Mitigating vulnerabilities through forecasting and crime trend analysis

Orong, Markdy Y.; Sison, Ariel M.; Hernandez, Alexander A.

doi:10.1109/icbir.2018.8391166

Cited by 11 publications

(9 citation statements)

References 19 publications

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“…ARIMA(p,d,q) model is used to forecast the data pattern of diseases for the next fourteen years. Time-series predictions are basically based on the changes over time in historical data sets and can produce mathematical models by using statistical data that can be extrapolated [28]- [30]. The ARIMA (p,d,q) model is defined as follows:…”

Section: Methodsmentioning

confidence: 99%

Alleviating Vulnerabilities of the Possible Outbreaks of Measles: A Data Trend Analysis and Prediction of Possible Cases

Talirongan¹,

Orong²,

Talirongan³

2020

MJBAS

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Measles is considered as a highly contagious disease that leads to serious complications around the world. Thus, the paper determined the trend and the five-year forecasted data of the Measles in the Philippines. This study utilized the time series data for trend analysis and data forecasting using the ARIMA model to visualize the measles cases. Figures for the time-series and forecasted results are individually presented with the use of GRETL software. Results showed that there was an increasing pattern of the disease from 2016 to 2019. However, there was a decreasing pattern of its occurrence in the next five years based on the five-year forecast. Nevertheless, with the results of the study, there is still a need to improve the different intervention plans of the authority in alleviating the occurrence of the disease though it yielded a decreasing pattern in the future since it is evident that the figure of the forecasted data is still approximately 15,000 and above.

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Section: Methodsmentioning

confidence: 99%

Alleviating Vulnerabilities of the Possible Outbreaks of Measles: A Data Trend Analysis and Prediction of Possible Cases

Talirongan¹,

Orong²,

Talirongan³

2020

MJBAS

Self Cite

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“…The study was anchored on the concept of [19] but differs in many ways. Although K-Means algorithm was also used, it was utilized to find clusters within the city and municipalities in the province of Surigao del Norte to identify areas with much, more, and most number of crimes recorded.…”

Section: Methodsmentioning

confidence: 99%

“…In the Philippines, a recent study of [19] clustered the index crime data in the province of Misamis Occidental using K-Means algorithm and forecasted its occurrence for the next five years using ARIMA(1,0,1) model. Based on the result, 3 of the indexed crime data were categorized in the first cluster and the other five index crimes are found to be in the cluster 2.…”

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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“…Fig. 2 shows the graphical representation of the processes of the K-means algorithm from the concept of [31]. Table I shows the indexed datasets for the category Methodology.…”

Section: B Clusteringmentioning

confidence: 99%

Rank-Based Variable Minimization Using Clustering Algorithm

Delima¹,

Francisco²

2019

IJMLC

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This paper introduces variable minimization with the advent of data mining techniques, particularly the cluster analysis using the K-means algorithm. The results of the simulation process validated the actual ranking of the variables in each category from the 407 records of student-respondents. A noticeable decrease of variables in determining the instructional performance of teachers in the Caraga Region, Philippines is evident in the simulation results. The variables that were clustered by the algorithm that conforms to have the lowest rank based on the actual survey was removed. Results showed that there were a total of fourteen (14) variables removed from the thirty-item survey questionnaire. In this case, it only proved that like any other data mining algorithms, the cluster analysis, particularly with the K-Means algorithm, is also effective in determining what variables in the data set to omit based on the groupings with the lowest rank it generated.

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Mitigating vulnerabilities through forecasting and crime trend analysis

Cited by 11 publications

References 19 publications

Alleviating Vulnerabilities of the Possible Outbreaks of Measles: A Data Trend Analysis and Prediction of Possible Cases

Alleviating Vulnerabilities of the Possible Outbreaks of Measles: A Data Trend Analysis and Prediction of Possible Cases

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

Rank-Based Variable Minimization Using Clustering Algorithm

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