Related Experiment Video
Updated: Jan 3, 2026
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
10.9K
Spatial Regression Analysis of Poverty in R
Maria Kamenetsky1, Guangqing Chi2, Donghui Wang3
1Department of Population Health Sciences, University of Wisconsin-Madison.
Spatial Demography
|November 19, 2019
Summary
This study introduces spatial regression models for poverty research, detailing their application using R. It highlights how understanding spatial poverty distribution reveals place-based inequalities.
Area of Science:
- Social Sciences
- Spatial Analysis
- Econometrics
Background:
- Poverty research spans multiple social science disciplines.
- The spatial distribution of poverty is crucial for understanding structural inequalities.
- Existing spatial regression models present a learning curve for poverty researchers.
Purpose of the Study:
- To introduce spatial regression modeling concepts for poverty research.
- To provide a practical guide for applying spatial regression models using R.
- To facilitate the analysis of spatial poverty data and heterogeneity.
Main Methods:
- Exploratory Data Analysis (EDA)
- Standard Linear Regression
- Spatial Weight Matrix construction
- Exploratory Spatial Data Analysis (ESDA)
- Spatial Linear Regression
- R programming language for data analysis
Main Results:
- Demonstration of spatial regression techniques applied to poverty data.
- Code examples for conducting spatial poverty analysis in R.
- Discussion of spatial heterogeneity and spatial panel data in poverty.
Conclusions:
- Spatial regression models are valuable tools for analyzing poverty distribution.
- R provides a robust environment for implementing these spatial analyses.
- This guide empowers researchers to explore place-based poverty inequalities effectively.
More Related Videos
Related Concept Videos
01:11Residuals and Least-Squares Property
8.9K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.9K
01:11Regression Analysis
7.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.7K
01:21Friedman Two-way Analysis of Variance by Ranks
459
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
459
01:25Statistical Methods for Analyzing Epidemiological Data
851
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
851
01:20Comparing the Survival Analysis of Two or More Groups
514
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
514
01:08Outliers and Influential Points
5.8K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
5.8K