**Instructions:**

- Enter the X and Y coordinates for Point 1 and Point 2.
- Click "Calculate Distance" to find the distance between the two points.
- Results will be displayed as a chart, text, and in the Calculation History section below.
- Click "Clear Results" to clear both the chart and the history.
- Click "Copy Result" to copy the latest result to the clipboard.

**Distance Result:**

**Calculation Details:**

**Formula used:** Distance = √((X2 - X1)^2 + (Y2 - Y1)^2)

**Explanation:** The formula calculates the Euclidean distance between two points in a 2D plane. It measures the straight-line distance between Point 1 (X1, Y1) and Point 2 (X2, Y2).

**Detailed Calculation:** The calculation involves finding the difference in X and Y coordinates, squaring them, summing the squares, and taking the square root of the sum to get the distance.

**Calculation History:**

## What is 2D Distance?

The 2D distance, also known as the Euclidean distance, between two points (x1, y1) and (x2, y2) in a two-dimensional plane can be calculated using the following formula:

Distance = √((x2 – x1)^2 + (y2 – y1)^2)

Here:

- (x1, y1) are the coordinates of the first point.
- (x2, y2) are the coordinates of the second point.
- √ denotes the square root operation.
- (x2 – x1)^2 represents the squared difference of the x-coordinates.
- (y2 – y1)^2 represents the squared difference of the y-coordinates.

## All Formulae Related to 2D Distance

- Euclidean Distance:
- Formula: √((x2 – x1)^2 + (y2 – y1)^2)
- Calculates the straight-line distance between two points in a 2D plane.

- Manhattan Distance:
- Formula: |x2 – x1| + |y2 – y1|
- Calculates the distance between two points in a grid-like fashion, where you can only move horizontally or vertically (no diagonal movement).

- Minkowski Distance:
- Formula: [Σ(|xi – yi|^p)]^(1/p) for i = 1 to n (for n-dimensional space)
- Generalizes distance calculation by introducing a parameter ‘p’. When p=1, it’s the Manhattan distance; when p=2, it’s the Euclidean distance.

- Chebyshev Distance (Chessboard Distance):
- Formula: max(|x2 – x1|, |y2 – y1|)
- Measures the distance when movement is allowed in all directions (horizontal, vertical, diagonal).

- Hamming Distance (for binary vectors):
- Formula: Σ(|xi – yi|) for i = 1 to n
- Measures the difference between two binary vectors of equal length.

- Cosine Distance (for vectors):
- Formula: 1 – (Σ(xi * yi) / (sqrt(Σ(xi^2)) * sqrt(Σ(yi^2))))
- Measures the cosine of the angle between two vectors, used in vector space similarity analysis.

- Jaccard Distance (for sets):
- Formula: 1 – (|A ∩ B| / |A ∪ B|)
- Measures the dissimilarity between two sets by comparing their intersection and union.

## Applications of 2D Distance Calculator in Various Fields

A 2D distance calculator, which computes distances between points in a two-dimensional space, finds applications in various fields. Here are some common areas where it is utilized:

**Geographic Information Systems (GIS)**:- Calculating distances between geographical coordinates to determine travel distances, shortest routes, or proximity analysis for mapping and navigation.

**Computer Graphics**:- Determining the distance between pixels or vertices in 2D images, which is essential for rendering, image processing, and computer games.

**Robotics**:- Robot path planning, obstacle avoidance, and control algorithms use 2D distance calculations to ensure safe and efficient movement.

**Machine Learning and Data Analysis**:- Feature engineering: Calculating distances between data points for clustering, dimensionality reduction, or similarity analysis.

**Statistics**:- In hypothesis testing and data analysis, distance measures can help assess the similarity or dissimilarity between data points or samples.

## Benefits of Using the 2D Distance Calculator

Using a 2D distance calculator offers several benefits in a wide range of applications and fields. Here are some of the key advantages:

**Accuracy**: 2D distance calculators provide precise measurements, ensuring accurate results in applications where precise spatial relationships are critical, such as geographic analysis and engineering.**Efficiency**: They automate the distance calculation process, saving time and effort compared to manual calculations, especially when dealing with large datasets or complex geometries.**Consistency**: Calculations are consistent and repeatable, reducing the likelihood of human errors that can occur with manual measurements.**Versatility**: 2D distance calculators can handle various distance metrics, allowing users to choose the most appropriate measure for their specific needs, whether it’s Euclidean, Manhattan, Minkowski, or others.**Speed**: These calculators can quickly process a large number of distance calculations, making them suitable for real-time or near-real-time applications, such as robotics and navigation.**Optimization**: In fields like logistics and supply chain management, distance calculators help optimize routes, reducing transportation costs and delivery times.

## References

**“Beyond Cartesian Coordinates: Distance Measures in Riemannian Geometry and Computer Graphics”**by SIGGRAPH Asia**“From Robotics to Path Planning: Distance Metrics in Sensor Fusion and Autonomous Systems”**by IEEE Transactions on Robotics and Automation

Last Updated : 27 February, 2024

Sandeep Bhandari holds a Bachelor of Engineering in Computers from Thapar University (2006). He has 20 years of experience in the technology field. He has a keen interest in various technical fields, including database systems, computer networks, and programming. You can read more about him on his bio page.

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