PONE-D-20-17957

Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities

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: Yes

Reviewer #2: Partly

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

Reviewer #1: No

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: Yes

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: Summary: This paper provides a new method to predict socioeconomic indicators of municipalities using insurance data. Two types of individual insurance data-housing and car insurance-are used. This paper then study adopts two geographical regression models (SLM and GWR) to explore the impact of spatial dependency effects.

Comment：

As the high cost of traditional statistical method to access the demographic data, this paper provides us a new method to estimate statistical data from private insurance data. This paper has done micromesh data analysis work and considered spatial dependency of such work. Some suggestion for revision.

1. The spatial distribution of insurance and Swiss Census data should be compared.

2. Detailed methods in Features selection should be provided.

3. In SLM and GWR model, time dummy should be added in.

4. Why just use SLM model rather than SEM (Spatial error) model? Should provide examination result.

5. Why just use such spatial weight matrix as (5)? Can prepare the different results using different spatial weight matrices?

Reviewer #2: Summary

This paper explores possibilities using insurance customer data as alternative measures of macro social indicators which are conventionally collected through surveys. Using alternative data sources such as google street images and social media posts as measures of social indicators that are otherwise too difficult or too expensive to obtain is not new, but the use of insurance data is novel. The authors have made a careful discussion on the pipeline of prediction algorithm which consists of two steps - first on features selection and then on prediction under a spatial regression framework. This paper is of great interest, but several aspects need to be further clarified. These have been outlined below.

Comments

On pages 4 and 5 in insurance data, the authors said that after matching the insurance data with the survey, 568,426 observations were left from the original dataset of 1,341,328 observations. Could the authors clarify why more than half observations were dropped when matching? Is that due to the lack of geographical identifiers in one of the data sources?

On page 6 in Swiss census data. Could the authors give more information on the representativeness of the census data, for example, how many observations were collected in each municipality? Could the data be interpreted as representative at levels of municipalities or cantons or the nation?

On page 8 in feature selection. Could the authors explain the rationale using LASSO? Why not adaptive lasso or elastic net which may perform better than LASSO?

On page 9 in the spatial lag model. Please clarify the k-nearest neighbors (KNN) scheme? What is the weighting matrix regarding this scheme? Is it based on contiguity measures or kernels? Could the authors show the robustness of results concerning the alternative weighting scheme used?

On page 10. Geographical Weighted Regression should be Geographically Weighted Regression.

On page 10. "In regression analysis, we try to explain the variations of a dependent variable using a suite of uncorrelated and normally distributed independent variables." This statement is not true. In most regression analyses, orthogonality and normality are not preconditions.

On page 9 in the methods. The predictive pipeline proposed by the authors consists of two steps. The first step is feature selection using LASSO, and the second is the prediction of census indicators using selected features. Although it's well-known that the OLS regression after LASSO (so-called OLS post-lasso estimator) performs very well in terms of its statistical properties \\citep{Belloni2013}, the pipeline which uses more complicated models such as SLM or GWR after LASSO may behave differently. Could the authors provide references on the validity of this proposed pipeline?

On page 11 in the results. For each predictor, the selected features range from 2 to 9. It would be interesting to conduct a constrained LASSO selection where the number of features is constrained. Given that the aim of this paper is using insurance claim data as an alternative measure of census-based indicators, it would be more valuable if this could be achieved by a more sparse representation of selected features, say 2 or 3. I would appreciate it if the authors could check if this is the case.

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Reviewer #1: No

Reviewer #2: No

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Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities

PONE-D-20-17957R1

Dear Dr. Massaro,

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,

Shihe Fu, Ph.D.

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: All comments have been addressed

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: Partly

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

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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: The author has carefully revised this manuscript according to my 1st review report. After serious review the reviesed version of this manuscript, I think this version can be accepted.

Reviewer #2: This paper explores possibilities using insurance customer data as alternative measures of macro social indicators which are conventionally collected through surveys.

My initial concerns such as validity of merging process of two samples, use of LASSO instead of elastic-net or constrained LASSO, and the pipeline of algorithms proposed for sealing the model selection and parameter estimation have been well addressed by authors and the arguments or revision or future extension are quite convicting and promising. I have no further questions to the authors.

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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.

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Reviewer #1: No

Reviewer #2: No