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

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

320
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...
320
Manipulation and Analysis01:21

Manipulation and Analysis

325
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...
325
Introduction to GIS01:28

Introduction to GIS

718
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...
718
Levels of Use of a GIS01:29

Levels of Use of a GIS

451
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...
451
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

985
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...
985
Thematic Layering in GIS01:30

Thematic Layering in GIS

412
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)...
412

You might also read

Related Articles

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

Sort by
Same author

Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.

Allergy·2026
Same author

Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking.

Environmetrics·2026
Same author

Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.

Bayesian analysis·2026
Same author

Assessing spatial disparities: a Bayesian linear regression approach.

Biostatistics (Oxford, England)·2025
Same author

Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models.

Journal of machine learning research : JMLR·2025
Same author

Bayesian Data Sketching for Varying Coefficient Regression Models.

Journal of machine learning research : JMLR·2025
Same journal

Diminished Returns: The Hidden Health Costs of Upward Social Mobility for Black Americans.

Annual review of public health·2026
Same journal

The Epidemiology, Prevention, and Control of Type 2 Diabetes in Low- and Middle-Income Countries.

Annual review of public health·2026
Same journal

Community-Based Participatory Research: Evolution and Significant Developments.

Annual review of public health·2026
Same journal

Causal Inference in Health Disparities Research.

Annual review of public health·2026
Same journal

The Health Implications of Fatherhood: A Comprehensive Literature Review.

Annual review of public health·2026
Same journal

Practice-Focused Research Based on Public Health Critical Race Praxis.

Annual review of public health·2026
See all related articles

Related Experiment Video

Updated: Mar 26, 2026

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

14.3K

Spatial Data Analysis.

Sudipto Banerjee1

  • 1Department of Biostatistics, University of California, Los Angeles, California 90095;

Annual Review of Public Health
|January 21, 2016
PubMed
Summary
This summary is machine-generated.

This review explores statistical models for spatial data, focusing on disease mapping and survival analysis in public health. It highlights methods to understand geographic patterns and risks associated with location-referenced data.

Keywords:
Bayesian hierarchical modelingconditional autoregressive (CAR) modelscure rate modelsdisease mappingmultivariate CAR modelsmultivariate disease mappingspatial survival analysis

More Related Videos

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K

Related Experiment Videos

Last Updated: Mar 26, 2026

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

14.3K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K

Area of Science:

  • Statistics
  • Geographic Information Systems (GIS)
  • Public Health

Background:

  • Geographic Information Systems (GIS) software is increasingly accessible, leading to more location-referenced spatial data in scientific research.
  • Spatial data in public health often appears as aggregated areal data (e.g., counts over counties).
  • There is growing interest in statistical modeling for spatial dependence in areal data.

Purpose of the Study:

  • To provide an overview of statistical models that account for spatial dependence in areal data.
  • To illustrate these models within the contexts of disease mapping and spatial survival analysis.
  • To demonstrate the application of spatial statistical models in public health research.

Main Methods:

  • Review of statistical models designed for areal data with spatial dependence.
  • Application of models to disease mapping for identifying geographic disease rates and clusters.
  • Application of models to spatial survival analysis for geographically referenced time-to-event data.

Main Results:

  • Statistical models can effectively account for spatial dependence in areal data.
  • Disease mapping can reveal geographic disparities and identify potential disease hot spots.
  • Spatial survival analysis is valuable for understanding survival data clustered by geographic regions.

Conclusions:

  • Statistical modeling of spatial dependence is crucial for analyzing location-referenced areal data.
  • Disease mapping and spatial survival analysis are key applications in public health research.
  • Understanding spatial patterns enhances insights into disease prevalence, incidence, and survival outcomes.