Distance Raster overview

Distance Raster Analysis is to analyze the Spatial distance of each grid from its neighboring pixel (source), so as to reflect the relationship between each pixel and the nearest neighboring pixel. It not only considers the grid surface distance, but also considers the influence of various consumption factors such as cost. Through distance analysis, a lot of useful information can be obtained to guide people to manage and plan resources, such as the distance between the earthquake emergency rescue area and the nearest hospital, the service area evaluation of supermarket chains, etc.

Distance Raster Analysis mainly includes three aspects:

One is Generate Distance Raster, which calculates the distance between each cell and the nearest source (the object of interest), including straight-line distance, surface distance and cost distance. Respectively corresponding to the following functions:

Generate Line Distance Raster. : Generate the corresponding Line Distance Raster, line direction grid and line distribution grid according to the Source Dataset.
Generate Surface Distance Raster. : Generates the appropriate Surface Distance Raster, surface orientation grid, and surface distribution grid based on the Source Dataset and surface grid.
Build Custom Cost Distance Raster : Generate the corresponding cost Distance Raster, cost direction grid and cost distribution grid according to the Source Dataset and cost grid.

Condly, according to the direction grid and the distribution grid, the Shortest Path from the target to the source is analyzed and obtained;
The third is to calculate the Shortest Path between two points (source point and target point), including the minimum cost path and the shortest surface distance path.

Basic concepts

Let's

start with the basic concepts related to Raster Analysis.

Source: Objects or features of interest, such as schools, roads, or fire hydrants.

Source Dataset: Dataset containing the source, which can be a 2D point, line, Region Dataset, or Raster Dataset. For Raster Dataset, requires cells other than identity source to have no value.

Distance: includes Straight-line Distance, Cost Distance, and Surface Distance, as described below:

The Straight-line Distance is the Euclidean distance, which is the Straight-line distance from each cell to the nearest source.
The cost distance is the cost value required to actually reach the nearest source obtained according to the weighting of one or several attribute factors of the cell;
The surface distance is the actual surface distance from the cell to the nearest source, calculated from the surface grid.

Straight-line Distance can be seen as the cost of length, which is the simplest kind of cost. On the other hand, the actual land cover types are diverse, and it is almost impossible to reach the source through the straight-line distance, and it is necessary to make a detour to avoid obstacles such as rivers, mountains, and so on. Therefore, it can be said that the cost distance is the expansion and extension

of the straight-line distance.

Cost grid: The cost grid is required for Generate Cost Distance Raster and for calculating the minimum cost path. The cost grid is used to determine the cost required to pass through each cell. The value of the cell represents the cost of one unit passing through the cell and cannot be negative. For example, if the grid value of a cost grid representing the forward resistance of a vehicle in different ground environments represents the resistance value for each kilometer of advance when passing through the cell, then the total cost of passing through the cell is the unit cost value (i.e., the grid value) multiplied by the size of the cell. The units that cost the grid can be any unit type, such as length, time, money, etc., or unitless, such as regraded slope, aspect, Land Use type, etc.

Compute Shortest Path

The Shortest Path Analysis is based on the target point data and the Distance Raster and orientation grid generated by the Generate Distance Raster "feature. Calculate the Shortest Path of the target to the nearest source, such as the Shortest Path from a suburban point to the nearest shopping mall (Target Dataset).

For example, analyze how to get to the nearest shopping mall (Point Dataset) from each residential area (Region Dataset). First, the shopping mall is used as the source, Generate Cost Distance Raster and consumption direction grid; Taking the residential area as the target area, the Shortest Path from each residential area (target) to the nearest shopping mall (source) can be obtained by Shortest Path Analysis based on the generated cost Distance Raster and cost direction grid.

There are three Path Types for

Compute Shortest Path:

Pixel Path: Each grid pixel generates a path, that is, the distance from each target pixel to the nearest source.
As shown in the figure below, the red point is used as the source, and the black frame polygon is used as the target. In this way, the grid Shortest Path Analysis is performed, and the Shortest Path represented by the blue cell is obtained.

Figure: Pixel Path Analysis
Zonal Path: Each grid area generates a path. Here, the grid area refers to a continuous grid with equal grid values. The Zonal Path is the Shortest path from each target area to the nearest source.
As shown in the figure below, the red point is used as the source, and the black frame polygon is used as the target. In this way, the grid Shortest Path Analysis is performed, and the Shortest Path represented by the blue cell is obtained.

Figure: Zonal Path Analysis
Single Path: Only one path is generated for all pixels, which is the shortest path for the entire target Area Dataset.
As shown in the figure below, the red point is used as the source, and the black frame polygon is used as the target. In this way, the grid Shortest Path Analysis is performed, and the Shortest Path represented by the blue cell is obtained.

Figure: Single Path Analysis

Compute two-point Shortest Path

Calculate the Shortest Path between the source and destination points. Based on the specified surface grid or cost grid, you can calculate the shortest surface distance path, the minimum cost path, or the minimum cost path considering the surface distance between two points.

Cost distance

Computing the minimum cost path requires specifying the cost grid. The cost grid is used to determine the cost required to pass through each cell. The value of the cell represents the cost of one unit passing through the cell. For example, if the grid value of a cost grid representing the forward resistance of a vehicle in different ground environments represents the resistance value for each kilometer of advance when passing through the cell, then the total cost of passing through the cell is the unit cost value (i.e., the grid value) multiplied by the size of the cell. The units that cost the grid can be any unit type, such as length, time, money, etc., or unitless, such as regraded slope, aspect, Land Use type, etc. Usually, there may be many factors affecting the cost involved in an actual study. For example, when planning a new road, the factors affecting the cost may include the total length of construction, the Land Use type of the area it passes through, the slope, the distance from the population gathering area, etc. These factors need to be weighted to get a combined weight as Cost Data. In addition, it is important to note that the cost cannot be negative.

Basic concepts

Compute Shortest Path

Compute two-point Shortest Path

Cost distance

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