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

97
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...
97
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

146
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...
146
Introduction to GIS01:28

Introduction to GIS

186
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...
186
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

103
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...
103
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

65
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...
65
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

159
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...
159

You might also read

Related Articles

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

Sort by
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Sep 8, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K

A Clipped Gaussian Geo-Classification model for poverty mapping.

Richard Puurbalanta1

  • 1Department of Statistics, Faculty of Mathematical Sciences, CK Tedam University of Technology and Applied Sciences, Navrongo, Ghana.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

A new spatial model, Clipped Gaussian Geo-Classification (CGG-C), accurately classifies household poverty in Ghana. This model offers improved poverty mapping and policy design for targeted interventions.

Keywords:
Bayesian estimation via MCMCGaussian random fieldsOrdered responsespoverty classificationspatial correlation

More Related Videos

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

153
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K

Related Experiment Videos

Last Updated: Sep 8, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

153
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K

Area of Science:

  • Spatial statistics
  • Econometrics
  • Geographic Information Systems (GIS)

Background:

  • Discrete spatial models are crucial for measuring living standards, with geo-referenced data exhibiting spatial dependencies.
  • Categorical measurements are common in living standards surveys, necessitating models that handle ordered, spatially correlated data.

Purpose of the Study:

  • To introduce and evaluate the Clipped Gaussian Geo-Classification (CGG-C) model for classifying spatially-dependent ordered data.
  • To compare the performance of the CGG-C model against existing methods for household poverty classification using Ghana Living Standards Survey (GLSS 6) data.

Main Methods:

  • Development of the Clipped Gaussian Geo-Classification (CGG-C) model for ordered spatial data.
  • Application of Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling.
  • Model evaluation using classification and prediction accuracy metrics, including misclassification rates.

Main Results:

  • The CGG-C model demonstrated superior performance with a 14.2% misclassification rate, outperforming the Cumulative Probit (CP) model's 17.4% rate.
  • Statistically significant covariates were strongly related to the ordered poverty response variable.
  • Empirical evidence of spatial poverty character in Ghana, with households in specific locations exhibiting similar poverty levels.

Conclusions:

  • The CGG-C model provides a robust approach for spatial poverty analysis and mapping.
  • This methodology aids in designing effective policies and cost-efficient programs for poverty reduction and monitoring.
  • The study highlights the importance of spatial considerations in understanding and addressing poverty incidence.