Crime Forecasting Using Data Mining Techniques

Yu, Chung-Hsien; Ward, M.; Morabito, Melissa S.; Ding, Wei

doi:10.1109/icdmw.2011.56

Cited by 102 publications

(58 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Yu [20] have discussed the preliminary results of a crime forecasting model developed in collaboration with the police department of a United States city in the Northeast. Their approach is to architect datasets from original crime records.…”

Section: Spatial and Geo-location Based Methodsmentioning

confidence: 99%

Survey on Crime Analysis and Prediction Using Data Mining Techniques

David

Suruliandi

2017

IJSC

View full text Add to dashboard Cite

Data Mining is the procedure which includes evaluating and examining large pre-existing databases in order to generate new information which may be essential to the organization. The extraction of new information is predicted using the existing datasets. Many approaches for analysis and prediction in data mining had been performed. But, many few efforts has made in the criminology field. Many few have taken efforts for comparing the information all these approaches produce. The police stations and other similar criminal justice agencies hold many large databases of information which can be used to predict or analyze the criminal movements and criminal activity involvement in the society. The criminals can also be predicted based on the crime data. The main aim of this work is to perform a survey on the supervised learning and unsupervised learning techniques that has been applied towards criminal identification. This paper presents the survey on the Crime analysis and crime prediction using several Data Mining techniques.

show abstract

Section: Spatial and Geo-location Based Methodsmentioning

confidence: 99%

Survey on Crime Analysis and Prediction Using Data Mining Techniques

David

Suruliandi

2017

IJSC

View full text Add to dashboard Cite

show abstract

“…Brown and Oxford [6] study methods that pertain to predicting the number of breaking and enterings in sub-cities and correlate breaking and enterings with different factors including unemployment rates, alcohol sales and previous incidents of crime. Yu et al [44] also develop a crime forecasting model by employing different data mining classification techniques. They employ several classification techniques including Nearest Neighbor, Decision Tree and Support Vector Machines.…”

Section: Predictive Policingmentioning

confidence: 99%

Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

Malik

Maciejewski

Towers

et al. 2014

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Abstract-In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.

show abstract

“…Yu et al discuss crime forecasting using different classification techniques, SVM, J48, Neural, and INN [17]. They calculated the accuracy and F1 for each method.…”

Section: Literature Reviewmentioning

confidence: 99%