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Related Concept Videos

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...
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Levels of Use of a GIS01:29

Levels of Use of a GIS

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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...
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Thematic Layering in GIS01:30

Thematic Layering in GIS

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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)...
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Introduction to GIS01:28

Introduction to GIS

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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...
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Manipulation and Analysis01:21

Manipulation and Analysis

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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...
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Quantifying Heat02:46

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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...
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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
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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

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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.