Urban Indicators of China from Radiance-Calibrated Digital DMSP-OLS Nighttime Images

Abstract

in 30-arc-second grids in byte format is evaluated for its usefulness in extracting urban indicators data of China. With the aid of image-processing and Environmental Systems Research Institute's ArcView GIS software with 3-D Analysis extension, zonal variations of radiance were extracted and three-dimensional models created using Triangulated Irregular Network (TIN) functionality for thirty-five cities. Two variables were extracted from the TIN model: surface area and volume. These were used separately as independent variables in the form of an allometric growth model to estimate the following urban indicators for individual Chinese cities: nonagricultural population, gross domestic product (GDP), built-up area, and electricity consumption. The estimates obtained were checked against data supplied by the Statistical Bureau of China for 1997, the same period during which the DMSP-OLS images were acquired. It was found that urban indicators of acceptable accuracy could be obtained, provided that cities designated as Special Economic Zones (SEZs) were excluded. Volume appeared to be slightly better than surface area in estimating these urban indicators. In all cases, total nonagricultural population for all the cities combined could be very accurately estimated, indicating the usefulness of the radiance-calibrated DMSP-OLS nighttime images to determine the level of urbanization in China. The three-dimensional TIN model portrays an illuminated urban area (IUA) dome, the shape of which is affected by the internal structure of the city. The core of the city invariably exhibits the highest level of light intensity in the DMSP-OLS nighttime image. However, only a very limited amount of land-use information could be extracted. The image was also found to be useful in detecting the special urban phenomenon in China known as the extended metropolitan region, in which rural and urban uses intermixed and small towns developed. In conclusion, despite the low spatial resolution, the radiance-calibrated DMSP-OLS nighttime images are capable of providing useful demographic and socioeconomic data for cities.

Superlinear and sublinear urban scaling in geographical networks modeling cities

“…From Eqs. (5), (6), and (8), P (k) and R(l) depend on m and α only through d c . Therefore, the parameters m and α influence both distribution functions in a similar way.…”

“…Such similarities are found not only in the correspondence between constituent elements of cities and living organisms, but also in allometric scaling [1]. Similar to living organisms with the basal metabolic rate of an animal proportional to the 3/4 power of its body mass M [2,3] or the breathing rate (or heart rate) proportional to M −1/4 [4], there are various quantities related to activities or performances of cities such as urban road systems [5], night illuminations [6], and size distribution of buildings [7] that are described by power-law relations [8]. Power-law scaling has also been found in the morphology and evolution of cities and argued from a viewpoint of fractal cities [9][10][11][12][13].…”

Estimation of Mexico’s Informal Economy and Remittances Using Nighttime Imagery

“…Therefore, the DMSP-OLS can serve as a proxy measure of population and correlates of population such as economic activity and energy consumption [20]. Nighttime imagery has been used for myriad applications including estimation of urban populations [21][22][23][24], estimation of intra-urban population density [25,26], energy utilization or electric power consumption [21,22,24,27], delineating urban land cover [24,28], measuring anthropogenic impervious surface area [29], estimating GDP at the national and sub-national level [15,24,27,30,31], mapping marketed and non-marketed economic activity [32], estimation and mapping of CO 2 emissions [30], mapping 'exurban' areas [33], mapping nocturnal squid fishing [34], and mapping fire and fire-prone areas [35].…”

Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review

“…It records spatial brightness variation within stable light areas and helps minimize the blooming effect that lights "diffuse" into neighboring cells. The additional spatial information on light intensity helps increase the accuracy of human activity estimations [32], and allows researchers to explore more research topics, especially in studies of urban areas that are mainly concerned with saturation [33].…”