Research on Urban Carrying Capacity Based on Multisource Data Fusion—A Case Study of Shanghai
Abstract
:1. Introduction
2. Overview of the Study Area and Data Processing
2.1. Study Area
2.2. Data Processing
- (1)
- Point of interest (POI): this article collected all points of interest related to the infrastructure of Shanghai in 2018. After removal of useless information and outliers, relevant POIs were divided into eight categories: basic public facilities; scientific, educational and cultural services; medical and health care services; sports and leisure services; financial and insurance services; government agencies and social organizations; transportation facilities services; and scenic spots. The criteria of selecting interest points are whether they are related to social basic services. For example, some interest points record the activity information of the event, which are not related to this research. The standard of outlier removal is whether its information is complete and whether its category can be determined. The points of interest that cannot be judged are classified as outliers. These data contained 1,157,914 pieces of information. Due to the excessive number of observations, they were presented on a 300 m × 300 m grid in the form of point density, as shown in Figure 2a.
- (2)
- Remote sensing data of nighttime light: nighttime light is closely related to human activities [21]. These data can be used for the estimation of socioeconomic indicators and analysis of human activities [22,23]. Based on the principle of nonsaturation and the high resolution of the radiance in the city centre, this paper selected the nighttime light data produced by NOAA’s National Climatic Data Center for research. The products selected in this paper cover daily, monthly and annual synthetic data. They correspond to different application scenarios. In order to ensure the consistency of time, this paper selects the synthetic data of 2018. After processing steps such as projection and resampling of the original data, GeoPandas [24] was finally used for visualization, as shown in Figure 2b.
- (3)
- Data on PM2.5 concentrations: PM2.5 concentration data with a global average resolution of 0.01° * 0.01° were retrieved by the Atmospheric Composition Analysis Group of Dalhousie University. The data were estimated by combining the inversion values of aerosol optical depth from MODIS, MISR and SeaWiFS sensors with the simulated values of the GEOS-Chem chemical transport mode. The accuracy verification results showed that the estimated values were highly consistent with the ground monitoring values (with an R-squared of 0.81) [25]. Through inverse distance weight interpolation and resampling, the spatial distribution of the PM2.5 concentration data of Shanghai for 2018 was finally obtained (Figure 2c).
- (4)
- Surface heat island data: most studies focus on air temperature heat islands and surface heat islands [26,27]. Temperature measurement points are limited, and most of them are located far from the Shanghai urban area. While interpolation of temperature heat island data cannot meet the accuracy requirements of this paper, surface heat islands can be retrieved by means of remote sensing data. This kind of data not only covers a wide range but also ensures the time consistency of heat island intensity. Usually, surface heat island data can be obtained by using MODIS satellite data products or thermal infrared data received by the Landsat 8 satellite. As MODIS satellite products are usually used for long time series analysis across multiple periods and have low resolution, they are not used in this paper. Landsat 8 satellite images with fewer clouds were thus selected, and the atmospheric correction method was used for inversion [28,29]. The inversion process is shown in Figure 3, and the results are presented in Figure 2d.
3. Methodology
3.1. Weight Determination Method
- (1)
- Creating original matrix R.
- (2)
- Index standardization.
- (3)
- Calculating the entropy of the j-th target.
- (4)
- Computing information entropy redundancy.
- (5)
- Calculating the weight of each index.
3.2. Multisource Data Fusion Method
3.3. Space Exploration Methods
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Schroeder, P.; Anggraeni, K.; Weber, U. The Relevance of Circular Economy Practices to the Sustainable Development Goals. J. Ind. Ecol. 2019, 23, 77–95. [Google Scholar] [CrossRef] [Green Version]
- Rees, W.E. Ecological footprints and appropriated carrying capacity: What urban economics leaves out. Environ. Urban. 1992, 4, 121–130. [Google Scholar] [CrossRef]
- Wei, Y.; Huang, C.; Lam, P.T.; Yuan, Z. Sustainable urban development: A review on urban carrying capacity assessment. Habitat Int. 2015, 46, 64–71. [Google Scholar] [CrossRef]
- Central People’s Government of the People’s Republic of China. Bulletin of the State Council of the People’s Republic of China. Available online: http://www.gov.cn/gongbao/2020/issue_8266.htm (accessed on 17 April 2020).
- Taagepera, R. A world population growth model: Interaction with earth’s carrying capacity and technology in limited space. Technol. Forecast. Soc. Chang. 2014, 82, 34–41. [Google Scholar] [CrossRef] [Green Version]
- Bush, J.C.; Guhathakurta, S.; Keane, J.L.; Dworkin, J.M. Examination of the Phoenix regional water supply for sustainable yield and carrying capacity. Nat. Resour. J. 2006, 46, 925–958. [Google Scholar]
- Ait-Aoudia, M.N.; Berezowska-Azzag, E. Water resources carrying capacity assessment: The case of Algeria’s capital city. Habitat Int. 2016, 58, 51–58. [Google Scholar] [CrossRef]
- Alberti, M.; Marzluff, J.M.; Shulenberger, E.; Bradley, G.; Ryan, C.; Zumbrunnen, C. Integrating humans into ecology: Opportunities and challenges for studying ur-ban ecosystems. BioScience 2003, 53, 1169–1179. [Google Scholar] [CrossRef] [Green Version]
- Anfuso, G.; Williams, A.T.; Hernández, J.A.C.; Pranzini, E. Coastal scenic assessment and tourism management in western Cuba. Tour. Manag. 2014, 42, 307–320. [Google Scholar] [CrossRef]
- Martire, S.; Castellani, V.; Sala, S. Carrying capacity assessment of forest resources: Enhancing environmental sustainability in energy production at local scale. Resour. Conserv. Recycl. 2015, 94, 11–20. [Google Scholar] [CrossRef]
- Kessler, J. Usefulness of the human carrying capacity concept in assessing ecological sustainability of land-use in semi-arid regions. Agric. Ecosyst. Environ. 1994, 48, 273–284. [Google Scholar] [CrossRef]
- Chandra, A.; Thompson, E. Does public infrastructure affect economic activity?: Evidence from the rural interstate highway system. Reg. Sci. Urban Econ. 2000, 30, 457–490. [Google Scholar] [CrossRef]
- Papageorgiou, K.; Brotherton, I. A management planning framework based on ecological, perceptual and economic carrying capacity: The case study of Vikos-Aoos National Park, Greece. J. Environ. Manag. 1999, 56, 271–284. [Google Scholar] [CrossRef]
- Shi, Y.; Wang, H.; Yin, C. Evaluation method of urban land population carrying capacity based on GIS—A case of Shanghai, China. Comput. Environ. Urban Syst. 2013, 39, 27–38. [Google Scholar] [CrossRef]
- Pitkänen, K.; Antikainen, R.; Droste, N.; Loiseau, E.; Saikku, L.; Aissani, L.; Hansjürgens, B.; Kuikman, P.; Leskinen, P.; Thomsen, M. What can be learned from practical cases of green economy? –studies from five European countries. J. Clean. Prod. 2016, 139, 666–676. [Google Scholar] [CrossRef]
- Rezaei, R.; Yari, G. Keyfitz entropy: Investigating some mathematical properties and its application for estimating survival function in life table. Math. Sci. 2021, 1–12. [Google Scholar] [CrossRef]
- Fahim, A.; Tan, Q.; Naz, B.; Ain, Q.; Bazai, S. Sustainable Higher Education Reform Quality Assessment Using SWOT Analysis with Integration of AHP and Entropy Models: A Case Study of Morocco. Sustainability 2021, 13, 4312. [Google Scholar] [CrossRef]
- Önüt, S.; Soner, S. Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment. Waste Manag. 2008, 28, 1552–1559. [Google Scholar] [CrossRef]
- Bekesiene, S.; Vasiliauskas, A.V.; Hošková-Mayerová, Š.; Vasilienė-Vasiliauskienė, V. Comprehensive Assessment of Distance Learning Modules by Fuzzy AHP-TOPSIS Method. Mathematics 2021, 9, 409. [Google Scholar] [CrossRef]
- Elzainy, A.; El Sadik, A.; Al Abdulmonem, W. Experience of e-learning and online assessment during the COVID-19 pandemic at the College of Medicine, Qassim University. J. Taibah Univ. Med Sci. 2020, 15, 456–462. [Google Scholar] [CrossRef] [PubMed]
- Mann, M.L.; Melaas, E.K.; Malik, A. Using VIIRS day/night band to measure electricity supply reliability: Preliminary results from Maharashtra, India. Remote Sens. 2016, 8, 711. [Google Scholar] [CrossRef] [Green Version]
- Li, J.; Pan, J. Study on urban spatial expansion in Gansu Province from 1992 to 2012 based on night light. J. Glaciol. Geocryol. 2016, 38, 829–835. (In Chinese) [Google Scholar]
- Liu, H.; Du, G. Regional disparity and stochastic convergence test of China’s economic development: Based on DMSP/OLS night light data from 2000 to 2013. Quant. Tech. Econ. Res. 2017, 34, 43–59. (In Chinese) [Google Scholar]
- Jordahl, K.; Bossche, J.D.; Wasserman, J.; McBride, J.; Fleischmann, M.; Gerard, J. Geopandas/Geopandas: v0.8.1 (Version v0.8.1). Zenodo. Available online: http://doi.org/10.5281/zenodo.3946761 (accessed on 15 July 2020).
- Van Donkelaar, A.; Martin, R.V.; Li, C.; Burnett, R.T. Regional estimates of chemical composition of fine particulate matter using a combined geoscience-statistical method with information from satellites, models, and monitors. Environ. Sci. Technol. 2019, 53, 2595–2611. [Google Scholar] [CrossRef] [Green Version]
- Cristóbal, J.; Jiménez-Muñoz, J.C.; Prakash, A.; Mattar, C.; Skoković, D.; Sobrino, J.A. An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band. Remote Sens. 2018, 10, 431. [Google Scholar] [CrossRef] [Green Version]
- Galdies, C.; Lau, H.S. Urban Heat Island Effect, Extreme Temperatures and Climate Change: A Case Study of Hong Kong SAR; Springer: Berlin/Heidelberg, Germany, 2020; pp. 369–388. [Google Scholar]
- Le, T.; Nie, S.; Pan, H.; Li, L. Land surface temperature inversion based on Landsat8 satellite images and analysis of urban heat island effect in Fuzhou in spring. J. Northwest For. Univ. 2019, 34, 154–160. (In Chinese) [Google Scholar]
- Cai, J.; Bai, L.; Xu, D.; Li, Y.; Liu, W. Remote sensing inversion of land surface temperature in irrigated area verified by ground infrared detection system. Trans. Chin. Soc. Agric. Eng. 2017, 33, 116–122. (In Chinese) [Google Scholar]
- Zheng, H.; Wu, C.; Shen, X. Research review and framework construction of land mixed use. Econ. Geogr. 2018, 38, 157–164. (In Chinese) [Google Scholar]
- Shi, Y.; Yin, C.; Wang, H.; Tan, W. Research progress and prospect of urban comprehensive carrying capacity. Geogr. Res. 2013, 32, 133–145. (In Chinese) [Google Scholar]
- Shi, K.; Yu, B.; Huang, Y.; Hu, Y.; Yin, B.; Chen, Z.; Chen, L.; Wu, J. Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A comparison with DMSP-OLS data. Remote Sens. 2014, 6, 1705–1724. [Google Scholar] [CrossRef] [Green Version]
- Wang, Z.; Lam, N.S. Extending Getis–Ord statistics to account for local space–time autocorrelation in spatial panel data. Prof. Geogr. 2020, 72, 411–420. [Google Scholar] [CrossRef]
- Ord, J.K.; Getis, A. Local spatial autocorrelation statistics: Distributional issues and an application. Geogr. Anal. 1995, 27, 286–306. [Google Scholar] [CrossRef]
- Vo, Q.T.; So, J.M.; Bae, D.H. An integrated framework for extreme drought assessments using the natural drought index, copula and Gi*statistic. Water Resour. Manag. 2020, 34, 1353–1368. [Google Scholar] [CrossRef]
- Hinman, S.E.; Blackburn, J.K.; Curtis, A. Spatial and temporal structure of typhoid outbreaks in Washington, DC 1906–1909: Evaluating local clustering with the Gi* statistic. Int. J. Health Geogr. 2006, 5, 1–17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhou, C. Some understandings on the geographic grid system. In Proceedings of the National Cartography and GIS Academic Conference, Fuzhou, China, 2004. (In Chinese). [Google Scholar]
- Wang, D.; Chen, S.; Gao, Q.; Yan, L. Spatial difference analysis method of urban carrying capacity: A case study of Changzhou city. Acta Ecol. Sin. 2011, 31, 1419–1429. (In Chinese) [Google Scholar]
- Bai, Y.; Liao, S.; Sun, J. Evaluation method of accuracy loss of raster attributes and its scale effect analysis: A case study of 1:250000 land cover data in Sichuan Province. Acta Geogr. Sin. 2011, 66, 709–717. (In Chinese) [Google Scholar]
- Hu, Z.; Luo, H.; Tang, Z.; Li, S. Evaluation of natural suitability of human settlement environment based on grid scale in Yunnan Province. Reg. Res. Dev. 2009, 28, 91–94. (In Chinese) [Google Scholar]
Number | Name1 | Name2 | Mixed Function Classification | Classification of Carrying Space | ||
---|---|---|---|---|---|---|
Production | Living | Ecology | ||||
1 | cultivated land | paddy field | * | * | agricultural production and ecology space | |
dry land | * | * | ||||
2 | woodland | economic forests | * | * | ||
shrubland | * | |||||
sparse woodlot | * | |||||
other woodlands | * | |||||
3 | grassland | high coverage | * | agricultural ecology space | ||
medium coverage | * | |||||
low coverage | * | |||||
4 | waters | ditches | * | * | other production and ecology space | |
lakes | * | * | ||||
reservoirs and ponds | * | * | ||||
glaciers and snows | * | other ecology space | ||||
tidal flats | * | |||||
beach land | * | |||||
5 | urban and rural, industrial and mining, residential land | built up area above county level | * | * | urban production and living space | |
rural residential areas | * | rural living space | ||||
oil field, salt field, quarry, etc. | * | urban production space | ||||
airports, etc. | * | |||||
factories and mines | * | |||||
large industrial areas | * | |||||
other special land | * | |||||
6 | unused land | deserts | * | other ecology space | ||
gobi | * | |||||
saline alkali land | * | |||||
marshland | * | |||||
bare land | * | |||||
bare rocky land | * | |||||
others | * |
District | Population | 300 m × 300 m | 500 m × 500 m | 1000 m × 1000 m |
---|---|---|---|---|
Pudong | 555.02 | 601.71 | 624.64 | 649.88 |
Huangpu | 65.38 | 29.65 | 32.62 | 41.64 |
Xuhui | 108.44 | 49.015 | 52.32 | 60.66 |
Changning | 69.40 | 39.63 | 44.24 | 53.75 |
Jingan | 106.28 | 42.03 | 46. | 56.46 |
Putuo | 128.19 | 49.07 | 52.44 | 62.43 |
Hongkou | 79.70 | 24.87 | 27.80 | 36.51 |
Yangpu | 131.27 | 42.10 | 44.45 | 50.45 |
Minhang | 254.35 | 183.32 | 191.17 | 210.70 |
Baoshan | 204.23 | 131.77 | 141.49 | 155.30 |
Jiading | 158.89 | 173.36 | 184.72 | 195.84 |
Jinshan | 80.50 | 172.66 | 190.56 | 203.72 |
Songjiang | 176.22 | 201.76 | 207.20 | 220.37 |
Qingpu | 121.90 | 191.77 | 206.11 | 221.86 |
Fengxian | 115.20 | 218.31 | 226.51 | 238.44 |
Chongming | 68.81 | 370.98 | 404.39 | 432.48 |
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Cao, X.; Shi, Y.; Zhou, L. Research on Urban Carrying Capacity Based on Multisource Data Fusion—A Case Study of Shanghai. Remote Sens. 2021, 13, 2695. https://doi.org/10.3390/rs13142695
Cao X, Shi Y, Zhou L. Research on Urban Carrying Capacity Based on Multisource Data Fusion—A Case Study of Shanghai. Remote Sensing. 2021; 13(14):2695. https://doi.org/10.3390/rs13142695
Chicago/Turabian StyleCao, Xiangyang, Yishao Shi, and Liangliang Zhou. 2021. "Research on Urban Carrying Capacity Based on Multisource Data Fusion—A Case Study of Shanghai" Remote Sensing 13, no. 14: 2695. https://doi.org/10.3390/rs13142695