Open map List

Univariate

In statistics, data analysis, and cartography, univariate refers to a type of analysis or dataset that involves only one variable. The term comes from "uni-" (meaning one) and "variate" (meaning variable).

Basic

Introduction

Key Characteristics of Univariate Data

  1. Single Variable – Focuses on analyzing one variable at a time.
  2. Descriptive in Nature – Typically involves summarizing, visualizing, and understanding the distribution of the variable.
  3. No Relationships – Unlike bivariate or multivariate analysis, univariate analysis does not study relationships between multiple variables.

Explanation

Univariate in Cartography and GIS

In mapping, univariate maps display information about a single variable using visual representation techniques such as:

  • Choropleth Maps – Color shading based on one variable (e.g., population density).
  • Proportional Symbol Maps – Symbol sizes based on one data attribute (e.g., number of hospitals per city).
  • Dot Density Maps – Dots represent frequency of a single variable (e.g., crime incidents in a region).

Univariate vs. Bivariate vs. Multivariate

  • Univariate – Analyzes one variable (e.g., temperature).
  • Bivariate – Analyzes two variables and their relationship (e.g., temperature vs. altitude).
  • Multivariate – Analyzes more than two variables at once (e.g., temperature, altitude, and humidity).

Examples

Examples of Univariate Analysis

  • Descriptive Statistics:

    • Mean, Median, Mode (Measures of Central Tendency)
    • Variance, Standard Deviation, Range (Measures of Dispersion)
    • Skewness, Kurtosis (Measures of Distribution Shape)

  • Visualization Methods:

    • Histograms – Show frequency distribution of a variable.
    • Box Plots – Display data distribution and identify outliers.
    • Bar Charts – Represent categorical data.

Outgoing relations