{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:12:55Z","timestamp":1774541575158,"version":"3.50.1"},"reference-count":91,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Funds, through the COMPETE 2020\u2014Operational Programme \u2018Competitiveness and Internationalization\u2019","award":["POCI-01-0145-FEDER-030748"],"award-info":[{"award-number":["POCI-01-0145-FEDER-030748"]}]},{"name":"European Regional Development Funds, through the COMPETE 2020\u2014Operational Programme \u2018Competitiveness and Internationalization\u2019","award":["UIDB\/04084\/2020"],"award-info":[{"award-number":["UIDB\/04084\/2020"]}]},{"name":"National Funds through the Portuguese Foundation for Science and Technology (FCT)","award":["POCI-01-0145-FEDER-030748"],"award-info":[{"award-number":["POCI-01-0145-FEDER-030748"]}]},{"name":"National Funds through the Portuguese Foundation for Science and Technology (FCT)","award":["UIDB\/04084\/2020"],"award-info":[{"award-number":["UIDB\/04084\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals.<\/jats:p>","DOI":"10.3390\/ijgi11070400","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T00:17:10Z","timestamp":1657844230000},"page":"400","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Crime Prediction and Monitoring in Porto, Portugal, Using Machine Learning, Spatial and Text Analytics"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5967-661X","authenticated-orcid":false,"given":"Miguel","family":"Saraiva","sequence":"first","affiliation":[{"name":"CEGOT\u2014Centre of Studies in Geography and Spatial Planning, Faculty of Arts and Humanities, University of Porto, Via Panor\u00e2mica s\/n, 4150-564 Porto, Portugal"}]},{"given":"Irina","family":"Matijo\u0161aitien\u0117","sequence":"additional","affiliation":[{"name":"Data Science Institute, Saint Peter\u2019s University, Jersey City, NJ 07306, USA"}]},{"given":"Saloni","family":"Mishra","sequence":"additional","affiliation":[{"name":"Data Science Institute, Saint Peter\u2019s University, Jersey City, NJ 07306, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1718-5323","authenticated-orcid":false,"given":"Ana","family":"Amante","sequence":"additional","affiliation":[{"name":"CEGOT\u2014Centre of Studies in Geography and Spatial Planning, Faculty of Arts and Humanities, University of Porto, Via Panor\u00e2mica s\/n, 4150-564 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,14]]},"reference":[{"key":"ref_1","unstructured":"Organisation for Economic Co-operation and Development (2020). 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