Abstract
The spatial structure of urban agglomeration reveals the self-organization process of internal factors from the outside. As China ushers into a critical stage with the boosting of urbanization and the booming of the economy, measuring the spatial structure of urban agglomerations is vital to urban planning, regional development, etc. Nighttime stable light data from the National Polar–Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) night-time light (NTL) series dataset offers a novel source that potentially simulates spatial variations in socio-economic activities. The present study aimed to scientifically guide wide applications of the NPP-VIIRS data to define and measure the evolution of urban agglomeration from a spatial–temporal perspective. Yangtze River Middle Reaches (YRMR) Urban Agglomeration (UA) in Central China was taken as the case study region to systematically identify and measure its spatial configuration of urban growth. The light index model and the method of the rank clock were performed to quantify the dynamics of rank-order distributions. In the meantime, spatial statistics and location coefficient index were used to identify the nature and heterogeneity of spatial structure. According to our findings, NPP-VIIRS data can offer insights into the applications to analyze urbanization processes and reveal the dynamics of urban expansion. The proposed framework will help understand the nature and unbalance of spatial configurations of urban economic growth within urban agglomeration and lay a theoretical basis for the policy-making of regional spatial planning.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Batty, M. (2006). Rank clocks. Nature, 444, 592. https://doi.org/10.1038/nature05302.
Batty, M. (2008). The size, scale, and shape of cities. Science, 319(5864), 769–771. https://doi.org/10.1126/science.1151419.
Baugh, K., Hsu, F.-C., Elvidge, C. D., & Zhizhin, M. (2013). Nighttime lights compositing using the VIIRS day-night band: preliminary results. Proceedings of the Asia-Pacific Advanced Network, 35, 70–86. https://doi.org/10.7125/APAN.35.8.
Cao, Z. (2016). Estimating the spatial distribution of GDP based on nighttime light image and analysis of correlation between it and PM_(2.5) concentration. (Doctor of [Science]), University of Chinese Academy of Sciences, Guangzhou.
Chen, Z., Yu, B., Wei, S., Liu, H., Wu, Q., Shi, K., & Wu, J. (2017). A new approach for detecting urban centers and their spatial structure with nighttime light remote sensing. IEEE Transactions on Geoscience & Remote Sensing, PP(99), 1–15. https://doi.org/10.1109/TGRS.2017.2725917
Dengsheng, L. U., Tian, H., Zhou, G., & Hongli, G. E. (2015). Regional mapping of human settlements in southeastern China with multisensor remotely sensed data. Remote Sensing of Environment, 112(9), 3668–3679. https://doi.org/10.1016/j.rse.2008.05.009.
Elvidge, C. D., Baugh, K. E., Zhizhin, M., & Hsu, F.-C. (2013). Why VIIRS data are superior to DMSP for mapping nighttime lights. Paper presented at the Proceedings of the Asia-Pacific Advanced Network.
Fang, C., Song, J., Zhang, Q., & Li, M. (2005). The formation, development and spatial heterogeneity patterns for the structures system of urban agglomerations in China. Acta Geographica Sinica, 60(5), 827–840. (in Chinese).
Fang, C., & Yu, D. (2017). Urban agglomeration: An evolving concept of an emerging phenomenon. Landscape and Urban Planning, 162, 126–136. https://doi.org/10.1016/j.landurbplan.2017.02.014.
Florence, P. S. (1948). Investment, location and size of plant. Cambridge: Cambridge University Press.
Garcia-López, M. -À., & Muñiz, I. (2010). Employment decentralisation: Polycentricity or scatteration? The case of Barcelona. Urban Studies,, 47(14), 3035–3056. https://doi.org/10.1177/0042098009360229.
Ghosh, S., & Das, A. (2017). Exploring the lateral expansion dynamics of four metropolitan cities of India using DMSP/OLS night time image. Spatial Information Research, 25(6), 779–789. https://doi.org/10.1007/s41324-017-0141-3.
Gong, J., Hu, Z., Chen, W., Liu, Y., & Wang, J. (2018). Urban expansion dynamics and modes in metropolitan Guangzhou, China. Land Use Policy, 72, 100–109. https://doi.org/10.1016/j.landusepol.2017.12.025.
He, J., Li, C., Yu, Y., Liu, Y., & Huang, J. (2017). Measuring urban spatial interaction in Wuhan Urban Agglomeration, Central China: A spatially explicit approach. Sustainable Cities and Society, 32, 569–583. https://doi.org/10.1016/j.scs.2017.04.014.
Hsu, W. (2012). Central place theory and city size distribution. The Economic Journal, 122(563), 903–932. https://doi.org/10.1111/j.1468-0297.2012.02518.x.
Kang, C., Liu, Y., Guo, D., & Qin, K. (2015). A generalized radiation model for human mobility: Spatial scale, searching direction and trip constraint. PLoS ONE, 10(11), e0143500. https://doi.org/10.1371/journal.pone.0143500.
Krehl, A., Siedentop, S., Taubenböck, H., & Wurm, M. (2016). A comprehensive view on urban spatial structure: Urban density patterns of German city regions. ISPRS International Journal of Geo-Information, 5(6), 76. https://doi.org/10.3390/ijgi5060076.
Lan, F., Da, H., Wen, H., & Wang, Y. (2019). Spatial structure evolution of urban agglomerations and its driving factors in mainland China: From the monocentric to the polycentric dimension. Sustainability, 11(3), 610. https://doi.org/10.3390/su11030610.
Letu, H., Hara, M., Tana, G., & Nishio, F. (2012). A saturated light correction method for DMSP/OLS nighttime satellite lmagery. IEEE Transactions on Geoscience & Remote Sensing, 50(2), 389–396. https://doi.org/10.1109/tgrs.2011.2178031.
Li, W., Sun, B., & Zhang, T. (2018). Spatial structure and labour productivity: Evidence from prefectures in China. Urban Studies. https://doi.org/10.1177/0042098018770077.
Li, X., Li, D., Xu, H., & Wu, C. (2017). Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria’s major human settlement during Syrian Civil War. International Journal of Remote Sensing, 38(21), 5934–5951. https://doi.org/10.1080/01431161.2017.1331476.
Li, X., Xu, H., Chen, X., & Li, C. (2013). Potential of NPP-VIIRS nighttime light lmagery for modeling the regional economy of China. Remote Sensing, 5(6), 3057–3081. https://doi.org/10.3390/rs5063057.
Li, Y., & Wu, F. (2013). The emergence of centrally initiated regional plan in China: A case study of Yangtze River Delta Regional Plan. Habitat International, 39, 137–147. https://doi.org/10.1016/j.habitatint.2012.11.002.
Liang, X., Zhao, J., Dong, L., & Xu, K. (2013). Unraveling the origin of exponential law in intra-urban human mobility. Scientific Reports, 3(1), 2983–2983. https://doi.org/10.1038/srep02983.
Liu, Z., He, C., Zhang, Q., Huang, Q., & Yang, Y. (2012). Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008. Landscape and Urban Planning, 106(1), 62–72. https://doi.org/10.1016/j.landurbplan.2012.02.013.
Ma, M., Lang, Q., Yang, H., Shi, K., & Ge, W. (2020). Identification of polycentric cities in China based on NPP-VIIRS nighttime light data. Remote Sensing, 12(19), 3248.
Ma, T., Zhou, C., Pei, T., Haynie, S., & Fan, J. (2014). Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities. Remote Sensing Letters, 5(2), 165–174. https://doi.org/10.1080/2150704X.2014.890758.
Mosammam, H. M., Nia, J. T., Khani, H., Teymouri, A., & Kazemi, M. (2017). Monitoring land use change and measuring urban sprawl based on its spatial forms: The case of Qom city. The Egyptian Journal of Remote Sensing and Space Science, 20(1), 103–116. https://doi.org/10.1016/j.ejrs.2016.08.002.
National Development and Reform Comission. (2015). The development plan for the urban agglomeration of the Yangtze River Middle Reaches (2015–2030). Retrieved from http://www.ndrc.gov.cn/zcfb/zcfbtz/201504/t20150416_688229.html
NOAA National Calibration Center. (2018). Visible Infrared Imaging Radiometer Suite (VIIRS). Retrieved from https://ncc.nesdis.noaa.gov/VIIRS/
Sahana, M., Hong, H., & Sajjad, H. (2018). Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India. Science of The Total Environment, 628–629, 1557–1566. https://doi.org/10.1016/j.scitotenv.2018.02.170.
Shi, K., Chang, H., Yu, B., Bing, Y., Huang, Y., & Wu, J. (2014). Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas. Remote Sensing Letters, 5(4), 358–366. https://doi.org/10.1080/2150704x.2014.905728.
Shi, K., Yun, C., Yu, B., Xu, T., Chen, Z., Rui, L., & Wu, J. (2016). Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis. Applied Energy, 168, 523–533. https://doi.org/10.1016/j.apenergy.2015.11.055.
The General Office of Hubei Provincial People’s Government. (2015). City clusters along middle reaches of the Yangtze River. Retrieved from http://en.hubei.gov.cn/news/newslist/201504/t20150422_644229.shtml
Tian, G., Jiang, J., Yang, Z., & Zhang, Y. (2011). The urban growth, size distribution and spatio-temporal dynamic pattern of the Yangtze River Delta megalopolitan region. China. Ecological Modelling,, 222(3), 865–878. https://doi.org/10.1016/j.ecolmodel.2010.09.036.
Vasanen, A. (2012). Functional polycentricity: Examining metropolitan spatial structure through the connectivity of urban sub-centres. Urban Studies, 49(16), 3627–3644. https://doi.org/10.1177/0042098012447000.
Wang, Y., Dong, L., Liu, Y., Huang, Z., & Liu, Y. (2019). Migration patterns in China extracted from mobile positioning data. Habitat International, 86, 71–80. https://doi.org/10.1016/j.habitatint.2019.03.002.
Wei, C., Taubenböck, H., & Blaschke, T. (2017). Measuring urban agglomeration using a city-scale dasymetric population map: A study in the Pearl River Delta, China. Habitat International, 59, 32–43. https://doi.org/10.1016/j.habitatint.2016.11.007.
Wei, Y., Liu, H., Song, W., Yu, B., & Xiu, C. (2014). Normalization of time series DMSP-OLS nighttime light images for urban growth analysis with Pseudo Invariant Features. Landscape & Urban Planning, 128(128), 1–13. https://doi.org/10.1016/j.landurbplan.2014.04.015.
Wu, B., Yu, B., Yao, S., Wu, Q., Chen, Z., & Wu, J. (2019). A surface network based method for studying urban hierarchies by night time light remote sensing data. International Journal of Geographical Information Science, 33(7), 1377–1398. https://doi.org/10.1080/13658816.2019.1585540.
Wu, K., & Wang, X. (2019). Aligning pixel values of DMSP and VIIRS nighttime light images to evaluate urban dynamics. Remote Sensing, 11(12), 1463. https://doi.org/10.3390/rs11121463.
Yang, J., Song, G., & Lin, J. (2015). Measuring spatial structure of China’s megaregions. Journal of Urban Planning and Development, 141(2), 04014021. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000207.
Yao, Y., & Li, Y. (2011). Spatial correlation of nocturnal light with social-economic factors at different scales of urban areas — Case study of Beijing. Paper presented at the 2011 19th International Conference on Geoinformatics, Shanghai, China.
Yücer, E., & Erener, A. (2018). Examining urbanization dynamics in Turkey using DMSP–OLS and socio-economic data. Journal of the Indian Society of Remote Sensing, 46(3), 1159–1169. https://doi.org/10.1007/s12524-018-0785-z.
Zhang, Q., & Seto, K. C. (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sensing of Environment, 115(9), 2320–2329. https://doi.org/10.1016/j.rse.2011.04.032.
Zhang, W., Derudder, B., Wang, J., & Shen, W. (2018). Regionalization in the Yangtze River Delta, China, from the perspective of inter-city daily mobility. Regional Studies, 52(4), 528–541. https://doi.org/10.1080/00343404.2017.1334878.
Zheng, W., Kuang, A., Wang, X., & Chen, J. (2020). Measuring network configuration of the Yangtze River Middle Reaches Urban Agglomeration: Based on modified radiation model. Chinese Geographical Science, 30(4), 677–694. https://doi.org/10.1007/s11769-020-1131-2.
Zheng, W., Run, J., Zhuo, R., Jiang, Y., & Wang, X. (2016). Evolution process of urban spatial pattern in Hubei Province based on DMSP/OLS nighttime light data. Chinese Geographical Science, 26(3), 366–376. https://doi.org/10.1007/s11769-016-0814-1.
Zheng, Z., & Bohong, Z. (2012). Study on spatial structure of Yangtze River Delta Urban Agglomeration and its effects on urban and rural regions. Journal of Urban Planning and Development, 138(1), 78–89. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000095.
Zhong, C., Arisona, S. M., Huang, X., Batty, M., & Schmitt, G. (2014). Detecting the dynamics of urban structure through spatial network analysis. International Journal of Geographical Information Science, 28(11), 2178–2199. https://doi.org/10.1080/13658816.2014.914521.
Zhou, N., Hubacek, K., & Roberts, M. (2015). Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data. Applied Geography, 63, 292–303. https://doi.org/10.1016/j.apgeog.2015.06.016.
Funding
This study was funded by National Social Science Foundation of China (grant number 17BJL052).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Informed consent
The research has not involved human participants or animals.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zheng, W., Kuang, A., Liu, Z. et al. Analysing the spatial structure of urban growth across the Yangtze River Middle reaches urban agglomeration in China using NPP-VIIRS night-time lights data. GeoJournal 87, 2753–2770 (2022). https://doi.org/10.1007/s10708-021-10381-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10708-021-10381-x