K Nearest Neighbours (KNN)

A spatial analysis method that defines the neighbourhood of a feature by identifying its k closest neighbours based on distance. This approach is widely used for spatial clustering, hotspot detection, and ensuring consistent neighbour counts regardless of area size.

How does the K Nearest Neighbours (KNN) identify?

By determining the 'K' nearest data points (neighbours) to a target point in a dataset using distance metrics like Euclidean distance, the K Nearest Neighbours (KNN) algorithm finds patterns or classifications.

How It Operates:

A dataset with established values or categories is the input.
Goal: A fresh, unlabelled data point
Distance Calculation: The algorithm determines how far the target point is from every other point in the dataset.
The 'K' closest spots are chosen to find neighbours.
Decision:
- Classification: Assigns the most common class among the K neighbours
- Regression: Averages the values of the K neighbours

Through the identification of neighbouring or geographically similar features, KNN is utilized in GIS for location-based predictions, land cover classification, and spatial pattern recognition.

Related Keywords

A straightforward machine learning approach for regression and classification is called K-Nearest Neighbours (KNN). The majority class or average of the "k" closest data points is used to predict results.

In machine learning, K-Nearest Neighbours (KNN) is a straightforward, non-parametric classification technique. It uses distance measures, such as Euclidean distance, to find the majority class of a data point's k closest neighbours in the feature space. Although KNN is simple to use, doesn't require a training phase, and performs well on small to medium-sized datasets, its processing costs may cause it to perform worse on large or high-dimensional datasets.

Here’s a concise KNN example in Python: from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
print("Accuracy:", accuracy_score(y_test, knn.predict(X_test)))

By examining the K nearest data points and applying majority voting for classification or averaging for regression, the K-Nearest Neighbour (KNN) method makes predictions. It is based on distance measurements and is easy to use.