ABORTION AFFILIATION, URBAN SIZE, AND GEOGRAPHIC BIAS OF RESPONSES TO LOST LETTERS

1998 ◽  
Vol 83 (7) ◽  
pp. 1107
Author(s):  
F. STEPHEN BRIDGE
Keyword(s):  
1994 ◽  
Vol 27 (4) ◽  
pp. 391
Author(s):  
Harry A. Miskimin
Keyword(s):  

2018 ◽  
Vol 10 (8) ◽  
pp. 2733 ◽  
Author(s):  
Yang Li ◽  
Hua Shao ◽  
Nan Jiang ◽  
Ge Shi ◽  
Xin Cheng

The development of the Yangtze River Economic Belt (YREB) is an important national regional development strategy and a strategic engineering development system. In this study, the evolution of urban spatial patterns in the YREB from 1990 to 2010 was mapped using the nighttime stable light (NSL) data, multi-temporal urban land products, and multiple sources of geographic data by using the rank-size distribution and the Gini coefficient method. Through statistical results, we found that urban land takes on the feature of “high in the east and low in the west”. The study area included cities of different development stages and sizes. The nighttime light increased in most cities from 1992 to 2010, and the rate assumed an obvious growth tendency in the three urban agglomerations in the YREB. The results revealed that the urban size distribution of the YREB is relatively dispersed, the speed of urban development is unequal, and the trend of urban size structure shows a decentralized distribution pattern that has continuously strengthened from 1990 to 2010. Affected by factors such as geographical conditions, spatial distance, and development stage, the lower reaches of the Yangtze River have developed rapidly, the upper and middle reaches have developed large cities, and a contiguous development trend is not obvious. The evolution of urban agglomerations in the region presents a variety of spatial development characteristics. Jiangsu, Zhejiang, and Shanghai have entered a phase of urban continuation, forming a more mature interregional urban agglomeration, while the YREB inland urban agglomerations are in suburbanization and multi-centered urban areas. At this stage, the conditions for the formation of transregional urban agglomerations do not yet exist, and there are many uncertainties in the boundary and spatial structure of each urban agglomeration.


Cities ◽  
2020 ◽  
Vol 98 ◽  
pp. 102590 ◽  
Author(s):  
Rafał Łopucki ◽  
Daniel Klich ◽  
Ignacy Kitowski ◽  
Adam Kiersztyn

2020 ◽  
pp. 1-10
Author(s):  
Zhongqi Deng ◽  
Shunfeng Song ◽  
Hongru Tan

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Xue Luo ◽  
Yuqing Zhang ◽  
Dongqi Sun

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.


1992 ◽  
Vol 23 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Alan Hedge ◽  
Yousif H. Yousif
Keyword(s):  

1981 ◽  
Vol 9 (1) ◽  
pp. 85-89 ◽  
Author(s):  
Thomas M. Power
Keyword(s):  

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