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相关概念视频

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
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...
27
Levels of Use of a GIS01:29

Levels of Use of a GIS

52
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
52
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

65
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
65
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
69
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

101
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
101

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相关实验视频

Updated: Jul 5, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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综合移动和运输解决方案的数据源和模型

Pierfrancesco Bellini1, Stefano Bilotta1, Enrico Collini1

  • 1DISIT Lab, University of Florence, 50139 Florence, Italy.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
概括

本文回顾了移动数据模型和标准,强调了它们的复杂性和整合潜力. 它探索使用这些模型进行城市交通管理和规划,利用Snap4City平台.

科学领域:

  • 运输科学 运输科学
  • 数据管理数据管理
  • 城市规划 城市规划

背景情况:

  • 在移动和运输领域数据源和模型的扩散导致了整合和管理的复杂性.
  • 现有的数据模型经常重叠,并且可以在类似的创新解决方案中互换使用.
  • 这种复杂性阻碍了智能城市应用程序对数据的有效利用.

研究的目的:

  • 提供移动领域的数据模型和标准及其相互关系的概述.
  • 探索这些数据模型对城市运输管理的潜在利用.
  • 研究数据模型在基础设施规划的料模拟和优化过程中的使用.

主要方法:

  • 对运输行业现有数据模型和标准的文献综述.
  • 对数据模型重叠的分析和替代利用的可能性.
  • 在Snap4City平台的运营过程中应用案例研究.

主要成果:

  • 确定了各种移动数据模型之间显著的复杂性和重叠.
  • 证明了使用数据模型用于运营城市交通管理的可行性.
  • 展示了用于模拟,优化和规划过程的数据模型的集成.

结论:

关键词:
大数据就是大数据.数据模型数据模型的数据模型.数据空间数据空间.移动性和交通运输.

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  • 移动数据模型的标准化和整合对于高效的智能城市解决方案至关重要.
  • Snap4City平台为增强运输管理提供了各种数据模型的利用.
  • 有效的数据模型利用支持城市交通基础设施的战术控制和战略规划.