Round 1

Reviewer 1 Report

Abstract

The study's objective is well represented in the abstract section, which includes activity-space-based segregation of different income groups based on time allocation. An overview of time allocation has also been highlighted in the area. The people mainly focused on China's transformation in time allocation in different urban spaces. Moreover, the desired outcome of this research has also been depicted in this section of this paper.

Introduction

The introductory section has established the importance of time in driving an individual's behavioral activities that can influence the quality of life. In addition, the application of time resources for managing transport and land use policies based on socio-spatial differentiation is outlandish. Hence convectional segregation focused on the residential neighbourhoods that proposed a paradigm to integrate human mobility based on the temporal decision of time allocation. This section also identifies the urban structures driven by the economic reformation that increase urbanisation in China by approximately 64.72% in the year 2021. Societal evaluation based on the urban spatial differentiation and contribution of different income groups has also been highlighted in the section to analyse the contribution of time allocation.

Literature review

A clarified literature review section has not been depicted in the section but the literary ideas have been identified in the introductory section methodological section and discussion section simultaneously. Additionally, evaluation based on the employment skill physical limitations and others has been depicted for a transparent outcome. An idea related to the contribution of time in suburban areas based on their income abilities and advantageous-disadvantageous position has been described that can significantly influence the level of education Living Style housing and others. For example, car ownership is another significant area to evaluate individual mobility that analyses activities of the community in Suburban areas.

Method

Nanjing from China is selected as the study area which compasses approximately 7.09 million population and covers an area of 4723 square kilometres. Evaluation within the inner Suburban and Outer shower one areas and its activities have been analysed for this research. Sample participants from 6 typical neighbourhoods of various types have been selected based on the random sampling method for conducting the survey. Hence descriptive statistics have been analysed to define the location quotient and utility of different income groups for analysing the overall distribution pattern of daily activities.

Research result

Comparative analysis based on the collected data has highlighted significant differences in time allocation among diverse income groups. The application of pictures and tables has defined the activities of low-income participants and non-income participants in urban spaces to differentiate the space utility. The OLS regression model has been identified to investigate how the diverse factors affect the use of urban spaces.

Conclusion

This section has major reference to the analytical understanding of the contribution of different socio-economic factors to the usage of urban spaces. Hence the application of big data is suggested along with the usage of traditional data to generate empirical research in a future study. This part, as it together with discussion need to improve with more details about the findings

Comments

 

Generally, is a well written article the researcher has selected only a specific region of China which has created a lack of transparency. In order to minimise the research gap researchers are suggested to define the agglomeration pattern of activities of different income groups in spaces for analysing the influence for quantifying and characterising the present data. Along with this, the application of theoretical data is highly required to improve research relevance and a clear understanding on the subject matter.

Author Response

Response to Reviewer 1 Comments

 

Point 1: The introductory section has established the importance of time in driving an individual's behavioral activities that can influence the quality of life. In addition, the application of time resources for managing transport and land use policies based on sociospatial differentiation is outlandish.

 

Response 1: Thank you for the comment. The accessibility of social resources and urban space is an important dimension of sociospatial differentiation and exclusion (Kwan M P, Weber J. Individual accessibility revisited: implications for geographical analysis in the twenty‐first century[J]. Geographical analysis, 2003, 35(4): 341-353.; Cass N, Shove E, Urry J. Social exclusion, mobility and access[J]. The sociological review, 2005, 53(3): 539-555.), which can be measured by the calculating the time allocation of different social groups in various urban space. There are significant differences in the spatial patterns of residents' activities under different built environments. In general, high-density, mixed-use, high-accessibility transport and land use policies can promote equitable use of urban space and other resources by different income groups. As Ashiru et al.(2004) noted “Time has long been recognized by researchers as playing an important role in the travel and activity behavior of individuals and its theoretical and practical is important for the development of transport and land use policies.”(Ashiru O, Polak J W, Noland R B. Utility of schedules: theoretical model of departure-time choice and activity-time allocation with application to individual activity schedules[J]. Transportation Research Record, 2004, 1894(1): 84-98.)

 

Point 2: This section (conclusion) has major reference to the analytical understanding of the contribution of different socio-economic factors to the usage of urban spaces. Hence the application of big data is suggested along with the usage of traditional data to generate empirical research in a future study. This part, as it together with discussion need to improve with more details about the findings.

 

 

Response 2: Thank you for the suggestion. We have provided a lot of detail to the findings and discussion. Please see the highlighted text Line 216-226 on page 7, Lines 326-341 on Page 14, Lines 396-425 on Page 17-18.

 

Point 3: Generally, is a well written article the researcher has selected only a specific region of China which has created a lack of transparency. In order to minimise the research gap researchers are suggested to define the agglomeration pattern of activities of different income groups in spaces for analysing the influence for quantifying and characterising the present data. Along with this, the application of theoretical data is highly required to improve research relevance and a clear understanding on the subject matter.

 

Response 3: Thank you for the suggestion. We have provided a description of the agglomeration pattern of activities of different income groups in spaces. Please see the highlighted text Line 400-406 on page 17.

Please see the attachment the responses to the reviewer’s comments in Word version.

Author Response File: Author Response.docx

Reviewer 2 Report

This opinion will be very short. The article is of high quality, the topic is excellent, current. The research method is compliant, classically conservative. The results are comprehensibly interpreted and clearly shown. The authors naturally concentrate on Chinese urban culture.

The only question is the conservatism and quantitative limits of the questionnaire research method. In the conclusions, the authors themselves admit the limited potential of the methodology.

The article would benefit from an explanation, in one short paragraph, why the methodology of combining big data sources and tools was not used. Appropriate approaches should be obtainable for academic research. Protection of personal data can be guaranteed. The presence of people in different places and their movements can be substituted, for example, by localization sim cards. Subsequently, other big data sources can be clustered.

Author Response

Thank you very much for the suggestion. We have provided a discussion on why big data was not adopted in this study. Please see the highlighted text Line 441-448 on page18. In addition, we revised and explained the shortcomings of traditional data and the improvement direction of future research. Please see the highlighted text Line 449-462 on page18-19.

Please see the attachment the responses to the reviewer’s comments in Word version.

Author Response File: Author Response.docx