A New Approach for Detecting Urban Centers and Their Spatial Structure With Nighttime Light Remote Sensing

Chen, Zuoqi; Yu, Bo; Song, Wei; Liu, Hongxing; Wu, Qiusheng; Shi, Kaifang; Wu, Jianping

doi:10.1109/tgrs.2017.2725917

Cited by 153 publications

(96 citation statements)

References 66 publications

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“…Finally, internal urban structure changes must be investigated from DMSP/OLS data to better understand changing economic activities. The topographical metaphor for light intensity values can be used to identify urban centers and subcenters, and the surface slope of light intensity can be used to determine urban land use intensity gradients [60].…”

Section: Discussionmentioning

confidence: 99%

Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP/OLS Nighttime Light Imagery for 1993 to 2012

Zhang

Sun

et al. 2018

IJGI

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Abstract:Investigating the characteristics of urban expansion is helpful in managing the relationship between urbanization and the ecological and environmental issues related to sustainable development. The Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) collects visible and near-infrared light from the Earth's surface at night without moonlight. It generates effective time series data for mapping the dynamics of urban expansion. As a major urban agglomeration in the world, the Yangtze River Delta Urban Agglomeration (YRDUA) is an important intersection zone of both the "Belt and Road Initiative" and the "Yangtze River Economic Belt" in China. Therefore, this paper analyses urban expansion characteristics of the YRDUA for 1993-2012 from urban extents extracted from the DMSP/OLS for 1993, 1997, 2002, 2007, and 2012. First, calibration procedures are applied to DMSP/OLS data, including intercalibration, intra-annual composition, and inter-annual series correction procedures. Spatial extents are then extracted from the corrected DMSP/OLS data, and a threshold is determined via the spatial comparison method. Finally, three models are used to explore urban expansion characteristics of the YRDUA from expansion rates, expansion spatial patterns, and expansion evaluations. The results show that the urban expansion of the YRDUA occurred at an increasing rate from 1993-2007 and then declined after 2007 with the onset of the global financial crisis. The Suxichang and Ningbo metropolitan circles were seriously affected by the financial crisis, while the Hefei metropolitan circle was not. The urban expansion of the YRDUA moved from the northeast to the southwest over the 20-year period. Urban expansion involved internal infilling over the first 15 years and then evolved into external sprawl and suburbanization after 2007.

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Section: Discussionmentioning

confidence: 99%

Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP/OLS Nighttime Light Imagery for 1993 to 2012

Zhang

Sun

et al. 2018

IJGI

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“…Several variations on this approach have been proposed (Craig and Ng, 2001;Redfearn, 2007 (Lee, 2007). Though this approach has been most frequently used on employment data, it has been applied to other data sources as well, including nighttime light emissions (Chen et al, 2017).…”

Section: Methods Of Identifying Employment Centersmentioning

confidence: 99%

The Spatial Structure of US Metropolitan Employment: New Insights from LODES Data

Manduca¹

2018

Preprint

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Urban researchers have long debated the extent to which urban employment is monocentric, polycentric, or diffuse. In this paper I use high-resolution data based on unemployment insurance records to show that employment in US metropolitan areas is not centralized but is spatially concentrated. Unlike residents, who form a continuous surface covering most parts of each MSA, jobs have a bimodal spatial distribution, with most blocks containing no jobs whatsoever and a small number having extremely high employment densities. Across the 100 largest MSAs about 75% of jobs are located on the 10% of built land in Census blocks with at least twice as many jobs as people. Further, most of these jobs are in clustered business districts of more than 5 contiguous employment blocks. These relative proportions are extremely consistent across MSAs, even though cities vary greatly in the physical density at which they are constructed. Motivated by these empirical regularities, I introduce an algorithm to identify contiguous business districts and classify them into four major types. Based solely on the relative densities of employment and population, this algorithm is both simpler to implement and more flexible than current approaches, requiring no metro-specific tuning parameters and no assumptions about urban form. As one output, it provides an inductive, data-driven method of identifying city centers for the purposes of urban economic analysis.

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“…Moreover, specific POI categories are more closely related to nighttime leisure activities than other categories [65]. Therefore, the POI density and NTL intensity for a region can be conceptualized as a continuous surface of the intensity of human nighttime activity, in which the nighttime leisure space is analogous to a mountain on the Earth's surface topography [66]. Accordingly, by analogy, the detection of UNLSs can be regarded as identifying a mountain, namely, a set of contours, from a numerical composite data surface.…”

Section: Theoretical Basismentioning

confidence: 99%

Urban Nighttime Leisure Space Mapping with Nighttime Light Images and POI Data

et al. 2020

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Urban nighttime leisure spaces (UNLSs), important urban sites of nighttime economic activity, have created enormous economic and social benefits. Both the physical features (e.g., location, shape, and area) and the social functions (e.g., commercial streets, office buildings, and entertainment venues) of UNLSs are important in UNLS mapping. However, most studies rely solely on census data or nighttime light (NTL) images to map the physical features of UNLSs, which limits UNLS mapping, and few studies perform UNLS mapping from a social function perspective. Point-of-interest (POI) data, which can reflect social activity functions, are needed. As a result, a novel methodological UNLS mapping framework, that integrates NTL images and POI data is required. Consequently, we first extracted high-NTL intensity and high-POI density areas from composite data as areas with high nightlife activity levels. Then, the POI data were analyzed to identify the social functions of leisure spaces revealing that nighttime leisure activities are not abundant in Beijing overall, the total UNLS area in Beijing is 31.08 km2, which accounts for only 0.2% of the total area of Beijing. In addition, the nightlife activities in the central urban area are more abundant than those in the suburbs. The main urban area has the largest UNLS area. Compared with the nightlife landmarks in Beijing established by the government, our results provide more details on the spatial pattern of nighttime leisure activities throughout the city. Our study aims to provide new insights into how multisource data can be leveraged for UNLS mapping to enable researchers to broaden their study scope. This investigation can also help government departments better understand the local nightlife situation to rationally formulate planning and adjustment measures.

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A New Approach for Detecting Urban Centers and Their Spatial Structure With Nighttime Light Remote Sensing

Cited by 153 publications

References 66 publications

Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP/OLS Nighttime Light Imagery for 1993 to 2012

Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP/OLS Nighttime Light Imagery for 1993 to 2012

The Spatial Structure of US Metropolitan Employment: New Insights from LODES Data

Urban Nighttime Leisure Space Mapping with Nighttime Light Images and POI Data

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