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Manipulation and Analysis

Manipulation and Analysis

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...

Design Example: Alignment of a Road Line Using GIS

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...

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Thematic Layering in GIS

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)...

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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Related Experiment Video

Updated: May 25, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

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Infiltration route analysis using thermal observation devices (TOD) and optimization techniques in a GIS environment.

Soonam Bang¹, Joon Heo, Soohee Han

¹Agency for Defense Development, P.O. Box 35 Yuseong, Daejeon, Korea. snbang@hanafos.com

Sensors (Basel, Switzerland)

|February 9, 2012

Summary

This study uses geospatial information system (GIS) technology and optimal-path algorithms to identify vulnerable infiltration routes by analyzing thermal observation device (TOD) detection probabilities. Dynamic simulations generated a thousand route sets, creating an infiltration vulnerability map for strategic planning.

Keywords:

A* algorithm GIS dynamic simulation infiltration-route analysis thermal observation device

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Last Updated: May 25, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

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Published on: May 1, 2018

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Published on: November 7, 2016

Area of Science:

Geospatial Information Systems (GIS)
Military Operations Research
Computational Geography

Background:

Infiltration-route analysis is crucial for military strategy.
Geospatial Information System (GIS) technology offers advanced analytical capabilities.
Optimal-path-searching algorithms are key to identifying efficient routes.

Purpose of the Study:

To develop a method for identifying susceptible infiltration routes using GIS.
To apply optimal-path-searching algorithms to minimize detection probability costs.
To simulate dynamic conditions for realistic route vulnerability assessment.

Main Methods:

Utilized GIS for spatial analysis and cost function calculation.
Incorporated thermal observation device (TOD) detection probability and viewshed analysis.
Employed the A* algorithm to determine optimal routes by minimizing summed costs.
Generated 1000 cost surfaces with random TODs and start points for dynamic simulation.

Main Results:

Successfully identified 1000 sets of vulnerable infiltration routes.
Developed an infiltration vulnerability map by accumulating simulation results.
Demonstrated the effectiveness of dynamic simulation in GIS for optimization problems.

Conclusions:

Dynamic GIS simulation provides a powerful solution for optimizing infiltration routing.
The methodology can inform both optimal infiltration route planning and surveillance network design.
The approach is adaptable for dynamic optimal routing in civil infrastructure applications.