Volunteered Geographical Information and Recreational Uses within Metropolitan and Rural Contexts

Round 1

Reviewer 1 Report

This manuscript tries to assess the value of VGI and recreational uses within metropolitan and rural contexts. The manuscript has many expression and formatting errors (Line 119, Line 197, Table 1 etc.), and I recommend proper proof reading. I suggest to ask for major review. My general comments are as follow.

The manuscript is more like a survey report and less like a research paper. It only conducts simple data analysis, and the methodological innovation is weak. This manuscript does not go far enough in the theoretical analysis of VGI data quality and usage. Furthermore, the references are rather outdated.

Author Response

Thank you for your observations and suggestions. The manuscript has been proofread.

The comments are answered point-by-point in the following paragraphs:

REVIEWER: The manuscript is more like a survey report and less like a research paper. It only conducts simple data analysis, and the methodological innovation is weak.

AUTHORS: As indicated, the paper is an experimental essay regarding the use of VGI on a regional scale for recreational outdoor activities. These data sources have proved valid in such studies but have been mainly used for local analysis. Here we go a step forward and demonstrate its utility for wider scales of analysis, and different contexts (urban vs. rural). This innovation was reinforced at Line 161:

“Furthermore, and since these data sources are traditionally used only at the local level, we intend to test their applicability at a regional level and see if they can be used to support policies for the development of tourist activities.”

REVIEWER: This manuscript does not go far enough in the theoretical analysis of VGI data quality and usage.

AUTHORS: A paragraph concerning VGI data quality was included as follows, along with more references about the board use of these sources of data, at Line 77:

“Although VGI relies on volunteer generated data, it does not mean that it is of low quality. Many authors have studied the quality of citizen derived data for mapping [12–19] concluding that VGI is becoming a reliable source to validate and improve relevant spatial information[20]. In fact, while some sites require no expertise (e.g., disaster mapping sites like GEOCAN), others apply different levels of quality control (e.g., OpenStreetMap includes peer review). Furthermore, data quality is an issue when there are few instances, but with the advance of the era of big data, this becomes a less relevant constraint [15,21].”

REVIEWER: Furthermore, the references are rather outdated.

AUTHORS: 19 more references were introduced, and from that set, the following ones are from the last 4 years:

Chu, Y.-T.; Li, D.; Chang, P.-J. Effects of Urban Park Quality, Environmental Perception, and Leisure Activity on Well-Being among the Older Population. International Journal of Environmental Research and Public Health 2021, 18, 11402, doi:10.3390/ijerph182111402.

Teles da Mota, V.; Pickering, C. Geography of Discourse about a European Natural Park: Insights from a Multilingual Analysis of Tweets. Society & Natural Resources 2021, 34, 1492–1509, doi:10.1080/08941920.2021.1971809.

Teles da Mota, V.; Pickering, C. Assessing the Popularity of Urban Beaches Using Metadata from Social Media Images as a Rapid Tool for Coastal Management. Ocean & Coastal Management 2021, 203, 105519, doi:10.1016/j.ocecoaman.2021.105519.

Balletto, G.; Milesi, A.; Ladu, M.; Borruso, G. A Dashboard for Supporting Slow Tourism in Green Infrastructures. A Methodological Proposal in Sardinia (Italy). Sustainability 2020, 12, 3579. https://doi.org/10.3390/su12093579

Li, C.-L. Quality of Life: The Perspective of Urban Park Recreation in Three Asian Cities. Journal of Outdoor Recreation and Tourism 2020, 29, 100260, doi:10.1016/j.jort.2019.100260.

Fagerholm, N.; Torralba, M.; Moreno, G.; Girardello, M.; Herzog, F.; Aviron, S.; Burgess, P.; Crous-Duran, J.; Ferreiro-Domínguez, N.; Graves, A.; et al. Cross-Site Analysis of Perceived Ecosystem Service Benefits in Multifunctional Landscapes. Global Environmental Change 2019, 56, 134–147, doi:10.1016/j.gloenvcha.2019.04.002.

Kondo, M.C.; Fluehr, J.M.; McKeon, T.; Branas, C.C. Urban Green Space and Its Impact on Human Health. Int J Environ Res Public Health 2018, 15, 445, doi:10.3390/ijerph15030445.

Foody, G.; See, L.; Fritz, S.; Moorthy, I.; Perger, C.; Schill, C.; Boyd, D.; Foody, G.; See, L.; Fritz, S.; et al. Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data. ISPRS International Journal of Geo-Information 2018, 7, 80, doi:10.3390/ijgi7030080.

Reviewer 2 Report

The authors conclude that: "This study aimed to test the potential of VGI data regarding recreational activities in urban and rural areas and evaluate the attractiveness of tourist products such as Grand Routes. The results show that it is possible to do it with volunteer data uploaded by user to webshare services, outdoor/sports apps and social media." The conclusion of this last sentence seems to me to need to be relativized or justified in more detail. Indeed, there is still the question of whether the activities represented by the data provided by the VGI services are representative of all activities, including here that of tourists or inhabitants who do not use these services to monitor their activities. It would probably be necessary to compare these data with observations in the field. Moreover, the authors relativize the place to be given to the analysis of data from VGI services a few paragraphs later. It would be very interesting to be able to evaluate how representative these data are and what place can be given to them with the other tools available to define policies for the development of tourist activities. The authors could elaborate a bit more on this aspect of their analysis. Finally, a last axis on which the authors could extend their analysis would be to propose which data analysis tools from VGI services could be developed in a generic way on the basis of the particular study they have conducted.

Author Response

Thank you for your observations and suggestions. 

The comments are answered point-by-point in the following paragraphs:

REVIEWER: The conclusion of this last sentence seems to me to need to be relativized or justified in more detail

AUTHORS: The conclusions were supported with the following information and references at Line 584:

“These data sets are always a sample of a larger population. As with other digital and social media apps and platforms, VGI follows fashions and trends [19] and sometimes can be biased depending on different motivations or behaviours. Nevertheless, this virtual expression of outdoor recreational use is extremely large, both concerning intensive sports (e.g., trail running) and soft leisure activities (e.g., light hiking). On the other hand, visitor surveys involving interviews with users on site have demonstrated the wide use of these apps for recreational outdoor activities [50,69] and other studies also show a positive correlation between tracks and actual users [19,24,57]. Therefore, it is reasonable to assume that these data sources as a reliable representation of the spatial use of outdoor activities.”

REVIEWER: Finally, a last axis on which the authors could extend their analysis would be to propose which data analysis tools from VGI services could be developed in a generic way on the basis of the particular study they have conducted.

AUTHORS: In the last paragraphs we developed further this topic at Line 666:

“Regarding future work on recreational activities and VGI, there are still dimensions of these datasets that can be further explored. For example, mobility of recreational users, with special attention to privacy issues, could be done with free access data from platforms where users identify themselves and tracks or POIs are freely available. Nowadays, most popular apps make data available through API. However, some apps, not only due to privacy issues but also due to their business model, only release processed data, limiting this type of analysis.“

Reviewer 3 Report

The authors offer a new exploratory perspective to test the potential of VGI data regarding recreational activities in urban and rural areas and evaluate the attractiveness of tourist products using the Lisbon Metropolitan Area (LMA) and the Southwest region of Portugal as example. The results show that it is possible to evaluate recreational activities and the attractiveness of metropolitan and rural region using volunteer data uploaded by users to web-share services, outdoor/sports apps and social media. The English is used correct and readable. But several figures and tables are not nice, and needed to be revised. For these reasons, I recommend a minor revision.

1) Pg8. The meaning of Figure 4 is not clear. What does the symbol “×” denote?

2) The boundary of all tables is not clear. It is recommended to add an upper border for each table.

3) Several figures, e.g., Figure 1, Figure 5, Figure 6, etc. still include several small figures. It is recommended to add a frame and a caption for each small figures.

Author Response

Thank you for your observations and suggestions. The comments are answered point-by-point in the following paragraphs:

REVIEWER: 1) Pg8. The meaning of Figure 4 is not clear. What does the symbol “×” denote?

AUTHORS: The caption of figure 4 was clarified.

REVIEWER: 2) The boundary of all tables is not clear. It is recommended to add an upper border for each table.

AUTHORS: Done

REVIEWER: 3) Several figures, e.g., Figure 1, Figure 5, Figure 6, etc. still include several small figures. It is recommended to add a frame and a caption for each small figures.

AUTHORS: All figures were improved, and frames and captions were included when needed to ensure a correct interpretation

Reviewer 4 Report

Thank you for your paper. The piece of research carried on appears as an interesting and promising one, worth an investigation.

I did appreciate the paper organization and design and the sysematic framework in which the research is put. 

Before considering the paper publishable, I consider that some important points need to be addressed for improving your paper. 

• Analytical methods. You correctly proposed a method of 'counting' the times a VGI - Track crosses a cell. The method needs to be addressed and justified in a sounder way, as it appears as a quite basic, nonetheless effective, way of doing this job. Literature and applications, however, are quite vast in terms of using applications of variations of kernel density estimations in showing smoothed density surfaces on point pattern distributions. The same work by Silverman (1986) you cite, contains a vast amount of details and description on the different methods and parameters used of density estimation. 
  It would be necessary to better justify and reinforce your choice, explaining, nonetheless, while a KDE was not performed. 
• What is the original contribution of your research work, considering that some platforms exist allowing similar comparisons. Please see Strava heatmap that, in some way, does a similar job. 
  (https://www.strava.com/heatmap#8.00/-8.93077/40.51615/hot/all)
• Data. Please provide some more statistics on the tracks downloaded and used in the present research, justifying in a sounder way the need and benefits of their use. 

The rest of the paper appears as well organized and structured.

Please considering the following possible references to be added with reference to similar works carried on.

Àvila Callau, A.; Pérez-Albert, Y.; Serrano Giné, D. Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience. ISPRS Int. J. Geo-Inf. 2020, 9, 727. https://doi.org/10.3390/ijgi912072

Balletto, G.; Milesi, A.; Ladu, M.; Borruso, G. A Dashboard for Supporting Slow Tourism in Green Infrastructures. A Methodological Proposal in Sardinia (Italy). Sustainability 2020, 12, 3579. https://doi.org/10.3390/su12093579

Heikinheimo, V.; Minin, E.D.; Tenkanen, H.; Hausmann, A.; Erkkonen, J.; Toivonen, T. User-Generated Geographic Information for Visitor Monitoring in a National Park: A Comparison of Social Media Data and Visitor Survey. ISPRS Int. J. Geo-Inf. 2017, 6, 85. https://doi.org/10.3390/ijgi6030085

 

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Author Response

Thank you for your observations and suggestions. The comments are answered point-by-point in the following paragraphs:

REVIEWER: It would be necessary to better justify and reinforce your choice, explaining, nonetheless, while a KDE was not performed. 

 

AUTHORS: We used line density to analyze spatial distribution of recreational activities within the study areas (as indicated in section 2.4, step 3) and it proved to be a very straightforward method to use at regional scale and with such a large dataset. This was included in the conclusions, at Line 647:

“The methodology proved to be adequate, namely the use of line-density analysis which was quite efficient at regional levels and with the amount of data used (with a total of 22 031 tracks submitted by 3 297 identified users, corresponding to 1 578 017 km).”

After depicting a great adherence between GR and tracks from SWPT, we wanted to further analyze the attractiveness/weight that GR has on the recreational use of the region (section 2.4, step 4), which we called compliance with the Grand Route. Density tools could not perform this type of analysis. They are great for showing smoothed density surfaces on point pattern distributions, but we needed a method, that could measure the weight of the GR. Since no tool is available for doing this type of analyzes, we developed the method we have presented.

 

REVIEWER: What is the original contribution of your research work, considering that some platforms exist allowing similar comparisons. Please see Strava heatmap that, in some way, does a similar job. (https://www.strava.com/heatmap#8.00/-8.93077/40.51615/hot/all)

 

AUTHORS: Heatmaps are very interesting tools to identify patterns of use, but they present a “smooth” observation of the territory, and local/or fewer common uses are left out the map. Besides, heatmaps disregard users and all vector attributes of the tracks. The following sentences were added to the conclusions, at Line 649:

“Furthermore, while heatmaps (such as Strava heatmap) are very useful tools to identify patterns of use, they present a “smooth” observation of the territory, and local/or fewer common uses are left out of the map. Moreover, heatmaps disregard users’ profiles and all vector attributes of the tracks are lost. The approach proposed by the authors goes a step forward and takes into consideration all public dimensions when analyzing recreational activities.”

 

REVIEWER: Data. Please provide some more statistics on the tracks downloaded and used in the present research, justifying in a sounder way the need and benefits of their use. 

AUTHORS: The total Km for each activity was included in table 1, adding more information to tables 1, 2, 3, 4 and 5, and appendix A. Further statistics would be useful to fully understand the potential of these data sets but adding it to the current paper would create more noise and lose focus on our analysis.

Also, in the conclusion, we reinforced the amount Km that were analyzed at Line 647:

“The methodology proved to be adequate, namely the use of line-density analysis which was quite efficient at regional scales and with the amount of data used (with a total of 22 031 tracks submitted by 3 297 identified users, corresponding to 1 578 017 km).”

REVIEWER: Please considering the following possible references to be added with reference to similar works carried on.

AUTHORS: All suggested references were appreciated and included in the text.

 

Round 2

Reviewer 1 Report

The revised paper is much better than its previous version.

Author Response

Thanks for your comments. Section 2.4 of the methodology was improved, and conclusions were also enhanced.

Reviewer 4 Report

I am partially satisfied with the replies provided. 

Conclusions were revised, data and methods were justified in the replies, not widely in the text. 

Revisions provided in the text are minimal with respect to the notes and comments expressed.

Please read thorughly again what written in the first round of reviews and produce an updated version of your paper. 

Please refer to literature on point pattern analysis when justifying your method (i.e., grid superimposed over the study region to collect point data).

Author Response

Thank you again for your comments. Our manuscript was updated with detailed attention, through the entire text.

Conclusions were rewritten with more data about the originality of our study.

Concerning Strava heatmap comment, new text was introduced at Line 496:

“Moreover, heatmaps are based on track points, losing all vector attributes of the tracks and disregarding users’ profiles. For example, many track points can indicate places where people walk/ride slower and not more users. Also, activities are grouped into few categories (riding, run, on water, winter activities), losing the richness of the original data.”

Concerning KDE/point analysis suggestions, please note that our analysis was not performed with point data. The approach was further explained in the methodology section at line 223 and 239, and references for similar works were added:

“This resolution produced accurate results, and at the same time was large enough to accommodate positional errors that commonly go up to 10~30 m with handheld GPS and assisted GPS smartphones.”

“This was applied to the entire dataset as well as for both national and foreign users.”

“This proposed approach for testing the GR attractiveness is an adaptation of [27], which is often used to analize spatial distribution of VGI tracks (data lines) [6,59]. Kernel density tools were not used at this stage as one needed a method that could measure the weight of the GR.”