# PostGIS Raster SoC Idea 2012 - Distance Analysis Tools for PostGIS Raster

**Student:**Qing Liu

**Mentor: Pierre Racine
**

**Idea**:

The idea for this proposed project is to add two spatial analysis functions to PostGIS Raster that
implement two main ways of performing distance analysis: Euclidean distance calculation and
cost-weighted distance calculation.

Euclidean distance function will create a distance surface representing the Euclidean distance from each cell in the source layer to the starting point or the closest source (as designated by user). The basic concepts of the algorithm would be using the center of the source cell to calculate the distance from it to the rest cells in the raster layer or within the user-defined extent.

Cost-weighted distance will create a raster layer representing the accumulative cost distance for each cell to the starting point or the closest source (as designated by user). A cost raster layer will be generated using one or several factor layers representing the impedance of passing through each cell according to user’s criteria. User can also put weights on the input factor layers to represent different levels of importance for those factors. Factors such as: elevation, slope, orientation, land use / land cover type; vehicle speed, accessibility; and a binary map layer could also be used as a mask for defining geographic extent or as a filter for the output cost layer. The accumulative cost values will be then assigned to each cell representing the cost per unit distance for moving through this cell.

**GSoC 2012 Proposal: **

http://google-melange.appspot.com/gsoc/proposal/review/google/gsoc2012/kya_na/1

**Document Page:**

http://trac.osgeo.org/postgis/wiki/PostGIS_Raster_SoC_Idea_2012/Distance_Analysis_Tools/document

**Code Page:**

http://trac.osgeo.org/postgis/wiki/PostGIS_Raster_SoC_Idea_2012/Distance_Analysis_Tools/code

**Test Page:**

http://trac.osgeo.org/postgis/wiki/PostGIS_Raster_SoC_Idea_2012/Distance_Analysis_Tools/test

# Weekly Reports

- Week 1 Report (May 21st - May 25th)
- Week 2 Report (May 28th - June 1st)
- Week 3 Report (June 4th - June 8th)
- Week 4 Report (June 11th - June 15th)
- Week 5 Report (June 18th - June 23th)
- Week 6 Report (June 25th - June 29th)
- Week 7 Report (July 2nd - July 6th)
- Week 8 Report (July 9th - July 13th)
- Week 10 Report (July 23th - July 27th)
- Week 11 Report (July 30th - Aug. 3rd)
- Week 12 Report (Aug. 6th - Aug. 10th)
- Week 13 Report (Aug. 13th - Aug. 17th)

## Week 1 Report

**What did you get done this week?**

- Compiled PostGIS 2.0 sucessfully.
- Loaded raster data into PostgreSQL and practiced some query by finishing the raster/vector tutorial.
- Wrote an analysis of how Euclidean distance and Cost-weighted distance are computed in ArcGIS and GRASS.
- Setup wiki page.

** What do you plan on doing next week?**

- Learn more about PostGIS Raster concepts and functions
- Write an analysis of how Euclidean distance and Cost-weighted distance are computed in Oracle
- Start to write a proposal on how to adopt the concepts and algorithms in PostGIS.

**Are you blocked on anything?**

- As of now I was not blocked on anything yet, but working in a spatial database is something new and challenging to me. I will need to read and reserch more about it.
- However, it took me some time to understand how raster coverage is stored in PostGIS, and how Raster type works.

### Analysis Report

How Euclidean distance and Cost-weighted distance are computed in ArcGIS and GRASS

**Euclidean Distance****ArcGIS****Concepts:**

The Euclidean Distance generates a raster layer that contains the measured distance from every cell to the nearest source. (Illustration from ArcGIS Online Help)

**Function:**

EucDistance (in_source_data, {maximum_distance}, {cell_size}, {out_direction_raster})- in_source_data: Raster Layer / Feature Layer (vector)
- Identifies the cells(raster) or locations(feature) to which the Euclidean distance for every output cell location is calculated.
- If the source is a raster layer, it must contain only the values of the source cells, while other cells must be NoData.
- If the source is a feature layer, it will internally be transformed into a raster. The resolution of the converted raster can be controlled with the output cell_size parameter.

- maximum_distance(Optional): Double Precision
- Defines the threshold that the accumulative distance values cannot exceed.
- Any cell location that has accumulative Euclidean distance value exceed this value will be assigned NoData for output value.
- The default is to the edge of the output raster.

- cell_size(Optional): Cell size of the output raster
- Either set in the environment settings of the workspace
- Or depends on if the input source is:
- raster: same cell size as the input raster
- feature: determined by the shorter of the width or height of the extent of input feature divided by 250.

- out_direction_raster(Optional): Output raster dataset
- The calculated direction (in degrees) each cell center is from the closest source cell center.

- Return value: Raster Layer
- The distance raster identifies the Euclidean distance for each cell to the closest source cell, set of source cells, or source location(s).
- The distances are measured as Euclidean distance in the projection units of the raster, and are computed from cell center to cell center.

- in_source_data: Raster Layer / Feature Layer (vector)
**Algorithm:**- Euclidean distance is calculated from the center of the source cell to the center of each of the surrounding cells. The Euclidean algorithm works as follows: for each cell, the distance to each source cell is determined by calculating the hypotenuse with x and y distances between cell centers as the other two legs of the triangle.

(Illustration from ArcGIS Online Help)

- If the shortest distance to a source is less than the specified maximum distance, the value is assigned to the cell location on the output raster.
- If the cell is at an equal distance to two or more sources, the cell is assigned to the source that is first encountered in the scanning process. User cannot control this scanning process.
- The actual algorithm computes the information using a two-scan sequential process(need to figure it out?). This process makes the speed of the tool independent from the number of source cells, the distribution of the source cells, and the maximum distance specified. The only factor that influences the speed with which the tool executes is the size of the raster. The computation time is linearly proportional to the number of cells in the Analysis window.

- Euclidean distance is calculated from the center of the source cell to the center of each of the surrounding cells. The Euclidean algorithm works as follows: for each cell, the distance to each source cell is determined by calculating the hypotenuse with x and y distances between cell centers as the other two legs of the triangle.
**Reference:**

http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//009z0000001p000000.htm

**Euclidean Distance****GRASS**- r.grow.distance or GDAL tools\ Proximity (Raster Distance) in QGIS
**Concepts:****r.grow.distance**metric=euclidean (default):

generates raster maps representing the distance to the nearest non-null cell in the input map and/or the value of the nearest non-null cell.**gdal_proximity**generates a raster proximity map indicating the distance from the center of each pixel to the center of the nearest target pixel in the source raster.

**Function:**

**r.grow.distance**[**-m**]**input**=name [**distance**=name] [**value**=name] [**metric**=string] [—**overwrite**] [—**verbose**] [—**quiet**]- input: Raster dataset
- Source cell locations are represented with non-null cell value

- distance: Output Raster dataset
- Identifies distances for each cell to the nearest source cell(s)

- metric:
- User specified four different metrics which control the geometry in which grown cells are created
**Euclidean**(by default), Squared, Manhattan, and Maximum

**gdal_proximity.py**srcfile dstfile [-srcband n] [-dstband n] [-of format][-co name=value]* [-ot Byte/Int16/Int32/Float32/etc][-values n,n,n] [-distunits PIXEL/GEO] [-maxdist n] [-nodata n][-fixed-buf-val n] - srcfile: Raster file, identifies target pixels
- dstfile: Output Raster file of proximity (distance)
- -values:

A list of target pixel values in the source image to be considered target pixels. If not specified, all non-zero pixels will be considered target pixels. - -mxdist:

The maximum distance to be generated. All pixels beyond this distance will be assigned the nodata value.

- input: Raster dataset
**Algorithm:**- d(dx,dy) = sqrt(dx
^{2}+ dy^{2}) - Cells grown would form isolines of distance that are circular from a given point, with the distance given by the radius.

- d(dx,dy) = sqrt(dx
**Reference:**

http://grass.osgeo.org/manuals/html70_user/r.grow.distance.html

http://www.gdal.org/gdal_proximity.html

**Cost-weighted Distance****ArcGIS****Concepts:****Function:**

CostDistance (in_source_data, in_cost_raster, {maximum_distance}, {out_backlink_raster})- in_source_data: Raster Layer / Feature Layer (vector)
- Identifies the cells(raster) or locations(feature) to which the Euclidean distance for every output cell location is calculated.
- If the source is a raster layer, it must contain only the values of the source cells, while other cells must be NoData.
- If the source is a feature layer, it will internally be transformed into a raster. By default, the first valid available field will be used, otherwise the ObjectID field will be used.

- in_cost_raster: Raster Layer
- The value of each cell represents the cost per unit distance for moving through the cell. Each cell location value is multiplied by the cell resolution while also compensating for diagonal movement to obtain the total cost of passing through the cell.
- If the input source data and the cost raster are different extents, the default output extent is the intersection of the two. Or you can Union the input rasters to get a cost distance surface for the entire extent.
- Cell locations with NoData in the Input cost raster act as barriers in the cost surface. Any cell location that is assigned NoData on the input cost raster will receive NoData on the output raster.
- The values of the cost raster cannot be negative or zero. If the cost raster does contain values of zero, and:
- If these values represent areas of lowest cost, change values of zero to a small positive value (such as 0.01) before running Cost Distance.
- If areas with a value of zero represent barriers, these values should be turned to NoData before running Cost Distance.

- maximum_distance: Double Precision
- Defines the threshold that the accumulative cost values cannot exceed.
- Any cell location that has accumulative cost distance value exceed this value will be assigned NoData for output value.
- The default is to the edge of the output raster.
- The unit is specified the same cost units as those on the cost raster.

- out_backlink_raster: Raster Layer
- Defines the direction or identify the next neighboring cell along the least accumulative cost path from a cell to reach its least cost source.
- The first succeeding cell from the source cell will be assigned the value 1 and 2,3,… ,8 continuing clockwise. The value 0 is reserved for source cells.

- Return Value: Raster Layer
- Identifies the least accumulative cost distance for each cell over a cost surface to the closest source cell, set of source cells, or source location(s).
- The least-cost distance of a cell to a set of source locations is the lower bound of the least-cost distances from the cell to all source locations.

- in_source_data: Raster Layer / Feature Layer (vector)
**Algorithm:**- The algorithm utilizes the node/link cell representation, in which each center of a cell is considered a node and each node is connected to its adjacent nodes by multiple links.
- Every link has an impedance derived from the costs associated with the cells at each end of the link and from the direction of movement through the cells. The cost assigned to each cell represents the cost per unit distance for moving through the cell, that is the cell size multiplied by the cost value.
- Perpendicular node cost:

The cost moving from cell1 to cell2 = (cost1 + cost2) / 2 - Diagonal node cost:

The cost moving from cell1 to cell2 = sqrt(2)*(cost1+cost2)/2

- Perpendicular node cost:
- Creating an accumulative cost-distance raster is an iterative process that attempts to identify the lowest-cost cell and add it to an output list.
- The process begins with the source cells.
- Accumulative cost distances are calculated for all the neighboring cells, activated, put into a cost cell list and arranged rom the lowest to the highest.
- The lowest cost cell is chosen from the active accumulative cost cell list, and the value is assigned to the output cost-distance raster for that cell location.
- The neighborhood is expanded from the lowest cost cell, the new costs are calculated, and the new cost cells are added to the active list.
- This allocation process continues until all cells have been chosen from the active list. The result is the accumulative cost or weighted-distance raster.
- If there are multiple zones or disconnected sets of source cells on the input source raster, the growing process continues and allocates the cheapest cost cell from the active list regardless of which source it is from.
- No travel is allowed through cells containing NoData values. The least accumulative cost for cells on the back side of a group of NoData cells is determined by the cost to travel around these locations. If a cell location is assigned NoData on the input cost raster, NoData will be assigned to the cell location on the cost distance output raster.

**Reference:**

http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/Cost_Distance/009z00000018000000/

**Cost-weighted Distance****GRASS****r.cost****Concepts:**- Creates a raster map showing the cumulative cost of moving to each cell on a cost surface from other user-specified cell(s) whose locations are specified by their geographic coordinate(s) or from a raster points map.
- Each cell value in the output raster presents the lowest total cost of traversing the cost surface between each cell and the user-specified points.

**Function:**

**r.cost**[-**knrv**]**input**=name**output**=name [**start_points**=name] [**stop_points**=name] [**start_rast**=name] [**coordinate**=x,y[,x,y,…]] [**stop_coordinate**=x,y[,x,y,…]] [**max_cost**=cost] [**null_cost**=null cost] [**percent_memory**=percent memory] [—**overwrite**] [—**verbose**] [—**quiet**]- input: raster cost map
- Value of each grid cell represents cost information

- output: output raster map of cummulative cost
- start_points:
- Point(s) with geographic coordinates (coordinate)
- Vector points file
- Raster map with non-null cells representing starting points (start_rast)

- stop_points:
- Point(s) with geographic coordinates (stop_coordinate)
- Vector points file

- max_cost: maximum cumulative cost
- The execution will stop accumulating costs when either max_cost is reached, or one of the stop points is reached. Once the cumulative cost to all stopping points has been determined, processing stops.

- null_cost:
- By default null cells in the input raster map are excluded from the algorithm, and thus retained in the output map.
- The null cells in the input cost map can be assigned a positive cost with the null_cost option.
- When input map null cells are given a cost with the null_cost option, the corresponding cells in the output map are no longer null cells. When specified null_cost=0.0, the movement transparently cross null cells which then propagate the adjacent costs.
- By using the -n flag, the null cells of the input map will be retained as null cells in the output map.

- -
**k**flag:- Use Knight’s move to improve the accuracy of the output by growing cost distance surface outwards in 16 directions instead of 8 directions by default.
- Computes slower, but more accurate

- input: raster cost map
**Algorithm:**- Calculation begins with starting points.
- Cells (from starting points to the neighboring cells growing in 8 or 16 directions) are put into a list, on which the cell with the lowest cumulative cost is selected for computing costs to the neighboring cells.
- Neighbors are put into the list and originating cells are removed from the list.
- This process continues until the list is empty.
- A binary tree with an linked list at each node in the tree is used for efficiently holding cells with identical cumulative costs.
- As the algorithm works through the dynamic list of cells it can move almost randomly around the entire area. It divides the entire area into a number of pieces and swaps these pieces in and out of memory as needed.

**Reference:**

http://grass.fbk.eu/gdp/html_grass64/r.cost.html

## Week 2 Report

**What did you get done this week?**

- Read Documents and Pierre Racine's presentations on PostGIS Raster; Learned more about the raster data storage and manipulation in PostGIS
- Read Oracle Spatial Documents on GeoRaster; Learned about raster data storage in Oracle.

** What do you plan on doing next week?**

- Learn more about PostGIS Raster development
- Write a comparison of raster data storage and manipulation in PostgreSQL and Oracle
- Write a proposal on how to adopt the concepts and algorithms of distance calculation in PostGIS.

**Are you blocked on anything?**

- It seems that Oracle Sptial dosenot provide distance analysis functions for GeoRaster data.
**Please let me know if I missed it.**

However, by reading documents of GeoRaster, I got a better understanding of the raster data storage in spatial database. So I feel it would be helpful for me to write an analysis to compare the concepts of raster data storage and manipulation with PostGIS Raster and Oracle Spatial GeoRaster.

**Comment from Mentor:**

I really think we want to avoid having to produce an intermediate raster of sources. PostGIS is strong on raster/vector interaction and I really don't see why someone would prefer to provide sources as raster. The sources should come from a table of point. Now this raise a certain number of issues:

-What if the table contains more than one point?

-What if some of those points are outside the raster extent?

-Another issue is: What if the raster I want to produce does not fit into the PostgreSQL maximum field size?

These are the kind or difficulties one encounters when working in a database.

Think about large tiled coverage, Delaunay triangulation, aggregate functions and all the combinatory of "small/very large number of point" and "small/very large raster coverage". Ideally we want to provide a solution working for every limit cases.

## Week 3 Report

**What did you get done this week?**

- Wrote a comparison of raster data storage and manipulation in PostgreSQL and Oracle
- Wrote a proposal on how to adopt the concepts and algorithms of Euclidean distance calculation in PostGIS.

** What do you plan on doing next week?**

- Write a proposal on how to adopt the concepts and algorithms of Cost-weighted distance calculation in PostGIS

**Are you blocked on anything?**

- Based on the feed back I got from Pierre last week, I agreed that we want to avoid having to produce an intermediate raster of the source point data. Since PostGIS Raster provide the capability of seamless vector-raster interactions, it is preferable that we expect the input source point data to be a vector layer, which is stored in PostgreSQL as a table of points with geomegry. The concepts of the distance calculation in ArcGIS and GRASS were all based on raster source data (ArcGIS will first rasterize the vector layer if necessary). So I was stucked at this point on how to avoid generating this intermediate raster layer if we want the source data to be vector points.

### Analysis Reports

**Compare Raster Data Storage and Manipulation in PostgreSQL and Oracle GeoRaster**

- Raster Data Storage:
- PostGIS Raster
- Oracle GeoRaster

- Georeferencing:
- Spatial Indexing:
- Pyramids:
- Loading Data:
- PostGIS Raster:
- All GDAL accepted formats
- Easy

- Oracle GeoRaster:
- Few formats accepted
- Hard

- PostGIS Raster:
- Raster Data Implementaion (take vector-raster intersection for example):
- PostGIS Raster:
- Implement seamless vector-raster analyses
- Do analysis independently of the data presentation
- Vector-Raster intersection:
- Really intersect Vector Data with Raster Data
- Raster data is first polygonized to be intersected with Vector data

- Oracle GeoRaster:
- Was primarily designed for raster data storage not for anlysis
- Vector-Raster intersection:
- Intersect Vector data with MBR of raster data not with the raster data itself
- Process is faster though

- PostGIS Raster:

**Generate Euclidean Distance Surface in PostGIS (Proposal) (first working on a single raster row)**

- Concepts:
- We first think of working on a single raster row (a single tile) containing only one band and then maybe apply it on a tiled raster coverage with multi-bands. We first assume the source point(s) is/are within the raster extent.
- Generate a raster tile in which the value for each pixel represents the Euclidean distance from the pixel to the nearest source point.
- Euclidean distance is calculated from the center of the source pixel to the center of each of the surrounding pixels.

- What should be the input source data?
- ArcGIS accepts both raster and vector layers as input source data, but will first transform the source layer into raster layer if it is in the vector format; Thus, it produces an intermediate raster of sources.
- Grass and GDAL proximity tools accept only raster layer as the input source data.
- We want to avoid having to produce an intermediate raster of sources, since PostGIS implements seamless vector-raster interactions.
- Thus, we expect input source data to be a vector point layer, which is stored as a table of point with geometries.

- What should be the output result?
- Results are stored as pixel values in the raster

- Algorithms:
- Make an empty raster new_rast, using the same extent as the input vector point data source_point, set pixel value to NoData value.
**Comment from Mentor:**I don't think this is a good idea as you would have no control on the alignment of the raster produced. I want to be able to control the dimensions and how the raster is georeferenced. I think your best friend here is ST_AsRaster() which allows passing a geometry and some raster properties and burns the geometry in the raster. Basically ST_AsDistanceRaster() should not be so different from ST_AsRaster(), the only difference is that instead of burning the geometry in the raster, we compute a distance to the geometry for each pixel. It is very possible in this case that the geometry will be outside the extent of the requester raster. Should we want this to work for lines and polygons?

- Utilize ST_SetValue(raster rast, geometry pt, double newvalue) function to designate source pixels in the resulting raster where vector source point(s) intersect(s) with the new raster.We want to assign “0” as new pixel value to pixels as source pixel(point) since the Euclidean distance from source to itself is zero.
- Calculate Euclidean distance from the center of source pixel(s) to each pixel in the raster:

d(dx,dy) = sqrt(dx^{2}+ dy^{2})

Unit is pixel/cell - Set pixel value to the resulted distance

**Need to consider scan algorithm in case of more than one point in the source data** - Utilize ST_MapAlgebraExpr() function to multiply resulting distance raster with pixel size to get the real geographic distance in the unit assigned in the georeferencing info.
**Comment from Mentor:**You should avoid to do two passes to compute the final pixel values: one to compute the distance in pixel/cell and another to convert into the geographical distance.

- Make an empty raster new_rast, using the same extent as the input vector point data source_point, set pixel value to NoData value.
- Issues to be considered:
- Scanning method in case of source point data containing more than one source point:
- Shortest distance will be assigned to resulted raster
- In case there is an equal distance to more than one source, the pixel is assigned to the source that is first encountered in the scanning process

- Specify maximum distance as the threshold that the accumulative distance values cannot exceed
- Any pixel that has accumulative Euclidean distance value exceed this value will be assigned NoData for output value.
- The default threshold is to the edge of the output raster.

- How to apply to multi-band tiled raster coverage
- Some of the source points are outside the raster extent

- Raster storage in PostgreSQL
- The resulting raster does not fit into the PostgreSQL maximum field size

- Scanning method in case of source point data containing more than one source point:

**Comment from Mentor:**

-What do ArcGIS and GRASS do when there is more than one point? We need a more detailed approach for this case.

-I want also a kind of approach to the problem of billions of points. Does the knn indexing help us in quickly identifying what is the nearest point to the pixel without rescanning all the point for every pixels.

-In general I don't think just copying what ArcGIS or GRASS used to do is what we want. We want a true raster/vector integrated approach, scalable to billions of points, enabling the production of very large tiled rasters. I know this is a much more difficult problem but we want to end up with a variety of easy to use tools. I think this would be a much greater addition to PostGIS than just converting one raster to another raster. Think about aggregate functions or passing a geometry table name to the function. ST_AsRaster was done to work on only one geometry but how could it work to burn many geometries into one raster? Look at ST_UnionToRaster in the PostGIS raster working specifications. (http://trac.osgeo.org/postgis/wiki/WKTRaster/SpecificationWorking03)

-Look also at this mail: http://postgis.refractions.net/pipermail/postgis-users/2012-April/033875.html it might be a general solution working on small and big raster but might be slower than a specific solution. We could simply implement a bunch of custom function in C...

-Start thinking about the signature of some prototype SQL functions. Think about passing the name of a point table

-I think the constraints should be explicited first in your document so we know in which context we want all this to work.

I think the solution to this multipoint problem is the key to the algorithm we want to implement. There would be only one point, there would be no problem. The problem is: How do I know which distance to compute (to which point) for each pixel while I iterate on them. What if the number of point is greater than the number of pixel? Should we iterate on the point in this case instead of iterating on the pixels?

The point here is what to scan first and how many times? Do they rescan the whole raster for every points? That's ok if you have a low number of points but what if you have millions?

I think with the knn index already built you could scan pixels only once assigning the distance to the closest point. and that would be fast even if there are millions. Is this correct?

Pierre Wrote:

>> I want also a kind of approach to the problem of billions of points. Does

>>the knn indexing help us in quickly identifying what is the nearest point to the

>>pixel without rescanning all the point for every pixels.

Qing Wrote:

> Yes, KNN index will definitely help us to identify the nearest point to the pixel.

> With the capability of PostGIS 2.0 to return sorted results from a GiST index, we

> can have the results ordered by the geometry distance between pixel centroid

> and identify the shortest one without having to rescan all the points. However,

> the "order by distance between bounding box centroid" works now for

> geometries. When it comes to point data, the bounding boxes are equivalent to

> the points themselves. So the same would apply to the center of pixel in our

> calculation. We now have a GiST index over the raster column. However, I am

> not sure about how we build spatial index embedded in the pixels or the raster

> geometry.

Not sure I understand well. Could converting pixel centroids into points and computing the knn index on the mix of points and centroids converted to points be an avenue?

I think, considering all the constraints, that we might have to end up with different approaches depending on different constraints. A simple one if some constraints are involved, a more complex one if more constraints are involved.

So in your analyses you have to put into relationship: constraints, features (like the possibility to define a limited distance or the possibility to work with line and polygons), possibilities (limits of the data structure imposed by the DB like the fact that all the geometries are on a different row), scalability, flexibility (we want a generic solution to implement other kind of distances) and performance.

## Week 4 Report

**What did you get done this week?**

- Revised proposal for creating distance surface based on the comments and discussions with Pierre.

** What do you plan on doing next week?**

- Revise the proposal from last week according to the feedback from the mentor
- Write more detailed approaches according to different constraints
- Transfer all the Google docs documents to the wiki page

**Are you blocked on anything?**

- Pierre's comments and feedbacks are very helpful in guiding me through the problems I've encountered.

### Analysis Report

**Revised Proposal of Creating Distance Surface in PostGIS Raster**

**Case 1 - One-Point Source**

To create distance surface from source data containing only one point geometry as the source point.- Inputs:
- geometry - One single PostGIS geometry to create distance surface from
- geographic extent - Dimension of the output raster
- georeferencing - Information of how the raster is georeferenced
- maximum distance - The threshold that the accumulative distance values cannot exceed. When exceeding this value, NoData will be assigned for the output pixel value. The default threshold is to the edge of the output raster extent.

- Output:

Generate one single raster (rectan) in which pixel values represent the Euclidean distance from the center of the pixels to the center of the source point. - Proposed Algorithm:
- Calculate Euclidean distance using the coordinates of the center of pixel and the center of the source point (d(dx,dy) = sqrt(dx
^{2}+ dy^{2})). If distance exceed max_distance specified, NoData value is used instead. - Create a single raster with distance as pixel value (ST_RasterFromText()?) using dimensions and georeferencing information specified

- Calculate Euclidean distance using the coordinates of the center of pixel and the center of the source point (d(dx,dy) = sqrt(dx

- Inputs:

**Case 2 - Multi-point Source**

To create distance surface from source data containing multiple point geometries as the source points.- Inputs:
- geometry - Multiple PostGIS geometries to create distance surface from
- geographic extent - Dimension of the output raster
- georeferencing - Information of how the raster is georeferenced
- maximum distance - The threshold that the accumulative distance values cannot exceed. When exceeding this value, NoData will be assigned for the output pixel value. The default threshold is to the edge of the output raster extent.

- Output:

Generate one single raster (rectan) in which pixel values represent the Euclidean distance from the center of the pixels to the center of its nearest source point. - Proposed Algorithm:
- Scan through pixels, compute the KNN index for each pixel to its nearest source point, assign Euclidean distance using the coordinates of the center of the pixel and the center of its nearest source point (d(dx,dy) = sqrt(dx
^{2}+ dy^{2})). If distance exceed max_distance specified, NoData value is used instead. - Create a single raster with distance as pixel value (ST_RasterFromText()?) using dimensions and georeferencing information specified

- Scan through pixels, compute the KNN index for each pixel to its nearest source point, assign Euclidean distance using the coordinates of the center of the pixel and the center of its nearest source point (d(dx,dy) = sqrt(dx
- Scalability:
- In case of source data being large size dataset (e.g. containing billions of points), the KNN indexing approach should be able to help us in quickly identifying the nearest point to the pixel without rescanning all the points for every pixel.

- Possibility:
- It is possible to work with line geometries and polygon geometries utilizing the algorithms for the multi-point solution
- distance will be computed from the center of each pixel to the center of its nearest pixel on the line or polygon
- In case of single line or polygon geometry, the default output raster extent will be the same as the source line or polygon.
- Note that pixels within the polygon will be assigned “0” distance or NoData value

- distance will be computed from the center of each pixel to the center of its nearest pixel on the line or polygon

- It is possible to work with line geometries and polygon geometries utilizing the algorithms for the multi-point solution
- Performance:
- The speed depends on the number of source points, as well as the size of the produced raster.
- By utilizing KNN indexing, we expect the speed to be independent from the number of the source points.
- There are limits of the data structure imposed by the database needed to be considered like the fact that all the geometries are stored in different rows.

- Flexibility:
- The solution for calculating Euclidean distance can be implemented for other kinds of distances (e.g. cost-weighted distance). Euclidean distance will be used as the base layer. The cost-weighted distance could be calculated from the Euclidean distance surface with the cost surface. ST_MapAlgebraExpr two raster bands version could be utilized for computing.

- Inputs:

**Comment from Mentor:**

I find the constraints are not really well stated as a first part of the document. Could you list those constraints, right here, so we agree on them?

Also:

To produce a distance raster we need to know which point is the nearest from the pixel centroid.

To produce a more generic interpolation surface (as we would like to be able to do), which points do we need to know? Only the three nearest points? The three nearest points forming a triangle encompassing the pixel centroid? More points?

Maybe those two problems can resume to one if we would build a Delauney triangulation with the point before trying to produce any surface. So the process would be two steps: 1) Build a Delauney surface 2) Compute some metric for each pixel centroid.

**Comment from PostGIS-Devel:**

Paul Ramsey Wrote:

On the algorithm side, the first case is "OK", though of limited value
I would imagine (most use cases will involve multiple inputs, and not
just points).

On the multiple inputs side, you really have to grapple with the fact
that people will be starting from many different kinds of geometry, so
your input is best not thought of as "a multipoint" but "a grid of
starting locations", which could be derived from any kind of geometry.

I also note that the distance grid is actually just a specialization
of a generalized cost surface, where grid values are calculated as
using input of a starting point grid and a friction grid. In the case
of the distance grid you are just assuming a uniform friction grid,
but for maximum utility you might want to consider doing a cost
surface instead.

The algorithm is much less parallelizable, because you have to work
out from the start points, however it's a nice little recursive
function. From each start point you calculate the cost of the
neighbors as a product of the friction of the neighbor and the
distance to the neighbor (one unit in left-right-up-down directions,
root-2 units in the diagonals). If a cell already has a value, skip
it. You can use a threshold as in your algorithm as a stopping
condition.

With a friction grid of 1, this nets out to the distance grid.

Start only from points seems fundamentally limiting for no good
reason. The added "precision" you get by working directly against the
points seems pretty pointless for most grids. You'll get a lot of
users forced to convert their input geometries into point sets before
starting, and then their question will be "why is it slow" and you'll
say "because you have too many points, then them" and they'll say
"how" and you'll say "snap them to a grid basis" and at that point
they might as well have rasterized anyways.

I still think a generic cost calculator would be more useful than a
single-purpose distance calculator.

## Week 5 Report

**What did you get done this week?** & **Are you blocked on anything?**

- Posted project to postgis-devel hoping to get feedback and suggestions from the community
- Discussed with mentor and people from postgis-devel list (see comments part from Week 4 Report). Found myself got blocked at this point:

As to the source data, I think I would agree that we don't really want to rasterize the source geometries because we want to utilize the vector/raster seamless interactions featured and provided by PostGIS Raster. The solution I could think of was to calculate the distance based on the coordinates of the source geometries and the coordinates of the center of the resulted pixel derived from georeferencing info and then create a raster with those distance values as the pixel values.

Now with the feedback from the mentor and the list, I think I got confused about whether we want to avoid creating intermediate raster layer from rasterizing source geometries or not.

I hope I can work this out next week.

## Week 6 Report

**What did you get done this week?**

- Discussed with mentor,
- Came up with a preliminary approach briefly discribed as below:

Based on the discussion, I think the way that ST_MapAlgebraFct() works could be the way for computing Euclidean distance and interpolation in terms of creating a new raster based on a SQL function. And the function should be already set like returning the hypotenuse value for Euclidean distance and more complicated functions for interpolation according to different interpolation methods. The problem is to determine to which pixel (the nearest neighbour) the function (formula) should be applied for the current pixel. As we agreed we want to use KNN indexing for this problem.

I come up with an approach which I think should work for a single raster situation where the source points are within the geographic extent of the result raster: First create an empty raster based on the source data (a table of points) as the "container" for the resulted Euclidean distance raster. The raster will have the same georeference as the source data points; The SQL function could be like:

CREATE FUNCTION euclidean_fct(pixel float, pos integer[], source geometry, variadic args text[]) RETURNS float AS $$ BEGIN

SELECT ST_Distance(ST_Point(pos), source) as eucliean_dis ORDER BY $1 ↔ $2 LIMIT 1; Return euclidean_dis;

END; $$ LANGUAGE 'plpgsql';

Then we can just UPDATE new_rast SET rast = ST_MapAlgebraFct(rast,NULL,'euclidean_fct(float,integer[],geometry,text[])'::regprocedure);

**Comments from the Mentor:**

Why would the point have to be in the raster extent if what you pass to the custom function is the name of a geometry table (or view)?

You don't pass a raster representing the point. How would you do that if you have many point and this is not in accordance with the idea of not converting points to raster.

You pass a raster description based on parameter (similar to ST_AsRaster) or an existing raster and the name of a table (with the schema) and a geometry column (much like in ST_Count but passing a geometry column instead of a raster column) containing the points you want to compute the distance from for every pixels. Those points do not have to be in the raster extent and there can be as many as you want since you will be using the KNN index to find the nearest one.

Generally you won't want to UPDATE an existing raster but rather create a new one based on an existing raster or even better an existing raster coverage. More something like:

CREATE TABLE distcoverage AS

SELECT rid, ST_MapalgebraFct(rast, 'euclidean_fct(list of parameters including the table name and column name of the geometry table)'::regprocedure) rast

FROM myexistingcoveragetable

We should afet encapsulate the ST_Mapalgebra function with a name and parameter list corresponding to a series of ST_EucledianDistance functions (some taking raster creation parameter, some taking a reference raster).

Not sure whether I can pass in a geometry in the function above. The ways that raster and point geometry works are logically pretty similar. Like we can make point geometry from a given coordinate (x,y) pair, pixels in a raster are also organized in a relative coordinate system with the defined height and width of the raster in which the (x,y) pair of coordinate indicates the relative position of this pixel in the raster. My thoughts are when we create the result euclidean raster from the source table of points with the same extent, the points geometry can be then projected into this relative coordinate system of the raster, so that the position of a pixel and the relatively projected coordinate of a point geometry could be exchangeable with or equivalent to each other.

But there will be situation where the source data is like billions of points and the extent would be too large to create a single raster (and the computation will be very inefficient). Then we will have to consider approaches for a tiled raster as the resulted Euclidean surface for the input source.

**Comments from the Mentor:**

So the trick is to pass a geometry table name instead. That solve the issue of the point having to be in the raster extent (they don't have to). that also make it possible to create a tiled raster coverage perfectly aligned to an existing one...

That approach can be reused to compute interpolation. But is not the way to go for cost distance which is more complicated.

** What do you plan on doing next week?**

**Commented and Suggested by the Mentor:**

Write a document summarizing the approach above including, in order:

1- Objective

2-The list of constraints

3-At least three approaches (converting point to raster, creating a TIN first, the approach described here) and the pros and cons for each approaches in relation with the constraints

4-Your choice of approach and the reason why

5-A series of function signature offering flexibility to the user

Document is here: https://docs.google.com/document/d/1GAnvNDHhwUKoCOeY5GIfnAaTqya9I_HOEdotQleExCY/edit

## Week 7 Report

**What did you get done this week?**

- Disscussed with the Mentor, Came up with a final document of "Creating an Euclidean Distance Raster in PostGIS" with the link:

http://trac.osgeo.org/postgis/wiki/PostGIS_Raster_SoC_Idea_2012/Distance_Analysis_Tools/document

** What do you plan on doing next week?**

- Start to work on implementing a plpgsql prototype of the preferred solution in the document

## Week 8 Report

**What did you get done this week?**

- Did research on the scanning algorithm used by GDAL and GRASS

- Second revised document of "Creating an Euclidean Distance Raster in PostGIS" is here:

http://trac.osgeo.org/postgis/wiki/PostGIS_Raster_SoC_Idea_2012/Distance_Analysis_Tools/document

**"gdalproximity" for euclidean distance in GDAL and the "r.grow.distance" in GRASS**

- Both of them require source features in raster format
- Both of them create distance raster (and temporary file during computation) from the source raster, which dosenot allow source features to fall outside of the resulted distance raster
- Both of them use linescan to iterate among pixels to find the nearest target to the current pixel and the shortest distance
- "gdalproximity" in GDAL allocates buffer for two scanlines of distance, one loop from top to bottom of the raster, the other loop from bottom to top. For each current rows, it scans colums from left to right, and right to left. Each scanline also iterate among the source points, and replace current distance value with the shorter one (from above,below,left,right) if that applies. (Although ArcGIS doesn't reveal their scanning algorithm, from what was briefly described in their documentation, it is very likely that they are using a similar way of scanning method)
- "r.grow.distance" in GRASS dose it in another way. It computes the distance to the source points from current pixel at the same time of creating the output distance raster. It iterates among rows and then columns in each scan row, scanning left, right, and then top-left,above,top-right, replacing distance value while shorter one found. And then does another scanning while writing the output distance raster, in a reversed way of scanning (first top-left,above, top-right, and then left and right).
- Both methods could be very inefficient if the target points (features) come in a very large size.

**Source code:**

- "gdalproximity" (both c++ and java implementation): http://code.metager.de/source/xref/gdal/alg/gdalproximity.cpp http://code.metager.de/source/xref/gdal/swig/java/apps/GDALProximity.java

- grass/r.grow.distance (C implementation) http://trac.osgeo.org/grass/browser/grass/branches/develbranch_6/raster/r.grow.distance/main.c

Comments from Mentor > Do those algorithms have known names? They are called "sequential algorithms" in distance mapping, with which the distance surface will ideally be created across the entire image(raster) in one scan. ArcGIS also uses sequential algorithms in the scanning process. The distance for the current pixel under scanning is computed using recently computed values from the present scan in the neighborhood. For example, in one row scan, Dist_row(col) is determined by comparing current Dist_row(col) with the distance value assigned in its neighbor (say, Dist_row(col-1)) plus the distance from current pixel to its neighbor. If the computed distance is greater than Dist_row(col), then do nothing; otherwise Dist_row(col) will be replaced with the newly computed distance. GRASS's "r.grow.distance" is computing octagonal distance, while GDAL "gdalproximity" is doing a chessboard scanning manner. GRASS "r.grow.distance" creates the distance surface in a "growing" manner from the source points, computing distance from it's neighbor to the left/right/above/top-left/top-right. GDAL computes from the neighbor to the left/right, above/below, topright/bottomleft. > What is important here is "how many times each algorithm scan the whole > raster?". Is one more efficient than the other one? In terms of scanline, both approaches do only one scan for the whole raster, row by row. In terms of pixels, GDAL "gdalproximity" scan all the pixel once. It has two scanlines from top to bottom and bottom to top, then for each row, it also has two scanlines from right to left and left to right. (ArcGIS says they are using a "two-scan sequential process", however, they didn't explain it in details in the document, so I would assume they are using similar algorithms as GDAL.) GRASS actually scans columns in each row 4 times. The first time is to assign distance value of "0" to source pixels. Then 3 times for the neighbor to the left, right, and topleft/above/topright. So in terms of scanning times, it looks like GDAL is more efficient. > To which one of those two algorithms our approach is similar (or > comparable)? Our approach will scan the whole raster only once. So we could use a similar scanning algorithm as GDAL in terms of utilizing scanlines to do multiple scans simultaneously. > Could you describe, in two short sentences, how each of them decide which > source is the nearest for each pixel? I think both approaches don't have a specific process to actually determine the nearest source pixel to the current one, but use this sequential scanning to replace distance with shorter one from their neighbors. So it's like an accumulative method and scanning in a "growing" manner. I actually think this algorithms could be reusable for cost-weighted distance computation. > Are you still confident in our approach now that you understand better > those two algorithms? Why? Yes, I think our approach utilizing KNN indexing will show its efficiency while dealing with vary large source dataset and very high resolution resulted distance raster. Because both methods in GDAL and GRASS require the source data come in a raster format, and create resulted raster from the input source raster, and also will create temporary files during the computing process which will be very memory intensive when dealing with large dataset. Also, they don't provide the scalability for large tiled coverage and flexibility for source data being outside of the resulted distance extent.

** What do you plan on doing next week?**

- I will be taking a vacation next week. And I will have to speed it up and continue to work on implementing a plpgsql prototype for Approach 3 in the document after I come back.

## Week 10 Report

**What did you get done this week?**

- Wrote plpgsql prototype for implementing Euclidean distance with KNN indexing approach.
---------------------------------------------------------------------- -- $Id: st_euclideandistance.sql 2012-07-28 $ ---------------------------------------------------------------------- -------------------------------------------------------------------- -- ST_EuclideanDistance - (for one raster) Return a raster generated from a one band reference raster -- which values are the Euclidean distance -- from pixel to the points (for now only concerning point geometries) -- from the input source table of geometries -- (points for now, lines or polygons in the future). -- Arguments -- refrast raster - Reference raster align with which Euclidean distance raster will be created. -- pixeltype text - Pixeltype assigned to the resulting raster. By default -- to be the pixeltype of the reference raster if specified. -- sourceschema text - Database schema of the source geometry table. -- sourcetable text - Source geometry table name. -- sourcegeomcolumn text - Source geometry table geometry column name. -- maxdist double precision - Maximum distance from pixel to the source -- when the source is too far from the pixel. -- Pixel that exceeds it will be assigned nodatavalue. -------------------------------------------------------------------- DROP FUNCTION IF EXISTS ST_EuclideanDistance(refrast raster, pixeltype text, sourceschema text, sourcetable text, sourcegeomcolumn text, maxdist double precision); CREATE OR REPLACE FUNCTION ST_EuclideanDistance(refrast raster, pixeltype text, sourceschema text, sourcetable text, sourcegeomcolumn text, maxdist double precision) RETURNS raster AS $$ DECLARE width integer; height integer; x integer; y integer; sourcex float8; sourcey float8; sourcexr integer; sourceyr integer; newx float8; newy float8; newsrid integer; exesql text; sourcegeom geometry; newnodatavalue float8; newinitialvalue float8; newrast raster; newval float8; newpixeltype text; newhasnodatavalue boolean := FALSE; -- Assuming reference raster has only one band band integer := 1; BEGIN -- Check if reference raster is NULL IF refrast IS NULL THEN RAISE NOTICE 'ST_EuclideanDistance: Reference raster is NULL. Returning NULL'; RETURN NULL; END IF; width := ST_Width(refrast); height := ST_Height(refrast); newsrid := ST_SRID(refrast); -- Create a new empty raster having the same georeference as the provided raster newrast := ST_MakeEmptyRaster(width, height, ST_UpperLeftX(refrast), ST_UpperLeftY(refrast), ST_ScaleX(refrast), ST_ScaleY(refrast), ST_SkewX(refrast), ST_SkewY(refrast), newsrid); -- Check if the new raster is empty (width = 0 OR height = 0) IF ST_IsEmpty(newrast) THEN RAISE NOTICE 'ST_EuclideanDistance: Raster is empty. Returning an empty raster'; RETURN newrast; END IF; -- Set the new pixeltype newpixeltype := pixeltype; -- If pixeltype not specified then use the pixeltype of the reference raster IF newpixeltype IS NULL THEN newpixeltype := ST_BandPixelType(refrast, band); ELSIF newpixeltype != '1BB' AND newpixeltype != '2BUI' AND newpixeltype != '4BUI' AND newpixeltype != '8BSI' AND newpixeltype != '8BUI' AND newpixeltype != '16BSI' AND newpixeltype != '16BUI' AND newpixeltype != '32BSI' AND newpixeltype != '32BUI' AND newpixeltype != '32BF' AND newpixeltype != '64BF' THEN RAISE EXCEPTION 'ST_EuclideanDistance: Invalid pixeltype "%". Aborting.', newpixeltype; END IF; -- Check for notada value newnodatavalue := ST_BandNodataValue(refrast, band); IF newnodatavalue IS NULL OR newnodatavalue < ST_MinPossibleValue(newpixeltype) OR newnodatavalue > (-ST_MinPossibleValue(newpixeltype) - 1) THEN RAISE NOTICE 'ST_EuclideanDistance: Reference raster do not have a nodata value or is out of range for the new raster pixeltype, nodata value for new raster set to the min value possible'; newnodatavalue := ST_MinPossibleValue(newpixeltype); END IF; -- We set the initial value of the resulting raster to nodata value. -- If nodatavalue is null then the raster will be initialise to ST_MinPossibleValue newinitialvalue := newnodatavalue; -- Create the raster receiving all the computed distance values. Initialize it to the new initial value. newrast := ST_AddBand(newrast, newpixeltype, newinitialvalue, newnodatavalue); -- Scan pixels in the raster set pixel value to zero if pixel is source point -- There is place for optimization here for a more efficient scanning method FOR x IN 1..width LOOP FOR y IN 1..height LOOP exesql := "SELECT " || sourcegeomcolumn || " FROM " || sourcetable || ";"; FOR geom IN EXECUTE(exesql) LOOP sourcex := ST_X(geom); sourcey := ST_Y(geom); sourcexr := ST_World2RasterCoordX(newrast, sourcex, sourcey); sourceyr := ST_World2RasterCoordY(newrast, sourcex, sourcey); IF x = sourcexr AND y = sourceyr THEN newrast := ST_SetValue(newrast, band, x, y, 0); END IF; END LOOP; END LOOP; END LOOP; -- There is place for optimization here for a more efficient scanning method FOR x IN 1..width LOOP FOR y IN 1..height LOOP newx := ST_Raster2WorldCoordX(newrast, x, y); newy := ST_Raster2WorldCoordY(newrast, x, y); -- If pixel is not the source point then compute Eculidean distance IF ST_Value(newrast, band, x, y) <> 0 THEN exesql := "SELECT ST_Distance(ST_GeomFromText('POINT(" || newx || " " || newy || ")'," || newsrid || "),ST_Transform(" || sourcegeomcolumn || "," || newsrid || ")) FROM " || sourcetable || " ORDER BY $1 <-> $2 LIMIT 1;"; newval := EXECUTE(exesql); END IF; newrast := ST_SetValue(newrast, band, x, y, newval); END LOOP; END LOOP; RETURN newrast; END; $$ LANGUAGE 'plpgsql';

** What do you plan on doing next week?**

- Test the prototype.
- Start writing document for cost-weighted distance.
- Start writing C implementation for Euclidean distance.

## Week 11 Report

**What did you get done this week?**

- Revised plpgsql scripts for implementing Euclidean distance with KNN indexing approach for two signatures.
- Wrote document for cost-weighted distance.

**What do you plan on doing next week?**

- Revise the cost-weighted distance document.
- Test the plpgsql scripts.
- Write C implementation for Euclidean distance.

## Week 12 Report

**What did you get done this week?**

- Finished plpgsql scripts for implementing Euclidean distance with KNN indexing approach for two signatures.
- Get scripts tested.
- Started working on the C implementation for Euclidean distance.
- Started working on plpgsql prototype for cost-weigted distance.

**What do you plan on doing next week?**

- Get final versoin of document for both Euclidean Distance and Cost-weighted distance.
- Finish writing C implementation for Euclidean distance.
- Finish writing plpgsql prototype for cost-weigted distance.
- Final revision for all the codes.

**PL/pgSQL script for Euclidean Distance Raster: ST_EuclideanDistance()**

---------------------------------------------------------------------- -- $Id: st_euclideandistance.sql 2012-07-28 $ ---------------------------------------------------------------------- -------------------------------------------------------------------- -- ST_EuclideanDistance - (for one raster) Return a raster generated from a one band reference raster -- which values are the Euclidean distance -- from pixel to the points (for now only concerning point geometries) -- from the input source table of geometries -- (points for now, lines or polygons in the future). -- Arguments -- width,height,rastulx,rastuly,rastscalex,rastscaley,rastskewx,rastskewy -- - Dimenssions and georeference of resulted raster. -- rastsrid - SRID of the resulted raster -- rastndv - Nodata value for resulted raster -- pixeltype text - Pixeltype assigned to the resulting raster. By default -- to be the pixeltype of the reference raster if specified. -- sourceschema text - Database schema of the source geometry table. -- sourcetable text - Source geometry table name. -- sourcegeomcolumn text - Source geometry table geometry column name. -- maxdist double precision - Maximum distance from pixel to the source -- when the source is too far from the pixel. -- Pixel that exceeds it will be assigned nodatavalue. -------------------------------------------------------------------- DROP FUNCTION IF EXISTS ST_EuclideanDistance(width integer, height integer, rastulx float8, rastuly float8, rastscalex float8, rastscaley float8, rastskewx float8, rastskewy float8, rastsrid integer, rastndv float8, pixeltype text, sourceschema text, sourcetable text, sourcegeomcolumn text, double precision = -1); CREATE OR REPLACE FUNCTION ST_EuclideanDistance(width integer, height integer, rastulx float8, rastuly float8, rastscalex float8, rastscaley float8, rastskewx float8, rastskewy float8, rastsrid integer, rastndv float8, pixeltype text, sourceschema text, sourcetable text, sourcegeomcolumn text, double precision = -1) RETURNS raster AS $$ DECLARE maxdist ALIAS FOR $15; x integer; y integer; sourcex float8; sourcey float8; sourcexr integer; sourceyr integer; sourcesrid integer; newx float8; newy float8; exesql text; newnodatavalue float8; newinitialvalue float8; newrast raster; newval float8; newpixeltype text; -- Assuming reference raster has only one band band integer := 1; geom geometry; BEGIN -- Assuming source point table has one SRID for all geometries EXECUTE 'SELECT DISTINCT ST_SRID(' || sourcegeomcolumn || ') FROM ' || sourceschema || '.' || sourcetable || ';' INTO sourcesrid; -- Create a new empty raster using provided georeference newrast := ST_MakeEmptyRaster(width, height, rastulx, rastuly, rastscalex,rastscaley, rastskewx, rastskewy, rastsrid); -- Check if the new raster is empty (width = 0 OR height = 0) IF ST_IsEmpty(newrast) THEN RAISE NOTICE 'ST_EuclideanDistance: Raster is empty. Returning an empty raster'; RETURN newrast; END IF; -- Set the new pixeltype newpixeltype := pixeltype; IF newpixeltype != '1BB' AND newpixeltype != '2BUI' AND newpixeltype != '4BUI' AND newpixeltype != '8BSI' AND newpixeltype != '8BUI' AND newpixeltype != '16BSI' AND newpixeltype != '16BUI' AND newpixeltype != '32BSI' AND newpixeltype != '32BUI' AND newpixeltype != '32BF' AND newpixeltype != '64BF' THEN RAISE EXCEPTION 'ST_EuclideanDistance: Invalid pixeltype "%". Aborting.', newpixeltype; END IF; -- Check for notada value newnodatavalue := rastndv; IF newnodatavalue IS NULL OR newnodatavalue < ST_MinPossibleValue(newpixeltype) OR newnodatavalue > (-ST_MinPossibleValue(newpixeltype) - 1) THEN RAISE NOTICE 'ST_EuclideanDistance: Reference raster do not have a nodata value or is out of range for the new raster pixeltype, nodata value for new raster set to the min value possible'; newnodatavalue := ST_MinPossibleValue(newpixeltype); END IF; -- We set the initial value of the resulting raster to nodata value. -- If nodatavalue is null then the raster will be initialise to ST_MinPossibleValue newinitialvalue := newnodatavalue; -- Create the raster receiving all the computed distance values. Initialize it to the new initial value. newrast := ST_AddBand(newrast, newpixeltype, newinitialvalue, newnodatavalue); -- Set raster pixel value to zero if pixel is source point exesql := 'SELECT ' || sourcegeomcolumn || ' FROM ' || sourceschema || '.' || sourcetable || ';'; FOR geom IN EXECUTE(exesql) LOOP sourcex := ST_X(geom); sourcey := ST_Y(geom); sourcexr := ST_World2RasterCoordX(newrast, sourcex, sourcey); sourceyr := ST_World2RasterCoordY(newrast, sourcex, sourcey); -- If source point within raster range, then set pixel value to zero IF sourcexr <= width AND sourceyr <= height THEN newrast := ST_SetValue(newrast, band, sourcexr, sourceyr, 0); END IF; END LOOP; -- Scan pixels in the raster set pixel value to the computed Eculidean distance -- to its nearest source point. -- There is place for optimization here for a more efficient scanning method FOR x IN 1..width LOOP FOR y IN 1..height LOOP newx := ST_Raster2WorldCoordX(newrast, x, y); newy := ST_Raster2WorldCoordY(newrast, x, y); -- If pixel is not the source point then compute Eculidean distance IF ST_Value(newrast, band, x, y) != 0 OR ST_Value(newrast, band, x, y) IS NULL THEN exesql := 'SELECT ST_Distance(ST_SetSRID(ST_MakePoint(' || newx || ',' || newy || '),' || sourcesrid || '), (SELECT ' || sourcegeomcolumn || ' FROM ' || sourceschema || '.' || sourcetable || ' ORDER BY ' || sourcegeomcolumn || ' <-> ST_SetSRID(ST_MakePoint(' || newx || ',' || newy || '),' || sourcesrid || ') LIMIT 1));'; EXECUTE exesql INTO newval; -- If max distance specified IF maxdist != -1 THEN -- Any distance exceeds max distance is assigned nodata value IF newval > maxdist THEN newval = newnodatavalue; END IF; END IF; END IF; newrast := ST_SetValue(newrast, band, x, y, newval); END LOOP; END LOOP; RETURN newrast; END; $$ LANGUAGE 'plpgsql'; -- test -- CREATE TABLE test_eudist_2 AS (SELECT 1 AS rid,ST_EuclideanDistance(10, 10, 0, 0, 9, 9, 0, 0, 4326,-1,'32BF','public','test_geom','the_geom') AS rast FROM test_raster); -- CREATE TABLE test_eudist_22 AS (SELECT 1 AS rid,ST_EuclideanDistance(10, 10, 0, 0, 9, 9, 0, 0, 4326,-1,'32BF','public','test_geom','the_geom',4954880) AS rast FROM test_raster); -------------------------------------------------------------------- -- ST_EuclideanDistance - (for one raster) Return a raster generated from a one band reference raster -- which values are the Euclidean distance -- from pixel to the points (for now only concerning point geometries) -- from the input source table of geometries -- (points for now, lines or polygons in the future). -- Arguments -- refrast raster - Reference raster align with which Euclidean distance raster will be created. -- pixeltype text - Pixeltype assigned to the resulting raster. By default -- to be the pixeltype of the reference raster if specified. -- sourceschema text - Database schema of the source geometry table. -- sourcetable text - Source geometry table name. -- sourcegeomcolumn text - Source geometry table geometry column name. -- maxdist double precision - Maximum distance from pixel to the source -- when the source is too far from the pixel. -- Pixel that exceeds it will be assigned nodatavalue. -------------------------------------------------------------------- DROP FUNCTION IF EXISTS ST_EuclideanDistance(refrast raster, pixeltype text, sourceschema text, sourcetable text, sourcegeomcolumn text, double precision = -1); CREATE OR REPLACE FUNCTION ST_EuclideanDistance(refrast raster, pixeltype text, sourceschema text, sourcetable text, sourcegeomcolumn text, double precision = -1) RETURNS raster AS $$ DECLARE maxdist ALIAS FOR $6; width integer; height integer; x integer; y integer; sourcex float8; sourcey float8; sourcexr integer; sourceyr integer; sourcesrid integer; newsrid integer; newx float8; newy float8; exesql text; newnodatavalue float8; newinitialvalue float8; newrast raster; newval float8; newpixeltype text; -- Assuming reference raster has only one band band integer := 1; geom geometry; BEGIN -- Check if reference raster is NULL IF refrast IS NULL THEN RAISE NOTICE 'ST_EuclideanDistance: Reference raster is NULL. Returning NULL'; RETURN NULL; END IF; width := ST_Width(refrast); height := ST_Height(refrast); newsrid := ST_SRID(refrast); -- Assuming source point table has one SRID for all geometries EXECUTE 'SELECT DISTINCT ST_SRID(' || sourcegeomcolumn || ') FROM ' || sourceschema || '.' || sourcetable || ';' INTO sourcesrid; -- Create a new empty raster having the same georeference as the provided raster newrast := ST_MakeEmptyRaster(width, height, ST_UpperLeftX(refrast), ST_UpperLeftY(refrast), ST_ScaleX(refrast), ST_ScaleY(refrast), ST_SkewX(refrast), ST_SkewY(refrast), newsrid); -- Check if the new raster is empty (width = 0 OR height = 0) IF ST_IsEmpty(newrast) THEN RAISE NOTICE 'ST_EuclideanDistance: Raster is empty. Returning an empty raster'; RETURN newrast; END IF; -- Set the new pixeltype newpixeltype := pixeltype; -- If pixeltype not specified then use the pixeltype of the reference raster IF newpixeltype IS NULL THEN newpixeltype := ST_BandPixelType(refrast, band); ELSIF newpixeltype != '1BB' AND newpixeltype != '2BUI' AND newpixeltype != '4BUI' AND newpixeltype != '8BSI' AND newpixeltype != '8BUI' AND newpixeltype != '16BSI' AND newpixeltype != '16BUI' AND newpixeltype != '32BSI' AND newpixeltype != '32BUI' AND newpixeltype != '32BF' AND newpixeltype != '64BF' THEN RAISE EXCEPTION 'ST_EuclideanDistance: Invalid pixeltype "%". Aborting.', newpixeltype; END IF; -- Check for notada value newnodatavalue := ST_BandNodataValue(refrast, band); IF newnodatavalue IS NULL OR newnodatavalue < ST_MinPossibleValue(newpixeltype) OR newnodatavalue > (-ST_MinPossibleValue(newpixeltype) - 1) THEN RAISE NOTICE 'ST_EuclideanDistance: Reference raster do not have a nodata value or is out of range for the new raster pixeltype, nodata value for new raster set to the min value possible'; newnodatavalue := ST_MinPossibleValue(newpixeltype); END IF; -- We set the initial value of the resulting raster to nodata value. -- If nodatavalue is null then the raster will be initialise to ST_MinPossibleValue newinitialvalue := newnodatavalue; -- Create the raster receiving all the computed distance values. Initialize it to the new initial value. newrast := ST_AddBand(newrast, newpixeltype, newinitialvalue, newnodatavalue); -- Set raster pixel value to zero if pixel is source point exesql := 'SELECT ' || sourcegeomcolumn || ' FROM ' || sourceschema || '.' || sourcetable || ';'; FOR geom IN EXECUTE(exesql) LOOP sourcex := ST_X(geom); sourcey := ST_Y(geom); sourcexr := ST_World2RasterCoordX(newrast, sourcex, sourcey); sourceyr := ST_World2RasterCoordY(newrast, sourcex, sourcey); -- If source point within raster range, then set pixel value to zero IF sourcexr <= width AND sourceyr <= height THEN newrast := ST_SetValue(newrast, band, sourcexr, sourceyr, 0); END IF; END LOOP; -- Scan pixels in the raster set pixel value to the computed Eculidean distance -- to its nearest source point. -- There is place for optimization here for a more efficient scanning method FOR x IN 1..width LOOP FOR y IN 1..height LOOP newx := ST_Raster2WorldCoordX(newrast, x, y); newy := ST_Raster2WorldCoordY(newrast, x, y); -- If pixel is not the source point then compute Eculidean distance IF ST_Value(newrast, band, x, y) != 0 OR ST_Value(newrast, band, x, y) IS NULL THEN exesql := 'SELECT ST_Distance(ST_SetSRID(ST_MakePoint(' || newx || ',' || newy || '),' || sourcesrid || '), (SELECT ' || sourcegeomcolumn || ' FROM ' || sourceschema || '.' || sourcetable || ' ORDER BY ' || sourcegeomcolumn || ' <-> ST_SetSRID(ST_MakePoint(' || newx || ',' || newy || '),' || sourcesrid || ') LIMIT 1));'; EXECUTE exesql INTO newval; -- If max distance specified IF maxdist != -1 THEN -- Any distance exceeds max distance is assigned nodata value IF newval > maxdist THEN newval := newnodatavalue; END IF; END IF; END IF; newrast := ST_SetValue(newrast, band, x, y, newval); END LOOP; END LOOP; RETURN newrast; END; $$ LANGUAGE 'plpgsql'; -- test -- CREATE TABLE test_eudist AS (SELECT 1 AS rid,ST_EuclideanDistance(rast,NULL,'public','test_geom','the_geom') AS rast FROM test_raster); -- CREATE TABLE test_eudist2 AS (SELECT 1 AS rid,ST_EuclideanDistance(rast,NULL,'public','test_geom','the_geom',4954880) AS rast FROM test_raster);

## Week 13 Report

**What did you get done this week?**

- Finished plpgsql scripts for implementing Cost-weighted distance for two signatures.
- Finished documentations for both Euclidean and Cost-weighted distance plpgsql scirpt.

**What do you plan on doing after gsoc?**

- Optimize plpgsql scirpts for Euclidean and Cost-weighted distance.
- Work on C implementations.

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