Using Nighttime Light Data and POI Big Data to Detect theUrban Centers of Hangzhou
1 Department of Regional and Urban Planning,College of Civil Engineering and Architecture,Zhejiang University, Hangzhou 310058, China
2 Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental
and Resource Sciences, Zhejiang University,Hangzhou 310058, China
* Correspondence: chen_qiuxiao@zju.edu.cn
Abstract: The worldwide development ofmulti-center structures in large cities is a prevailing development trend.In recent years,China’s large citiesdeveloped from a predominantly mono-centric to a multi-center urbanspace structure. However, the definition and identification city centers iscomplex. Both nighttime light data and point of interest (POI) data areimportant data sources for urban spatial structure research, but there are few integrated applications for these two kinds of data.In this study, visibleinfrared imaging radiometer suite (NPP-VIIRS) nighttime imagery and POI datawere combined to identify the city centers in Hangzhou, China. First, theoptimal parameters of multi-resolution segmentation were determined by experiments. The POI densitywas then calculated with the segmentation resultsas the statistical unit. High–highclustering units were then definedas the main centersby calculating theAnselin Local Moran’sI, and a geographically weighted regression model was used to identify the subcenters accordingto the square root of the POI density and the distances between the units andthe city center. Finally, acomparison experiment was conducted betweenthe proposed methodand the relative cut-off_threshold method, and the experiment results were compared with the evaluation report of the masterplan. The results showed that the optimal segmentation parameters combinationwas 0.1 shape and 0.5 compactness factors. Twomain city centers and ten subcenters were detected. Comparison with the evaluation report of the master plan indicated that the combination of nighttime light data and POI data could identifythe urban centers accurately. Combinedwith the characteristics of the two kinds of data, the spatial structure of the city could be characterized properly. This study provided a newperspective for the study of the spatial structure of polycentric cities.
Keywords: nighttime light image; NPP-VIIRS; POI; imagesegmentation; polycentric structure
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1. Introduction
InChina, with the acceleration of urbanization and the increasing scale ofcities, urban spatial structures have undergone significant changes in many citiesover recent decades.The primary change has been the transition from a single-center structure to a multi-center or polycentric structure. For the latter case,a city contains multiple urban centers, including one or more main urbancenter(s) and several sub-urban centers.Urban center is a large and denselypopulated urban area and may includeseveral independent administrative districts [1].It shows an urban development pattern with active clustering of the urbanpopulation and the economic elements. A polycentric structure is commonlyadopted in the master plans of many large cities in China [2]. However, the implementation of such master plansis a long-term process. There has been a lack of effective analysisand evaluation methods,
Remote Sens. 2019, 11, 1821; doi:10.3390/rs11151821 www.mdpi.com/journal/remotesensing
especiallyrelatively objective and rational methods, to evaluate whether theimplementation results are consistent with the planning intention.
Thestudy of a polycentric urban spatial structure always relies on statisticaldata, such as a population census or socio-economic indicators. However, to some extent, traditional methods relying onsocio-economic and statistical data may have the following problems:
• Low spatial resolution.
• Low temporal resolution.
• Access to spatial disaggregated data.
• Insufficient background and priorknowledge of the research area.
Comparedwith traditional methods, remote sensing data, especially nighttime lightremote sensing data, have been widely used in urban studies [3] because of their free availability, globalcoverage, and high temporal resolution [4,5]. Sensorson a satellite can capturethe brightness of cities,farms, industrial areas, fishing vessel lights, forest fires, and other humanactivity areas at night and form a nighttime light image [6,7]. Nighttimelight data can make up for the deficiency of statistical data for urban researchin some respects,and can be applied to studies relatedto human activities due to the strong correlation between human activities and thelightmaps of population, GDP, orpower consumption [8].
Atpresent, nighttime light data are widely used in research on urban expansion [9], urban morphology and structure [10,11],estimation of socioeconomic status [12–15], fisheries [16,17], and energy [18,19]. It has been found that populationdistributions and light intensities have significant correlations [20]. Other researchers have evaluated the abilityof composite nighttime light data to estimate poverty and revealed thatNPP-VIIRS data can be used to effectively evaluate poverty at the county level in China [21]. For the study of urban structures, nighttimelight images have been considered as a new potential source [10]. It was found that new data from the visibleinfrared imaging radiometersuite (VIIRS) enabled more detailed inner-city structure monitoring [22].
Althoughnighttime light imagery has relatively high spatial stability and objectivity,it cannot record the distribution forms of the social economyand the activitystatus of humans[23]. For example, at night,lights are emittednot only from urban centersbut also from roads, industrial areas, and port areas, which can make it impossibleto accurately estimate population concentrations.
With the rapiddevelopment of big data, in addition to the use of statistical data, remote sensing images, and other types of data,open access data type play an increasing role in related studies. In recentyears, the number of urban studies using large samples of network or navigationdata has increased, and some geospatial big data, such as LBS (location-based service)data [10], point of interest(POI) [24], and open network data [25] have been applied to the research of urban structure.
POI data,also known as point of interest data, as a new spatialdata source, have the advantages of spatial and attributeinformation such as high accuracy, widecoverage, fast updates, and large amounts of data. It represents point datafrom real geographical entities. At present, POI data have been widely used in urban studies.Most urban studiesperformed by scholarsbased on POI data focus on urban land use mapping [24,26], urbanboundary extraction [27,28], population spatialization, or distribution [29,30].Compared with traditional survey methods, using POI data to identify urban centerscan save time and improve accuracy. However,these data have rarely been used to research urban multi-center structures. Thepolycentric structure of Chongqing and its scope of influence on the city as awhole were identified based on POI data [31].The boundaries of Guangzhou City’s multi-type commercial center were identifiedusing POI data and were used to explore the city’s commercial spatialstructures and modes [32]. The primary and secondary urbancenters of the Beijingmetropolitan area were identified by applying the point pattern analysis methodand the clustering effect of employment centers for different industries and were discussed by comparing the clusteringdegree before and after the removal of employment centers [33].
Fewstudies have combined nighttime light data with POI data [34,35]. It hasbeen found that nighttime light data and POI data have a strongly coupledspatial relationship [35]; hence, in this paper, we attempted to combine nighttimelight data with POI big data to identify the city centers of Hangzhou, a citythat is at the forefront of China’s rapid urbanization. The main objectives ofthis study were (1) to developa methodological frameworkwith the methodsof multi-scale segmentation, Anselin Local Moran’s I, and geographically weighted regression to identify the main city center and subcenters using a combination of the two types of data. (2) To use different data sourcesand threshold methods forcomparative experiments and use population data to verify the accuracy quantitatively.
(3) To compare the performance of our method with theevaluation report of the master plan.