Addendum to “Spatiotemporal evolution in a (2+1) -dimensional chemotaxis model” [Physica A 391 (2012) 107–112]

2012 ◽  
Vol 391 (15) ◽  
pp. 4061-4062 ◽  
Author(s):  
Santo Banerjee ◽  
Lamberto Rondoni
Keyword(s):  
Spatiotemporal Evolution ◽  
Chemotaxis Model

scholarly journals Spatiotemporal evolution in a (2+1)-dimensional chemotaxis model

2012 ◽  
Vol 391 (1-2) ◽  
pp. 107-112 ◽  
Author(s):  
Santo Banerjee ◽  
Amar P. Misra ◽  
L. Rondoni
Keyword(s):  
Spatiotemporal Evolution ◽  
Chemotaxis Model

Spatiotemporal evolution of the chlorophyll a trend in the North Atlantic Ocean

2018 ◽  
Vol 612 ◽  
pp. 1141-1148 ◽  
Author(s):  
Min Zhang ◽  
Yuanling Zhang ◽  
Qi Shu ◽  
Chang Zhao ◽  
Gang Wang ◽  
...  
Keyword(s):  
Atlantic Ocean ◽  
Chlorophyll A ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Spatiotemporal Evolution ◽  
The North ◽  
The North Atlantic

scholarly journals Spatiotemporal Effects and Driving Factors of Water Pollutants Discharge in Beijing–Tianjin–Hebei Region

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1174
Author(s):  
Qilong Ren ◽  
Hui Li
Keyword(s):  
Economic Development ◽  
Industrial Structure ◽  
Oxygen Demand ◽  
Ammonia Nitrogen ◽  
Pollutant Emissions ◽  
Spatial Correlations ◽  
Time Data ◽  
Spatiotemporal Evolution ◽  
Water Pollutants ◽  
Pollutant Discharge

The problem of water pollution is a social issue in China requiring immediate and urgent solutions. In the Beijing–Tianjin–Hebei region, the contradiction between preserving the ecological environment and facilitating sustainable economic development is particularly acute. This study analyzed the spatiotemporal evolution of water pollutants and their factors of influence using statistics on the discharge of two water pollutants, namely chemical oxygen demand (COD) and NH3-N (ammonia nitrogen), in 154 counties in both 2012 and 2016 as research units in the region. The study employed Exploratory Spatial-Time Data Analysis (ESTDA), Standard Deviational Ellipse (SDE), and the Geographically Weighted Regression (GWR) models, as well as ArcGIS and GeoDa software, obtaining the following conclusions: (1) From 2012 to 2016, pollutant discharge dropped significantly, with COD and NH3-N emissions decreasing 65.9% and 47.2%, respectively; the pollutant emissions possessed the spatial feature of gradual gradient descent from the central districts to the periphery. (2) The water pollutants discharge displayed significant and positive spatial correlations. The spatiotemporal cohesion of the spatiotemporal evolution of the pollutants was higher than their spatiotemporal fluidity, representing strong spatial locking. (3) The level of economic development, the level of urbanization, and the intensity of agricultural production input significantly and positively drove pollutant discharge; the environmental regulations had a significant effect on reducing the emission of pollutants. In particular, the effect for NH3-N emissions reduction was stronger; the driving effect of the industrial structure and the distance decay was not significant.

scholarly journals Spatiotemporal Evolution and Determinant Factors of the Intra-Regional Trade Community Structures of the Indian Ocean Region

2021 ◽  
Vol 10 (4) ◽  
pp. 214
Author(s):  
Lihua Yuan ◽  
Xiaoqiang Chen ◽  
Changqing Song ◽  
Danping Cao ◽  
Hong Yi
Keyword(s):  
Indian Ocean ◽  
Explanatory Power ◽  
Community Structures ◽  
Indian Ocean Region ◽  
Spatiotemporal Evolution ◽  
Regional Trade ◽  
Determinant Factors ◽  
Ocean Region ◽  
The Indian Ocean ◽  
The Impact

The Indian Ocean Region (IOR) has become one of the main economic forces globally, and countries within the IOR have attempted to promote their intra-regional trade. This study investigates the spatiotemporal evolution of the community structures of the intra-regional trade and the impact of determinant factors on the formation of trade community structures of the IOR from 1996 to 2017 using the methods of social network analysis. Trade communities are groups of countries with measurably denser intra-trade ties but with extra-trade ties that are measurably sparser among different communities. The results show that the extent of trade integration and the trade community structures of the IOR changed from strengthening between 1996 and 2014 to weakening between 2015 and 2017. The largest explanatory power of the formation of the IOR trade community structures was the IOR countries’ economic size, indicating that market remained the strongest driver. The second-largest explanatory power was geographical proximity, suggesting that countries within the IOR engaged in intra-regional trade still tended to select geographically proximate trading partners. The third- and the fourth-largest were common civilization and regional organizational memberships, respectively. This indicates that sharing a common civilization and constructing intra-regional institutional arrangements (especially open trade policies) helped the countries within the IOR strengthen their trade communities.

Sign in / Sign up

Export Citation Format

Share Document