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

52
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...
52
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
Manipulation and Analysis01:21

Manipulation and Analysis

26
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...
26
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

65
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
65
Plotting of Topographic Maps01:29

Plotting of Topographic Maps

47
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,...
47
Introduction to GIS01:28

Introduction to GIS

68
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...
68

You might also read

Related Articles

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

Sort by
Same author

MatchY in practice: Factors influencing pedigree-based Y-STR match probabilities.

Forensic science international. Genetics·2026
Same author

MatchY: A software implementation of pedigree-based calculation of Y-STR match probabilities.

Forensic science international. Genetics·2026
Same author

First-Line Pembrolizumab Monotherapy for Advanced Non-Small Cell Lung Cancer: A Multicenter Real-World Study from Vietnam.

Current oncology (Toronto, Ont.)·2026
Same author

Real-world trends in diagnosis, treatment, and survival of non-small cell lung cancer in Vietnam.

BMC cancer·2026
Same author

Novel Y-STRs with elevated mutation rates further improve male relative differentiation.

Forensic science international. Genetics·2026
Same author

Author Correction: Forensic genetics in the omics era.

Nature reviews. Genetics·2026

Related Experiment Video

Updated: Jul 8, 2025

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

6.6K

SMapper: visualizing spatial prevalence data of all types, including sparse and incomplete datasets.

Lynn Khellaf1, Arwin Ralf2, Khanh Toan Nguyen3

  • 1Cologne Center for Genomics, University of Cologne, 50931 Cologne, Germany.

Bioinformatics Advances
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

SMapper is a new tool for visualizing spatial prevalence data, even with incomplete information. It helps overcome limitations in existing tools for epidemiological, anthropological, and forensic applications.

More Related Videos

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

13.3K
Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

10.1K

Related Experiment Videos

Last Updated: Jul 8, 2025

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

6.6K
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

13.3K
Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

10.1K

Area of Science:

  • Genetics
  • Epidemiology
  • Anthropology
  • Forensic Science

Background:

  • Spatial prevalence data visualization is crucial for understanding population-level patterns.
  • Existing tools often struggle with incomplete geographic coverage and insufficient sample sizes.
  • Interpretational challenges arise from data limitations in spatial analyses.

Purpose of the Study:

  • Introduce SMapper, a novel web and software tool.
  • Enable visualization of diverse spatial prevalence data, including incomplete datasets.
  • Demonstrate SMapper's utility in addressing limitations of current visualization tools.

Main Methods:

  • Development of a web-based implementation of SMapper.
  • Creation of a stand-alone software version compatible with Singularity containers and native Linux Python installations.
  • Application of SMapper to human genotype and phenotype data.

Main Results:

  • SMapper effectively visualizes spatial prevalence data, accommodating incomplete coverage and sample sizes.
  • The tool mitigates interpretational issues caused by data limitations.
  • Successful application to human genetic and phenotypic data in various scientific contexts.

Conclusions:

  • SMapper offers a robust solution for visualizing spatial prevalence data.
  • The tool enhances the interpretation of data with inherent limitations.
  • SMapper is applicable across epidemiological, anthropological, and forensic research domains.