PCSY-D-25-00050

Food purchase data reveals the locations of London's 'food deserts'

PLOS Complex Systems

Dear Dr. Broadbridge,

Thank you for submitting your manuscript to PLOS Complex Systems. After careful consideration, we feel that it has merit but does not fully meet PLOS Complex Systems'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.

Please submit your revised manuscript within 60 days Sep 02 2025 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 complexsystems@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcsy/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to any formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Zhen Zhu

Academic Editor

PLOS Complex Systems

Zhen Zhu

Academic Editor

PLOS Complex Systems

Hocine Cherifi

Editor-in-Chief

PLOS Complex Systems

Journal Requirements:

1. We ask that a manuscript source file is provided at Revision. Please upload your manuscript file as a .doc, .docx, .rtf or .tex.

2. Please provide separate figure files in .tif or .eps format.

For more information about figure files please see our guidelines:  

https://journals.plos.org/complexsystems/s/figures 

https://journals.plos.org/complexsystems/s/figures#loc-file-requirements 

3. We do not publish any copyright or trademark symbols that usually accompany proprietary names, eg (R), (C), or TM  (e.g. next to drug or reagent names). Please remove all instances of trademark/copyright symbols throughout the text, including © on page 6, 10, 14, 15, 16, 17, 19, 20, 21.

4. Your manuscript is missing the following sections: Methods. Please ensure these are present, and in the correct order, and that any references to subheadings in your main text are correct. An outline of the required sections can be consulted in our submission guidelines here: 

https://journals.plos.org/complexsystems/s/submission-guidelines#loc-parts-of-a-submission

5. Please upload a copy of Figure 1, 2 , 3, 4, 5, 6, 7, 8 which you refer to in your text on page 6, 7, 9, 10, 14, 15, 16, 17. Or, if the figure is no longer to be included as part of the submission please remove all reference to it within the text.

6. Some material included in your submission may be copyrighted. According to PLOS’s copyright policy, authors who use figures or other material (e.g., graphics, clipart, maps) from another author or copyright holder must demonstrate or obtain permission to publish this material under the Creative Commons Attribution 4.0 International (CC BY 4.0) License used by PLOS journals. Please closely review the details of PLOS’s copyright requirements here: PLOS Licenses and Copyright. If you need to request permissions from a copyright holder, you may use PLOS's Copyright Content Permission form.

Please respond directly to this email or email the journal office and provide any known details concerning your material's license terms and permissions required for reuse, even if you have not yet obtained copyright permissions or are unsure of your material's copyright compatibility. 

Potential Copyright Issues:

Figure 1, 3, 4, 5, 6, 7, 8: please (a) provide a direct link to the base layer of the map (i.e., the country or region border shape) and ensure this is also included in the figure legend; and (b) provide a link to the terms of use / license information for the base layer image or shapefile. We cannot publish proprietary or copyrighted maps (e.g. Google Maps, Mapquest) and the terms of use for your map base layer must be compatible with our CC-BY 4.0 license. 

Note: if you created the map in a software program like R or ArcGIS, please locate and indicate the source of the basemap shapefile onto which data has been plotted.

If your map was obtained from a copyrighted source please amend the figure so that the base map used is from an openly available source. Alternatively, please provide explicit written permission from the copyright holder granting you the right to publish the material under our CC-BY 4.0 license.

Please note that the following CC BY licenses are compatible with PLOS license: CC BY 4.0, CC BY 2.0 and CC BY 3.0, meanwhile such licenses as CC BY-ND 3.0 and others are not compatible due to additional restrictions. 

If you are unsure whether you can use a map or not, please do reach out and we will be able to help you. The following websites are good examples of where you can source open access or public domain maps: 

* U.S. Geological Survey (USGS) - All maps are in the public domain. (http://www.usgs.gov) 

* PlaniGlobe - All maps are published under a Creative Commons license so please cite “PlaniGlobe, http://www.planiglobe.com, CC BY 2.0” in the image credit after the caption. (http://www.planiglobe.com/?lang=enl) 

* Natural Earth - All maps are public domain. (http://www.naturalearthdata.com/about/terms-of-use/)

7. We notice that your supplementary figures are included in the manuscript file. Please remove them and upload them with the file type 'Supporting Information'. Please ensure that each Supporting Information file has a legend listed in the manuscript after the references list.

Additional Editor Comments:

Dear authors,

Thank you for submitting your paper to PLOS Complex Systems. I found the topic current, interesting, and to be a good fit for the scope of our journal. I have now received the reviewers' reports. All reviewers have identified substantial concerns that will require major revisions before the manuscript can be reconsidered.

Please find their comments attached below. I invite you to carefully address each point raised and resubmit with a detailed response highlighting your changes.

Best wishes,

Zhen Zhu

[Note: HTML markup is below. Please do not edit.]

Reviewers' Comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Complex Systems’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: Yes

**********

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

Reviewer #1: N/A

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. 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

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Complex Systems 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: This manuscript Tesco transaction data to identify London’s food deserts by analyzing spatial patterns of nutritionally deficient purchases. Using PCA, the study identifies a dominant purchasing pattern characterized by high sugar/carbohydrate intake, which clusters in east and west boroughs. Geographically Weighted Regression reveals spatial heterogeneity in drivers: household income, BAME population, and car ownership show varying impacts on purchasing patterns. Hot spots correlate with lower income and higher BAME demographics, while cold spots align with affluent areas. The study highlights the need for context-specific interventions, as one-size-fits-all policies fail to address local nuances in access barriers. However, I have a few questions before this work can be accepted

1. The analysis relies solely on Tesco purchase data, ignoring complementary data sources. Integrating diverse datasets would better capture multifaceted access barriers. For instance, combining Tesco purchases with transport network data could reveal how commuting patterns influence food choices, enhancing the model’s ability to distinguish between supply-side and demand-side factors.

2. The study uses cross-sectional 2015 data, missing temporal dynamics. Implementing a longitudinal analysis with sliding windows would track how food desert dynamics evolve. For example, comparing pre- and post-lockdown purchasing patterns could isolate the effect of economic shocks on dietary choices, using event-driven analysis to validate if policy interventions alter spatial clusters.

3. LSOA-level analysis asks intra-neighborhood heterogeneity. Upgrading to street-level data would enable fine-grained modeling of "micro-food deserts." For instance, combining LSOA-level sociodemographics with street-level store density could identify pockets of food insecurity within ostensibly well-served areas, using kernel density estimation to map food access gradients at sub-LSOA scales.

Reviewer #2: This manuscript presents a data-rich, spatially explicit analysis of food purchasing behaviors across London using Tesco loyalty card transactions. The authors develop a geographically weighted regression framework to identify areas exhibiting nutritionally deficient purchasing patterns and explore their sociodemographic correlates. The use of large-scale, individual-level purchase data offers considerable promise for refining the food desert concept beyond traditional access-based metrics. However, several key points—particularly related to framing, methodological clarity, and interpretation—need to be addressed.

• The authors claim that most existing studies identify food deserts without direct links to food purchase data or dietary patterns. However, recent studies have used behavioral proxies such as restaurant visits or mobility data (https://doi.org/10.1038/s41467-024-46425-2, https://doi.org/10.1186/s12889-021-11953-9

). How does this study differ methodologically and conceptually from such approaches? Are these works fundamentally less valid, or simply limited in precision?

• What specific insights are gained by using actual purchase data that cannot be derived from traditional accessibility-based metrics? Does the use of loyalty card data lead to the identification of different food desert areas compared to conventional methods?

• On what basis were alcohol and non-calorific beverages excluded from the analysis? Could their inclusion have altered the principal component structure or the nutritional interpretation of food purchasing patterns?

• The authors refer to the fraction of food category purchases at the LSOA level. Is this fraction calculated by number of items or by expenditure? If both were available, why was one chosen over the other?

• Is there more detailed product-level data available in the Tesco dataset (e.g., product names, nutrient values)? If so, why were broader food categories used rather than finer-grained classification?

• The manuscript includes a section labeled 2.4.1 without a corresponding 2.4.2. Is there a reason for this structure? Does the subsection warrant a separate label, or could it be integrated?

• In handling collinearity, the authors excluded public transport accessibility and education level, retaining walk time and income. Why were these particular variables prioritized? Were any diagnostics (e.g., VIF) used to guide this choice?

• The Yeo–Johnson transformation was applied to the response variable. What was the empirical distribution of the data before and after transformation? Was this transformation critical for model assumptions or merely for symmetry?

• The spatial variation in predictor effects is clearly mapped, but how do the authors interpret these findings in relation to known patterns of social inequality in London? For example, what mechanisms might explain the strong association between BAME population and nutritionally deficient purchases in some boroughs but not others?

• The analysis is conducted at the LSOA level, using aggregated purchase data. If individual-level transaction and demographic data were available, what additional questions could this study answer? Could it support causal inference or subgroup analysis that is not possible here?

Reviewer #3: This paper presents a novel approach to identifying and characterizing urban food deserts in London by analyzing supermarket transaction data from Tesco Clubcard customers and linking this to sociodemographic data via PCA and GWR. The use of large-scale transaction data linked to spatially-resolved census variables is a significant methodological contribution. It offers a richer, behaviorally grounded proxy for food accessibility compared to traditional metrics such as store distance or availability. The authors provide a clear, data-driven operationalization of food deserts based on actual purchasing behavior, moving beyond theoretical definitions. The study’s findings hold strong potential for informing targeted public health interventions. In particular, the ability to identify local factors of food deserts (e.g., income vs. car ownership) offers valuable insights for designing future policy responses.

However, I have several concerns regarding the clarity of data coverage, justification for methodological choices, and the structure and presentation of results.

Clarity of Data Coverage and Representativeness

The paper repeatedly refers to areas with “better” or “more” data coverage but does not define what this means. It is unclear whether this refers to the number of Tesco stores or transaction volume per LSOA. Since selection bias and spatial representativeness are important concerns, the authors may clarify how coverage was measured and provide thresholds or criteria used for inclusion. Ideally, a summary table or spatial coverage map showing variation in data density would improve transparency.

Principal Component Analysis (PCA): Need for Justification of Dominance

The analysis centers on the first principal component (PC1) as representing the “dominant purchasing pattern” used to define food deserts. While PC1 explains 30.4% of the variance, the manuscript should clarify how much variance is explained by subsequent components. If PC2 explains a similar amount (e.g., 25–30%), ignoring it may overlook important variation in purchasing behavior. The authors can provide a scree plot and discuss the eigenvalue distribution to justify the focus on PC1 alone.

Overextended Explanation of Basic Statistical Concepts

While methodological transparency is commendable, the explanations of methods such as PCA and linear regression are unnecessarily detailed and, at times, overly technical. The PCA section includes algebraic derivations and matrix formulations that may distract rather than inform the general readership. Similarly, the linear regression section adds limited interpretive value, especially given that GWR is the main analytical tool (Since the GWR model offers a more nuanced spatial interpretation and already outperforms OLS in explanatory power, retaining the linear regression results in the main text adds little value.) These sections could be significantly shortened or moved to supplementary material.

Risk of Causal Overinterpretation

Although the authors acknowledge that their analysis is correlational, some language—such as referring to certain sociodemographic variables as “drivers of food deserts”—risks implying causality. These terms may be revised or carefully qualified. Furthermore, potential confounding factors are not addressed. For example, pricing strategies, or availability of culturally preferred foods in different neighborhoods could influence purchasing patterns independently of income or ethnicity. These limitations can be more explicitly discussed.

**********

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.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

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.]

Figure resubmission:

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. If there are other versions of figure files still present in your submission file inventory at resubmission, please replace them with the PACE-processed versions.

Reproducibility:

To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit 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. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Attachments

Attachment

Submitted filename: Review Reports.docx

Food purchase data reveals the locations of London's 'food deserts'

PCSY-D-25-00050R1

Dear Dr. Broadbridge,

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 https://www.editorialmanager.com/pcsy/ click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. For questions related to billing, please contact billing support at https://plos.my.site.com/s/.

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 complexsystems@plos.org.

Kind regards,

Zhen Zhu

Academic Editor

PLOS Complex Systems

Additional Editor Comments (optional):

Reviewer #1:

Reviewer #2:

Reviewer #3:

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

Reviewer #3: All comments have been addressed

--------------------

2. Does this manuscript meet PLOS Complex Systems's publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

--------------------

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

--------------------

4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. 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

Reviewer #3: Yes

--------------------

5. Is the manuscript presented in an intelligible fashion and written in standard English?<br/><br/>PLOS Complex Systems 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: Yes

--------------------

6. Review Comments to the Author<br/><br/>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 revised MS has addressed all my questions, and the current version meets the publication criteria

Reviewer #2: The revision satisfactorily addresses my earlier concerns, with clearer framing, tighter methodological exposition, and a more transparent presentation of results and limitations. While the contribution is incremental and some constraints (e.g., coverage and causal interpretation) remain, the authors’ changes are adequate to support the main claims. In my view, the manuscript is acceptable for publication pending minor editorial polishing.

Reviewer #3: Thank you for addressing my questions

--------------------

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.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Yunke Zhang

Reviewer #3: None

--------------------