Modeling Polycentric Urbanization Using Multisource Big Geospatial Data

Xie, Zhiwei; Ye, Xinyue; Zheng, Zihao; Liu, Dong; Sun, Li; Li, Ruren; Benya, Samuel

doi:10.3390/rs11030310

Cited by 24 publications

(18 citation statements)

References 38 publications

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“…Through the comparison, it was found that the two main centers in the plan were consistent with the experimental results in this paper: Yan'an Road and the area near West Lake on the north side of 3 Xiasha City Center…”

Section: Comparison Of Main Centers and Analysissupporting

confidence: 86%

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Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

Lou

Chen

et al. 2019

Remote Sensing

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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.2 of 20 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: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 correlatio...

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Section: Comparison Of Main Centers and Analysissupporting

confidence: 86%

“…(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.…”

Section: Study Areamentioning

confidence: 99%

Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

Lou

Chen

et al. 2019

Remote Sensing

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“…At present, the data-centric [7,8] "smart city" is still in its infancy, and its development is challenged by none ective process of huge amount of data and restricted extraction of data values [9]. New technologies such as big data [6,10,11] processing, data mining, arti cial intelligence, and deep learning are important tools to build smart cities [6,12]. However, the analysis of geospatial data in many developed cities ignores some important social and economic management elements.…”

Section: Introductionmentioning

confidence: 99%

In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning

2020

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The coordinated development of smart cities has become the goal of world urban development, and the railway network plays an important role in this progress. This paper proposes a solution that integrates data acquisition, storage, GIS visualization, deep learning, and statistical correlation analysis to deeply analyze the distribution data of companies collected in the past 40 years in the Yangtze River Delta. Through deep learning, we predict the spatial distribution of the company after the opening of the train stations. Through statistical and correlation analysis of the company’s registered capital and quantity, the urban development relationship under the influence of the opening of the railway is explored. Going forward, the use and application of such analysis can be tested for use and application in the context of other smart cities for specific aspects or scale.

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“…Various Internet map services such as Google Map, AMap, OpenStreetMap [50,67], and TianDiTu (IMap) can be directly connected to the BiGeo platform as reference data for a basemap service. Downloadable entity data, such as OSM data from OpenStreetMap [68][69][70], can also be opened, browsed, and queried directly in BiGeo. BiGeo can also publish managed data as a map service that complies with the OGC interface standard [29,71], and can mix and match these Internet map services.…”

Section: Toolkitmentioning

confidence: 99%

BiGeo: A Foundational PaaS Framework for Efficient Storage, Visualization, Management, Analysis, Service, and Migration of Geospatial Big Data—A Case Study of Sichuan Province, China

Liu

Hao

Yang

2019

IJGI

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With the rapid development of big data, numerous industries have turned their focus from information research and construction to big data technologies. Earth science and geographic information systems industries are highly information-intensive, and thus there is an urgent need to study and integrate big data technologies to improve their level of information. However, there is a large gap between existing big data and traditional geographic information technologies. Owing to certain characteristics, it is difficult to quickly and easily apply big data to geographic information technologies. Through the research, development, and application practices achieved in recent years, we have gradually developed a common geospatial big data solution. Based on the formation of a set of geospatial big data frameworks, a complete geospatial big data platform system called BiGeo was developed. Through the management and analysis of massive amounts of spatial data from Sichuan Province, China, the basic framework of this platform can be better utilized to meet our needs. This paper summarizes the design, implementation, and experimental experience of BiGeo, which provides a new type of solution to the research and construction of geospatial big data.

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Modeling Polycentric Urbanization Using Multisource Big Geospatial Data

Cited by 24 publications

References 38 publications

Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning

BiGeo: A Foundational PaaS Framework for Efficient Storage, Visualization, Management, Analysis, Service, and Migration of Geospatial Big Data—A Case Study of Sichuan Province, China

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