scholarly journals Spatio-temporal characteristics of cultivated land fragmentation in different landform areas with a case study in Northeast China

2020 ◽  
Vol 6 (1) ◽  
pp. 1800415
Author(s):  
Fengkui Qian ◽  
Yanru Chi ◽  
Rattan Lal ◽  
Klaus Lorenz
Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 120
Author(s):  
Haoran Zhai ◽  
Jiaqi Yao ◽  
Guanghui Wang ◽  
Xinming Tang

Based on measurement data from air quality monitoring stations, the spatio-temporal characteristics of the concentrations of particles with aerodynamic equivalent diameters smaller than 2.5 and 10 μm (PM2.5 and PM10, respectively) in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2018 were analysed at yearly, seasonal, monthly, daily and hourly scales. The results indicated that (1) from 2015 to 2018, the annual average values of PM2.5 and PM10 concentrations and the PM2.5/PM10 ratio in the study area decreased each year; (2) the particulate matter (PM) concentration in winter was significantly higher than that in summer, and the PM2.5/PM10 ratio was highest in winter and lowest in spring; (3) the PM2.5 and PM10 concentrations exhibited a pattern of double peaks and valleys throughout the day, reaching peak values at night and in the morning and valleys in the morning and afternoon; and (4) with the use of an improved sine function to simulate the change trend of the monthly mean PM concentration, the fitting R2 values for PM2.5 and PM10 in the whole study area were 0.74 and 0.58, respectively. Moreover, the high-value duration was shorter, the low-value duration was longer, and the concentration decrease rate was slower than the increase rate.


2019 ◽  
Vol 2 ◽  
pp. 1-3
Author(s):  
Nahye Cho ◽  
Youngok Kang

<p><strong>Abstract.</strong> In this study, we visualized and analyzed log data in order to analyze the spatiotemporal characteristics of “moving” and “staying activities”. As a case study, we collected and preprocessed GPS log data generated by students participating in field activities. STP (Space-Time Path) was used to visualize movement logs. “Movement” and staying places were distinguished through density-based clustering, and the time “stayed” and activities performed at staying places were examined. The problem of over-measuring time at some staying places was examined. To resolve this, the 3D Density-Based Spatial Clustering of Application with Noise (DBSCAN) was used to more accurately measure the time spent at staying places. We propose 3D DBSCAN as methodology to accurately measure spatiotemporal data. We believe this method will remain effective even as this data becomes more numerous.</p>


2014 ◽  
Vol 34 (21) ◽  
Author(s):  
解文娟 XIE Wenjuan ◽  
杨晓光 YANG Xiaoguang ◽  
杨婕 YANG Jie ◽  
刘利民 LIU Limin ◽  
叶清 YE Qing ◽  
...  

2018 ◽  
Vol 38 (24) ◽  
Author(s):  
习慧鹏 XI Huipeng ◽  
王世杰 WANG Shijie ◽  
白晓永 BAI Xiaoyong ◽  
唐红 TANG Hong ◽  
吴路华 WU Luhua ◽  
...  

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