Emerging Location-Based Service Data on Perceiving and Measuring Multifunctionality of Rural Space: A Study of Suzhou, China

Round 1

Reviewer 1 Report

The paper is based on quantitative approach and it is quite innovative and constructively engages with complex problems about human mobility on rural spaces. The methodology and the case studies are properly presented and the results are significant. The bibliography is appropriate.

Author Response

Thanks to the anonymous reviewer.

Since there's no specific suggestion for revise, no attachment with response to your comment is upload. 

Reviewer 2 Report

The subject of the article is relevant, novel and of great interest. The objective of the article is clearly defined, the methodology is adequate and correctly applied. The results are significant and interesting. The formal aspects, cartography and tables, are correct.

However, there are some aspects to improve:

- There is no review of the abundant literature on the use of big data applied to the analysis of mobility, its limitations and results.

- There is no criticism of the sources in relation to the possible bias of the source used, in particular with respect to the age structure of social network users. Likewise, we are not provided with information on the numerical relationship that exists between the data collected and the total of the resident or temporary population in the municipalities studied. This information is necessary to assess the significance of the sample.

- The comment of the results is excessively descriptive. It would be necessary to go beyond the contrast of the results obtained with the processes known by other sources, and underline what the use of this source gives us beyond corroborating the existence of territorial processes that can be known from other types of information. In the same way, the conclusions should show more clearly what are the contributions of the analysis carried out for the purposes of territorial planning.

Author Response

Three materials have been uploaded.

1) Cover Letter

Dear Editor,

Thank you so much for the effort to improve this manuscript, and please regard our best wishes to anonymous reviewers for their valuable comments and suggestions, which have been carefully revised, using the "Track Changes" function in Microsoft Word as recommend.

Changes in the revised version of manuscript are as follows.

1) Line 66-78(All Markup, the same below) is overwritten.

2) Line 503-528 is overwritten, and an extra reference (No. 69) is added.

3) A rearrangement of references from No. 23 to 36 due to the revise.

We are willing to hear from you if there is any questions or further discussion regarding the revised manuscript.

Yours Sincerely,

Authors

2019.10.18

2) Response to Reviewer 2 Comments

Point 1: There is no review of the abundant literature on the use of big data applied to the analysis of mobility, its limitations and results.

Response 1:

Literature on the use of big data applied to the analysis of mobility, and its limitation is added in the end of the second paragraph in Introduction part, there’s also a rearrangement of references from No. 23 to 36, as follows.

“Further, recent emergence of geographic big-data, e.g. location-aware technologies (LAT) data[23], location-based services (LBS) data [24], has created an emerging research stream, especially in interdisciplinary fields of big data and human geography including, in particular, urban computing and social sensing [25-27], which offers a new opportunity for human geography study within multi-scales. For example, mobile phone signaling data [28-29], smart card of bus/metro data and taxi GPS trajectory data [30-31] are used to explore mobility patterns on macro-scale, social media data, POI data [32-33] and Google maps [34-35] are new-style tools for vitality assessment of activities on micro-scale such as in a community level. A growing body of research regards application of big data analysing human mobility though, however most existing studies are limited in urban areas [36], ignores contemporary rural space also as an important case study area for geographic big-data application.”

Point 2: There is no criticism of the sources in relation to the possible bias of the source used, in particular with respect to the age structure of social network users. Likewise, we are not provided with information on the numerical relationship that exists between the data collected and the total of the resident or temporary population in the municipalities studied. This information is necessary to assess the significance of the sample.

Response 2:

It’s true that the RTUQ data has limitations, e.g. it only provide information about location and quantity of Tencent App users at specified epoch, details regarding the socio-demographic attributes of observables are unavailable. The age structure of social network users as reviewer mentioned may not be available (for protecting privacy issues, no personal private information is provided by Tencent company), but it seems reasonable to assume that tourists visiting Luxiang and young migrant workers in Zhonganqiao and Jishan are all social network users, especially using mobile applications like Weixin and QQ. So as a source combined MAU of Weixin and WeChat increased to approximately 1,098 million by the end of 2018, and the overall MAU of QQ increased to 807 million, as well as cover the range both in urban and rural areas, the RTUQ data do fit the need to be validly and reliably used to analyse the spatio-temporal variation of human mobilities in small-scale areas like villages.

Another fact is that there is barely any official open access to the numerical statistics of resident living in incorporated villages in China (three typical villages in this study are all incorporated villages), let alone in the unincorporated village level. Even there is an annual census, it’s hard to tell temporary population in the study case area due to their multifunctional differentiation, which causes flows of population coming in and out of the village for purposes like visit as a tourist or go out as a migrant worker. That’s why new type of geographic big-data like RTUQ data could provide a new perspective for understanding the population dynamics in rural areas, perceive and measure multifunctionality of residence, employment and consumption in rural space by distinguishing mobility patterns and functional indices during different periods (weekdays, weekends, and holidays).

Point 3: The comment of the results is excessively descriptive. It would be necessary to go beyond the contrast of the results obtained with the processes known by other sources, and underline what the use of this source gives us beyond corroborating the existence of territorial processes that can be known from other types of information. In the same way, the conclusions should show more clearly what are the contributions of the analysis carried out for the purposes of territorial planning.

Response 3:

As far as we know, there is few study with quantitative approach engages with human mobility and multifunctional differentiation on rural spaces in village level due to limited sources. It is a sword with double blades, for one thing, the proposed methodology is novel and of interest, for another, it’s hard to contrast the result with other sources, which doesn’t mean validity and reliability of the result cannot be proved. As a matter of fact, spatio-temporal patterns of human mobilities in three typical villages and their function index change during different periods according to RTUQ data manifest that the proposed method is suitable for identifying and comparing multifunctionality of rural space in various times and places, the main methodological goal in this study is achieved. But the secondary empirical goal, as reviewer mentioned, “…show more clearly what are the contributions of the analysis carried out for the purposes of territorial planning” still needs some improvement, especially its influence to decision-making mechanisms of rural development trajectories in China and rural areas in transition all over the world. So the last paragraph in Conclusion part is overwritten, and an extra reference is added, as follows.

“Understanding the transition of rural multifunctionality by the data of human mobilities is great significance to reconstruct the rural space. The mobilities big data at micro-scale of rural population proves to be a useful tool to distinguish and compare perceive and measure the multifunctionality transformation of rural space. Based on the RTUQ data at village level, the paper reveals the changing process of the human mobilities, which have a profound impact on rural land use, especially affect the intensity and mode of land use. Likewise, spatio-temporal patterns of human mobilities in village level manifest that there are profound differentiation within dominant function and development trajectories considering multiple purposes of human activities. Multifunctional transition in rural area indicates that the substitution is more than a gradual linear model use to be, but as a breakthrough of traditional modernization development paradigm. With the process of rural-urban integration in contemporary China and many other developing countries in the near future, transition in rural area and its functions is predictably become much more pluralistic and complicated. Therefore, a new research agenda for combining traditional land use data with the emerging big data in exploring and understanding the contemporary rural space towards diversity and multifunctionality is urgently needed, for example, provide scientific reference for pushing forward rural revitalization by classifying and realizing the multifunctional characteristics of the village [69], especially human geographers are now in the era of geospatial big data.”

3) Revised Manuscript using the "Track Changes" Function in MS

Please see the attachment. 

Author Response File: Author Response.docx