Peer Review History
| Original SubmissionFebruary 2, 2020 |
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PONE-D-20-03060 Association of violence with urban points of interest PLOS ONE Dear Mr Redfern, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. As your research is interdisciplinary, I have invited three qualified reviewers, an econometrician, a computational geographer, and an urban economist. Their recommendations are mixed. I myself do see some merits of your study by combining machine learning techniques to study the association between POI and crime. Please try to address the reviewers' concerns as much as you can. We would appreciate receiving your revised manuscript by May 18. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols Please include the following items when submitting your revised manuscript:
Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. We look forward to receiving your revised manuscript. Kind regards, Shihe Fu, Ph.D. Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Yes Reviewer #3: No ********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: No ********** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: No Reviewer #3: Yes ********** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: No ********** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: 1. Equation 2 only describes one type of POI. There are N crime locations (\\vec{x_1} to \\vec{x_N}), M locations of point interests (\\vec{p_1} to \\vec{p_M}). However, the equation should also include different types of POIs, such as pubs, bus stops, supermarkets, etc., and the set of locations and weights are different for each type of POI. 2. The selection of the penalty term in Lasso regression should have theoretical justification. On page 6, it is stated that “We determine a value of \\alpha such that ten points of interest are selected”. Why only ten, given that there are 619 classes of POIs. Typically, for Lasso regression, one first determines a range for the penalty parameter, with the maximum being the smallest value such that no variable is selected. A grid of the penalty parameter is then constructed. The penalty term can be determined based on cross validation, looping over the possible grid points. One may also select the penalty term using plugin method based on theoretical justifications. The authors should provide some justification on the selection of the penalty term. 3. The paper considers the central 3kmx3km of cities, and density is estimated using univariate kernel density estimation. Note that for points on the boundary, only data points on one side are observed, which can lead to bias in density estimations. A solution may be to use a larger grid for density estimation and use the density estimates on a smaller grid to do the Lasso regressions. 4. The candidate variables in Lasso regression can include interactions between different classes of POIs. For example, the risk of crime may be high if alcohol outlets are mixed with cash machines in close distance. 5. On the first line on page 6, it is stated that “\\vec{y} represents the ground-truth vectorized crime density map”. I thought ground truth crime locations are only available for South Wales? (page 3, line 83) 6. How are the cities in the study selected? Some big cities are missing, such as London, Nottingham and Glasgow. The performance of the method in cities with more complicated structures is worth investigating. 7. Figure 10 (page 8) is missing from the manuscript. 8. The combined model performance (table 6) is worse than the baseline alcohol-outlet only model (table 3) for Liverpool. Some discussions can help clarify the issue. Reviewer #2: Redfern et al investigated the association between violent crime and various urban points of interest. This is an interesting topic. In the meantime, I have some comments and suggestions for the authors: On Page 3 Table 1. “One of 15 crime types” Crime Type: violent vs non-violent It would be of interest to describe what are the 15 types of crimes the authors are referring to. Considering that the authors investigate the relationship between violent crime and points of interest, are all 15 types of crimes belong to the category of violent crime? Or some of them may belong to non-violent crimes (e.g. property crimes (such as theft), bribery, etc.)? On Page 6 Line 157 Please give more details on how the authors get the ground-truth vectorized crime density map (y) and the basis matrix (B) since the definitions of them are missing. How the density of the violent crime was calculated at each point on the map from the locations of the offense in the open police data? I know that Eq (3) is based on the assumption that POIs within each category have the same weight. Eq(4) is the LASSO regression on the POI classes. Please give out the detailed derivation of the basis matrix (B) in Equation (4). It would be useful to give out the relationship of Eq(3) to the basis matrix (B) in the derivation. On Page 7, Line 205, “Example model output for Cardiff is shown in Fig 9” What are the values of the R-squared (in both Baseline model and Proposed model) for the example model output in Fig 9? Does higher R-squared always indicate a better prediction power in the Lasso based model (a higher level of similarity between Lasso predication and ground truth crime density) in authors' results? On page 7, Line 207 and table 9 Definition is missing for R-squared score(s) authors put on line 207. How authors calculate the R-squared score? From Table 9, is R-squared score the same as the R-squared mean? On page 7, Line 210 “with R2 scores ranging from 0.51 to 0.70” If R-squared scores are defined as the same as R-squared mean in the authors’ manuscript, then the R-squared mean for the proposed model ranges from 0.52 to 0.65 (not from 0.51 to 0.70) based on the results from table 3. Where does this range '0.51 - 0.70' come from? On page 8, Figure 10 missing Figure 10 is missing. Please add figure 10. Other Comments: It is an interesting idea to utilize the density of points of interest in cities to study the violent crime density and to use the lasso regression to select the most important POI classes for each city. However, the violent crime density can be closely related to community factors of neighborhoods in a city (e.g. education level, median income level, etc). Various levels of community factors can play an important role on the quality of a POI. Due to this fact, the quality of each POI in the same POI class can still be very different from each other. This fact could be an important reason that the performance of the combined model (utilizing the other 9 cities' information and predicating the 10th city) displayed in table 6, is worse than the model for an individual city (except Birmingham). Reviewer #3: This paper presents a study of the association between the density of different types of POIs and the density of violence crime counts. Though the topic itself would be interesting, I have several major concerns with the merit of this paper. 1. The contributions of this paper are not clear and convincing to me. First, the paper is not trying to contribute to the theories, as there is no discussion about the theories on what determine violence crime and the related literature. The interpretation of the results and the effects of different types of poi businesses is lacking and discussions in relation to relevant literature is missing. Second, the contribution to methodology is minor. Standard regression approach with variable selection is employed. The baseline model is too simple to make your main models good. The construction of poi related variables with kernel density was done a lot in the literature. Although the authors claim that one advantage of the work is to conduct the analysis at a fine spatial scale, the definition of unit of analysis is not clear to me. Third, regarding the empirical implication for policing and city-specific initiatives, why the found associations could be helpful and what reason or logical this conclusion was made are not explained and thus not convincing. 2. A comprehensive literature review regarding the theories on violence crime and related determining factors is needed. The current review is thin and mostly focuses on the method side. 3. The lack of details of models results and definition of variables. The dependent variable, density of crime counts, is not detailed. Coefficients of poi types for city-specific model are missing, which is critical for the explanation of why those factors vary across cities. Given the above issues, I do not recommend the consideration of this paper for publication in this journal. ********** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No Reviewer #3: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step. |
| Revision 1 |
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PONE-D-20-03060R1 Association of violence with urban points of interest PLOS ONE Dear Dr. Redfern, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Both reviewers still have concerns. Reviewer 2 raised the question whether sexual offense is violent or not. You may consider replacing "violence" with "crime" in the title if you cannot clarify this. Please try to address their other conerns as much as you can. Please submit your revised manuscript by Sep 20 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript:
If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Shihe Fu, Ph.D. Academic Editor PLOS ONE [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: (No Response) Reviewer #2: (No Response) ********** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Partly ********** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: N/A ********** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ********** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Thanks for revising the paper. I have only a minor concern regarding the response to my previous comment 1. In the Methods section, I think equations 1 and 2 should be dropped. The analysis begins with estimating kernel densities of crimes and each POI separately using equation 3, which has nothing to do with explaining the crime density by POIs. Then with the estimated densities, Lasso regression is performed to predict the crime density using the densities of POIs (equation 4). In this line of reasoning, there is no need to assume “POIs within each category have the same weight” (line 188, page 6). Please clarify if my interpretation is not right. Reviewer #2: I appreciate for your revision. Thanks for explaining your data categories to me, which addresses some of my concerns – the crime type you use, and which let me know the details of the crime categories through the added appendix 2. I know you are investigating the relationship of violence with POI. However, after look into the details of the crime categories, you provided in the revision, I do not think (at least at this point) the database you use is persuasive to me, and therefore, I am recommending a further revision. First of all, you choose to use violence and sex offense. But is this category of a crime necessary to belong to violent crime? Sex offense may or may not involve violence. Secondly, from the other crime types you provide in the revision (Table 1 in appendix 2), how can you tell the other crime category is not belong to violent crime? I think it is worth investigating which crime type is exactly violent which is not? At least, I can tell that Robbery belongs to violent crime and it is the type of crime that is available in the dataset, but you did not include it in your study. Are you certain that sex offense in the database is all belong to violent sex offense? I also suggest you add other violent crime categories (e.g. Robbery and other violent categories) into your data and to perform the analysis. In respect to the combined model, I think it is meaningless because every city is different, (such as income level, educational level). I do not find that it would be useful to use a model trained from other cities to perform predication in another city. The database you have is only about the POI in a very general way, but we know that even a restaurant, there is a high ending one, and there is so-so one, which depends on the income level of a city. I think those differences have an impact on violence. ********** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. |
| Revision 2 |
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Association of violence with urban points of interest PONE-D-20-03060R2 Dear Dr. Redfern, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Shihe Fu, Ph.D. Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: |
| Formally Accepted |
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PONE-D-20-03060R2 Association of violence with urban points of interest Dear Dr. Redfern: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Shihe Fu Academic Editor PLOS ONE |
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