Using Nighttime Light Data and POI Big Data to Detect the Urban 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 of multi-center structures in large cities is a prevailing development trend. In recent years, China’s large cities developed from a predominantly mono-centric to a multi-center urban space structure. However, the definition and identification city centers is complex. Both nighttime light data and point of interest (POI) data are important data sources for urban spatial structure research, but there are few integrated applications for these two kinds of data. In this study, visible infrared imaging radiometer suite (NPP-VIIRS) nighttime imagery and POI data were combined to identify the city centers in Hangzhou, China. First, the optimal parameters of multi-resolution segmentation were determined by experiments. The POI density was then calculated with the segmentation results as the statistical unit. High–high clustering units were then defined as the main centers by calculating the Anselin Local Moran’s I, and a geographically weighted regression model was used to identify the subcenters according to the square root of the POI density and the distances between the units and the city center. Finally, a comparison experiment was conducted between the proposed method and the relative cut-off_threshold method, and the experiment results were compared with the evaluation report of the master plan. The results showed that the optimal segmentation parameters combination was 0.1 shape and 0.5 compactness factors. Two main 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 identify the urban centers accurately. Combined with the characteristics of the two kinds of data, the spatial structure of the city could be characterized properly. This study provided a new perspective for the study of the spatial structure of polycentric cities.
Keywords: nighttime light image; NPP-VIIRS; POI; image segmentation; polycentric structure
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1. Introduction
In China, with the acceleration of urbanization and the increasing scale of cities, urban spatial structures have undergone significant changes in many cities over 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 urban center(s) and several sub-urban centers. Urban center is a large and densely populated urban area and may include several independent administrative districts [1]. It shows an urban development pattern with active clustering of the urban population and the economic elements. A polycentric structure is commonly adopted in the master plans of many large cities in China [2]. However, the implementation of such master plans is a long-term process. There has been a lack of effective analysis and evaluation methods,
Remote Sens. 2019, 11, 1821; doi:10.3390/rs11151821 www.mdpi.com/journal/remotesensing
especially relatively objective and rational methods, to evaluate whether the implementation results are consistent with the planning intention.
The study of a polycentric urban spatial structure always relies on statistical data, such as a population census or socio-economic indicators. However, to some extent, traditional methods relying on socio-economic and statistical data may have the following problems:
• Low spatial resolution.
• Low temporal resolution.
• Access to spatial disaggregated data.
• Insufficient background and prior knowledge of the research area.
Compared with traditional methods, remote sensing data, especially nighttime light remote sensing data, have been widely used in urban studies [3] because of their free availability, global coverage, and high temporal resolution [4,5]. Sensors on a satellite can capture the brightness of cities, farms, industrial areas, fishing vessel lights, forest fires, and other human activity areas at night and form a nighttime light image [6,7]. Nighttime light data can make up for the deficiency of statistical data for urban research in some respects, and can be applied to studies related to human activities due to the strong correlation between human activities and the lightmaps of population, GDP, or power consumption [8].
At present, 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 population distributions and light intensities have significant correlations [20]. Other researchers have evaluated the ability of composite nighttime light data to estimate poverty and revealed that NPP-VIIRS data can be used to effectively evaluate poverty at the county level in China [21]. For the study of urban structures, nighttime light images have been considered as a new potential source [10]. It was found that new data from the visible infrared imaging radiometer suite (VIIRS) enabled more detailed inner-city structure monitoring [22].
Although nighttime light imagery has relatively high spatial stability and objectivity, it cannot record the distribution forms of the social economy and the activity status of humans [23]. For example, at night, lights are emitted not only from urban centers but also from roads, industrial areas, and port areas, which can make it impossible to accurately estimate population concentrations.
With the rapid development 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 recent years, the number of urban studies using large samples of network or navigation data 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 spatial data source, have the advantages of spatial and attribute information such as high accuracy, wide coverage, fast updates, and large amounts of data. It represents point data from real geographical entities. At present, POI data have been widely used in urban studies. Most urban studies performed by scholars based on POI data focus on urban land use mapping [24,26], urban boundary extraction [27,28], population spatialization, or distribution [29,30]. Compared with traditional survey methods, using POI data to identify urban centers can save time and improve accuracy. However, these data have rarely been used to research urban multi-center structures. The polycentric structure of Chongqing and its scope of influence on the city as a whole were identified based on POI data [31]. The boundaries of Guangzhou City’s multi-type commercial center were identified using POI data and were used to explore the city’s commercial spatial structures and modes [32]. The primary and secondary urban centers of the Beijing metropolitan area were identified by applying the point pattern analysis method and the clustering effect of employment centers for different industries and were discussed by comparing the clustering degree before and after the removal of employment centers [33].
Few studies have combined nighttime light data with POI data [34,35]. It has been found that nighttime light data and POI data have a strongly coupled spatial relationship [35]; hence, in this paper, we attempted to combine nighttime light data with POI big data to identify the city centers of Hangzhou, a city that is at the forefront of China’s rapid urbanization. The main objectives of this study were (1) to develop a methodological framework with the methods of 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 sources and threshold methods for comparative experiments and use population data to verify the accuracy quantitatively.
(3) To compare the performance of our method with the evaluation report of the master plan.