Spatial Structure Analysis And Characteristics Of Urban Spatial Clusters Based On Remote Sensing Data Of Night Lights

An urban spatial cluster(USC)describes one or more geographic agglomerations and the linkages among cities based on transportation and information networks.As the basic characteristic of USC'structures,the spatial structure of USCs can reflect USCs'development degree,stage and process.The development of USCs is accompanied by the spatial structure evolution of USCs.The theory of USC spatial structure is the basic theory for studying urban areas.Analyzing the spatial structure of USCs provides further insight for the understanding of high-density and high-efficiency growth of USCs,and assists policymakers in their planning practices at national and regional scales.The spatial structure of USCs can be summarized into two parts:physical spatial structure and socio-economic spatial structure.The spatial extent of USCs is the basis for studying the physical spatial structure of USCs,and the evolution identification of USCs can enable decision makers to formulate corresponding policies based on the actual extent of USCs.USCs are conventionally delineated based on predefined administrative boundaries of cities.The spatial extents of these administrative units are changing over time.The change in the spatial extent of the administrative unit often lead to biases and obscurity when examining the spatiotemporal evolution of urban areas,as well as when comparing urban development in different countries and regions.Meanwhile,the fact that the spatial extent of USCs is dynamic and evolving over time is often overlooked.Nighttime light(NTL)images have been proven useful in identifying the spatial structures of USCs.However,related studies are largely influenced by the selection of optimal threshold values and are unsuitability for long-term identification.Within USCs,each city has the functions of gathering and radiating other cities.Quantitative exploration of the spatial interactions between cities can improve the functions of USCs.The urban network measured by spatial data or socio-economic data reflects the relationship between cities from a single aspect with uncertainty.The model method is usually an undirected measure suffering the accuracy verification and cannot provide the directional measurement of the relationship between cities.A reasonable urban functional structure within USCs means a higher economic efficiency and productivity,and is an important component for measuring the development of USCs.Traditional methods mostly rely on the population data from different sectors,resulting in the recognition results relying too much on statistical data.Considering the statistical data are often delayed or exist missing data,it is impossible to accurately predict the functional structure of different cities.In addition,related studies are mostly concentrated in a certain city or a certain USC,which lack the advantage of efficiently estimating the functional structure on a large scale.Considering the above issues,we uses multi-source night light remote sensing data to identify the spatial structures of USCs,including the spatial evolution of USCs,urban spatial interaction of cities within USCs,and urban functional structure of cities within USCs.The main research results are as follows.This study uses Defense Meteorological Satellite Program/Operational Linescan System(DMSP-OLS)nighttime light satellite images to quantitatively detect and characterize the evolution of USCs.We propose a dynamic minimum spanning tree(DMST)and a subgraph partitioning method to identify the evolving USCs over time,which considers both the spatial proximity of urban built-up areas and their affiliations with USCs at the previous snapshot.Four DMSTs are generated for China using the urban built-up areas extracted from DMSP-OLS NTL satellite images collected in 2000,2004,2008,and2012.Each DMST is partitioned into various subtrees and the urban built-up areas connected by the same subtree are identified as a potential USC.By inspecting the evolution of USCs over time,we can obtain the spatial extent of USCs.In total,twenty,thirty-three,thirty-one,and thirty-one USCs were detected in 2000,2004,2008,and 2012,respectively.The USCs can be classified into three types:the newly emerging cluster,the single-core cluster,and the multicore cluster.The USCs in the eastern coastal or southern regions appear to develop faster than those in the western region.The spatial and temporal patterns of USCs are analyzed using the rank-size distribution and the standard deviational ellipse(SDE)methods.Overall,the development of large-sized USCs is more prominent than the small and medium-sized USCs.The small and medium-sized USCs are developed faster than the large-sized USCs from 2000 to 2004.The large-sized USCs are developed faster than the small and medium-sized USCs from 2004 to 2012.A clear directionality and heterogeneity are observed in the expansions of the ten largest USCs.Our study provides further insight for the understanding of urban system and its spatial structures,and assists policymakers in their planning practices at national and regional scales.This study improves the Radiation Model for population flow and builds a directional measurement method based on nighttime light data.The methodological framework consists of four main components,including the spatial extent of USCs,the gravity of cities within USCs,the directional network-based city interaction nighttime light index(CNLI),and the quantitative relationship between cities.We have proven that the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite(NPP-VIIRS)nighttime light data can be used to analyze the directional relationship of cities within USCs.Firstly,the DMST and the subgraph partitioning method is used to identify the spatial extent of USCs in 2018.Secondly,based on the city quality measured by the total nighttime light intensity and the spatial distance between the gravity of cities,the directional CNLI between cities is calculated.Finally,the total input and output-CNLI of cities within USCs are further measured to explore the radiation and agglomeration capacity of cities.The Baidu's population migration index data are adopted to validate the CNLI result.The R~2 ranges from 0.47 to 0.86 in the top ten USCs.The Yangtze River Delta(YRD),the Pearl River Delta(PRD),and the Eastern Fujian(EFJ)are three typical USCs with the large-scale and well-balanced development.The Liaodong Peninsula(LDP)and the Guanzhong Plain(GZP)are also relatively balanced although with the small-scale.The cities in the following five USCs,including Beijing-Tianjin-Hebei(BTH),Changsha USC(CHS),Central Plain(CPL),Chengdu USC(CHD),and Wuhan USC(WUH),have the uneven input CNLI and output CNLI.The social and economic flows within the five USCs are more concentrated in cities that have a higher ouput CNLI than input CNLI.This study provides an effective way of measuring the intensity of radiation and agglomeration capacity of cities within USCs,which can be easy to apply in other regions.Based on the points-of-interest(POIs)and nighttime light data,we propose Gaussian process regression models t for measuring the number of employees in different sectors of cities within USCs.We have confirmed that the POIs and nighttime light data can be used to identify the urban functional structures within USCs.POIs and nighttime light data are used in Gaussian process regression(GPR)models to fit the population of cities from different sectors.Several variables extracted from POI and NTL data can explaine the population of cities from different sectors,with a good R~2 from 0.63 to 0.80,respectively.The urban function of cities from different sectors can be classified into four intensities,including lacking this function,general function,dominant function and superior function.The accuracy of the functional intensity from GPR models ranges from 55.09%to 78.24%.And the accuracy for top ten USCs ranges from 53.28%to 81.02%.With the advantage of the easy accessible and real-time of POIs and NTL data,our methods can be easily applied to other cities or regions which lacking of socio-economic data.Our results have the important practical significance for rationally guiding the healthy development of USCs.Exploring the evolution of USC from a dynamic perspective assists policymakers in their planning practices at national and regional scales.The directional relationships of cities within USCs also provide a new perspective for studying the agglomeration and radiation patterns of the inner cities.The measurement of urban functions of USCs broadens the understanding of urban function structures.This paper confirms the importance of nighttime light remote sensing data in analyzing the spatial structure of USCs.Future research can fully consider the application of this data.