What is Interpolation?

Interpolation is a method of estimating unknown values from known values. Interpolation is a fundamental mathematical method used in various fields, including Geographic Information Systems (GIS) and data analysis. It plays a crucial role in estimating or filling in missing values between known data points.

In GIS applications, interpolation is particularly valuable for spatial data analysis and visualization. Selecting the appropriate interpolation method depends on factors such as data characteristics, distribution of data points, and desired accuracy. It is essential to choose the right method and validate the results to ensure reliable and meaningful estimates in GIS and data analysis workflows.

By employing interpolation effectively, professionals in GIS and data analysis can enhance spatial analysis, visualize data trends, and make informed decisions based on comprehensive and accurate information.

In Geographic Information Systems (GIS) these following steps will ensure effective interpolation:

1. Data Preparation: Collect and organize relevant data points with associated attributes. Properly format the data to ensure compatibility with interpolation methods.
2. Method Selection: Choose the most appropriate interpolation method based on your data characteristics, spatial distribution, and desired accuracy. Common methods include linear interpolation, nearest neighbor interpolation, spline interpolation, and kriging interpolation.
3. Interpolation Process: Apply the chosen method to estimate values at unknown locations. The method utilizes nearby data points and their attributes to determine values at the target locations accurately.
4. Evaluation and Analysis: Assess the quality and reliability of the interpolation results. Validate the estimates by comparing them with known values or conducting additional analysis to ensure accuracy.

Triangulated Irregular Network (TIN): The triangulation method creates a raster surface by forming triangles from the real values of the nearest neighbor data (input) points. These triangles are then used to estimate the values in each cell of the raster to be created through interpolation. The TIN method is a suitable interpolation technique for representing surface morphology.

Inverse Distance Weighting (IDW): The IDW method aims to find the source of a sampled substance within a specified search radius by utilizing samples taken from a specific region. This method calculates the raster surface by taking the average of all sample values within the specified search radius and inversely proportional to the distances of unknown cell values. Areas close to the source will have higher values, while areas far from the source will have lower values. For example, it can be used to determine where air pollution, a chemical leak (such as oil spillage), or water leakage is concentrated.

Minimum Curvature: The Minimum Curvature method models the values between points where transitions are not sharp to be curved. This method aims to create a flexible and continuously smooth raster surface rather than averaging known point values like the IDW method.

Nearest Neighbor: The Nearest Neighbor method calculates the unknown cell values in a raster surface by considering the closest neighbor pixels to the data points. For example, this method can be used to create a distribution map by assigning the same value as the nearest sample point to points outside the locations where various types of soil (sandy, clayey, loamy, etc.) were measured.

Natural Neighbor: The Natural Neighbor method models and generates a raster by adding intermediate values between neighboring points geometrically. Natural neighbor interpolation estimates the value of each raster cell by utilizing the properties of Voronoi polygons of the source data points. This method can be used to create raster models from data that exhibit clustering and distribution patterns.

Distance: The Distance method is a spatial analysis process that records the distance from each cell in the raster to the input data points and creates zones by increasing the values based on distance. The process of creating a raster using the Distance method is equivalent to buffer creation. For example, this method can be used to determine the risk distribution of the explosion effect of fuel stations.

Stamp: The Stamp method samples data based on specific point intervals. It takes the known values and assigns them to the corresponding raster cells. This method is useful when each cell corresponds to a single point, as otherwise, there would be gaps in the raster surface. For example, lidar data can be used for this method.

Heatmap: Heatmap is a method of visualizing data using a color spectrum to represent variations in the data. The darker the shade, the higher the density or volume, depending on the amount of data. Heatmaps are used in weather reports, e-commerce websites, real estate studies, transportation/healthcare services, insurance, banking, marketing, telecommunications, and data analysis in various sectors.

Kriging: Kriging is a geostatistical interpolation method that takes into account spatial autocorrelation and variability. By modeling the spatial dependency of attribute values, kriging generates more precise estimates. It considers both nearby points and the overall spatial pattern of the data.

In Geographic Information Systems (GIS), linear interpolation is a widely utilized method for estimating values between two known points. This straightforward approach assumes a linear relationship between attribute values and the distance or position of the unknown location in relation to the known points.

With linear interpolation, calculating the attribute value at any point along a straight line between two known points becomes achievable. The estimation considers the known values and the relative distance of the target location from each point. Linear interpolation assumes a consistent rate of change between the known points, making it an efficient and reliable estimation technique.

Linear interpolation in GIS facilitates precise value estimation, particularly in scenarios where data is distributed linearly or follows a linear trend. This method supports various applications, such as terrain analysis, map generation, and spatial modeling.