Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention

Shah, Neil; Shah, Manan

doi:10.1186/s42492-021-00075-z

Cited by 82 publications

(37 citation statements)

References 61 publications

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“…We have chosen state-of-the-art New York City crime data [38] that has been used in several research articles. However, we now compare the results of this study under the same experimental setup with Random forest [39], ARIMA [4], SARIMA [5], and ZeroR [40]. To compare the results with state-of-the-art approaches, the same regions of New York City are used.…”

Section: Comparison Of the Proposed Approach With State-of-the-artmentioning

confidence: 99%

Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy

et al. 2022

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The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been made in the crime prediction area due to the deficiency of spatiotemporal crime data. Several machine learning, deep learning, and time series analysis techniques are exploited, but accuracy issues prevail. Thus, this study proposed a Bidirectional Long Short Term Memory (Bi-LSTM) and Exponential Smoothing (ES) hybrid for crime forecasting. The proposed technique is evaluated using New York City crime data from 2010–2017. The proposed approach outperformed as compared to state-of-the-art Seasonal Autoregressive Integrated Moving Averages (SARIMA) with low Mean Absolute Percentage Error (MAPE) (0.3738, 0.3891, 0.3433,0.3964), Root Mean Square Error (RMSE)(13.146, 13.669, 13.104, 13.77), and Mean Absolute Error (MAE) (9.837, 10.896, 10.598, 10.721). Therefore, the proposed technique can help law enforcement agencies to prevent and control crime by forecasting crime patterns.

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Section: Comparison Of the Proposed Approach With State-of-the-artmentioning

confidence: 99%

Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy

et al. 2022

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“…Neil Shah, Nandish Bhagat et. al., [3] presented a crime forecasting approach using ML and computer vision methods for the prediction and prevention of crime. In this paper, they described the results of certain cases where such approaches were used, and which are motivated to pursue further research in this field The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate.…”

Section: Literature Surveymentioning

confidence: 99%

Crime data anlysis using machine learning

Kiran¹,

Mounika²,

Naveen³

et al. 2022

sajet

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Crime is one of the biggest and dominating problems in our society and its prevention is an important task. Daily there are huge numbers of crimes committed frequently. This requires keeping track of all the crimes and maintaining a database for same which may be used for future reference. The current problem faced are maintaining of proper dataset of crime and analyzing this data to help in predicting and solving crimes in future. The objective of this project is to analyze dataset which consist of numerous crimes and predicting the type of crime which may happen in future depending upon various conditions. In this project, Machine Learning and data science techniques are used for crime prediction of Chicago crime data set. For this supervised classification Random Forest algorithm is used. This approach involves predicting crimes classifying, pattern detection and visualization with effective tools and technologies. Use of past crime data trends helps us to correlate factors which might help understanding the future scope of crimes. In this work, various visualizing techniques and machine learning algorithms are adopted for predicting the crime distribution over an area. In the first step, the raw datasets are processed and visualized based on the need.

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“…For instance, the introduction of machine learning algorithm in crime analysis and prediction brings a whole new perspective in one's security agency. This intelligent approach in crime eradication outstands the human approach in analysis by mimicking the human concept (Shah et al, 2021). The usage of machine learning algorithms can be helpful to the crime analysts to their fight against crimes and in saving humanity (ToppiReddy et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Building Model for Crime Pattern Analysis Through Machine Learning Using Predictive Analytics

Asor¹,

Balahadia²,

Catedrilla³

et al. 2022

IJSTEM

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Crime has a big impact in both the human lives and the society’s growth, which needs to be addressed and controlled. Machine learning algorithms as the fanciest technology to assist decision makers in policy making has proven its reliability in showing unseen patterns in crime. This research aims to examine the capability of trees and ensemble trees in classifying crime through model development. Experiments were done to enhance the capability of the ensembles in both classification and regression. Feature extraction like synthetic minority oversampling technique was applied in order to address the problem in the imbalanced data. Different metrics relevant to classification and regression were considered in evaluating the performance of each model used. With the use of different metrics, Gradient boosted tree was found to have better classification capability in crime dataset after outperforming decision tree and random forest in both classification and regression problem. Furthermore, random forest was also found to have a promising capability in classification by regression. Therefore, it is highly recommended that this ensemble algorithm be further examined and considered in developing model in other datasets.

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Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention

Cited by 82 publications

References 61 publications

Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy

Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy

Crime data anlysis using machine learning

Building Model for Crime Pattern Analysis Through Machine Learning Using Predictive Analytics

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