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

Levels of Use of a GIS01:29

Levels of Use of a GIS

139
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...
139
Statgraphics01:10

Statgraphics

220
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
220
Manipulation and Analysis01:21

Manipulation and Analysis

111
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...
111
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

154
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...
154
Plotting of Topographic Maps01:29

Plotting of Topographic Maps

188
Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
188
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

217
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...
217

You might also read

Related Articles

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

Sort by
Same author

BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation.

IEEE transactions on visualization and computer graphics·2025
Same author

Correction: Urban violence as a predictor factor of obesity: longitudinal evidence from Sao Paulo, Brazil.

BMC public health·2025
Same author

TiVy: Time Series Visual Summary for Scalable Visualization.

IEEE transactions on visualization and computer graphics·2025
Same author

Urban violence as a predictor factor of obesity: longitudinal evidence from Sao Paulo, Brazil.

BMC public health·2025
Same author

CounterCrime - Using Counterfactual Explanations to Explore Crime Reduction Scenarios.

IEEE transactions on visualization and computer graphics·2025
Same author

Visagreement: Visualizing and Exploring Explanations (Dis)Agreement.

IEEE transactions on visualization and computer graphics·2025
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: Oct 20, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.8K

CriPAV: Street-Level Crime Patterns Analysis and Visualization.

Germain Garcia-Zanabria, Marcos M Raimundo, Jorge Poco

    IEEE Transactions on Visualization and Computer Graphics
    |September 13, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for analyzing crime patterns in large cities, addressing data sparsity and computational challenges. The developed tool, CriPAV, visualizes probable crime hotspots and identifies similar crime behaviors across distant locations.

    More Related Videos

    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
    05:12

    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

    Published on: January 16, 2019

    11.6K
    Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands
    07:26

    Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

    Published on: January 31, 2025

    560

    Related Experiment Videos

    Last Updated: Oct 20, 2025

    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    13.8K
    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
    05:12

    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

    Published on: January 16, 2019

    11.6K
    Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands
    07:26

    Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

    Published on: January 31, 2025

    560

    Area of Science:

    • Spatio-temporal data analysis
    • Urban crime pattern recognition
    • Geospatial intelligence

    Background:

    • Analyzing urban crime patterns presents significant spatiotemporal challenges due to data sparsity and large spatial extents.
    • Existing methods struggle with sparse crime data and high computational costs for large urban areas.
    • Visualizing diverse crime time series patterns is difficult.

    Purpose of the Study:

    • To develop a novel methodology for analyzing spatiotemporal crime patterns at a street-level detail.
    • To address the challenges of spatial sparsity, large urban areas, and pattern visualization in crime data analysis.
    • To create an integrated tool for crime pattern analysis and visualization.

    Main Methods:

    • A two-component approach: 1) A stochastic mechanism for analyzing probable crime hotspots, revealing patterns missed by intensity-based methods. 2) A deep learning mechanism to embed crime time series, enabling identification of locations with similar crime behaviors.
    • Integration of these components into a web-based analytical tool named CriPAV (Crime Pattern Analysis and Visualization).
    • Validation using real crime data from São Paulo, Brazil, in collaboration with domain experts.

    Main Results:

    • The methodology effectively handles spatial sparsity and large urban areas for crime pattern analysis.
    • CriPAV enables visualization of probable, non-intense crime hotspots, offering new insights.
    • The deep learning embedding successfully identifies spatially distant locations with similar crime time series behaviors.
    • Case studies demonstrate CriPAV's effectiveness in uncovering subtle crime patterns.

    Conclusions:

    • The proposed methodology and CriPAV tool offer an effective solution for detailed spatiotemporal crime pattern analysis in urban environments.
    • CriPAV provides valuable insights for crime prevention and urban planning by identifying both probable hotspots and geographically dispersed similar crime patterns.
    • The approach enhances the understanding of complex urban crime dynamics.