 440\u2013443 (2015)"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Mohd Shamsuddin, N.H., Ali, N.A., Alwee, R.: An overview on crime prediction methods. In: Proceedings of the 6th ICT International Student Project Conference (ICT-ISPC), pp. 1\u20135, Malaysia (2017)","DOI":"10.1109\/ICT-ISPC.2017.8075335"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Wang, H., Kifer, D., Graif, C., Li, Z.: Crime rate inference with big data. In: Proceedings of the International Conference on Knowledge Discovery in Database, pp. 635\u2013644. ACM (2016)","DOI":"10.1145\/2939672.2939736"},{"key":"17_CR15","unstructured":"Isafiade, O., Bagula, A.: Series mining for public safety advancement in emerging smart cities. Future Gener. Comput. Syst. 108, 777\u2013802 (2020)"},{"key":"17_CR16","first-page":"1","volume":"54","author":"T Almanie","year":"2015","unstructured":"Almanie, T., Mirza, R., Lor, E.: Crime prediction based on crime types and using spatial and temporal criminal hotspots. Int. J. Data Min. Knowl. Manage. Process (IJDKP) 54, 1\u201319 (2015)","journal-title":"Int. J. Data Min. Knowl. Manage. Process (IJDKP)"},{"key":"17_CR17","unstructured":"Isafiade, O., Bagula, A., Berman, S.: A revised frequent pattern model for crime situation recognition based on floor-ceil quartile function. Procedia Comput. Sci. 15, 251\u2013260 (2015)"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Isafiade, O., Bagula, A., Berman, S.: On the use of Bayesian network in crime suspect modelling and legal decision support. In: Data Mining Trends and Applications in Criminal Science and Investigations, pp. 143\u2013168, USA (2016)","DOI":"10.4018\/978-1-5225-0463-4.ch006"},{"key":"17_CR19","unstructured":"Greenberg, D.: Time series analysis of crime rates. J. Quant. Criminol. 17(4), 291\u2013327 (2001)"},{"key":"17_CR20","unstructured":"Flaxman, S., Chirico, M., Pereira, P., Loeffler, C.: Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ Real-Time Crime Forecasting Challenge. Mach. Learn. 13, 1\u201330 (2018). https:\/\/arxiv.org\/abs\/1801.02858"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Isafiade, O., Bagula A: Efficient frequent pattern knowledge for crime situation recognition in developing countries. In: Proceedings of the 4th Annual Symposious on Computing for Development, pp. 1\u20132, ACM (2013)","DOI":"10.1145\/2537052.2537073"},{"issue":"1","key":"17_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18642\/ijamml_7100121446","volume":"2","author":"L Mcclendon","year":"2015","unstructured":"Mcclendon, L., Meghanathan, N.: Using machine learning algorithms to analyze crime data. Mach. Learn. Appl. Int. J. (MLAIJ) 2(1), 1\u201312 (2015)","journal-title":"Mach. Learn. Appl. Int. J. (MLAIJ)"},{"key":"17_CR23","unstructured":"Almanie, T., Mirza, R., Lor, E.: Crime prediction based on crime types and using spatial and temporal criminal hotspots. Int. J. Data Min. Knowl. Manage. Process 5(4), 1\u20139 (2015)"},{"key":"17_CR24","doi-asserted-by":"publisher","unstructured":"Lin, Y.-L., Yen, M.-F., Yu, L.-C.: Grid-based crime prediction using geographical features. ISPRS Int. J. Geo Inf. 7(8), 298 (2018). https:\/\/doi.org\/10.3390\/ijgi7080298","DOI":"10.3390\/ijgi7080298"},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Azeez, J., Aravindhar, D.J.: Hybrid approach to crime prediction using deep learning. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1701\u20131710. IEEE, Kochi (2015)","DOI":"10.1109\/ICACCI.2015.7275858"},{"key":"17_CR26","unstructured":"Stec, A., Klabjan, D.: Forecasting crime with deep learning. arXiv preprint arXiv:1806.01486l, pp. 1\u201320 (2018)"},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Yang, L.T., Chen, Z., Li, P.: A survey on deep learning for big data. J. Inf. Fusion 42, 146\u2013157 (2018)","DOI":"10.1016\/j.inffus.2017.10.006"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Towards new e-Infrastructure and e-Services for Developing Countries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-70572-5_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T07:13:12Z","timestamp":1619766792000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-70572-5_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030705718","9783030705725"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-70572-5_17","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"4 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AFRICOMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on e-Infrastructure and e-Services for Developing Countries","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Eb\u00e8ne","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mauritius","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"africom2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/africommconference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ConfyPlus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"89","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID-19 pandemic the conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}