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

Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

65
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
65
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

43
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...
43
Manipulation and Analysis01:21

Manipulation and Analysis

23
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...
23
Levels of Use of a GIS01:29

Levels of Use of a GIS

48
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...
48
Response Surface Methodology01:16

Response Surface Methodology

119
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
119
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

You might also read

Related Articles

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

Sort by
Same author

Monitoring open landfill fires using integrated UAV and Sentinel-2 satellite imagery.

Waste management (New York, N.Y.)·2026
Same author

Microplastics footprint in Indonesian edible salt: A comparative study across Western, Central, and Eastern regions.

The Science of the total environment·2026
Same author

Understanding the spatial pattern and determinants of Airbnb revenue through a spatial regression approach: Perspective from Indonesian cities.

PloS one·2025
Same author

Genome sequence of Ralstonia syzygii subsp. celebesensis, the causal agent of banana blood disease on Musa balbisiana cultivar Kepok in bali, Indonesia.

BMC research notes·2025
Same author

A computational simulation appraisal of banana lectin as a potential anti-SARS-CoV-2 candidate by targeting the receptor-binding domain.

Journal, genetic engineering & biotechnology·2023
Same author

Machine learning-based spatial data development for optimizing astronomical observatory sites in Indonesia.

PloS one·2023

Related Experiment Video

Updated: Jun 24, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K

Machine learning based urban sprawl assessment using integrated multi-hazard and environmental-economic impact.

Anjar Dimara Sakti1,2, Albertus Deliar3,4, Dyah Rezqy Hafidzah2

  • 1Geographic Information Sciences and Technology Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, 40132, Indonesia.

Scientific Reports
|June 11, 2024
PubMed
Summary

This study introduces a novel urban sprawl priority index using remote sensing and machine learning to manage hazardous development in Bandung, Indonesia. The index identifies high-risk areas needing urgent government intervention for sustainable urban planning.

More Related Videos

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.3K
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

Related Experiment Videos

Last Updated: Jun 24, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.3K
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

Area of Science:

  • Urban and Regional Planning
  • Environmental Science
  • Geographic Information Systems

Background:

  • Urbanization driven by land development demand causes environmental issues.
  • Uncontrolled urban sprawl creates an imbalance between resource supply and demand.
  • Effective management strategies are needed for hazardous urban sprawl.

Purpose of the Study:

  • To develop an integrated model for evaluating and prioritizing hazardous urban sprawl management.
  • To create a unique urban sprawl priority index using remote sensing and machine learning.
  • To identify high-priority areas for intervention in the Bandung metropolitan region.

Main Methods:

  • Application of long-term remote sensing data.
  • Utilizing machine learning techniques to formulate an urban sprawl priority index.
  • Integrating human economic activity, environmental degradation, and multi-disaster levels into the index.

Main Results:

  • The 1993-2008 period showed the highest increase in human economic activity (172,776 ha).
  • The 1985-1993 period exhibited the highest environmental degradation.
  • The 1993-2008 period had the highest concentration of multi-hazard locations.
  • Outskirts of urban areas in West Bandung Regency, Cimahi, Bandung Regency, and East Bandung Regency were identified as highest priority.

Conclusions:

  • The developed model and index provide a scientific basis for prioritizing hazardous urban sprawl management.
  • High-priority regions require immediate government attention to mitigate negative impacts.
  • The model supports sustainable urban development and natural resource preservation through efficient urban environment monitoring.