The Regional Disparity of Urban Spatial Expansion Is Greater than That of Urban Socioeconomic Expansion in China: A New Perspective from Nighttime Light Remotely Sensed Data and Urban Land Datasets
Round 1
Reviewer 1 Report
In this manuscript, authors attempt to define urban expansion from urban spatial expansion and urban socioeconomic expansion based on the nighttime light (NTL) data and urban land datasets. The manuscript is well structured and the conclusion is valuable. However, I still had some questions:
1. the correlation between the information extracted from nighttime light remoted sensed data and the USS/USE needs further explanation. In other words, why the nighttime light remoted sensed data was selected ?
2. I feel confused about what time (per year) data is used for comparison? What is the temporal resolution/time span of the experimental data? From my side, I think a sufficient and detailed explanation is needed here, but unfortunately I didn't see it.
3. Some suggestions about the written:
a) It will be much better if the table or figure can be shown before the corresponding analysis (like figure.7 in the manuscript)
b) Some conjunctions appear too often, for example, however.
Author Response
In this manuscript, authors attempt to define urban expansion from urban spatial expansion and urban socioeconomic expansion based on the nighttime light (NTL) data and urban land datasets. The manuscript is well structured and the conclusion is valuable. However, I still had some questions:
Response: We sincerely thank the reviewer’s comments and suggestions. We have revised our paper carefully according to your comments. Please see our item-by-item responses below and the revised version for details.
Comment 1: The correlation between the information extracted from nighttime light remoted sensed data and the USS/USE needs further explanation. In other words, why the nighttime light remoted sensed data was selected?
Response: Thank you for your comment. We have verified the accuracy of urban land and socioeconomic size extracted from nighttime light data with traditional statistical data like PGDP and urban built-up area in Section 4.1.The digital number of lighting pixels represents the intensity of socioeconomic activities, and the number of lighting pixels reflects spatial coverage of urban socioeconomic activities, therefore, nighttime light data will be a good proxy to statistics to quantify the evolution of socioeconomic activities.
Comment 2: I feel confused about what time (per year) data is used for comparison? What is the temporal resolution/time span of the experimental data? From my side, I think a sufficient and detailed explanation is needed here, but unfortunately.
Response: Thank you for your comment. The time span of this study is from 1993 to 2015, and the evolution differences between urban built-up area and socio-economic development are compared within this time span. However, due to the availability of data, urban land data in some years (such as 1994, 1997, 2011, 2013 and 2014) were missing during the period, but what we studied was the evolution trend of regional economic development and regional development differences. Therefore, on the premise of ensuring data integrity and consistency, we could assume that the final results would not be affected.
Comment 3: It will be much better if the table or figure can be shown before the corresponding analysis (like figure.7 in the manuscript).
Response: Thank you for your suggestions. Referring to the layout of previous manuscripts, tables or figures were placed after corresponding analysis.
Comment 4: Some conjunctions appear too often, for example, however.
Response: Thank you for your suggestions. Accepted and revised.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Dear Authors,
the paper idea is very interesting and can be considered for this journal. The regional disparity is an important topic which very often is not considered in studies. The paper in general is well written and well structured but the following changes are recommended:
- Method: it is necessary to define whether the method can also be used in other contexts or is it specific for this case study
- Case study: after defining the method we proceed with the results. it is necessary to introduce a chapter with the description of the case study in which all the data considered and their sources are highlighted
Author Response
The paper idea is very interesting and can be considered for this journal. The regional disparity is an important topic which very often is not considered in studies. The paper in general is well written and well structured but the following changes are recommended.
Response: We sincerely thank the reviewer’s comments and suggestions. We have revised our paper carefully according to your comments. Please see our item-by-item responses below and the revised version for details.
Comment 1: Method: it is necessary to define whether the method can also be used in other contexts or is it specific for this case study.
Response: Thank you for your comment. Thank for your comment. Taking China’s 241 prefecture-level cities within different provinces as experimental subjects, the Dagum Gini (DG) coefficient and stochastic convergence test were employed to assess the disparity of urban expansion from two different dimensions in this study. The DG coefficient can decompose the total regional development gap into the gaps formed by different sources to analyze the impact of different subsamples on the overall regional differences. As an important method of convergence test, the stochastic convergence method can test whether one variable has a continuous impact on another variable, which is information that can be used to avoid the possible state between convergence and non-convergence in a short term. Both methods are universal that can be used in other contexts.
Comment 2: Case study: after defining the method we proceed with the results. it is necessary to introduce a chapter with the description of the case study in which all the data considered and their sources are highlighted
Response: Thank you for your comment. We supplemented the description and data sources.
“Two kinds of data were used to evaluate the regional disparity of urban expansion in China, including the urban land datasets, DMSP-OLS nighttime stable light (NSL) data. And collected urban land data and per capita GDP (PGDP) data from China Urban Statistical Yearbook (http://www.stats.gov.cn/tjsj/) for regression analysis to verify the accuracy of USS and USE.
Referring to the study by He et al. [20], urban land datasets were extracted by a hi-erarchical support vector machine (SVM) using the NTL data (https://ngdc.noaa.gov/eog/dmsp.html), land surface temperature (LST) (http://ladsweb.nascom.nasa.gov), and normalized difference vegetation index (NDVI) data (http://edc2.usgs.gov/ and http://free.vgt.vito.be/origin) from 1992 to 2015 (Figure. 3) [21]. The data were projected to the equivalent region of the Albers cone and resampled to a 1-km resolution before processing. Fine-scale urban land data generated by Landsat data ((http://www.geodata.cn/Portal/index.jsp) were used to verify the accuracy of the extraction results, with the average Kappa value reaching 0.66 [22].
The NSL data were collected from the National Geophysical Data Center of the US National Oceanic and Atmospheric Administration (https://ngdc.noaa.gov/eog/dmsp.html) . The NSL data exclude erratic lights that do not drive from cities, towns, or other sites of human activity. The digital number (DN) of the data is 6 bits (i.e., 0-63), with a spatial resolution of 30 arc-seconds (approximately 1-km). However, the NSL data have two flaws: 1) DN oversaturation and 2) the lack of continuity and comparability. Thus, this study adopted the method developed by Shi et al. [23] to processed the NSL data , and the corrected NSL data from 1993-2013 are shown in Figure. 4.”
Author Response File: Author Response.docx
Reviewer 3 Report
The manuscript is very interesting and quite well-designed. However, besides a general remark that the results could be presented in a less textual and more visual format (e.g. pie charts instead of descriptive paragraphs), here are some concrete remarks:
1 Lines 38-39. Logical error (words ‘per capita’ are obsolete): „From 1978 to 2020, China's per capita gross domestic product (GDP) grew from $ 195.90 billion to $ 11.06×104 billion, an increase of 751.40 times.”
2 Line 49. Reference is required: “It has been acknowledged that urban expansion is the process of the rural population moving to cities.”
3 Line 55. Reference is required: “The USS usually manifests in decentralized urban forms, including jump-55 type expansion, fill-type expansion, and marginal expansion.”
4 Lines 265-268. A better explanation of the causes for the trends to make it clearer for an international readership is necessary: “This difference is due to factors such as the 1995 strict control region of the scale of fixed-asset development, the control of land leases, the large area of land requisitioned between 2002 and 2003 [28], and the “Eleventh Five-year Plan” mid-west development policy, which caused these trends.”
5 Lines 350-354. Some logical inconsistency (why measures to revitalise the NER are necessary while the results show that NER cities are on the track to sustainable development? Please, explain more clearly): “This result shows that in the sustainable development of cities, there are obvious differences in development in NER, which is consistent with the shrinkage of some cities in NER in recent years. However, the Chinese government has promoted the development of some cities in the strategy of revitalizing the NER, which has led to an increase in USS [36], while the difference in USE has changed from large to small.” Also, see lines 464-465.
6 Conclusions: Still remains unclear, how the results of this investigation lead to the goal declared in Lines 101-102, especially in the light of the UN SDGs: "provide reference opinions for the construction of sustainable cities in China.”
Author Response
The manuscript is very interesting and quite well-designed. However, besides a general remark that the results could be presented in a less textual and more visual format (e.g. pie charts instead of descriptive paragraphs), here are some concrete remarks.
Response: We sincerely thank the reviewer’s comments and suggestions. We have revised our paper carefully according to your comments. Please see our item-by-item responses below and the revised version for details.
Comment 1: Lines 38-39. Logical error (words ‘per capita’ are obsolete): „From 1978 to 2020, China's per capita gross domestic product (GDP) grew from $ 195.90 billion to $ 11.06×104 billion, an increase of 751.40 times.”
Response: Thank you for your comment. Accepted and revised.
“From 1978 to 2020, China's gross domestic product (GDP) grew from $ 1149.5 billion to $11.06 trillion, an increase of 96.3 times.”
Comment 2: Line 49. Reference is required: “It has been acknowledged that urban expansion is the process of the rural population moving to cities.”
Response: Thank you for your comment. We cited a paper.
[5] Zhang X, Brandt M, Tong X, et al. A large but transient carbon sink from urbanization and rural depopulation in China [J]. Nature Sustainability, 2022, 5(4): 321-328.
Comment 3: Line 55. Reference is required: “The USS usually manifests in decentralized urban forms, including jump-type expansion, fill-type expansion, and marginal expansion.”
Response: Thank you for your comment. We cited a paper.
[8] Du P, Hou X, Xu H. Dynamic Expansion of Urban Land in China’s Coastal Zone since 2000 [J]. Remote Sensing, 2022, 14(4): 916.
Comment 4: Lines 265-268. A better explanation of the causes for the trends to make it clearer for an international readership is necessary: “This difference is due to factors such as the 1995 strict control region of the scale of fixed-asset development, the control of land leases, the large area of land requisitioned between 2002 and 2003 [28], and the “Eleventh Five-year Plan” mid-west development policy, which caused these trends.”
Response: Thank you for your comment. This difference is due to factors such as the 1995 strict control region of the scale of fixed-asset development, the control of land leases, the large area of land requisitioned between 2002 and 2003 [30], and the “Eleventh Five-year Plan” mid-west development policy, which caused these trends. By standardizing the use of urban land and improving the land contract system, we accelerate the marketization and commercialization of real estate in large, medium and small cities, support the urban construction in less developed areas in the central and western regions, and improve the efficiency and benefit of urban land use.
Comment 5: Lines 350-354. Some logical inconsistency (why measures to revitalise the NER are necessary while the results show that NER cities are on the track to sustainable development? Please, explain more clearly): “This result shows that in the sustainable development of cities, there are obvious differences in development in NER, which is consistent with the shrinkage of some cities in NER in recent years. However, the Chinese government has promoted the development of some cities in the strategy of revitalizing the NER, which has led to an increase in USS [36], while the difference in USE has changed from large to small.” Also, see lines 464-465.
Response: Thank you for your comment. The panel unit root test found that the IPS test of all regions does not reject the null hypothesis, while the Hadri test significantly rejects the null hypothesis. The results show that all regions have a unit root. Through comparison, it can be concluded that the confidence of the IPS test of USE in NER and ER is higher than that of the USS IPS test. Therefore, we can see that the USE in the study sample area, NER, and ER has a trend of more stable development than the USS. Also, we know, during the course of the development of the city because of resource depletion and restricted development of contraction appear a large number of cities in NER, cause in most cities in the NER of socio-economic activities in the phase of low level, while China's city construction and move forward at a rapid pace, so the NER is the same in expanding in the city proper, although the NER revitalization strategy advocates that urban development should follow the principle of sustainable development, the northeast cities that are on the path of sustainable development have not effectively adjusted the organic combination between USS and USE development, so it is necessary to revitalize the NER cities in multiple dimensions.
Comment 6: Conclusions: Still remains unclear, how the results of this investigation lead to the goal declared in Lines 101-102, especially in the light of the UN SDGs: "provide reference opinions for the construction of sustainable cities in China.”
Response: Thank you for your comment. The results showed that the difference in overall USS in China was greater than the difference in USE. On a regional scale, the difference was the largest in WR, followed by ER and CR, and the difference was the smallest in the northeast region. Except for the ER, the difference between USS and USE in the other three regions was expanding, and the difference between regions was the main source of uneven USS and USE. This difference was the largest in ER and WR the NER and CR, followed by other regions, but the difference in USS in NER showed a relatively widening trend. Thus, the sustainable development of Chinese cities should pay attention to the organic integration between urban spatial structure and socio-economic development, especially the development policy should pay attention to the urban development among different regions and promote the coordinated development of the whole cities through a series of effective measures.
Author Response File: Author Response.docx
Reviewer 4 Report
The article is interesting and shows the difference between urban development in cities in China and overall socioeconomic development. It fills an important knowledge gap in the context of socio-economic and urban expansion. The authors comprehensively presented the obtained results, formulated the goals of the research and the motives behind them. Please refer to the following minor corrections and suggestions.
1. What are the methodological limitations of the presented research methods? What are their errors resulting from the presented statistics and tools?
2. I am not convinced about including the drawing in the introduction. In my opinion, this should be added elsewhere in the article.
3. The maps shown in Figure 4 show vividly nighttime lights as a sign of urban development. However, I would suggest using a different color scale to make this phenomenon visible. Please let me know if there are newer data than from 2013.
4. Please include the legend for Figures 5 and 6.
5. Please explain all the symbols used in the tables.
Author Response
Responses to reviewer #4’s comments
The article is interesting and shows the difference between urban development in cities in China and overall socioeconomic development. It fills an important knowledge gap in the context of socio-economic and urban expansion. The authors comprehensively presented the obtained results, formulated the goals of the research and the motives behind them. Please refer to the following minor corrections and suggestions.
Response: We sincerely thank the reviewer’s comments and suggestions. We have revised our paper carefully according to your comments. Please see our item-by-item responses below and the revised version for details.
Comment 1: What are the methodological limitations of the presented research methods? What are their errors resulting from the presented statistics and tools?
Response: Thank you for your comment. It is undeniable that there are several aspects worthy of further exploration in this study. First, the time coverage of DMSP-OLS data has only updated to 2013, limiting the application after 2013. As the successor to DMSP-OLS data, NPP-VIIRS data has been released in 2012 and is still updated. Thus, the integration of DMSP-OLS and NPP-VIIRS data to remeasure the regional disparities could improve the reliability of conclusions for future urban planning. Then, we have divided the urban expansion into physical space and socio-economic change and measured by the Dagum Gini coefficient failing to consider the regional difference from the spatial effect and delve into the mechanism of urban expansion difference, therefore, borrowing the direction of urban expansion in distance function to measure the overall performance, to deepen the analysis.
Comment 2: I am not convinced about including the drawing in the introduction. In my opinion, this should be added elsewhere in the article.
Response: Thank you for your comment. We introduced the redefinition of urban expansion in Section 1. It is a complex changing process of complex systems, including the population, society, economy, ecology, land, culture, and other subsystems. Horizontal expansion and vertical improvements are deployed in urban expansion. A horizontal expansion is represented by an increase in the number and scale of cities and the expansion of the urban built-up area, i.e., improvements in the urbanization level and urban size. A vertical expansion represents an increase in the population and GDP growth, i.e., the socio-economic development of cities. Thus, the two aspects of urban expansion cannot be shown in detail using only a single data source.
Figure 1. Definition of urban expansion.
Comment 3: The maps shown in Figure 4 show vividly nighttime lights as a sign of urban development. However, I would suggest using a different color scale to make this phenomenon visible. Please let me know if there are newer data than from 2013.
Response: Thank you for your comment. The DMSP-OLS data were employed to detect the evolution of socioeconomic activities and urban land expansion, and it was released up to 2013. And the display of nighttime lights was changed to clearly represent the urban development (Figure 4).
Figure 4. Spatial distribution of the NSL data in China. Note: according the data availability, the 2015 NSL data were replaced by the 2013 NSL data.
Comment 4: Please include the legend for Figures 5 and 6.
Response: Accepted and revised.
Figure 5. Regression analysis between NSL data and PGDP. Note: All cities (a), NER (b), ER (c), CR (d), and WR (e).
Figure 6. Regression analysis between NSL data and PGDP. Note: All cities (a), NER (b), ER (c), CR (d), and WR (e).
Comment 5: Please explain all the symbols used in the tables.
Response: Thank you for your comment. We note the full names of the relevant symbols below the table.
Table 1. DG coefficient and its decomposition of the USS.
|
Year |
Overall regional disparity |
Intra-regions |
Between regions |
Contribution rate (%) |
||||||||||
|
ER |
NER |
CR |
WR |
NER-ER |
CR-ER |
CR-NER |
WR-ER |
WR-NER |
WR-CR |
Gw |
Gnb |
Gt |
||
|
1993 |
0.785 |
0.772 |
0.617 |
0.61 |
0.747 |
0.823 |
0.794 |
0.623 |
0.86 |
0.696 |
0.698 |
30.486 |
51.845 |
17.669 |
|
1995 |
0.783 |
0.763 |
0.613 |
0.591 |
0.734 |
0.829 |
0.797 |
0.613 |
0.857 |
0.686 |
0.680 |
30.468 |
54.098 |
15.434 |
|
1996 |
0.773 |
0.75 |
0.548 |
0.582 |
0.725 |
0.806 |
0.793 |
0.573 |
0.854 |
0.657 |
0.671 |
30.312 |
55.061 |
14.627 |
|
1998 |
0.764 |
0.744 |
0.538 |
0.568 |
0.713 |
0.799 |
0.781 |
0.560 |
0.852 |
0.646 |
0.662 |
30.278 |
55.713 |
14.009 |
|
1999 |
0.758 |
0.734 |
0.540 |
0.557 |
0.702 |
0.797 |
0.776 |
0.556 |
0.846 |
0.638 |
0.650 |
30.158 |
56.307 |
13.535 |
|
2000 |
0.754 |
0.730 |
0.542 |
0.553 |
0.694 |
0.793 |
0.773 |
0.554 |
0.843 |
0.634 |
0.643 |
30.096 |
56.309 |
13.595 |
|
2001 |
0.753 |
0.726 |
0.546 |
0.551 |
0.693 |
0.797 |
0.777 |
0.555 |
0.837 |
0.634 |
0.638 |
30.031 |
56.094 |
13.876 |
|
2002 |
0.742 |
0.707 |
0.551 |
0.543 |
0.690 |
0.798 |
0.765 |
0.558 |
0.827 |
0.635 |
0.633 |
29.730 |
55.961 |
14.310 |
|
2003 |
0.734 |
0.691 |
0.551 |
0.54 |
0.688 |
0.805 |
0.754 |
0.565 |
0.821 |
0.634 |
0.631 |
29.458 |
56.422 |
14.121 |
|
2004 |
0.730 |
0.684 |
0.551 |
0.538 |
0.688 |
0.808 |
0.750 |
0.566 |
0.816 |
0.636 |
0.629 |
29.335 |
56.416 |
14.249 |
|
2005 |
0.727 |
0.678 |
0.554 |
0.538 |
0.685 |
0.807 |
0.744 |
0.569 |
0.812 |
0.636 |
0.627 |
29.252 |
56.406 |
14.341 |
|
2006 |
0.723 |
0.675 |
0.556 |
0.539 |
0.680 |
0.808 |
0.739 |
0.575 |
0.809 |
0.634 |
0.625 |
29.200 |
56.233 |
14.568 |
|
2007 |
0.721 |
0.671 |
0.556 |
0.543 |
0.677 |
0.809 |
0.736 |
0.579 |
0.807 |
0.633 |
0.626 |
29.134 |
56.229 |
14.637 |
|
2008 |
0.719 |
0.669 |
0.558 |
0.545 |
0.675 |
0.807 |
0.733 |
0.582 |
0.805 |
0.634 |
0.626 |
29.103 |
56.081 |
14.816 |
|
2009 |
0.712 |
0.660 |
0.560 |
0.544 |
0.670 |
0.798 |
0.727 |
0.579 |
0.799 |
0.630 |
0.623 |
28.982 |
55.873 |
15.145 |
|
2010 |
0.708 |
0.654 |
0.567 |
0.544 |
0.665 |
0.794 |
0.725 |
0.581 |
0.793 |
0.630 |
0.619 |
28.890 |
55.654 |
15.456 |
|
2012 |
0.681 |
0.629 |
0.572 |
0.520 |
0.634 |
0.781 |
0.698 |
0.575 |
0.759 |
0.620 |
0.588 |
28.630 |
54.740 |
16.630 |
|
2015 |
0.670 |
0.619 |
0.565 |
0.512 |
0.623 |
0.776 |
0.686 |
0.573 |
0.746 |
0.612 |
0.579 |
28.543 |
54.365 |
17.092 |
Note: ER is eastern region, NER is northeastern region, CR is central region, WR is western region, Gw is the regional differences in the contribution, Gnb is the regional difference contribution, Gt is the ultra-variable density contribution, USS is the urban spatial expansion.
Responses to reviewer #4’s comments
The article is interesting and shows the difference between urban development in cities in China and overall socioeconomic development. It fills an important knowledge gap in the context of socio-economic and urban expansion. The authors comprehensively presented the obtained results, formulated the goals of the research and the motives behind them. Please refer to the following minor corrections and suggestions.
Response: We sincerely thank the reviewer’s comments and suggestions. We have revised our paper carefully according to your comments. Please see our item-by-item responses below and the revised version for details.
Comment 1: What are the methodological limitations of the presented research methods? What are their errors resulting from the presented statistics and tools?
Response: Thank you for your comment. It is undeniable that there are several aspects worthy of further exploration in this study. First, the time coverage of DMSP-OLS data has only updated to 2013, limiting the application after 2013. As the successor to DMSP-OLS data, NPP-VIIRS data has been released in 2012 and is still updated. Thus, the integration of DMSP-OLS and NPP-VIIRS data to remeasure the regional disparities could improve the reliability of conclusions for future urban planning. Then, we have divided the urban expansion into physical space and socio-economic change and measured by the Dagum Gini coefficient failing to consider the regional difference from the spatial effect and delve into the mechanism of urban expansion difference, therefore, borrowing the direction of urban expansion in distance function to measure the overall performance, to deepen the analysis.
Comment 2: I am not convinced about including the drawing in the introduction. In my opinion, this should be added elsewhere in the article.
Response: Thank you for your comment. We introduced the redefinition of urban expansion in Section 1. It is a complex changing process of complex systems, including the population, society, economy, ecology, land, culture, and other subsystems. Horizontal expansion and vertical improvements are deployed in urban expansion. A horizontal expansion is represented by an increase in the number and scale of cities and the expansion of the urban built-up area, i.e., improvements in the urbanization level and urban size. A vertical expansion represents an increase in the population and GDP growth, i.e., the socio-economic development of cities. Thus, the two aspects of urban expansion cannot be shown in detail using only a single data source.
Figure 1. Definition of urban expansion.
Comment 3: The maps shown in Figure 4 show vividly nighttime lights as a sign of urban development. However, I would suggest using a different color scale to make this phenomenon visible. Please let me know if there are newer data than from 2013.
Response: Thank you for your comment. The DMSP-OLS data were employed to detect the evolution of socioeconomic activities and urban land expansion, and it was released up to 2013. And the display of nighttime lights was changed to clearly represent the urban development (Figure 4).
Figure 4. Spatial distribution of the NSL data in China. Note: according the data availability, the 2015 NSL data were replaced by the 2013 NSL data.
Comment 4: Please include the legend for Figures 5 and 6.
Response: Accepted and revised.
Figure 5. Regression analysis between NSL data and PGDP. Note: All cities (a), NER (b), ER (c), CR (d), and WR (e).
Figure 6. Regression analysis between NSL data and PGDP. Note: All cities (a), NER (b), ER (c), CR (d), and WR (e).
Comment 5: Please explain all the symbols used in the tables.
Response: Thank you for your comment. We note the full names of the relevant symbols below the table.
Table 1. DG coefficient and its decomposition of the USS.
|
Year |
Overall regional disparity |
Intra-regions |
Between regions |
Contribution rate (%) |
||||||||||
|
ER |
NER |
CR |
WR |
NER-ER |
CR-ER |
CR-NER |
WR-ER |
WR-NER |
WR-CR |
Gw |
Gnb |
Gt |
||
|
1993 |
0.785 |
0.772 |
0.617 |
0.61 |
0.747 |
0.823 |
0.794 |
0.623 |
0.86 |
0.696 |
0.698 |
30.486 |
51.845 |
17.669 |
|
1995 |
0.783 |
0.763 |
0.613 |
0.591 |
0.734 |
0.829 |
0.797 |
0.613 |
0.857 |
0.686 |
0.680 |
30.468 |
54.098 |
15.434 |
|
1996 |
0.773 |
0.75 |
0.548 |
0.582 |
0.725 |
0.806 |
0.793 |
0.573 |
0.854 |
0.657 |
0.671 |
30.312 |
55.061 |
14.627 |
|
1998 |
0.764 |
0.744 |
0.538 |
0.568 |
0.713 |
0.799 |
0.781 |
0.560 |
0.852 |
0.646 |
0.662 |
30.278 |
55.713 |
14.009 |
|
1999 |
0.758 |
0.734 |
0.540 |
0.557 |
0.702 |
0.797 |
0.776 |
0.556 |
0.846 |
0.638 |
0.650 |
30.158 |
56.307 |
13.535 |
|
2000 |
0.754 |
0.730 |
0.542 |
0.553 |
0.694 |
0.793 |
0.773 |
0.554 |
0.843 |
0.634 |
0.643 |
30.096 |
56.309 |
13.595 |
|
2001 |
0.753 |
0.726 |
0.546 |
0.551 |
0.693 |
0.797 |
0.777 |
0.555 |
0.837 |
0.634 |
0.638 |
30.031 |
56.094 |
13.876 |
|
2002 |
0.742 |
0.707 |
0.551 |
0.543 |
0.690 |
0.798 |
0.765 |
0.558 |
0.827 |
0.635 |
0.633 |
29.730 |
55.961 |
14.310 |
|
2003 |
0.734 |
0.691 |
0.551 |
0.54 |
0.688 |
0.805 |
0.754 |
0.565 |
0.821 |
0.634 |
0.631 |
29.458 |
56.422 |
14.121 |
|
2004 |
0.730 |
0.684 |
0.551 |
0.538 |
0.688 |
0.808 |
0.750 |
0.566 |
0.816 |
0.636 |
0.629 |
29.335 |
56.416 |
14.249 |
|
2005 |
0.727 |
0.678 |
0.554 |
0.538 |
0.685 |
0.807 |
0.744 |
0.569 |
0.812 |
0.636 |
0.627 |
29.252 |
56.406 |
14.341 |
|
2006 |
0.723 |
0.675 |
0.556 |
0.539 |
0.680 |
0.808 |
0.739 |
0.575 |
0.809 |
0.634 |
0.625 |
29.200 |
56.233 |
14.568 |
|
2007 |
0.721 |
0.671 |
0.556 |
0.543 |
0.677 |
0.809 |
0.736 |
0.579 |
0.807 |
0.633 |
0.626 |
29.134 |
56.229 |
14.637 |
|
2008 |
0.719 |
0.669 |
0.558 |
0.545 |
0.675 |
0.807 |
0.733 |
0.582 |
0.805 |
0.634 |
0.626 |
29.103 |
56.081 |
14.816 |
|
2009 |
0.712 |
0.660 |
0.560 |
0.544 |
0.670 |
0.798 |
0.727 |
0.579 |
0.799 |
0.630 |
0.623 |
28.982 |
55.873 |
15.145 |
|
2010 |
0.708 |
0.654 |
0.567 |
0.544 |
0.665 |
0.794 |
0.725 |
0.581 |
0.793 |
0.630 |
0.619 |
28.890 |
55.654 |
15.456 |
|
2012 |
0.681 |
0.629 |
0.572 |
0.520 |
0.634 |
0.781 |
0.698 |
0.575 |
0.759 |
0.620 |
0.588 |
28.630 |
54.740 |
16.630 |
|
2015 |
0.670 |
0.619 |
0.565 |
0.512 |
0.623 |
0.776 |
0.686 |
0.573 |
0.746 |
0.612 |
0.579 |
28.543 |
54.365 |
17.092 |
Note: ER is eastern region, NER is northeastern region, CR is central region, WR is western region, Gw is the regional differences in the contribution, Gnb is the regional difference contribution, Gt is the ultra-variable density contribution, USS is the urban spatial expansion.
Author Response File: Author Response.docx
