PONE-D-20-05006

Using 311 data to develop an algorithm to identify urban blight for public health improvement

PLOS ONE

Dear Dr Athens,

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Changshan Wu

Academic Editor

PLOS ONE

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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

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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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: No

Reviewer #2: Yes

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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

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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: The authors used natural language processing to categorize 311 data from NYC into different blight-related categories, and constructed models to identify blight-related calls using keywords/tokens. In my opinion, the manuscript presents an interesting and promising use of municipal open data to better understand how residents perceive and report blight in a city. However, the manuscript needs to be improved in several areas before it should be considered for publication.

First, the ACS vacancy data were analyzed as both raw counts and as percentages, and the authors found that the percentage data are likely more appropriate because the count data largely reflect the size of the census tract (p13). Given this strong rationale for analyzing data as percentages, why was the USPS/HUD data analyzed as counts but not as a percentage? The USPS/HUD data set provides total residential/business addresses in each tract, so this analysis is feasible and should be added unless there is a compelling reason not to do so.

Writing:

There is room to improve the writing and organization throughout the manuscript, but particularly in the Introduction. This will help the reader better understand the motivations and implications of the study. Some primary suggestions are as follows:

* Better transitions within and between sentences and paragraphs would be helpful. For example, on p3 para1, the colon in “…social ills: Residential overcrowding…” is not appropriate and the R should not be capitalized. On p4 para2, the sentence would read smoother as “…are highly problematic, because they rely…” On p4 para3, the colon after numerous does not work there, and I suggest placing a period after numerous to end the sentence.

* More strategic organization of text. For example, on p3 para2 the first sentence is out of order chronologically with the second sentence, and it actually seems to me like the first sentence should be dropped or moved because it doesn’t fit very well with the rest of the paragraph.

* Be more specific where possible. On p3 para 2, both ‘many aspects’ and ‘the facets of’ are vague and could be described more specifically. Other examples from the Discussion are noted below.

* Per journal instructions, please include line numbers. This makes it much easier to suggest edits to the manuscript.

Line item comments:

Abstract:

* p<0.001 instead of >

* ‘different facets’: Which facets? Can you be more specific here, or offer a couple examples?

Introduction:

* p3 para1: How can a direct quote have two sources? Cite the original source only.

* p3 para3: (physical decay) could be incorporated more smoothly into the sentence to avoid parentheses. Same with (physical) disorder on the next page.

* p4 para3: ‘affects’ not ‘affect.’

* p4 para3: References are needed after ‘well documented’, and in the following sentence. In the sentence after that, please place each reference after the specific outcome it addresses rather than grouping the references at the end.

* p5 para3: Maybe ‘validated’ is the best word here, but I wonder if ‘compared to’ isn’t a more appropriate verb. I understand model validation to be the process of making sure the model performs as expected; here, the authors use the ACS & USPS data sets to draw inferences about the relationships between blight-related calls and vacancy rates rather than using ACS & USPS data to characterize the quality of the algorithm itself. To me, this seems less like model validation and more like a statistical comparison in which the algorithm results are assumed to be valid.

Methods:

* This section is much clearer than the Introduction.

Results:

* ‘46% of token’ should be tokens

* p12 para1: missing ‘the’ in first sentence

* p12 para1: Table 1, not Table 4

* p12 para 2: Noting specific locations within NYC is only useful to those familiar with NYC because these locations are not shown on the map figure

* p13 and elsewhere: Inconsistent use of ACS, American Community Survey, and Census. Please define the abbreviation at first use and then use it consistently thereafter.

* p13 para1: correlation, not correlations

* p13 para1: why is count italicized?

Discussion:

* p14: The paragraph beginning ‘Nevertheless’ is not a complete paragraph.

* p14: change to ‘further develop our algorithm’

* p15 para1: Why would calls differ between these two places? I am not familiar with these places, so please explain why we should expect differences.

* p15 para1: Please add an explanation for why flooding the 311 lines would make estimates of blight less reliable.

* p15 para1: Please explain how blight on vacant commercial properties (which I take to be an important finding based on earlier discussion material) shows disinvestment by municipal government. And why is municipal government italicized here?

Table 1: Please provide a more descriptive caption so the table can stand alone

Fig 1: Please explain the axis ranges here. How can a word frequency go below 0%?

Fig 2: This is not a density map, so the caption should be reworded to more accurately describe the contents. In addition, the map really ought to show & label the borough outlines because boroughs are discussed in the text. As someone who is largely unfamiliar with NYC, labeling places within boroughs that are discussed in the text would be helpful for context (JFK, Brownsville, etc.).

Reviewer #2: This manuscript attempted to identify urban blights using 311 data. Particularly, several major types of "blights" were identified, and regression analysis was performed to examine the effectiveness of the model. This is a well-written and straightforward paper with contributions to the literature. My major comments are as follows.

1) Methodology: this paper does not provide a detailed algorithm for extracting the "blight" information from natural languages. As the authors claimed that one major contribution is to develop a new algorithm. The authors have specify the algorithm, and highlight their contributions.

2) Conclusions: this paper does not have a conclusion session. The authors need summarize their major results of the paper.

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Reviewer #1: No

Reviewer #2: No

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Using 311 data to develop an algorithm to identify urban blight for public health improvement

PONE-D-20-05006R1

Dear Dr. Athens,

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.

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Kind regards,

Changshan Wu

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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: All comments have been addressed

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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: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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: One very minor remaining issue: update figure numbers in the text, because figure 1 was deleted.

Also, consider changing the color ramp on the map. It seems strange to have low percentages of blight shown as red, as red is typically used to connote warning and draw the eye.

Reviewer #2: The authors have successfully addressed my comments. I am happy to see this manuscript to be accepted for publication.

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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