Normalization
The process of adjusting values measured on different scales to a common scale, often used in spatial statistics to compare data meaningfully (inferred from standard GIS usage).
What does Normalization mean?
In order to make data more comparable or to get it ready for analysis, normalization is the act of converting values measured on various scales to a common scale. Normalization is frequently used to remove the impacts of different units, population numbers, or data ranges in the context of GIS and spatial data. For instance, data may be normalized to show crimes per 1,000 people when comparing crime rates among cities rather than using total crime counts, which can be deceptive owing to population variations. This guarantees an equitable comparison. Normalization can also entail converting attribute data to proportions, percentages, or z-scores, or changing data values to lie inside a particular range, like 0 to 1. All things considered, normalization guarantees more accurate statistical or spatial analysis, improves visualization, and helps to improve data quality.
Related Keywords
A preprocessing method called data normalization reduces numerical data to a standard range to improve analysis and model performance. Decimal scaling, Z-score normalization (mean 0, standard deviation 1), and Min-Max normalization (scales data to 0–1) are common techniques. It guarantees that features with varying scales provide equitable contributions to the model.
In SQL, database normalization is the process of arranging data to enhance integrity and minimize redundancy. To guarantee effective, reliable, and maintainable data storage, it breaks up tables into smaller, linked tables and establishes rules (normal forms).
In machine learning, normalization scales data to a shared range (such as 0–1) so that each characteristic contributes equally. It increases training efficiency and model accuracy.
Data Normalization vs. Standardization: Normalization rescales data to a predetermined range, typically 0–1, whereas standardization scales data to a mean of 0 and standard deviation of 1. The algorithm and data distribution determine the choice.