Does High Spatial Density Imply High Population Density? Spatial Mechanism of Population Density Distribution Based on Population–Space Imbalance
Abstract
:1. Introduction
2. Literature Review and Working Hypotheses
2.1. The Examination of Unbalanced Population Density Distribution
2.2. Main Influencing Factors of Population Density Distribution and Related Study Trends
2.3. Composition and Indicators of Spatial Characteristics
2.4. Existing Characteristics and Shortcomings of Research
3. Materials and Methods
3.1. Study Area and Data Acquisition
3.1.1. Study Area
3.1.2. Data Sources and Preprocessing
3.2. Methodology
3.2.1. Population Density and Spatial Density Indicators
3.2.2. Classification Based on the Correlation between Population Density and Spatial Density
3.2.3. Identifying the Blocks with Unbalanced Population Density
4. Empirical Results
4.1. Correlation between Spatial Density and Population Density
4.2. Distribution Principles of City Blocks with Uneven Population Density
4.3. Spatial Characteristics Affecting Population Density Distribution
5. Discussion
5.1. Influence of Spatial Density on Population Density Distribution
5.2. Formation of Zonal Space with Low Population Density
5.3. Population–Space Selection Preference in Different Types of Blocks
5.4. Policy Influence
5.5. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Yue, W.Z.; Chen, Y.; Thy, P.T.M.; Fan, P.L.; Liu, Y.; Zhang, W. Identifying urban vitality in metropolitan areas of developing countries from a comparative perspective: Ho Chi Minh City versus Shanghai. Sustain. Cities Soc. 2021, 65, 102609. [Google Scholar] [CrossRef]
- Small, K.A.; Song, S. Population and employment densities: Structure and change. J. Urban Econ. 1994, 36, 292–313. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lessmann, C. Spatial inequality and development-Is there an inverted-U relationship? J. Dev. Econ. 2014, 106, 35–51. [Google Scholar] [CrossRef] [Green Version]
- Lee, H.; Kim, U.Y.; Lee, J. The impact of density of land use and spatial structure on urban vitality: Focused on comparing two new town; Bundang and Ilsan in metropolitan area of Korea. SH Urban Res. Insight 2014, 4, 47–56. [Google Scholar] [CrossRef]
- Xia, C.; Yeh, A.G.O.; Zhang, A. Analysing spatial relationships between urban land use intensity and urban vitality at street block level: A case study of five Chinese megacities. Landsc. Urban Plan 2020, 193, 103669. [Google Scholar] [CrossRef]
- Shi, Y.; Yang, J.Y. The Study of Spatiotemporal Behaviour density algorithm based on mobile phone signalling data. Chin. Landsc. Archit. 2019, 5, 102–106. [Google Scholar]
- Lan, F.; Gong, X.Y.; Da, H.L.; Wen, H.Z. How do population inflow and social infrastructure affect urban vitality? Evidence from 35 large- and medium-sized cities in China. Cities 2020, 100, 102454. [Google Scholar] [CrossRef]
- Clark, C. Urban population densities. J. R. Stat. Soc. 1951, 114, 490–496. [Google Scholar] [CrossRef]
- Wang, L.M. Research on configuration theory and method of man earth relationship system for PRED problem. Geogr. Res. 2002, 2, 38–44. [Google Scholar]
- Brennan, E.M.; Richardson, H.W. Asian megacity characteristics, problems, and policies. Int. Reg. Sci. Rev. 1989, 12, 117–129. [Google Scholar] [CrossRef]
- Jae-won, J.; Na, J.S. Comprehensive renewal plan of low-rise residential area using appropriate blocks for the implementation of urban block renewal project–Focusing on the case of Incheon Metropolitan City. J. Urban Des. Insitute Korea 2022, 23, 53–68. [Google Scholar]
- Luthi, A. Ecological limits to population growth. Schweiz. Z. Fur Volkswirtsch. Und Stat. 1989, 125, 461–472. [Google Scholar]
- Hu, W.X. Study on spatial agglomeration and diffusion in coastal urban dense areas. City Plann. Rev. 1998, 6, 22–29. [Google Scholar]
- Shi, Y.; Yang, J.Y.; Shen, P.Y. Revealing the correlation between population density and the spatial distribution of urban public service facilities with mobile phone data. ISPRS Int. J. Geo-Inf. 2020, 9, 38. [Google Scholar] [CrossRef] [Green Version]
- Liu, Y.S.; Zhao, P.J.; Liang, J.S. Study on urban vitality based on LBS data: A case of Beijing within 6th Ring Road. Areal Res. Dev. 2018, 37, 64–87. [Google Scholar]
- Chang, F.; Wang, L.; Ma, Y.; Yan, C.; Liu, H. Do urban public service facilities match population demand? Assessment based on community life circle. Prog. Geogr. 2021, 40, 607–619. [Google Scholar] [CrossRef]
- Szele, A.; Kisgyorgy, L. The vitality of traffic directions in road networks with recurrent congestion and its effect on road traffic design. In Proceedings of the Third International Conference on Traffic and Transport Engineering (CTTE), Tangier, Morocco, 27–28 May 2016; pp. 1012–1018. [Google Scholar]
- Gao, Y.P.; Xu, X.F.; Wei, Y. Analysis on the imbalance of population flow network during the Spring Festival travel rush in China in 2015. PLoS ONE 2021, 16, e0249520. [Google Scholar] [CrossRef]
- Zhang, F. Man-land relationship: Crisis, feature and thought on harmonisation. China Popul. Res. Environ 1993, 1, 9–14. [Google Scholar]
- Vinuela, A.; Posada, D.G.; Morollon, F.R. Determinants of immigrants’ concentration at local level in Spain: Why size and position still matter. Popul. Space Place 2019, 25, e2247. [Google Scholar] [CrossRef]
- Nie, X.J.; Zhang, Y.C.; Zhou, W.; Su, H.; Yang, T.T.; Li, X.H.; Lan, S.R. Research status and trend of urban space vitality comparative analysis of Chinese and English literature. J. Southwest Univ. Nat. Sci. Ed. 2021, 43, 147–155. [Google Scholar]
- Ariste, R. Availability of health workforce in urban and rural areas in relation to Canadian seniors. Int. J. Health Plann. Manag. 2019, 34, 510–520. [Google Scholar] [CrossRef] [PubMed]
- Masuda, J.R.; Zupancic, T.; Poland, B.; Cole, D.C. Environmental health and vulnerable populations in Canada: Mapping an integrated equity-focused research agenda. Can. Geogr.-Geogr. Can. 2008, 52, 427–450. [Google Scholar] [CrossRef]
- Bauer, T.; Epstein, G.; Gang, I.N. Herd effects or migration networks? The location choice of Mexican immigrants in the US. IZA Discuss. Pap. 2002, 551, 1–44. [Google Scholar]
- Bonaparte, H.M. Toward a population policy. Cuad. De Econ. Soc. 1982, 4, 77–95. [Google Scholar]
- Reboratti, C.A. Some considerations on population policy in Argentina. Cuad. Econ. Soc. 1982, 4, 45–55. [Google Scholar]
- Alogoskoufis, G.; Manning, A. Wage setting and unemployment persistence in Europe, Japan and the USA. Eur. Econ. Rev. 1998, 32, 698–706. [Google Scholar] [CrossRef]
- Geshev, G.; Tsekova, E.; Kalchev, I.; Spiridonova, I. The impact of migration on regional demography. Naselenie 1992, 5, 29–39. [Google Scholar]
- Alcala, C.M.; Moral-Pajares, E. Unbalanced distribution of the foreign population among the Spanish provinces: Determining factors. Rev. Estud. Reg. 2015, 103, 109–130. [Google Scholar]
- Diwakar, A. Processes and factors of metropolitanisation in India. Popul. Geogr. 1993, 15, 41–60. [Google Scholar]
- Duan, P.Z. Influence of China’s population flow in the change of regional disparity since 1978. China Popul. Res. Environ. 2008, 18, 27–33. [Google Scholar]
- Liu, Y.W.; Yan, Q.W.; Huang, S.; Jiang, C.L.; Jiang, L. Analysis on spatio-temporal dynamics of population distribution in Jiangsu. Sci. Survey. Mapp. 2015, 40, 30–35. [Google Scholar] [CrossRef]
- Liu, S.J.; Zhang, L.; Long, Y. Urban vitality area identification and pattern analysis from the perspective of time and space fusion. Sustainability 2019, 11, 4032. [Google Scholar] [CrossRef] [Green Version]
- Evans, A.W. The assumption of equilibrium in the analysis of migration and interregional differences—A review of some recent research. J. Reg. Sci. 1993, 30, 515–531. [Google Scholar] [CrossRef] [PubMed]
- Harrigan, F.J.; McGregor, P.G. Equilibrium and disequilibrium perspectives on regional labour migration. J. Reg. Sci. 1993, 33, 49–67. [Google Scholar] [CrossRef]
- Casado, M.; Molina, L.; Oyarzun, J. El analisis economico de los movimientos migratorios internacionales: Determinantes empiricos y nuevas propuestas de regulacion, Facultad de Ciencias Economicas y Empresariales de la UCM. Work. Pap. 2003, 7, 180–210. [Google Scholar]
- Mar, C. Los determinantes de los flujos migratorios internacionales: El caso espanol, 1995–2007. Princ. Estud. Econ. Politica 2009, 14, 49–68. [Google Scholar]
- Liu, J.S.; Chen, Y.G. GIS-based cellular automata models and researches on spatial complexity of man-land relationship. Geogr. Res. 2002, 21, 155–162. [Google Scholar]
- Li, X.J.; Wen, Y.Z.; Li, Y.Z.; Yang, H.M. High-quality development of the Yellow River Basin from a perspective of economic geography: Man-land and spatial coordination. Econ. Geogr. 2020, 40, 190–199. [Google Scholar]
- Minagawa, K.; Sumiyoshi, K. Studies on the optimal location of commerce-basic laws considering the distribution of population. Int. J. Prod. Econ. 1999, 60–61, 295–300. [Google Scholar] [CrossRef]
- Dorozhovets, M. Forward and inverse problems of Type A uncertainty evaluation. Measurement 2020, 165, 108072. [Google Scholar] [CrossRef]
- Yucong, H.; Haiwei, C.; Jian, O.; Lianhua, P.; Fengming, L. Land-Use Spatial Distribution Simulation and Traffic Demand Forecasting in New Town. J. South China Univ. Technol. Nat. Sci. Ed. 2014, 42, 27–34. [Google Scholar]
- Wu, Q.B.; Chen, Q.H. Population spatial change and urban spatial restructuring in Hangzhou from 2000 to 2010. City Plann. Rev. 2015, 39, 30–38. [Google Scholar]
- Meijers, E.J.; Burger, M. Spatial structure and productivity in US metropolitan areas. Environ. Plan A 2010, 42, 1383–1402. [Google Scholar] [CrossRef] [Green Version]
- Li, K.T. Population distribution and quality of life in the Taiwan area. Zi You Zhongguo Zhi Gong Ye 1983, 60, 17–31. [Google Scholar]
- Zhu, J.; He, B.J. Rebalance strategies for metropolitan area in the context of urban growth: Implications from Greater Sydney strategic planning 2056. Shanghai Urban Plan. Rev. 2017, 5, 83–89. [Google Scholar]
- Chi, G.Q.; Ventura, S.J. An integrated framework of population change: Influential factors, spatial dynamics, and temporal variation. Growth Change 2011, 42, 549–570. [Google Scholar] [CrossRef]
- Zhang, Z.C.; Luan, W.X.; Tian, C.; Su, M.; Li, Z.Y. Spatial distribution equilibrium and relationship between construction land expansion and basic education schools in Shanghai based on POI Data. Land 2021, 10, 1059. [Google Scholar] [CrossRef]
- Zhao, D.; Li, J.Y.; Yan, H.W.; Yin, H.Y.; Fu, Y.D.; Li, J.X. Spatial distribution characteristics of urban potential population in Shenyang City based on GIS and RS. Chin. J. Appl. Ecol. 2017, 28, 2697–2704. [Google Scholar]
- Lee, S.G.; Cho, I.S.; OHSEGYU. A Study on the planning characteristics of void spaces revealed in the external appearance of collective housings -focusing on the cases of collective housings in the architects working around the Netherlands. J. Archit. Inst. Korea 2014, 16, 11–20. [Google Scholar]
- Ma, B.; Jiang, J.; Xue, D.Q.; Li, M.; Cai, L. Spatial characteristics of historical and cultural block in the perspective of integration of subject and object:a case study of Xi’an Shuyuanmen. J. Shaanxi Normal Univ. 2018, 46, 102–109. [Google Scholar]
- Lee, J.H. A study on typology and property of the method of spatial composition in the block of street. J. Archit. Inst. Korea Plan Des. 2005, 21, 177–186. [Google Scholar]
- Jan, G.; Gemzoe, L. New City Spaces; Danish Architectural Press: Copenhagen, Denmark, 2000; ISBN 8774072552. [Google Scholar]
- Zhang, Z.B.; Pan, J.; Da, F.W. Population spatial structure evolution pattern and regulating pathway in Lanzhou City. Geogr. Res. 2012, 31, 2055–2068. [Google Scholar]
- Mao, Z.R.; Chen, X.K.; Xiang, Z.H.; Chen, Y.R. Research on the measurement and influencing factors of street vigour in historic districts: A case study of Wenming Street historic district in Kunming. SPE J. 2021, 4, 54–61. [Google Scholar]
- Jiang, C.J.; Gab-Geun, Y. Research on the design methods of historical and cultural blocks and public facilities -take Harbin Central Street for example. J. Humaniti. Soc. Sci. 2021, 12, 15–28. [Google Scholar]
- Mousavi, A.; Bunker, J.; Lee, J. Exploring socio-demographic and urban form indices in demand forecasting models to reflect spatial variations: Case study of childcare centres in Hobart, Australia. Buildings 2021, 11, 493. [Google Scholar] [CrossRef]
- Ratti, C.; Williams, S.; Frenchman, D.; Pulselli, R.M. Mobile landscapes: Using location data from cell phones for urban analysis. Environ. Plann. B Plann. Des. 2006, 33, 727. [Google Scholar] [CrossRef]
- Wang, H.J.; Li, X.Y.; Zhang, Z.L.; He, X.Y.; Chen, W.; Chen, Y.B.; Hu, J.B. Analysis on urban spatial expansion process in Shenyang City in 1979–2006. J. Appl. Ecol. 2008, 19, 2673–2679. [Google Scholar]
- Liu, L.B.; Peng, Z.H.; Wu, H.; Jiao, H.Z.; Yu, Y. Exploring urban spatial feature with dasymetric mapping based on mobile phone data and LUR-2SFCAe method. Sustainability 2018, 10, 2432. [Google Scholar] [CrossRef] [Green Version]
- Tian, G.J.; Jiang, J.; Yang, Z.F.; Zhang, Y.Q. The urban growth, size distribution and spatio-temporal dynamic pattern of the Yangtze River Delta megalopolitan region, China. Ecol. Modell. 2011, 222, 865–878. [Google Scholar] [CrossRef]
- Banerjee, A.; Saha, J. Population environment interface in urban India: A geographical analysis. Landsc. Ecol. Water Manag. 2014, 1, 147–163. [Google Scholar]
- Lafrombois, M.E.H.; Park, Y. The uneven shrinking city: Neighborhood demographic change and creative class planning in Birmingham, Alabama. Urban Geogr. 2021, 10, 4788. [Google Scholar] [CrossRef]
- Edwardes, M. Kendall Tau is Equal to the Correlation-Coefficient for the Be Distribution. J. South China Univ. Technol. Nat. Sci. Ed. Stat. Probab. Lett. 1993, 17, 415–419. [Google Scholar]
- Xu, W.C.; Ma, R.B.; Zhou, Y.Z.; Peng, S.G.; Hou, Y.H. Asymptotic properties of Pearson’s rank-variate correlation coefficient in bivariate normal model. Signal Process. 2015, 119, 190–202. [Google Scholar] [CrossRef]
- Pulselli, R.; Ramono, P.; Ratti, C.; Tiezzi, E. Computing urban mobile landscapes through monitoring population density based on cellphone chatting. Int. J. Des. Nat. Ecodyn. 2008, 3, 121–134. [Google Scholar]
- Louail, T.; Lenormand, M.; Ros, O.G.C.; Picornell, M.; Herranz, R.; Frias-Martinez, E.; Ramasco, J.J.; Barthelemy, M. From mobile phone data to the spatial structure of cities. Sci. Rep. 2014, 4, 5276. [Google Scholar] [CrossRef] [Green Version]
- Hu, X.Y.; Yang, J.Y. Spatial characteristics and boundary defining of shadow areas in public centre districts of megacities. J. Southeast Univ. Nat. Sci. Ed. 2014, 44, 1093–1098. [Google Scholar]
- Zhang, J.X.; Zhuang, L.D. Study on the evolution mechanism and countermeasures of metropolitan shadow area. J. Nanjing Univ. (Nat. Sci.) 2000, 6, 687–692. [Google Scholar]
- Frank, L.; Bradley, M.; Kavage, S.; Chapman, J.; Lawton, T.K. Urban form, travel time, and cost relationships with tour complexity and mode choice. Transportation 2008, 35, 37–54. [Google Scholar] [CrossRef]
- Ye, Y.; Li, D.; Liu, X.J. How block density and typology affect urban vitality: An exploratory analysis in Shenzhen, China. Urban Geogr. 2018, 39, 631–652. [Google Scholar] [CrossRef]
- Yi, Y.M.; Gim, T.H.T. What makes an old market sustainable? An empirical analysis on the economic and leisure performances of traditional retail markets in Seoul. Sustainability 2018, 10, 1779. [Google Scholar] [CrossRef] [Green Version]
- Gabriel, S.A.; Nothaft, F.E. Rental housing markets, the incidence and duration of vacancy, and the natural vacancy rate. J. Urban Econ. 2001, 49, 121–149. [Google Scholar] [CrossRef] [Green Version]
- Law of the People’s Republic of China on the Prevention and Control of Atmospheric Pollution. Available online: https://heinonline.org/HOL/LandingPage?handle=hein.journals/chinelgo37&div=39&id=&page= (accessed on 1 May 2014).
- Pawe, C.K.; Saikia, A. Decumbent development: Urban sprawl in the Guwahati Metropolitan Area, India. Singap. J. Trop. Geogr. 2020, 41, 226–247. [Google Scholar] [CrossRef]
- Yang, J.Y.; Hu, X.Y. Study on shadow area of city centres in circle-core structure mode. City Plann. Rev. 2012, 36, 26–33. [Google Scholar]
- Ou, G.L.; Zhou, M.; Zeng, Z.P.; He, Q.S.; Yin, C.H. Is there an equality in the spatial distribution of urban vitality: A case study of Wuhan in China. Open Geosci. 2021, 13, 469–481. [Google Scholar] [CrossRef]
- Ghaempanah, N.; Rahnamaei, M.T. Analysis success factors of the new cities establishment (case study: Pardisan town). Nexo Rev. Cient. 2021, 34, 1231–1242. [Google Scholar] [CrossRef]
- Kuang, W.H.; Du, G.M. Analysing urban population spatial distribution in Beijing Proper. J. Geo-Info Sci. 2011, 13, 506–512. [Google Scholar]
- Yigitcanlar, T.; Sarimin, M. Contributions of knowledge-based foundations of universities in knowledge city formation: A Malaysian case study. In Proceedings of the 2011 6th International Forum on Knowledge Asset Dynamics (IFKAD2011), Tampere, Finland, 15–17 June 2011; pp. 13–37. [Google Scholar]
- Xue, B.; Li, J.Z.; Xiao, X.; Xie, X.; Lu, C.P.; Ren, W.X.; Jiang, L. Overview of man-land relationship research based on POI data: Theory, method and application. Geogr. Geo-Info Sci. 2019, 35, 51–60. [Google Scholar]
- Rupasinghe, H.T.; Halwatura, R.U. Benefits of implementing vertical greening in tropical climates. Urban For. Urban Green. 2020, 53, 126708. [Google Scholar] [CrossRef]
- Yu, S.J.; Yuan, S.Q. Study on the spatial pattern of urban public green space based on GridsA case study of the main urban area of Fuzhou. J. Fujian Norm. Univ. Nat. Sci. 2011, 27, 88–94. [Google Scholar]
- Mu, B.; Liu, C.; Mu, T.; Xu, X.N.; Tian, G.H.; Zhang, Y.L.; Kim, G.W. Spatiotemporal fluctuations in urban park spatial vitality determined by on-site observation and behaviour mapping: A case study of three parks in Zhengzhou City, China. Urban For. Urban Green. 2021, 64, 127246. [Google Scholar] [CrossRef]
- Noh, S.C.; Park, J.H. Cafe and restaurant under my home: Predicting urban commercialisation through machine learning. Sustainability 2021, 13, 5699. [Google Scholar] [CrossRef]
Characteristic Indicator | Calculation |
---|---|
Locational centrality | , where K denotes the number of city-level central areas in the measured city; denotes the total floor area of public facilities within the scope of city-level central areas; denotes the total area of blocks in the central area k; denotes the straight-line distance between the spatial unit n and city-level central area k. |
Functional diversity | , where denotes the block area of type u in the block n; U denotes the total number of block types within a block range. |
Accessibility | , where L denotes the total length of road axes within a spatial unit; S denotes the total block area within a spatial unit. |
Scale intensiveness | , where M denotes the total base area of all buildings within a spatial unit; S denotes the area of a spatial unit. |
Morphological compactness | , where S denotes the total block area within a spatial unit; V denotes the perimeter of all planar shapes within a spatial unit. |
Landscape quality | , E denotes the green area in the buffer zone within a distance of 500 m from a spatial unit; S denotes the area of the buffer zone within a distance of 500 m from a spatial unit. |
Station | Longitude | Latitude | 0:00 | 1:00 | 2:00 | 3:00 | 4:00 | 5:00 |
2,079,730,131 | 120.09251 | 31.51191 | 184 | 182 | 184 | 172 | 182 | 169 |
6:00 | 7:00 | 8:00 | 9:00 | 10:00 | 11:00 | 12:00 | 13:00 | 14:00 |
190 | 203 | 240 | 269 | 309 | 277 | 297 | 274 | 273 |
15:00 | 16:00 | 17:00 | 18:00 | 19:00 | 20:00 | 21:00 | 22:00 | 23:00 |
277 | 288 | 242 | 260 | 254 | 241 | 223 | 213 | 210 |
Database | Data Layer | Content |
---|---|---|
Data of urban space | Block layer | This layer contains block boundaries and block area. |
Functional type layer | This layer contains the functional type and code of each block. | |
Building layer | This layer contains the base area of buildings, building stories, and building functions. | |
Road traffic layer | This layer contains road axes and stops or stations of urban public traffic. | |
National boundary environment | This layer contains rivers, river systems and mountains. |
Functional Type of Block | Coefficient of Correlation between Population Density and Spatial Density | Block Quantity | Bar Chart of Data |
---|---|---|---|
Administrative block | 0.264 * | 33 | |
Educational block | 0.283 * | 64 | |
Medical and health block | 0.130 | 26 | |
Sports block | 0.251 | 12 | |
Commercial block | 0.454 ** | 305 | |
Business block | 0.226 * | 126 | |
Entertainment block | 0.217 * | 17 | |
Utility block | 0.292 ** | 14 | |
High-grade residential block | −0.027 | 107 | |
General residential block | 0.326 ** | 386 | |
Shanty residential block | 0.235 ** | 144 | |
Traffic block | −0.128 ** | 15 | |
Industrial block | 0.156 | 83 |
Educational Block | Commercial Block | Business Block | General Residential Block | Shanty Residential Block | |
---|---|---|---|---|---|
Distribution of population density and spatial density | |||||
Degree of population–space correlation | |||||
When spatial density is higher than 10,000 m2/ha, unbalanced distribution of population density is obvious. | When spatial density is higher than 22,000 m2/ha, unbalanced distribution of population density is very obvious. | When spatial density is higher than 10,000 m2/ha, unbalanced distribution of population density is obvious. | When spatial density is higher than 30,000 m2/ha, unbalanced distribution of population density is obvious. | When spatial density is higher than 4000 m2/ha, unbalanced distribution of population density is obvious. |
Educational Blocks with Unbalanced Population Density | Commercial Blocks with Unbalanced Population Density | Business Blocks with Unbalanced Population Density | General Residential Blocks with Unbalanced Population Density | Shanty Residential Blocks with Unbalanced Population Density | |
---|---|---|---|---|---|
Distribution of blocks | |||||
Spatial characteristics of blocks |
Spatial density ≥ Critical value | |||||||||
Spatial density < Critical value |
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Shao, D.; Xiong, W. Does High Spatial Density Imply High Population Density? Spatial Mechanism of Population Density Distribution Based on Population–Space Imbalance. Sustainability 2022, 14, 5776. https://doi.org/10.3390/su14105776
Shao D, Xiong W. Does High Spatial Density Imply High Population Density? Spatial Mechanism of Population Density Distribution Based on Population–Space Imbalance. Sustainability. 2022; 14(10):5776. https://doi.org/10.3390/su14105776
Chicago/Turabian StyleShao, Dian, and Weiting Xiong. 2022. "Does High Spatial Density Imply High Population Density? Spatial Mechanism of Population Density Distribution Based on Population–Space Imbalance" Sustainability 14, no. 10: 5776. https://doi.org/10.3390/su14105776