Forecasting Crimes Using Autoregressive Models

Cesario, Eugenio; Catlett, Charlie; Talia, Domenico

doi:10.1109/dasc-picom-datacom-cyberscitec.2016.138

Cited by 42 publications

(25 citation statements)

References 13 publications

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“…The goal of computational experiments was to demonstrate a high-quality prediction of the number of crimes for each crime type in each community within the city of Chicago. This significantly extends experiments presented in [9] and introduces new research directions. Because predicting the crimes is a quite complicated task because of the dependence of data on a variety of social, economic and political factors, our second goal was to demonstrate that network constructed and used to fuse various data sources does improve the quality of prediction.…”

Section: Crime Predictionsupporting

confidence: 76%

“…The objective of [9] is similar to ours. The authors take the Chicago crime dataset (which is also part of our input) and creates the time series data to predict number of crimes at a specific area on a week by week basis.…”

Section: Related Workmentioning

confidence: 89%

“…To predict crime, several classical statistical techniques are being applied, whose results are then used to take correct steps taken by the police to prevent crimes. Statistical methods such as the auto-regression modeling [9], association of crime events [8], and random walks [24] are being used in state of the art analysis of crimes. In this section, we review several relevant crime analysis methods.…”

Section: Related Workmentioning

confidence: 99%

“…3) Auto-regressive model: In [9], the auto-regressive model was suggested for the analysis of crime data. Similar to other linear models, the variable of interest in auto-regressive model is predicted using a linear combination of its previous values.…”

Section: Crime Predictionmentioning

confidence: 99%

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Spatio-temporal prediction of crimes using network analytic approach

Dash

Safro

Srinivasamurthy

2018

2018 IEEE International Conference on Big Data (Big Data)

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It is quite evident that majority of the population lives in urban area today than in any time of the human history. This trend seems to increase in coming years. A study [5] says that nearly 80.7% of total population in USA stays in urban area. By 2030 nearly 60% of the population in the world will live in or move to cities. With the increase in urban population, it is important to keep an eye on criminal activities. By doing so, governments can enforce intelligent policing systems and hence many government agencies and local authorities have made the crime data publicly available. In this paper, we analyze Chicago city crime data fused with other social information sources using network analytic techniques to predict criminal activity for the next year. We observe that as we add more layers of data which represent different aspects of the society, the quality of prediction is improved. Our prediction models not just predict total number of crimes for the whole Chicago city, rather they predict number of crimes for all types of crimes and for different regions in City of Chicago. Reproducibility: our code is available at https://goo.gl/V1yVLk.

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Section: Crime Predictionsupporting

confidence: 76%

Section: Related Workmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Section: Crime Predictionmentioning

confidence: 99%

See 2 more Smart Citations

Spatio-temporal prediction of crimes using network analytic approach

Dash

Safro

Srinivasamurthy

2018

2018 IEEE International Conference on Big Data (Big Data)

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“…An approach based on auto-regressive models to reliably forecast crime trends in areas in Chicago was also performed. In particular, ARIMA as a model to forecast the number of crimes that is likely to occur in rolling time horizons was used to predict the number of crimes with an accuracy of 84% on one year-ahead forecasts and of 80% on two-year-ahead forecasts [18].…”

Section: Related Literaturementioning

confidence: 99%

Applying Data Mining Techniques in Predicting Index and non-Index Crimes

Delima¹

2019

IJMLC

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An increasing incidence of crime has led to the development and use of computer-aided diagnosis system, tools and methods in analyzing, classifying and predicting crimes. This paper clusters municipalities in Surigao del Norte using K-Means algorithm. This is instrumental in finding identical traits, patterns and values in categorizing municipalities with much, more, and most number of recorded index and non-index crimes from 2013-2017. Prediction of its occurrence for the year 2018-2022 was also provided using ARIMA (1,0,7) model. Results showed that Surigao City has the most number of recorded index and non-index crimes. Followed by the municipality of Placer, Claver, and Dapa of cluster 2 and Del Carmen of cluster 3. Further, physical injury, homicide, violation of special laws, car napping, reckless imprudence resulting to physical injury, and other non-index crimes has 26%, 25%, 25%, 24%, 24%, and 23% forecasted increase for the year 2018-2022 with the highest occurrence in years 2018, 2018, 2020, 2018, 2020, and 2020 respectively. Future researchers may utilize other data mining techniques supported by a better accuracy result.

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Modeling Daily Crime Events Prediction Using Seq2Seq Architecture

Alghamdi

Huang

2021

Lecture Notes in Computer Science

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Forecasting Crimes Using Autoregressive Models

Cited by 42 publications

References 13 publications

Spatio-temporal prediction of crimes using network analytic approach

Spatio-temporal prediction of crimes using network analytic approach

Applying Data Mining Techniques in Predicting Index and non-Index Crimes

Modeling Daily Crime Events Prediction Using Seq2Seq Architecture

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