Marijuana Dispensary Locations and Neighborhood Characteristics in New York City

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript titled “Marijuana Dispensary Locations and Neighborhood Characteristics in New York City” employs geographic information systems (GIS) to analyze the spatial distribution of marijuana dispensaries and assess their impacts on neighborhoods following New York State’s 2021 Marijuana Regulation and Taxation Act. While the manuscript presents maps to illustrate these goals, the absence of numerical evidence to substantiate findings raises questions about their reliability. Below are detailed comments:

Major Comments:

1)      Section 2.2: The section redundantly introduces fundamental GIS and LISA knowledge. To enhance the literature review, the authors should focus on studies applying GIS and LISA in related fields, supporting the relevance of these tools in similar research. Basic GIS and LISA definitions could be condensed into a few sentences, followed by relevant citations.

2)      Section 3.2: The compliance analysis introduces two additional buffer zones of shorter and longer distances, despite existing New York State guidelines specifying dispensary locations (500 feet from schools, 200 feet from places of worship, and 1,000 feet from the nearest dispensary). Without strong justification, these additional buffers appear unnecessary, as shorter distances would inherently yield more compliant dispensaries, while longer distances would reveal fewer. Justification for these additional buffers is needed, or analysis should be limited to current guidelines.

3)      Section 3.4:  The criteria for identifying significant spatial clusters are unclear, and there is no indication of the significance level applied.

4)      Section 4.1:  The term “noncompliance rate,” mentioned in the abstract and conclusion, is absent from Section 4.1. Additionally, it is unclear which buffer distance applies in the associated maps. The three buffer zones require clarification—are they intended to challenge current standards, examine a high noncompliance rate, or highlight the inadequacy of the existing guidelines? Clear research objectives and a reevaluation of the compliance analysis are recommended.

5)      Section 4.3:  The first paragraph describes the high-high (HH), low-low (LL), high-low (HL), and low-high (LH) categories, which would be more appropriately placed in Section 3.4. The second paragraph presents LISA results without statistical metrics, such as the local Moran’s I or Geary’s C statistic. Results appear to rely on descriptive explanations rather than scientific evidence, with no p-values or confidence intervals reported. Given the study’s aim of assessing neighborhood impacts, it is problematic that only a few dispensaries fall within significant clusters, suggesting limited impact. Scientific evidence supporting these impacts is essential, and Figure 4’s limited clusters question the broader claims.

6)      The manuscript lacks a dedicated Discussion section. While some IJGI publications combine Results and Discussion, the journal guidelines clearly require a Discussion section. Only the final sentence in Section 4.3 could be considered interpretive, as it includes two citations. Additionally, the Conclusion briefly compares findings to prior studies. Per journal guidelines, authors should provide a Discussion section addressing the broader context, limitations, and future research directions.

7)      Section 5: The opening paragraph references “highest noncompliance rates” and “LISA statistics,” which are absent in Section 4.

Minor Comments:

1)      Figures 2, 3, and 4: Avoid spanning a single figure across two pages by resizing as necessary. Additionally, as Figure 1 already labels Bronx, Manhattan, Brooklyn, Queens, and Staten Island, these labels can be omitted in subsequent figures to improve the clarity of the mapped data.

Author Response

See the attached file with  all of our responses to the reviewer's comments.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

I have no substantive criticism, but I would like to report some comments and suggestions for consideration by the author/s.

The methodological approach focuses on spatial analysis but lacks some technical details.

1) What kind of spatial contiguity is used (spatial weight matrix)?

2) Try more spatial weight matrix schemes to understand if the results are robust (e.g. band distances, queen and rook contiguity).

3) Moran index is missing for a global description of spatial autocorrelation;

4) I suggest trying a spatial explanatory model (e.g. spatial regression, GWR model etc..)

Minor revisions:

There is (maybe) a typo in this sentence:

 

Pag 9 "Conversely, the low-low (LL) category features census tracts with high values of the number of dispensaries surrounded by low values in these neighboring characteristics"

 

Author Response

See the attached file with  all of our responses to the reviewer's comments.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

The work analyses how recreational cannabis relates spatially to neighborhood characteristics using New York City as a case study, which is quite specific and local.

In Section 3.4. there is a need for more explanations regarding the methodology. What kind of matrix is being used? The results section mentioned high values and low values. What does it mean low and high in this context? I suggest reading Anselin et al. (2014) which you mentioned in the previous section.

Suggest replacing the visual comparison with a statistical analysis such Global Moran Index.

The results section mentions that The high-high (HH) category suggests that a census tract (or a census block group for the youth population) with a high value of the number of dispensaries is surrounded by neighbouring census tracts with a high youth population. However, the analysis of Figure 4 shows that only two dispensaries are in the HH cluster when it comes to the youth population. The number of non-significant relationships should also be mentioned. I suggest doing a table with the percentage of each cluster in the total number of dispensaries. As far as I can see, in most cases, there is no significant relationship between dispensaries and the remaining variables.

 

I have some difficulty seeing to what extent the results support conclusions such as this: This results in increased concentrations of businesses associated with crime and other vices due to this lapse in land use regulations. 

Author Response

See the attached file with  all of our responses to the reviewer's comments.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

In Table 2. Global Moran’s I Results.

1) put the type of spatial matrix used for Global Moran’s I ;

2) use same standard for decimal digits in the table 2

 

3) I advise the authors to include these considerations in the conclusions.

"Thank you for commenting on trying spatial explanatory models such as Geographically Weighted Regression (GWR) or spatial regression. While these approaches are powerful for understanding spatial relationships and variations, they are not the best fit for the objectives of our study. GWR (Brunsdon, 1998) models are primarily suited for explanatory analysis, where the goal is to quantify the relationship between variables and incorporate spatial dependence in regression outputs (Ward, 2018). Our study focuses on identifying and interpreting spatial clustering patterns of dispensary locations in relation to variables: 1) youth population, 2) education facilities, 3) adult businesses, and 4) crime. In addition, our study only has a small dataset (55 samples), and the GWR can lead to overfitting and vulnerable to multicollinearity (Fotheringham, 2016)"

Author Response

1) put the type of spatial matrix used for Global Moran’s I 

Response: we have added the type of spatial matrix used to the heading of Table 2. 

2) use same standard for decimal digits in the table 2

Response: we have revised the table so all decimals are reported to 3 decimal digits.

3) I advise the authors to include these considerations in the conclusions.

"Thank you for commenting on trying spatial explanatory models such as Geographically Weighted Regression (GWR) or spatial regression. While these approaches are powerful for understanding spatial relationships and variations, they are not the best fit for the objectives of our study. GWR (Brunsdon, 1998) models are primarily suited for explanatory analysis, where the goal is to quantify the relationship between variables and incorporate spatial dependence in regression outputs (Ward, 2018). Our study focuses on identifying and interpreting spatial clustering patterns of dispensary locations in relation to variables: 1) youth population, 2) education facilities, 3) adult businesses, and 4) crime. In addition, our study only has a small dataset (55 samples), and the GWR can lead to overfitting and vulnerable to multicollinearity (Fotheringham, 2016)"

Response: we have footnoted these considerations to the conclusion and included the citations in the references.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors answered all my questions, so I have nothing to add.

Author Response

Thanks you for your feedback. We are glad that our revisions addressed your earlier comments.