Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Light Acquisition02:16

Light Acquisition

8.6K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.6K
Manipulation and Analysis01:21

Manipulation and Analysis

59
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...
59
Levels of Use of a GIS01:29

Levels of Use of a GIS

101
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...
101
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

101
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
101
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

69
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...
69
Environmental Applications of Microorganisms01:30

Environmental Applications of Microorganisms

242
Microorganisms play a pivotal role in maintaining ecosystem balance by recycling essential elements such as carbon, nitrogen, and phosphorus, as well as supporting processes like bioremediation, wastewater treatment, and biofuel production.Microbes in Elemental CyclesIn the carbon cycle, microorganisms decompose organic matter, releasing carbon dioxide via aerobic respiration. This carbon dioxide is subsequently used by photosynthetic organisms to synthesize organic compounds, closing the...
242

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Real-time single-particle imaging of functional lungs reveals mosaic-like patterns of aerosol deposition in alveoli.

Nature biomedical engineering·2026
Same author

Conserved post-odor dynamics in the olfactory systems of mice and locusts.

iScience·2026
Same author

Engineering an l-Threonine Aldolase from <i>Staphylococcus epidermidis</i> for Enhanced Diastereoselectivity in the Synthesis of a Chloramphenicol Intermediate.

Journal of agricultural and food chemistry·2026
Same author

Mosaic pattern: lung functional heterogeneity at the alveolus level.

bioRxiv : the preprint server for biology·2025
Same author

Impact of Physiological Characteristics on Chylomicron Pathway-Mediated Absorption of Nanocrystals in the Pediatric Population.

ACS nano·2024
Same author

A new framework for assessment of park management in smart cities: a study based on social media data and deep learning.

Scientific reports·2024

Related Experiment Video

Updated: Sep 11, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K

AI big model and text mining-driven framework for urban greening policy analysis.

Li Li1,2, Xuesong Yang3, Sijia Liu4

  • 1School of Urban Design, Wuhan University, Wuhan, 430072, China.

Scientific Reports
|August 12, 2025
PubMed
Summary

This study introduces an AI-powered framework for analyzing urban greening policies, overcoming traditional biases. It enables systematic, real-time evaluation and tracking of policy evolution for smarter city planning.

Keywords:
AI big modelGreeningMethodological frameworkPolicy analysisText mining

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

652

Related Experiment Videos

Last Updated: Sep 11, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

652

Area of Science:

  • Environmental Policy
  • Urban Planning
  • Artificial Intelligence

Background:

  • Traditional policy analysis suffers from individual biases and limited scope.
  • Text mining offers efficiency but is underutilized in urban greening policy analysis.
  • Existing studies lack systematic analysis and real-time policy tracking.

Purpose of the Study:

  • To develop a multidimensional dynamic policy analysis framework using AI and text mining.
  • To systematically evaluate urban greening policies, track their evolution, and interpret findings.
  • To enable real-time policy tracking and visualization for improved urban planning.

Main Methods:

  • Constructed a novel framework integrating AI big models and text mining.
  • Applied the framework to analyze 15 years of urban greening policies in Wuhan.
  • Analyzed policy topic evolution, annual topic distribution, and greening indicator changes.

Main Results:

  • Revealed significant variations in Wuhan's greening policies over 15 years.
  • Identified a policy shift from basic greening to ecological remediation.
  • Observed a focus change from flower planning to wetland protection.

Conclusions:

  • The AI-driven framework offers a new paradigm for intelligent policy evaluation.
  • This methodology enhances the efficiency and accuracy of policy formulation and implementation.
  • The approach is crucial for developing smart cities with adaptive and rational policies.