Jove
Visualize
Contact Us

Related Concept Videos

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

245
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...
245
Levels of Use of a GIS01:29

Levels of Use of a GIS

319
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...
319
Thematic Layering in GIS01:30

Thematic Layering in GIS

300
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
300
Introduction to GIS01:28

Introduction to GIS

479
Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
479
Manipulation and Analysis01:21

Manipulation and Analysis

274
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...
274
Quantifying Heat02:46

Quantifying Heat

61.5K
Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a higher temperature. When the...
61.5K

You might also read

Related Articles

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

Sort by
Same author

An Evaluation of an Absorption Spectroscopy Based Non-invasive Technology (EzeCheck) Versus the Gold Standard Hematology Analyzer for Hemoglobin Estimation.

Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion·2026
Same author

Self-Healing Antibacterial Metallosupramolecular Soft-Networks: Comparative Studies on Mechanical Flexibility and Semiconducting Device Applications.

Inorganic chemistry·2026
Same author

Chronic stress, gut dysbiosis, and cholesterol metabolism: Implications for Alzheimer's disease.

Journal of neuroimmunology·2026
Same author

Osteogenic and angiogenic gelatin-coated Whitlockite nanoparticles incorporated chitosan composite scaffold for alveolar bone regeneration.

International journal of biological macromolecules·2025
Same author

Ecological Risk Assessment and Management of Forest Fires in Tamil Nadu, India: A MaxEnt Model-Based Approach for Strategic Resource Allocation and Fire Mitigation.

Risk analysis : an official publication of the Society for Risk Analysis·2025
Same author

Bridging the gaps in Universal Health Coverage using Digital Health Citizenship.

PLOS digital health·2025
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 Experiment Video

Updated: Jan 8, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.8K

Understanding the relationship between spatial urbanization and urban heat island using machine learning: a

Manob Das1, Arijit Das2, Ashis Mandal2

  • 1Department of Geography, Ramananda College, Bishnupur, West Bengal, India. dasmanob631@gmail.com.

Environmental Science and Pollution Research International
|December 12, 2025
PubMed
Summary

Urbanization significantly increased impervious surfaces and surface urban heat island (SUHI) intensity in Kolkata. Spatial urbanization positively correlates with SUHI, highlighting the need for sustainable urban planning and green infrastructure.

Keywords:
Impervious surfaceKolkata metropolitan areaLand surface temperatureUrban heat islandUrban management

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.5K

Related Experiment Videos

Last Updated: Jan 8, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.8K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.5K

Area of Science:

  • Environmental Science
  • Urban Planning
  • Remote Sensing

Background:

  • Urbanization drives the urban heat island (UHI) effect, a critical environmental issue.
  • Understanding the link between urban expansion and UHI is essential for city planning.

Purpose of the Study:

  • To analyze the relationship between urbanization and surface urban heat island (SUHI) in Kolkata Metropolitan Area (KMA) using machine learning.
  • To develop a spatial urbanization index (SUI) and assess its correlation with SUHI.

Main Methods:

  • Developed a spatial urbanization index (SUI) and employed spatial landscape metrics.
  • Utilized a spatial gradient approach to analyze land surface temperature (LST) along an urban-rural gradient (URG).
  • Applied correlation, regression, and Random Forest (RF) models to determine key urbanization and SUHI drivers.

Main Results:

  • Impervious surface (IS) increased by 128% from 2000-2020, with high/very high SUHI intensity areas growing by 287% and 207%, respectively.
  • Built-up area (AREA_AM) was the primary driver of spatial urbanization, while the largest patch index (LPI) most influenced SUHI.
  • Spatial urbanization showed a significant positive correlation with SUHI intensity.

Conclusions:

  • Urban expansion directly intensifies the SUHI effect, necessitating immediate sustainable urban planning.
  • Integrating green and blue infrastructure is crucial for mitigating UHI and improving urban thermal environments.