Simulation of the spatial diffusion process

1992 ◽  
Vol 18 (8) ◽  
pp. 965-974 ◽  
Author(s):  
Robert P. Sechrist
Keyword(s):  
Diffusion Process ◽  
Spatial Diffusion

Spatial Diffusion Model Analysis and Trend of Shizuishan City

2012 ◽  
Vol 174-177 ◽  
pp. 2370-2373
Author(s):  
Bin Feng ◽  
Xiao Jian Chen
Keyword(s):  
Diffusion Process ◽  
Diffusion Model ◽  
Urban Space ◽  
Urban Expansion ◽  
Spatial Diffusion ◽  
Spatial Expansion ◽  
Weighted Distance ◽  
Zero Resistance ◽  
Coordination Development ◽  
Development Tendency

The urban expansion direction and mode of Shizuishan City is addressed based on GIS and improved spatial diffusion model-replacing the spatial straight distance with cost weighted distance. This paper divides its spatial expansion into four cases: zero resistance, resistance, incomplete barrier and isolation. The diffusion model of urban space in terms of the new understanding of the resistance barrier in the spatial diffusion process is discussed. Then, this paper puts forward new trends of spatial development. The results reveal that Dawukou city area and Pingluo city, and Wuhai city and Huinong district form the coordination development tendency.

scholarly journals Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing

Public Health ◽  
2005 ◽  
Vol 119 (12) ◽  
pp. 1080-1087 ◽  
Author(s):  
B. Meng ◽  
J. Wang ◽  
J. Liu ◽  
J. Wu ◽  
E. Zhong
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Diffusion Process ◽  
Spatial Diffusion

scholarly journals EMBEDDING DIFFUSION IN VARIATIONAL BAYES: A TECHNIQUE FOR SEGMENTING IMAGES

2008 ◽  
Vol 22 (05) ◽  
pp. 811-827 ◽  
Author(s):  
GIUSEPPE BOCCIGNONE ◽  
PAOLO NAPOLETANO ◽  
MARIO FERRARO
Keyword(s):  
Free Energy ◽  
Image Segmentation ◽  
Diffusion Process ◽  
Energy Minimization ◽  
Bayesian Learning ◽  
Variational Bayes ◽  
Spatial Diffusion ◽  
Spatial Constraints ◽  
Variational Techniques ◽  
Learning Procedure

In this paper, we discuss how image segmentation can be handled by using Bayesian learning and inference. In particular variational techniques relying on free energy minimization will be introduced. It will be shown how to embed a spatial diffusion process on segmentation labels within the Variational Bayes learning procedure so as to enforce spatial constraints among labels.

An Experiment to Model Spatial Diffusion Process with Nearest Neighbor Analysis and Regression Estimation

2014 ◽  
Vol 5 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Jay Lee ◽  
Jinn-Guey Lay ◽  
Wei Chien Benny Chin ◽  
Yu-Lin Chi ◽  
Ya-Hui Hsueh
Keyword(s):  
Diffusion Process ◽  
Nearest Neighbor ◽  
Diffusion Processes ◽  
Critical Role ◽  
Verbal Description ◽  
Spatial Diffusion ◽  
Time Step ◽  
Different Types ◽  
Spatio Temporal ◽  
Over Time

Spatial diffusion processes can be seen in many geographic phenomena that spread or migrate across space and over time. Studies of these processes were mostly done with verbal description until Hägerstrand (1966) started to approach it with quantitative models. A variety of attempts were made to continue this effort, but only with various degrees of success. Recognizing the critical role that distances between geographic objects or events play in a spatial diffusion process, we experimented with a new approach that uses these distances to detect and distinguish different types of spatial diffusion processes. Our approach is a two-step process that first calculates nearest neighbor ratios in a point process at each time step and then applies regression curve estimation to observe how these ratios change over time. We first report the results from applying this method to three spatio-temporal data sets which show the feasibility of our approach. We then report results of randomly simulated spatial diffusion processes to see if our approach is effective for the purpose of distinguishing different types of spatial diffusion processes. With only extreme cases as exceptions, our experiment found that using estimated regression curves of nearest neighbor ratios over time is usable in classifying spatial diffusion processes to either contagious/expansion or hierarchical/relocation diffusion processes.

Spatial Diffusion Processes 2: Numerical Analysis

1979 ◽  
Vol 11 (3) ◽  
pp. 335-347 ◽  
Author(s):  
M J Webber ◽  
A E Joseph
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Numerical Analysis ◽  
Diffusion Process ◽  
Diffusion Processes ◽  
Model Solution ◽  
Spatial Diffusion ◽  
System Of Cities ◽  
Reasonable Description ◽  
Parameter Values

An earlier paper (Webber and Joseph, 1978) proposed a model of the process whereby messages diffuse between a system of cities and provided a means of approximating the solution to that model if cities can ‘self-infect’ themselves with the message. This paper continues the analysis of this model by investigating the case in which a city cannot send the message to itself. The analysis is numerical, and an alternative to Monte Carlo simulation is used. The results indicate that the diffusion process described by the model is highly predictable if information on the accessibility of cities is available. A second part of the paper shows that the approximation used in the earlier paper provides a reasonable description of the model solution for at least some parameter values.

Saturn's E, G and F rings: modulated by the plasma sheet

1984 ◽  
Vol 75 ◽  
pp. 597
Author(s):  
E. Grün ◽  
G.E. Morfill ◽  
T.V. Johnson ◽  
G.H. Schwehm
Keyword(s):  
Solar Wind ◽  
Direct Contact ◽  
Transport Processes ◽  
Time Variable ◽  
Dust Particles ◽  
Spatial Diffusion ◽  
Drag Forces ◽  
Orbit Perturbation ◽  
Time Variations ◽  
F Ring

ABSTRACTSaturn's broad E ring, the narrow G ring and the structured and apparently time variable F ring(s), contain many micron and sub-micron sized particles, which make up the “visible” component. These rings (or ring systems) are in direct contact with magnetospheric plasma. Fluctuations in the plasma density and/or mean energy, due to magnetospheric and solar wind processes, may induce stochastic charge variations on the dust particles, which in turn lead to an orbit perturbation and spatial diffusion. It is suggested that the extent of the E ring and the braided, kinky structure of certain portions of the F rings as well as possible time variations are a result of plasma induced electromagnetic perturbations and drag forces. The G ring, in this scenario, requires some form of shepherding and should be akin to the F ring in structure. Sputtering of micron-sized dust particles in the E ring by magnetospheric ions yields lifetimes of 102to 104years. This effect as well as the plasma induced transport processes require an active source for the E ring, probably Enceladus.

Burgers fluid flow in perspective of Buongiorno’s model with improved heat and mass flux theory for stretching cylinder

2020 ◽  
Vol 92 (3) ◽  
pp. 31101
Author(s):  
Zahoor Iqbal ◽  
Masood Khan ◽  
Awais Ahmed
Keyword(s):  
Diffusion Process ◽  
Mathematical Formulation ◽  
Thermal Relaxation ◽  
Mass Diffusion ◽  
Stretching Cylinder ◽  
Discussion Section ◽  
Buongiorno’S Model ◽  
Homotopy Analysis ◽  
Burgers Fluid ◽  
Diffusion Phenomena

In this study, an effort is made to model the thermal conduction and mass diffusion phenomena in perspective of Buongiorno’s model and Cattaneo-Christov theory for 2D flow of magnetized Burgers nanofluid due to stretching cylinder. Moreover, the impacts of Joule heating and heat source are also included to investigate the heat flow mechanism. Additionally, mass diffusion process in flow of nanofluid is examined by employing the influence of chemical reaction. Mathematical modelling of momentum, heat and mass diffusion equations is carried out in mathematical formulation section of the manuscript. Homotopy analysis method (HAM) in Wolfram Mathematica is utilized to analyze the effects of physical dimensionless constants on flow, temperature and solutal distributions of Burgers nanofluid. Graphical results are depicted and physically justified in results and discussion section. At the end of the manuscript the section of closing remarks is also included to highlight the main findings of this study. It is revealed that an escalation in thermal relaxation time constant leads to ascend the temperature curves of nanofluid. Additionally, depreciation is assessed in mass diffusion process due to escalating amount of thermophoretic force constant.

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