This manuscript introduces a Python package named GNNWR, which integrates two neural network-based models, GNNWR and GTNNWR, to enhance the accuracy and interpretability of spatiotemporal data analysis. The GNNWR package is validated using case studies involving air quality and offshore dissolved silicate concentration data. The results, evaluated through various performance metrics, demonstrate the package's robustness and efficacy in capturing both spatial and temporal patterns. The comprehensive implementation and validation demonstrate the potential of this package as a valuable tool in the field of spatiotemporal modeling. Still, there are some points that need to be improved:

1. Since the GNNWR and GTNNWR are models that have been proposed in other articles. This article should focus less on the model review, while highlight the design and usage of the newly developed package.
2. Chapter 3 is named as "Usage Example", but it contains class design, function usage, and other content that is outside the scope of Usage Example. Therefore, I suggest to use a more appropriate chapter name.
3. The usage example for GNNWR and GTNNWR in Sections 3.2 and 3.3 can be further divided into subsections for easier reading.
4. The design of optimizers is mentioned in both the GNNWR and GTNNWR sections. In L197, a range of optional optimizers are indicated, while in L345, the passage in focus on explaining the SGD optimizer. Is the choice of optimizer model-dependent (determined by GNNWR or GTNNWR)? If so, then it should be specified in the article, if not, then it might be better to arrange all the discussion about optimizers in consecutive paragraphs
5. The fonts in the subscripts of the formulas need to be checked further, i.e., the "OLR" in formulas 3, 4, and 7 have both roman and italic types.
6. In L116, "the symbol ⊗ represents the operator", what is "the operator"?
7. In Figure 2, the abbreviations (STPNN&STWNN) and their full names are duplicated, instead, detailed descriptions about the network (i.e. the ds and dst nodes) should be added.
8. L325, the word "function" is in the same font as function name "init_dataset".

The manuscript presents the development and validation of the GNNWR package, which includes novel spatiotemporal intelligent regression models, namely GNNWR and GTNNWR. These models are designed to handle spatial and temporal non-stationarity using neural networks within a spatiotemporal weighted framework. The paper is well-structured and provides detailed explanations of the models and their applications. However, there are several areas that require clarification and improvement. The abstract is concise and informative, but it would benefit from a brief mention of the key results from the case studies to highlight the effectiveness of the proposed models.

General Comments:

The introduction provides a comprehensive overview of the importance of spatiotemporal regression and the limitations of existing methods. It effectively sets the stage for the introduction of the GNNWR and GTNNWR models. However, the manuscript mentions traditional methods but does not provide a detailed comparative analysis. Please find the general comments below:

• Please specify the traditional methods.
• It might be helpful to explicitly state the primary contributions of this work in the introduction to clearly differentiate it from previous research.
• A brief explanation of how these weights are derived using the SWNN would enhance understanding.
• For the GTNNWR model, the process of integrating the STPNN and STWNN should be explained with more detail, particularly the way spatiotemporal distances are calculated and used.
• The case studies provided (air quality data from China and offshore dissolved silicate concentration data from Zhejiang Province) are appropriate for demonstrating the models' capabilities. However, the results section should include more detailed comparisons with traditional methods to better highlight the advantages of GNNWR and GTNNWR. Add a table or figure that directly compares the performance metrics (e.g., mean squared error, R-squared) of GNNWR and GTNNWR against traditional methods like OLS, GWR, and other spatiotemporal models. It would be beneficial to include visualizations of the results (e.g., maps or plots) to illustrate the models' performance and the spatial/temporal patterns they capture.
• Ensure that any mention of parallel processing clearly explains its relevance and application within the context of the research. For instance, explain how parallel processing contributes to efficiency and performance in the GNNWR model or mention in limitations.
• The manuscript does not discuss the computational efficiency of the proposed models. Please provide benchmarks comparing the training and prediction times of GNNWR and GTNNWR with those of traditional models. Discuss strategies for improving efficiency, such as parallel processing.
• The conclusion summarizes the findings but does not address limitations or future work in detail. Potential limitations of the current models are not discussed. Acknowledge limitations such as computational demands, data requirements, and model interpretability. Discuss how these might be addressed in future work. 

Specific Comments:

1. Matrices and vectors in the entire manuscript should be denoted by boldface uppercase letters (e.g., Eq. 12).
2. In Eqs. 1, 7, and 2, 3, please clarify the distinction between  and .

1. Please review the sentence in lines 87-89 to enhance clarity.
2. In line 85, the sentence needs a period after the equation. Please check the whole manuscript (e.g., lines 93, 96).
3. In lines 4, 28, 43, and more, each word of abbreviations should begin with a capital letter.
4. In Eqs. 3, 4, and 6, why do you need to use the symbol “×”?
5. In Eq. 1, what do p and n represent?
6. Please check all symbols shown in the manuscript and mentioned in the text (such as 𝑦̂𝑖, 𝑤, i, j, etc.).
7. In Eq. 5, what is the range of values for “j”?