Introducing Friction of Space into the Geography of Cultural Consumption
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors1. In this study, the definition of high-frequency location-based data used is not clear. It's important to know that there's been a lot of research on location-based data, so be sure what HFLB data is.
2. Please describe the motivation and advantages of conducting research based on high-frequency location-based data. If the corresponding results can be achieved by using conventional location information, the research significance of this paper is limited.
3. What does Figure 1 illustrate? Why is there no explanation? In addition, Figure 1 does not fit into the journal specification, the subtitle should be below the subgraph, please redraw.
4. Does the conclusion of lines 411~423 take into account other factors of the user? Such as education, income, religious beliefs, etc.
5. Milan is a large city with relatively good municipal facilities, so do the conclusions of this article apply to cities with limited infrastructure? If not, the conclusions of this paper should be constrained in Section 7.
Author Response
We would like to express our sincere thanks to both reviewers for their thoughtful and constructive feedback. Their comments have been instrumental in improving the clarity, methodological transparency, and policy relevance of our manuscript. In response, we have implemented several revisions throughout the text. We clarified the conceptual focus of the study, particularly in relation to the definition of culture adopted and its alignment with the domain of CCIs. We also enriched the discussion of results by adding policy implications for both public decision-makers and CCI managers, with specific attention to themes such as spatial inequality, chrono-urbanism, and the “15-minute city” framework. Methodological explanations have been expanded and refined, including additional justification for selected parameters and clearer interpretation of model outputs. We also improved the overall structure and readability of the manuscript, revised figures and captions, and ensured consistency in terminology and references. We trust these changes address the reviewers' concerns and contribute to a more robust and accessible final version of the paper.
- In this study, the definition of high-frequency location-based data used is not clear. It's important to know that there's been a lot of research on location-based data, so be sure what HFLB data is.
ANSWER: Thank you for the opportunity of clarifying the definition we use for our dataset. The following sentence has been added after the first time HFLB data are mentioned in the manuscript: In this study, high-frequency location-based data (HFLB) refers to anonymized geospatial data collected from GPS-enabled mobile applications that track individuals' movements over time with high temporal granularity. HFLB data captures user locations at frequent intervals allowing for the detailed reconstruction of mobility patterns and visits to specific points of interest (POIs).
- Please describe the motivation and advantages of conducting research based on high-frequency location-based data. If the corresponding results can be achieved by using conventional location information, the research significance of this paper is limited.
ANSWER: Thank you for raising this relevant point. We have rephrased and enriched the introduction, which now includes the following narrative to make the contribution stand out in a clearer way. Text added: Although recent scholarship has explored the use of non-traditional data sources to investigate cultural phenomena, such as Spotify listening logs (Bello & Garcia, 2021), YouTube consumption patterns (Airoldi, 2021), or user engagement on social media (Oghina et al., 2012), these sources typically lack geographical specificity or spatial continuity. In contrast, high-frequency location-based data (HFLB), such as GPS traces from mobile devices, enable the study of cultural consumption in motion, linking individual behaviours to the geography of residence, urban structure, and travel behaviour. This approach makes it possible not only to identify where cultural consumption occurs, but also to reconstruct the mobility trajectories that precede and follow it, thus offering insights into who consumes culture, from where, and under what spatial constraints. While conventional data sources, such as surveys or ticketing systems, offer static snapshots limited to the moment and site of consumption, HFLB captures real-world behaviour as it unfolds over time and space, overcoming issues of recall bias and sampling limitations (Bail, 2014). This methodological innovation allows us to operationalise the friction of space and assess how spatial distance and remoteness impact consumption levels. Furthermore, it lays the groundwork for future research on mobility dynamics such as trip chaining and multimodal access, which remain largely invisible to traditional methods and are central to a deeper understanding of spatial inequality in cultural participation.
What does Figure 1 illustrate? Why is there no explanation? In addition, Figure 1 does not fit into the journal specification, the subtitle should be below the subgraph, please redraw.
ANSWER: Thank you for the comment. The figure’s caption was present but might have been non readable for formatting issues. It has now been re-added and reads: ​​Figure 1. Correlation of level of consumption with remoteness from cultural amenities. Figure 1.a. shows the scatter plot of on . Figure 1.b. shows the Pearson’s correlation of and . The scatter plot shows the Locally Weighted Scatterplot Smoothing algorithm (LOWESS), a supervised learning regression algorithm used for regression analysis where data attributes don’t allow linear regression models to produce a good fit (Dobilas, 2020). Note: to increase the readability of the data visualisations, and to reduce the noise of the data at the users’ level, remoteness values (x) have been rounded (1 decimal place) and y variables have been averaged over each rounded x. All correlations hold true for non-rounded and non-averaged values as well.
Regarding the comment on the sub-title, the sentence ‘Remotness to the cultural offer’ refers to the values plotted on the x-axis of the graph and is not a subtitle of the graph.
- Does the conclusion of lines 411~423 take into account other factors of the user? Such as education, income, religious beliefs, etc.
ANSWER: That’s a really good point indeed. Unfortunately they do not as, working with anonymised data those information are not available. At the same time, the interpretation we make in those lines refer to the mere spatial aggregation of the cultural consumption variable. When we say ‘a clear core-periphery spatial distribution pattern emerges’ we afterward specify that the pattern refers only to the two measures we are plotting, namely level of cultural consumption and remoteness from the cultural offer.
Milan is a large city with relatively good municipal facilities, so do the conclusions of this article apply to cities with limited infrastructure? If not, the conclusions of this paper should be constrained in Section 7.
ANSWER: Thank you for raising this important point and for giving us the chance to further clarify the interpretation of our investigation. The conclusions section has been enriched with the following sentence: While the methodological approach presented in this paper is scalable and replicable, the findings are derived from the specific context of Milan: a large, culturally vibrant city with relatively well-developed infrastructure and public amenities. As such, the spatial dynamics observed, particularly the patterns of core-periphery inequality and the 15–20 minute proximity threshold, may not be directly transferable to smaller or less infrastructure-rich urban areas. Further research is required to assess how different levels of urban infrastructure, density, and public cultural provision influence the relationship between proximity and cultural consumption in diverse city contexts. The conclusions of this study should therefore be interpreted with regard to the characteristics of Milan’s urban fabric.
Reviewer 2 Report
Comments and Suggestions for AuthorsShort summary
The Authors investigate the effect played by the geographical distance between consumers and suppliers on individual cultural consumption levels by using geographical data on human mobility in a continuous spatial setting. They base their analysis on a dataset that comprises 16,082,194 stops made by 330,929 unique users, covering the period from January 2017 to January 2018. The data is enriched with information from 50,396 OSM POIs (1,070 of which being cultural amenities) that fall in the territory of Milan’s functional urban area. From the HFLB dataset, the Authors compute for each individual: the overall frequency of cultural consumption, the variety of cultural amenities visited, the remoteness of cultural amenitites from user's home location, a measure of cultural consumption considering both variety and frequency. Also the Authors compute the average level of consumption for each (hexagonal) territorial unit.
The statistical analysis lead the Authors to hypothesize the existence of two main groups of places in the metropolitan area of Milan: places on average inhabited by individuals with high consumption levels that are spatially closer to cultural amenities; places on average inhabited by individuals with low consumption levels of cultural amenities. Also Authors hypothesize the existence of four distinct groups of individuals, according to their relative level of consumption and remoteness from cultural amenities: "consumers", "non-consumers", "averse", "prone".
Results suggest how the effect of proximity with the cultural offer in increasing the level of individual cultural participation is significantly stronger for those groups of people who are less used to consuming cultural goods (individuals averse to cultural consumption). Similar results are obtained when performing the correlation analysis aggregating individuals in two groups, according to the single criteria of having a cultural consumption level lower or higher than the sample’s mean. From the visual interpretation of the correlation graphs the Authors observe how for individuals less used to cultural consumption, distance from the city’s cultural amenities describes consumption levels through a strong negative correlation up to a cut-off value, after which the slope of the curve decreases. Also, they notice how, by considering all users at the same time, the correlation curve follows a power law function.
Broad comments
The paper is well-written and addresses a central topic (core-periphery relations) in a rather overlooked and innovative research field (CCIs). In my opinion, few changes might consistently improve the quality of the manuscript:
- at conceptual level, there is a misunderstanding between "culture" and "cultural and creative industries (CCIs)": research is focused on CCIs and marketable cultural services, not on a broader notion of culture, which also includes wellbeing, relational goods, forms of active citizenship, volunteering, etc. This should be specified in the introduction and in proximity of Tables 1 and 2, as well as in the Annex 1, as this "reductionst" view of culture instead valorizes the analytical approach adopted by the Authors, that implicitly is focused on the existence of market demand and supply curves. Furhtermore, CCIs are one of the 14 industrial ecosystems identified by the European Commission, and specifying this issue might provide additional value to the underlying research.
- concerning metrics, remoteness is computed as the average distance of the 20 nearest cultural POIs from the users'home location. Why "20"? Authors are invited to further explain their choice and to verify whether results are robusts to different choices of the denominator or different weights.
- at structural level, Authors are invited to merge "Analysis" and "Results" Section, eliminating repeated arguments. Also, as the paper has mainly a statistical flavour (rather than an econometric one, that emerges only in the estimates of the power function), there is no need to keep the Annexes separated from the main text. Authors are invited to relocate the Annexes within main text.
- Authors are invited to enrich the Section "Discussion and conclusions" with suggestions for policy makers and implications for CCIs' managers.
Minor comments
- in Figure 1 and other similar figures, Authors are invited to estimate the cutoff point and to interpret the result obtained. Also, in Figure 2 and similar figures, as indicators have different polarities, to the benefit of the unaquainted readers Authors are invited to specify the polarity (e.g., Q1 = worst, Q4 = best, and viceversa). At least in Figure 4, Authors are invited to illustrate the regression output in the convetional way (estimates, s.e., test, p-value, R^2, Obs.);
- Line 129: delete "one"
- Line 498: "mobility" is repeated twice: delete the second "mobility".
- Line 908: "Von Thunen", instead of "Von Thilnen"
Author Response
We would like to express our sincere thanks to both reviewers for their thoughtful and constructive feedback. Their comments have been instrumental in improving the clarity, methodological transparency, and policy relevance of our manuscript. In response, we have implemented several revisions throughout the text. We clarified the conceptual focus of the study, particularly in relation to the definition of culture adopted and its alignment with the domain of CCIs. We also enriched the discussion of results by adding policy implications for both public decision-makers and CCI managers, with specific attention to themes such as spatial inequality, chrono-urbanism, and the “15-minute city” framework. Methodological explanations have been expanded and refined, including additional justification for selected parameters and clearer interpretation of model outputs. We also improved the overall structure and readability of the manuscript, revised figures and captions, and ensured consistency in terminology and references. We trust these changes address the reviewers' concerns and contribute to a more robust and accessible final version of the paper.
Short summary
The Authors investigate the effect played by the geographical distance between consumers and suppliers on individual cultural consumption levels by using geographical data on human mobility in a continuous spatial setting. They base their analysis on a dataset that comprises 16,082,194 stops made by 330,929 unique users, covering the period from January 2017 to January 2018. The data is enriched with information from 50,396 OSM POIs (1,070 of which being cultural amenities) that fall in the territory of Milan’s functional urban area. From the HFLB dataset, the Authors compute for each individual: the overall frequency of cultural consumption, the variety of cultural amenities visited, the remoteness of cultural amenitites from user's home location, a measure of cultural consumption considering both variety and frequency. Also the Authors compute the average level of consumption for each (hexagonal) territorial unit.
The statistical analysis lead the Authors to hypothesize the existence of two main groups of places in the metropolitan area of Milan: places on average inhabited by individuals with high consumption levels that are spatially closer to cultural amenities; places on average inhabited by individuals with low consumption levels of cultural amenities. Also Authors hypothesize the existence of four distinct groups of individuals, according to their relative level of consumption and remoteness from cultural amenities: "consumers", "non-consumers", "averse", "prone".
Results suggest how the effect of proximity with the cultural offer in increasing the level of individual cultural participation is significantly stronger for those groups of people who are less used to consuming cultural goods (individuals averse to cultural consumption). Similar results are obtained when performing the correlation analysis aggregating individuals in two groups, according to the single criteria of having a cultural consumption level lower or higher than the sample’s mean. From the visual interpretation of the correlation graphs the Authors observe how for individuals less used to cultural consumption, distance from the city’s cultural amenities describes consumption levels through a strong negative correlation up to a cut-off value, after which the slope of the curve decreases. Also, they notice how, by considering all users at the same time, the correlation curve follows a power law function.
Broad comments
The paper is well-written and addresses a central topic (core-periphery relations) in a rather overlooked and innovative research field (CCIs). In my opinion, few changes might consistently improve the quality of the manuscript:
COMMENT: at conceptual level, there is a misunderstanding between "culture" and "cultural and creative industries (CCIs)": research is focused on CCIs and marketable cultural services, not on a broader notion of culture, which also includes wellbeing, relational goods, forms of active citizenship, volunteering, etc. This should be specified in the introduction and in proximity of Tables 1 and 2, as well as in the Annex 1, as this "reductionist" view of culture instead valorizes the analytical approach adopted by the Authors, that implicitly is focused on the existence of market demand and supply curves. Furthermore, CCIs are one of the 14 industrial ecosystems identified by the European Commission, and specifying this issue might provide additional value to the underlying research.
ANSWER: Thank you for the valuable suggestion. We have added one short paragraph in the introduction to better explain this, as well as in proximity of the two Tables and in the Annex 1. The discussion of CCIs as one of the 14 industrial ecosystem has been added to the discussion and conclusion section - in line with your comment below.
COMMENT: concerning metrics, remoteness is computed as the average distance of the 20 nearest cultural POIs from the users'home location. Why "20"? Authors are invited to further explain their choice and to verify whether results are robusts to different choices of the denominator or different weights.
ANSWER: We thank the reviewer for this relevant observation regarding the choice of using the 20 nearest cultural POIs in the computation of the remoteness index. This threshold was selected to ensure a balance between capturing a sufficient density of accessible amenities and maintaining comparability across spatial contexts. Additionally, this approach is consistent with that adopted in other peer-reviewed studies we have authored, where the threshold of 20 has proven effective in capturing meaningful spatial patterns in access to urban amenities. While we are currently redacting the citation to preserve anonymity during the review process, we will make the reference explicit in the final version.
We agree that testing sensitivity to alternative thresholds or weighting schemes could enrich the robustness analysis, and we plan to explore this in future developments of the study. However, to preserve clarity and methodological parsimony in the current version, and considering the computational demands of the dataset, we have retained the original metric. We have added a clarifying sentence in the Methods section to justify this choice.
COMMENT: at structural level, Authors are invited to merge "Analysis" and "Results" Section, eliminating repeated arguments. Also, as the paper has mainly a statistical flavour (rather than an econometric one, that emerges only in the estimates of the power function), there is no need to keep the Annexes separated from the main text. Authors are invited to relocate the Annexes within main text.
ANSWER: The Analysis and Results sections have been merged. Thank you for your precious suggestion. We appreciate the reviewer’s suggestion to integrate the annexes into the main text. However, we believe that keeping them separate supports the readability and narrative cohesion of the paper, which is structured to maintain a clear and uninterrupted flow of the main empirical analysis. While the study has a primarily statistical flavour, the annexes include technical details (e.g., taxonomy construction, spatial data checks, and clustering algorithms) that, if relocated within the main text, might risk overloading the reader and diluting the focus of the core arguments. For these reasons, we kindly propose to retain the current structure, ensuring both accessibility for general readers and full transparency for technically oriented ones.
COMMENT: Authors are invited to enrich the Section "Discussion and conclusions" with suggestions for policy makers and implications for CCIs' managers.
ANSWER: We thank the reviewer for this valuable suggestion. In response, we have substantially enriched the Discussion and Conclusions section by adding a dedicated reflection on the policy implications of our findings for both public decision-makers and managers operating within the Cultural and Creative Industries. We highlight how the observed spatial disparities in access to cultural amenities can inform place-based strategies, infrastructure investment, and audience development efforts. Furthermore, we discuss the relevance of our methodology as a decision-support tool for regional mapping and planning, in line with recent EU recommendations that frame CCIs as one of the 14 key industrial ecosystems. These additions aim to strengthen the practical relevance of the study.
Minor comments
In Figure 1 and other similar figures, Authors are invited to estimate the cutoff point and to interpret the result obtained. Also, in Figure 2 and similar figures, as indicators have different polarities, to the benefit of the unaquainted readers Authors are invited to specify the polarity (e.g., Q1 = worst, Q4 = best, and viceversa). At least in Figure 4, Authors are invited to illustrate the regression output in the convetional way (estimates, s.e., test, p-value, R^2, Obs.);
ANSWER: We thank the reviewer for this observations. In response, before Figure 1, an interpretation of the threshold has been added. Figure 2 caption has been updated to better explain the different polarities. We have added a sentence in the Results section to interpret the meaning of the estimated power law exponent, clarifying that a 1% increase in remoteness corresponds to a 0.27% decrease in cultural participation. At the same time, we would like to stress that the primary objective of fitting a power law function was not to provide a detailed empirical description of the relationship, but rather to highlight its modelling potential and the suitability of a non-linear functional form for capturing the heavy-tailed nature of spatial cultural consumption.
- Line 129: delete "one"
- Line 498: "mobility" is repeated twice: delete the second "mobility".
- Line 908: "Von Thunen", instead of "Von Thilnen"
ANSWER: Thank you for spotting such mistakes. They have all been corrected.
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe author has already answered my questions.
Author Response
Being all comments already addressed, the new manuscript presents a corrected version of the reference style, to align with the journal's guidelines.
I remain available for any further clarification needed.
Best regards