Forecasting Crime Using the ARIMA Model

Chen, Peng; Yuan, Huang; Shu, Xueming

doi:10.1109/fskd.2008.222

Cited by 131 publications

(84 citation statements)

References 4 publications

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“…Meanwhile, precise crime prediction for small areas like police precincts is proposed in [16]. In [17], autoregressive integrated moving average (ARIMA) is employed for near future prediction of property crime. Based on 50 weeks property crime data, an ARIMA model was built to predict crime number of 1 week ahead.…”

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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Section: Related Literaturementioning

confidence: 99%

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

Delima¹

2019

IJMLC

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“…• ARIMA: Auto-Regression-Integrated-Moving-Average is used to for short-term crime prediction which considers the recent T days for a moving average in [26]. • VAR: Vector Auto-Regression is a multi variate forecasting technique accounting for cross correlation and temporal correlation, which is leveraged to forecast crime in [27].…”

Section: Performance Comparison For Crime Predictionmentioning

confidence: 99%

Modeling Temporal-Spatial Correlations for Crime Prediction

Zhao

Tang

2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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Crime prediction plays an impactful role in enhancing public security and sustainable development of urban. With recent advances in data collection and integration technologies, a large amount of urban data with rich crime-related information and fine-grained spatio-temporal logs has been recorded. Such helpful information can boost our understandings about the temporal evolution and spatial factors of urban crimes and can enhance accurate crime prediction. In this paper, we perform crime prediction exploiting the cross-type and spatio-temporal correlations of urban crimes. In particular, we verify the existence of correlations among different types of crime from temporal and spatial perspectives, and propose a coherent framework to mathematically model these correlations for crime prediction. The extensive experimental results on real-world data validate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of different correlations in crime prediction.

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“…Classical prediction methods such as ARIMA models and artificial neural networks [2], [3], [4], [5] have been successfully applied in crime-related prediction, like drug market or other specially designed safety indices. Another direction is how to build up sensor networks that can quickly respond in an emergency event like fires and traffic accidents [6], [7], [8].…”

Section: Related Workmentioning

confidence: 99%

A bipartite-graph based approach for disaster susceptibility comparisons among cities

Zhou

Shen

et al. 2014

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)

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Abstract-People are attracted to large cities because of more employment opportunities, convenient facilities, and rich cultural activities. However, large cities are also more vulnerable to natural disasters, which have caused widespread physical destructions, great loss of life and property, and immense havoc. "Which city is less susceptible to natural disasters?" is thus one of the most critical questions one faces when making decisions on travelling or job and business relocation. In this work, we propose a bipartite-graph based framework to compare the impacts of disasters on two cities by answering different queries using textual documents collected online. Besides intuitive simple comparison using statistics, our system also generates textual comparative summaries to better describe the differences between the two cities in terms of safety. Although a number of online services provide disaster events statistic information for cities, our framework compares the impacts of disasters on cities in a more straightforward and comprehensive way.

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Forecasting Crime Using the ARIMA Model

Cited by 131 publications

References 4 publications

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

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

Modeling Temporal-Spatial Correlations for Crime Prediction

A bipartite-graph based approach for disaster susceptibility comparisons among cities

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