SARIMA: A Seasonal Autoregressive Integrated Moving Average Model for Crime Analysis in Saudi Arabia
, Abdulqader M. Almars 1,†, Majed Alwateer 1,†, Malik Almaliki 1,†, Ibrahim Gad 2,†and El-Sayed Atlam 1,2,*Round 1
Reviewer 1 Report (Previous Reviewer 2)
Dear Editors, the authors have thoroughly replied to the
reviewers' comments. They have also revised the paper carefully. I think this
paper can be accepted in its current form.
Reviewer 2 Report (Previous Reviewer 1)
All my concerns have been addressed.
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
This study proposed a SARIMA model to capture and predict the crime pattern and trend in Saudi Arabia, which in my opinion presents some novelty. Generally, the logic of this study is clear. The authors used a relatively new method for crime prediction and found that it performs better than others. However, there are still some issues need to be addressed in order to meet the criterion for the publication.
1. In the abstract, the authors states that “This study aims to investigate which Saudi Arabian areas will experience increased crime rates in the coming years…,and identifying the likelihood of crime distribution based on various locations.” But I am not able to find any contents that is related with these objectives. In other words, the spatial characteristics of crime are not discussed in the study at all.
2. In the part of introduction, the authors proposed several points in terms of the main contributions of this research. I think it needs to be rewritten because it seems that these sentences can be largely simplified.
3. As for the SARIMA model, section 3.4, the authors had better provide more discussion to explain why to extend the ARIMA model to SARIMA model. Some references justifying the inclusion of the seasonal factors should be supplemented. Further, the so called “backward shift operator” should be explained.
4. The authors stated in the manuscript that “there are a few promising factors that could allow us to predict crime rates with maximum accuracy in previous studies.”and “previous work lacks some promising features that might allow us to predict crime rates with the highest degree of accuracy”. However, the authors did not clarify what are these promising factors or features. I think it is necessary to make these factors clear for it relates with the innovation of this study.
5. Some more discussions on the value of the methodology, the application perspective and the limitation of the research are required before or after the conclusion.
6. Some details should be noted. In figures 4, 5, and 6, the authors should make clear the meaning of lines with different colors. A mistake appears in the sentence of“Those proposed models can able to analyze crime, identify crime patterns and predict it.”
Reviewer 2 Report
Major negatives:
1. What is the novelty of the paper? Applying SARIMA for crime prediction is not new. What are the real contributions of this study?
2. The authors need to use the journal template to prepare the manuscript.
3. The authors need to check the citation style for this journal if it were to be published in this journal.
4. Is there something wrong with the crime data? As presented in Figure 1, this is a huge jump in 1999. The authors should explain why. I think there are some data quality issues.
Reviewer 3 Report
Paper deals wih important task. Hovewer there is no scientific novelty.
The authors only apply existing SARIMA model to the existing dataset.
Paper cant be published in this strong Journal.
