Analyzing Spatio-Temporal Trends of Deforestation due to Industrial Complex Development in Republic of Korea: A Case Study of Gyeong-buk and Gyeong-nam Provinces

2016 ◽  
Vol 19 (3) ◽  
pp. 103-111
Author(s):  
Oh Seok Kim ◽  
Jeong Eun ◽  
Young-Joon Lee
Keyword(s):  
Temporal Trends ◽  
Republic Of Korea ◽  
Industrial Complex ◽  
Complex Development ◽  
Spatio Temporal

Spatio-temporal approach for identification of critical conservation areas: a case study with two pine species from a threatened temperate forest in Mexico

2019 ◽  
Vol 28 (7) ◽  
pp. 1863-1883 ◽  
Author(s):  
Agustín Molina Sánchez ◽  
Patricia Delgado ◽  
Antonio González-Rodríguez ◽  
Clementina González ◽  
A. Francisco Gómez-Tagle Rojas ◽  
...  
Keyword(s):  
Temperate Forest ◽  
Conservation Areas ◽  
Spatio Temporal ◽  
Pine Species

scholarly journals A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data

2021 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Adina Iftimi ◽  
Juan Francisco Correcher ◽  
Jose De Andrés ◽  
Manuel Lozano ◽  
...  
Keyword(s):  
Model Fitting ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals Spatio-temporal analysis of COVID-19 incidence rate using GIS: a case study—Tehran metropolitan, Iran

GeoJournal ◽  
2021 ◽  
Author(s):  
R. Nasiri ◽  
S. Akbarpour ◽  
AR. Zali ◽  
N. Khodakarami ◽  
MH. Boochani ◽  
...  
Keyword(s):  
Incidence Rate ◽  
Temporal Analysis ◽  
Spatio Temporal

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

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