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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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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...
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

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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...
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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...
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Related Experiment Video

Updated: Jul 5, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Data Sources and Models for Integrated Mobility and Transport Solutions.

Pierfrancesco Bellini1, Stefano Bilotta1, Enrico Collini1

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

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
Summary
This summary is machine-generated.

This paper reviews mobility data models and standards, highlighting their complexity and potential for integration. It explores using these models for city transport management and planning, leveraging the Snap4City platform.

Keywords:
big datadata modelsdata spacemobility and transport

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Area of Science:

  • Transportation Science
  • Data Management
  • Urban Planning

Background:

  • The proliferation of data sources and models in mobility and transport has led to complexity in integration and management.
  • Existing data models often overlap and can be used interchangeably for similar innovative solutions.
  • This complexity hinders the efficient exploitation of data for smart city applications.

Purpose of the Study:

  • To provide an overview of data models and standards in the mobility domain and their interrelationships.
  • To explore the potential exploitation of these data models for operational city transportation management.
  • To investigate the use of data models in feeding simulation and optimization processes for infrastructure planning.

Main Methods:

  • Literature review of existing data models and standards in the transportation sector.
  • Analysis of data model overlaps and potential for alternative exploitation.
  • Case study application within the Snap4City platform context for operational processes.

Main Results:

  • Identified significant complexity and overlap among various mobility data models.
  • Demonstrated the feasibility of using data models for operational city transport management.
  • Showcased the integration of data models for simulation, optimization, and planning processes.

Conclusions:

  • Standardization and integration of mobility data models are crucial for efficient smart city solutions.
  • The Snap4City platform facilitates the exploitation of diverse data models for enhanced transport management.
  • Effective data model utilization supports both tactical control and strategic planning of urban transport infrastructure.