scholarly journals A Zipf’s Law-Based Method for Mapping Urban Areas Using NPP-VIIRS Nighttime Light Data

2018 ◽  
Vol 10 (1) ◽  
pp. 130 ◽  
Author(s):  
Wenjia Wu ◽  
Hongrui Zhao ◽  
Shulong Jiang
Keyword(s):  
Urban Areas ◽  
Zipf’S Law ◽  
Zipf's Law ◽  
Nighttime Light Data ◽  
Nighttime Light

scholarly journals Does China’s Urban Development Satisfy Zipf’s Law? A Multiscale Perspective from the NPP-VIIRS Nighttime Light Data

2020 ◽  
Vol 17 (4) ◽  
pp. 1460 ◽  
Author(s):  
Yizhen Wu ◽  
Mingyue Jiang ◽  
Zhijian Chang ◽  
Yuanqing Li ◽  
Kaifang Shi
Keyword(s):  
Urban Development ◽  
Infrared Imaging ◽  
Multiple Scales ◽  
Decision Makers ◽  
Zipf’S Law ◽  
Zipf's Law ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Zipf Law ◽  
The Ideal

Currently, whether the urban development in China satisfies Zipf’s law across different scales is still unclear. Thus, this study attempted to explore whether China’s urban development satisfies Zipf’s law across different scales from the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data. First, the NPP-VIIRS data were corrected. Then, based on the Zipf law model, the corrected NPP-VIIRS data were used to evaluate China’s urban development at multiple scales. The results showed that the corrected NPP-VIIRS data could effectively reflect the state of urban development in China. Additionally, the Zipf index (q) values, which could express the degree of urban development, decreased from 2012 to 2018 overall in all provinces, prefectures, and counties. Since the value of q was relatively close to 1 with an R2 value > 0.70, the development of the provinces and prefectures was close to the ideal Zipf’s law state. In all counties, q > 1 with an R2 value > 0.70, which showed that the primate county had a relatively stronger monopoly capacity. When the value of q < 1 with a continuous declination in the top 2000 counties, the top 250 prefectures, and the top 20 provinces in equilibrium, there was little difference in the scale of development at the multiscale level with an R2 > 0.90. The results enriched our understanding of urban development in terms of Zipf’s law and had valuable implications for relevant decision-makers and stakeholders.

scholarly journals Delineating the Urban Areas of a Cross-Boundary City with Open-Access Data: Guangzhou–Foshan, South China

2021 ◽  
Vol 13 (5) ◽  
pp. 2930
Author(s):  
Pengfei Ban ◽  
Wei Zhan ◽  
Qifeng Yuan ◽  
Xiaojian Li
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
City Size ◽  
Border City ◽  
Point Of Interest ◽  
Nighttime Light Data ◽  
Profile Line ◽  
Point Analysis ◽  
Nighttime Light ◽  
Single Data

Cities defined mainly from the administrative aspect can create impact and problems especially in the case of China. However, only a few researchers from China have attempted to identify urban areas from the morphology dimension. In addition, previous studies have been mostly based on the national and regional scales or a single prefecture city and have completely ignored cross-boundary cities. Defining urban areas on the basis of a single data type also has limitations. To address these problems, this study integrates point of interest and nighttime light data, applies the breaking point analysis method to determine the physical geographic scope of the Guangzhou–Foshan cross-border city, and then compares this city with Beijing and Shanghai. Results show that Guangzhou–Foshan comprises one core urban area and six suburban counties, among which the core urban area extends across the administrative boundaries of Guangzhou and Foshan. The urban area and average urban radius of Guangzhou–Foshan are larger than those of Beijing and Shanghai, and this finding contradicts the city size measurements based on the administrative division system of China and those published on traditional official statistical yearbooks. In terms of urban density value, Shanghai has the steepest profile followed by Guangzhou–Foshan and Beijing, and the profile line of Guangzhou–Foshan has a bimodal shape.

scholarly journals Rank-size Distribution of Cities and Municipalities in Bangladesh

2020 ◽  
Vol 12 (11) ◽  
pp. 4643
Author(s):  
Pankaj Bajracharya ◽  
Salima Sultana
Keyword(s):  
Size Distribution ◽  
Urban Areas ◽  
Urban Expansion ◽  
Population Data ◽  
Google Earth ◽  
Zipf’S Law ◽  
Zipf's Law ◽  
Spatial Inequalities ◽  
Planning Framework ◽  
Shed Light

This paper examines and updates the rank-size distribution of cities and municipalities in Bangladesh between 1990 and 2019 based on two criteria: (1) built-up urban areas; and (2) population. The distribution of built-up urban areas and population are compared to provide a robust theoretical underpinning of Zipf’s law for future urban developmental planning framework. The data on built-up urban areas is extracted from land cover classification using Google Earth Engine and the population data is obtained from the decennial censuses. The comparison of the conformity to Zipf’s law indicated contradictory results. While a greater proportion of the population has been increasingly concentrated in the smaller and midsized cities over the last three decades, built-up urban areas, on the other hand, have been mostly clustered in two largest cities— Dhaka and Chittagong—accounting for 50 to nearly 60 percent of the total built-up urban areas. These results shed light on the magnitude of continued spatial inequalities in urban development amongst cities and municipalities in Bangladesh despite there being an overall increase of evenness in the distribution of population over time. These results imply an unsustainable rate of urban expansion in Bangladesh and reinforce the need for the exploration of policies and regulations targeted at guiding the rate and direction of evenness in urban expansion.

scholarly journals Response Patterns of Vegetation Phenology along Urban-Rural Gradients in Urban Areas of Different Sizes

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Xue Luo ◽  
Yuqing Zhang ◽  
Dongqi Sun
Keyword(s):  
Urban Areas ◽  
Growing Season ◽  
Liaoning Province ◽  
Vegetation Phenology ◽  
Coastal Cities ◽  
Nighttime Light Data ◽  
Urban Rural ◽  
Nighttime Light ◽  
Urban Size ◽  
The Difference

On the basis of MODIS Enhanced Vegetation Index time series data and multisource data, such as nighttime light data and China City Statistical Yearbook data, we investigated the differences in vegetation phenology along urban-rural gradients in urban areas of different sizes between coastal and inland cities in Liaoning Province, China. The results showed that the following: (1) the iterative extraction of urban built-up areas using the threshold method based on nighttime light data combined with the definition of urban built-up areas had high accuracy. (2) Additionally, we found that the start of the growing season (SOS) in Liaoning Province occurred between day 100 and day 180, while the end of the growing season (EOS) occurred between days 260 and 330. The difference in the SOS between coastal cities (i.e., Dalian, Yingkou, Panjin, Jinzhou, Huludao, and Dandong) and inland cities (i.e., Chaoyang, Fuxin, Tieling, Shenyang, Fushun, Liaoyang, Benxi, and Anshan) was 1.70 days. However, the difference in the EOS was more significant, i.e., the EOS in coastal cities occurred 4.47 days later than that in the inland cities. (3) In urban areas of different sizes, the ∆SOS and ∆EOS of inland cities had negative correlations with urban size. Specifically, when the urban size increased 10-fold, the ∆SOS and ∆EOS advanced by 10.03 and 5.71 days, respectively. In contrast, the ∆SOS and ∆EOS of coastal cities had positive and negative correlations with the urban size, respectively. Specifically, when urban size increased 10-fold, ∆SOS was delayed by 11.29 days while EOS was advanced by 8.83 days.

Mapping Global Urban Areas From 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products

2019 ◽  
Vol 12 (4) ◽  
pp. 1143-1153 ◽  
Author(s):  
Zuoqi Chen ◽  
Bailang Yu ◽  
Yuyu Zhou ◽  
Hongxing Liu ◽  
Chengshu Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Urban Areas ◽  
Nighttime Light Data ◽  
Nighttime Light

scholarly journals Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information

2016 ◽  
Vol 26 (3) ◽  
pp. 325-338 ◽  
Author(s):  
Yang Cheng ◽  
Limin Zhao ◽  
Wei Wan ◽  
Lingling Li ◽  
Tao Yu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Nighttime Light Data ◽  
Nighttime Light

scholarly journals Research on spatial characteristics of metropolis development using nighttime light data: NTL based spatial characteristics of Beijing

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242663
Author(s):  
Yuli Yang ◽  
Mingguo Ma ◽  
Xiaobo Zhu ◽  
Wei Ge
Keyword(s):  
Urban Areas ◽  
Urban Land Use ◽  
Rapid Urbanization ◽  
Spatial Characteristics ◽  
Spatial And Temporal Scales ◽  
Island Effect ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Urban Heat ◽  
Research Period

As the capital and one of the metropolises in China, Beijing has met with a number of serious so-called "urban diseases" in the process of rapid urbanization such as blind expansion of urban areas, explosion of population and the increase of urban heat island effect. To treat these “urban diseases” and make the metropolis develop healthful and sustainable in Beijing in the future, the spatial characteristics of metropolis developments in Beijing are explored in this paper. The urban built-up areas in Beijing are extracted using the DMSP-OLS nighttime light data from 1992 to 2013. The characteristics of the urban developments of Beijing are studied, including spatial and temporal scales of urban developments, urban barycenter of Beijing and its transfer trajectory, variations of urban spatial forms and the differences of urban internal developments. The results have shown that the built-up areas had been increasing and circling extending from the central urban areas to the outer spaces in the last 21 years. The built-up area had expanded by 878km2 in 1992–2013, and the built-up area in 2013 had expanded to three times comparing to that of 1992. The expanding area of the built-up area in the northeast is the largest. The expansion of the urban had mainly occurred in 1996–2007, and the expanded area had accounted for 92% of the total research period. During the whole research period, the urban barycenter of Beijing had moved 5000.71 meters towards Northeast 28° of its original place from Dongcheng District to Chaoyang District. The development level of each municipal district had been increasing year by year, and the development differences among the municipal districts had been gradually reduced; the spatial forms of Beijing had been alternately changed between extensive and intensive expansion. The results of this study can help to plan urban land use and people migration of Beijing.

scholarly journals Demography and the emergence of universal patterns in urban systems

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Luís M. A. Bettencourt ◽  
Daniel Zünd
Keyword(s):  
Urban Areas ◽  
Small Towns ◽  
City Size ◽  
Zipf’S Law ◽  
Urban Systems ◽  
Size Distributions ◽  
Vital Rates ◽  
Zipf's Law ◽  
Large Cities ◽  
Population Sizes

Abstract Urban areas exist in a wide variety of population sizes, from small towns to huge megacities. No proposed form for the statistical distribution of city sizes has received more attention than Zipf’s law, a Pareto distribution with power law exponent equal to one. However, this distribution is typically violated by empirical evidence for small and large cities. Moreover, no theory presently exists to derive city size distributions from fundamental demographic choices while also explaining consistent variations. Here we develop a comprehensive framework based on demography to show how the structure of migration flows between cities, together with the differential magnitude of their vital rates, determine a variety of city size distributions. This approach provides a powerful mathematical methodology for deriving Zipf’s law as well as other size distributions under specific conditions, and to resolve puzzles associated with their deviations in terms of concepts of choice, symmetry, information, and selection.

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