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Related Concept Videos

Manipulation and Analysis01:21

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

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The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
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Updated: May 14, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Spatial effects in real networks: measures, null models, and applications.

Franco Ruzzenenti1, Francesco Picciolo, Riccardo Basosi

  • 1Center for the Study of Complex Systems, University of Siena, Via Roma 56, 53100 Siena, Italy. ruzzenenti@unisi.it

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 2, 2013
PubMed
Summary

This study introduces a new method to distinguish spatial effects from nonspatial factors in real-world networks. The approach successfully identifies subtle spatial influences, like those in the World Trade Web, even when nonspatial factors are dominant.

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

  • Network Science
  • Economic Geography
  • Complex Systems

Background:

  • Spatially embedded networks are influenced by both topological and spatial factors.
  • Disentangling these factors in real-world networks is challenging.
  • Existing methods struggle to isolate spatial effects from nonspatial constraints.

Purpose of the Study:

  • To develop a method for filtering out nonspatial constraints in spatially embedded networks.
  • To enable consistent comparison of spatial effects across different networks or time points.
  • To analyze the World Trade Web for underlying spatial influences.

Main Methods:

  • Introduction of global and local measures for spatial effects.
  • Utilizing null models to filter spurious nonspatial contributions.
  • Application to the World Trade Web, considering economic factors like GDP.

Main Results:

  • The proposed method effectively filters nonspatial constraints.
  • Weak but significant local and global spatial effects were detected in the World Trade Web.
  • Spatial information retrieval is successful even when nonspatial factors dominate.

Conclusions:

  • The developed measures provide a robust way to analyze spatial effects in embedded networks.
  • The findings offer insights into the interplay of space and nonspatial factors in global trade.
  • Results align with and extend economic literature on gravity models and globalization.