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Create Accurate Raster Datasets with the New Raster Tools Add-in for Esri ArcMap

Today is an exciting day with the release of our newest product, Raster Tools! Raster Tools is a powerful add-in to Esri’s ArcGIS for Desktop (ArcMap) software which creates accurate raster datasets. With Raster Tools, users have access to twelve advanced interpolation methods and control over all interpolation parameters.

Raster Tools: Interpolation Add-in for Esri ArcMap
Transform regularly or irregularly spaced data into accurate raster datasets.

The sophisticated interpolation methods available in Raster Tools are based on the same algorithms available in Golden Software’s well-known program, Surfer. Interpolation methods include Kriging, Inverse Distance, Minimum Curvature, Polynomial Regression, Triangulation with Linear Interpolation, Nearest Neighbor, Modified Shepard’s Method, Radial Basis Function, Natural Neighbor, Moving Average, Data Metrics, and Local Polynomial.

Raster Tools beta tester Paul Morrison comments, “You have so many options for creating the rasters. I also have a lot of trust in the algorithms.” Another beta tester, Tobais Spears, says, “All of my interpolation methods were there in front of me, and the over-arching workflow was very user friendly.”

Who should use Raster tools? Any ArcMap 10.3 user requiring more interpolation methods, more accurate algorithms, and absolute control over all interpolation parameters. Contact us if you would like to see Raster Tools available for other versions of ArcMap.

Why an add-in? Raster Tools differs from other Golden Software products as it’s an ArcMap add-in. Raster Tools was developed based on direct feedback from users who wanted the extensive interpolation controls offered by Surfer directly in the ArcMap environment. We answered those requests with Raster Tools and are excited to enhance the ArcMap environment!

For more information, visit the Raster Tools product page or test it out for yourself with the free 14 day trial!



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19 April 2016


A classified raster layer in ArcMap generated from Raster Tools overlaid with a roads shapefile.

Golden Software’s new Raster Tools add-in for ArcMap leverages Surfer’s 12 different gridding methods directly in the ArcMap ecosystem to create accurate and precise raster datasets from your point data with only a few clicks. Raster Tools is a wizard-based add-in that walks you through all of the necessary interpolation parameters that have been elegantly laid out on 3 pages, so you have quick access to select an interpolation method, customize neighborhood search parameters, choose output raster extents and resolution, and more.

For today’s blog post, I would like to walk you through an example of interpolating elevation point data using the Raster Tools add-in, so you can see how user friendly and easy this new tool is to use. To start things off, I am going to add some elevation data from Oahu (near Honolulu) to ArcMap. Now that the elevation data has been added to an ArcMap project, I am going to start the interpolation by clicking the Raster Tools tool bar and choosing Raster Tools | Interpolation Wizard.

The first page of the Interpolation Wizard opens. On the left-hand side, I can choose from any of the 12 interpolation methods, where each have a nice help tip describing the method.  On the right-hand side of the page, I can choose the dataset I want to interpolate, which field I am interpolating, and how to handle duplicate points with the data (if there are any). For this example, I am going to select the ever-popular Kriging as my interpolation method.


The first page of Raster Tools Interpolation Wizard.

After clicking Next, the second page of the Interpolation Wizard opens.  On this page, I get a quick look at the statistics surrounding the data and a preview of the data point dispersion with an overlapping search ellipse. Since I selected Kriging for my interpolation method, I have the option to pick Kriging-specific parameters on this page. I am working in ArcMap, so I will select the Block Kriging type, which estimates the average value of the cells centered on the grid nodes.  I am also going to use a custom search neighborhood because I don’t want all of the data to influence the resultant interpolated points. I think a search radius of 3000 will work well for the data dispersion of this dataset.


The second page of the Interpolation Wizard showing the search neighborhood options among other interpolation parameters.

I don’t have any breaklines for the area that need to be included in the interpolation, so I’m going to click Next to go to the final page of the wizard. On the third and final page, I can set the output raster’s resolution and extents. The data I’m working with is in meters and I’m working on a large scale mapping project, so it makes sense to generalize a little bit and use a cell size of  25. I am also going to leave the extent parameters as-is because I can clip the raster later in ArcMap, if necessary. Finally, Raster Tools writes the output raster in ADF, IMG, and TIF format in personal geodatabases, file geodatabases, or simple file folders.  The default output raster is ADF, which I’m OK with, and I’m going to click next to Filename to name the raster and click Finish to start the interpolation and save the output raster to the project’s default geodatabase.


The third page of the Interpolation Wizard, where the output raster parameters are assigned.

Now that the interpolation has quickly completed, a new raster layer has been added to my ArcMap project. I can now use the tools available in ArcMap for customizations like changing the symbology, adding a hill shade effect, adding vector files, etc. I also now have the ADF file that was added to the default geodatabase for use in other ArcMap projects and 3rd party applications.


The resulting raster layer created from Raster Tools added to the ArcMap project.

As you can see, Raster Tools is a quick and easy way to interpolate data directly inside of ArcMap, using the intuitive controls and powerful options Surfer offers! For more information, visit the Raster Tools product page, or test it our yourself with the free 14-day trial!

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05 April 2016

Golden Software recently hosted a training class on gridding and interpolating data in Surfer. Before the class was held, a user asked if we’d specifically cover the best options for gridding airborne geophysical data. At the time, it was not in the schedule, but as I looked at the data I thought this type of data could be very common and would make a great example. In this type of data, the data is taken in lines, where the data points along the lines are much closer together than the spacing between the lines.  Users generally want to interpolate the data to create a smooth color-filled image map while maintaining the data at sufficient resolution to show important anomalies.


A popular source of airborne geophysical survey data is the USGS aeromagnetic surveys, available at Some of the data sets have over one million data points, which is quite cumbersome to work with unless you have a lot of memory available on your computer. One data set that is more manageable to work with, and which we’ll use for this example, is for Cuprite, Nevada. This is a tough data set to interpolate, because of the high density of data in one direction (along the flight lines) versus the other direction (between the flight lines).

To find the best gridding method for this (or any) data set, these are the steps I perform when evaluating gridding options:

  1. Create a classed post map to visualize the data distribution.
  2. Run the sample script GridData_Comparison.bas to compare gridding methods and narrow down the gridding methods to just a few.
  3. Manually grid the data with the gridding methods previously selected with more specific gridding options.
  4. Calculate the residuals to determine the most accurate method (optional).

In detail, the steps I followed with this data set are outlined below.

Step 1: Create a Classed Post Map

The first step I always do is to create a classed post map of the data. I find that it is very useful to get a feel for the spatial distribution of the data, so I know what to expect when gridding the data. For example, areas with fewer data points may generate some odd-looking contours or contours that are significantly different depending upon gridding method. Or if the data points are regularly spaced, I know that Nearest Neighbor might be the best gridding method. In this case, I also want to find out how close the data points are along the lines and between the lines.

  1. Click Map | New | Classed Post Map, select NV_1152.dat and click Open.
  2. Select the Classed Post layer in the Object Manager, and in the Property Manager, on the General page, change the X,  Y and Z columns to C, D and K, respectively. I noticed the data points were very close together in the X direction, but father apart in the Y direction. 
  3. Click the Classes tab in the Property Manager.
  4. Click the Edit Classes button.
  5. In the Classes for Map dialog:
    1. Set the Binning method to Equal Intervals.
    2. Set the Number of classes to 7.  
    3. Note that interval between classes is about 23, the minimum of the first class is about -288, and the minimum of the last class is about -154.
    4. Click the Symbol button. In the Class Symbol Properties dialog, set the Line color to Black with 10% opacity, and change the symbol itself to a square shape. Click OK.
    5. Click the Size button.  Set the Minimum size and Maximum size to the same small value, 0.005, and click OK.
    6. Click each of the symbols for each class and change the Fill color for that class. I chose navy blue, cyan, green, yellow, red, magenta and pink for my classes.
    7. Click Save and save the class definitions to a CLS file (ClassedPostClasses.cls) for future use.
    8. Click OK.
  6. Then, I measure the distance of points. I zoom in to the map so I could see the points individually along the lines and click Map | Measure.
    1. In the Y direction, the flight lines were spaced apart about 0.005° on average.
    2. In the X direction, the points were spaced apart about every 0.0001° on average.
  7. Press the ESC key to exit measuring mode.
  8. I want to make the symbol sizes a little larger now. Click the Edit Classes button again and click the Size button.
  9. Set the Minimum size and Maximum size to the same small value, 0.02, and click OK and OK.


Step 2: Run the Script

After getting a feel for the data, I run the script GridData_Comparison.bas with the data file using columns 3, 4, 11 (C, D, K), and compare the results to see the best potential gridding methods. The script grids the data with the eight most popular gridding methods and creates a map from the result. It uses the default options, so it’s not perfect, but gives us a good idea for what the data looks like gridded with those methods.

  1. Open Scripter by clicking Windows Start | All Programs | Golden Software Surfer 13 | Scripter.
  2. Once Scripter is opened, click File | Open, navigate to the Surfer 13 SamplesScripts directory (within the installation folder), select GridData_Comparison.bas and click Open.
  3. Click Script | Run.
  4. When prompted for the data, select NV_1152.dat and click Open.
  5. When asked for the X data column, enter 3 and click OK.
  6. When asked for the Y data column, enter 4 and click OK.
  7. When asked for the Z data column, enter 11 and click OK. The script opens a new Surfer window and creates a map in it for each of the eight gridding methods.

Right away, I can eliminate Modified Shepard’s Method and Nearest Neighbor because I do not think the interpolation matches what I want the map to look like. I delete those maps. I also do not like how jagged the map is using Radial Basis Function. I delete that map also.


That still leaves five potential gridding method options. To try and eliminate others, I next check how well the contours match the original data.

  1. Click back to the plot window containing the classed post map previously created, copy it, click back to the window with the contour maps in it, and paste it five times.
  2. Overlay each of the classed post maps with each of the remaining contour maps by dragging a Classed Post layer into each of the maps above the Contour layers in the Object Manager
  3. Adjust the contours to match the classed post map colors. To do this, select one of the contour layers and click on the Levels tab in the Property Manager. On the Levels page:
    1. Change the Level method to Advanced, and click Edit Levels.
    2. Click the Level button, set the Minimum to -288 and the Interval to 23 (just like the classed post classes) and click OK.
    3. Double click on each of the levels under the Fill column and change the color fill for each level to the same as that of the classed post classes (navy blue, cyan, green, yellow, red, magenta, pink).
    4. You may want to fine tune the values for each level to match the minimum value of the classed post map classes exactly.
    5. Click Save and saved the levels to an LVL file (ContourLevels.lvl) and click OK.
    6. Check the check box next to Fill contours.
  4. Load the LVL file for each of the four remaining contour layers and fill the contours. For example:
    1. Select one of the other contour layers and click on the Levels tab in the Property Manager.
    2. Change the Level method to Advanced, and click Edit Levels.
    3. Click Load, select the LVL file and click Open and OK
    4. Check the check box next to Fill contours.
  5. All the methods seem relatively accurate to the data, so that is good. But I do not like how Triangulation with Linear Interpolation interpolated the data in between the flight lines. When I zoom in, it appears quite jagged. So I remove that option. That leaves four gridding methods.

Triangulation with Linear Interpolation did not interpolate the data between flight lines as desired.

Step 3: Grid the Data Manually with Options Specific to Data

With the 4 methods left (Inverse Distance, Kriging, Natural Neighbor, and Minimum Curvature), I wanted to grid them with the actual grid spacing of the data to help pick up any anomalies that the coarser default grid spacing missed. I do this manually.

  1. Click Grid | Data, select NV_1152.dat and click Open.
  2. In the Grid Data dialog:
    1. Set the XYZ columns to C, D and K, respectively.
    2. Select Inverse Distance to a Power gridding method
    3. Change the Spacing in the X direction to 0.0001 (the distance between points in the X direction, as measured above).
    4. Change the Spacing in the Y direction to 0.001 (about 1/5 the distance between flight lines).
    5. Check Blank grid outside convex hull of data so that the areas outside the flight lines are not interpolated.
    6. Change the Output Grid File name to include the gridding method (e.g. InverseDistance_HiRes.grd).
    7. Click OK. The grid is created.
  3. Now let’s update the map with the new grid. Select the contour layer for Inverse Distance, and in the Property Manager, on the General page, click the Open Grid button, select the new grid file and click Open. The contours are updated.
  4. Repeat steps 1-3 for Kriging, Natural Neighbor, and Minimum Curvature.
  5. Once all the maps are updated, I can see that gridding with Minimum Curvature at a higher resolution method really smeared the contours out. I don’t like that. Setting an anisotropy value (an advanced option for that gridding method) didn’t seem to help, so I eliminated that method. That left 3 potential gridding methods: Inverse Distance to a Power, Kriging, and Natural Neighbor

Minimum curvature smeared out the contours at high resolution.

Step 4: Calculate Residuals

At this point, all three remaining maps look pretty similar to me visually, and they all look “nice”. The next step is to see if I can tell a difference between the maps (and hence grids) mathematically, using the residuals. This is important because although I want the map to look nice, I also want it to be the most accurate relative to the data. For this, I will calculate the sum of the residuals for those three grids.

  1. Click Grid | Residuals.
  2. Select the high resolution Kriging grid and click Open.
  3. Select NV_1152.dat and click Open.
  4. Select columns C, D and K as XYZ and store the residuals in column L.
  5. Click OK. The worksheet opens with the data and the new Residuals column.
  6. Click File | Save to save the worksheet and then File | Close to close it.
  7. Click Grid | Residuals again.
  8. Select the high resolution Inverse Distance grid and click Open.
  9. Select NV_1152.dat and click Open.
  10. Select columns C, D and K as XYZ and store the residuals in column M.
  11. Click OK. The worksheet opens with the data and the new Residuals column.
  12. Click File | Save to save the worksheet and then File | Close to close it.
  13. Click Grid | Residuals a last time.
  14. Select the high resolution Natural Neighbor grid and click Open.
  15. Select NV_1152.dat and click Open.
  16. Select columns C, D and K as XYZ and store the residuals in column N.
  17. Click OK. Now all three residuals are there in the worksheet.
  18. Let’s calculate the square of the residuals.
    1. Select column L by clicking on the column header.
    2. Click Data | Transform, enter the function L=L*L
    3. Click OK.
    4. Click Data | Statistics, make sure Sum is checked and click OK. The sum of the square of the residuals (for the Kriging grid) is displayed. Note this value and click Close.
    5. Find the sum of the square of the residuals.
  19. Repeat Step 17 for columns M (Inverse Distance) and N (Natural Neighbor).
  20. The results show that Kriging has the lowest residuals and so is the most accurate relative to the original data. You can calculate the residuals for the original Kriging grid created by the script with the default settings to compare if decreasing the grid spacing (increasing the resolution) has increased the accuracy, and yes it has.

Compare the sum of the square of the residuals to compare gridding methods.

Based on the residuals, the Kriging grid best represents the data with a higher density of grid nodes. So my conclusion is that the best gridding parameters to use for this data set is the Kriging gridding method with a grid node spacing of 0.0001 in the X direction and 0.001 in the Y direction.

Although much time was spent to conclude the best gridding parameters, most of the time was spent eliminating the other methods or parameters. I can now be confident that I’m using an accurate set of gridding parameters to best display the data.

A stunning image map of the geophysical data is created with the Kriging gridding method.

The map above is created with proportional XY scaling (and the maps created by the script are scaled to a particular size, unrelated to their map coordinates). As many of you know, 1° of latitude does not equal 1° of longitude, except at the equator. Therefore, the map above is a little compressed in the Y direction. To scale lat/long maps correctly, multiply the X scaling by the cosine of the latitude, and then multiply the Y scaling by that value. Please see our KB article: How can I scale my Lat/Long maps correctly? 


Another option, suggested by a user, is to convert the data from lat/long to UTM (or another coordinate system with linear units) prior to gridding. Having linear, or proportional, XY units could have a slight effect on the search around the grid nodes, or the weight of the data points in the grid node calculation. For instructions on converting the coordinate system of raw data in Surfer, please see our KB article: How can I convert the coordinate system of raw data, such as from UTM to Lat/Long?

For more information about gridding data, please see these additional resources:

  1. The Help (Help | Contents): On the Contents page, review the topics under the Gridding | Gridding Methods book
  2. Webinar Gridding in Surfer
  3. Knowledge Base articles:
    1. What types of gridding methods are available?
    2. What gridding method is best for my data file?
    3. What advanced options are available for each of the different gridding methods?
  4. Newsletter articles:
    1. What gridding method should I use? Gridding for non-geostatisticians
    2. A Basic Understanding of Surfer Gridding Methods – Part 1
    3. A Basic Understanding of Surfer Gridding Methods – Part 2
  5. Paper by Chin-Shung Yang, Szu-Pyng Kao, Fen-Bin Lee, Pen-Shan Hung: TWELVE DIFFERENT INTERPOLATION METHODS: A CASE STUDY OF SURFER 8.0
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16 February 2016

Over the years, one of the most common questions asked is “How can I get my contour map out of Surfer mapping software and into ArcMap?”  It's actually quite easy to get maps from Surfer into ArcMap.  You can just click File | Export from Surfer and export to a shapefile (*.SHP).  There are other formats you can choose (e.g. DXF, MIF, GeoTIFF, etc.) but I will focus on SHP for this article.

You might ask, “What about attributes?” When exporting to a SHP file in Surfer 13, the Z value of the contour lines are exported as attributes to the associated DBF file. In addition, if you have objects in a base layer that have attributes, those attributes are exported to the SHP file as well. All attributes are stored in the associated DBF file for the SHP.

When exporting a SHP file from Surfer, you do have a few options to choose from regarding the storage of points, polygons, marker symbols and text. What options you choose depends on what is in the Surfer file you are exporting, what version of Surfer you are using, and ultimately what you want the SHP file to contain.

Let’s say you gridded some data in Surfer 13, created a contour map, overlaid a post map showing the original point locations, and wanted to export it to a SHP so you can import it into ArcMap.  Note that when exporting to a SHP file, it is best to not show contour line labels, as they would create a break in the otherwise continuous contour polyline.

Creating a contour map in Surfer to export into ESRI ArcMap

Export a contour map from Surfer 13 to a SHP file using File | Export and import it into ArcMap.

To export this map, you would:

  1. Click File | Export.
  2. In the Export dialog:
    1. Give the file a name.
    2. Next to Save as type, select the SHP ESRI Shapefile format.
    3. Make sure Show options dialog is checked.
    4. Click Save.
  3. In the Export Options dialog, there are three pages of options: 

a.    On the Scaling page, make sure the Scaling source is set to Map. This will set the File Rectangle coordinates to map units and allow the map to be exported in the map units. It is very important that the coordinates under File Rectangle are in map units. If you are using an older version of Surfer (Surfer 11 or earlier), then there are a few extra steps you may need to do to get map units in the File Rectangle boxes (see our KB article online). The only time you cannot export in map units is when you export 3D surface or wireframe maps. These map types are not suitable for export to SHP.

Creating a contour map in Surfer to export into ESRI ArcMap

On the Scaling page, make sure the File Rectangle units are in map units.

b.      On the Spatial References page, you most likely want the ESRI .PRJ file option checked so that Surfer can export the coordinate system for the map to a PRJ file, and then ArcMap knows the coordinate system information for the file when you import it. If you did not set a coordinate system for the map in Surfer, then no PRJ file is created.

Creating a contour map in Surfer to export into ESRI ArcMap

On the Spatial References page, make sure ESRI .PRJ file is checked.

c.       The SHP Options page is where you tell Surfer how to export the information to the SHP. You may or may not be aware that SHP files can only have one object type per file. You can have points, polylines or polygons. You cannot have more than one type of object (e.g. a point and a polyline) in a single SHP, and no other object types (e.g. text, images) are supported. When exporting multiple object types to SHP, Surfer can either convert all objects to polylines and save all objects to a single polyline file, or it can write points and polygons out to their own files.

Creating a contour map in Surfer to export into ESRI ArcMap

Select the desired options on the SHP Options page. 

                                                               i.      Under Areas, select whether Surfer should convert the polygons to polylines, or if it should keep the polygons as polygons and write them out to a new polygon file. It is really up to you, but most users create a separate file for polygon objects.

                                                             ii.      Under Points, select whether Surfer should convert any point objects to polylines, or if it should keep the point objects  and write them out to a new point file. Again, this is up to you but must users create a separate SHP file for point objects.

                                                            iii.      Since text is not supported in a SHP file, if you want to include the text objects (e.g. axis labels, contour labels, etc), you can check Render text. That will convert the text characters to individual polygons and write them out as polygons using the option selected under Areas. Most users don’t require text in their SHP files and don’t want extra polygons, so I will leave this unchecked. That means that no text (axis labels or contour labels) are exported to the SHP.

                                                           iv.      Marker symbols are also not supported in SHP files. Marker symbols are the actual symbol shape you use to define your points. For example, say you used circles, triangles and squares for a post map. If you exported that post map to a SHP file, you would get just plain point objects in ArcMap (no circles, triangles and squares) and it would use whatever symbol was the default in ArcMap. If you wish to render the shape of the point marker symbols, you can check the Render marker symbols box and Surfer will convert the marker symbols into polygons and write them out as polygons using the option selected under Areas.

                                                             v.      The Attribute translation [codepage] option allows you to specify a different codepage for the text in the DBF file (the attribute file) associated with the SHP. In most cases, you will want to leave this set to the default.

d.      Click OK and the SHP file(s) are created. In this case, Surfer created two SHP files: one for my polyline objects (the contour lines and axes) and one for the polygon objects (the contour fill and marker symbols, since I chose to render the marker symbols as polygons). Each SHP file created will have a number of associated files with it, such as CPG, DBF, PRJ and SHX.


When exporting to a SHP, the associated files (CPG, DBF, PRJ, and SHX) are also created.

Now the SHP file is exported, we can import it into ArcMap (I’m using version 10.3).

  1. In ArcMap, click File | Add Data | Add Data.
  2. Select one of the SHP files (e.g. ContourMap.shp) and click AddThe contour lines are displayed.
  3. If I enter into edit mode and select one of the polylines, I can see the Z value is the attribute in the Attributes window.

Adding contours from Surfer into ESRI ArcMap

Select one of the contour polylines and you can see the original Z value is stored as the ZLEVEL attribute.

If I add in the ContourMapPoly.shp layer, the polygons are added to the map. The shape of the original posted points is displayed since the markers are now polygons. You could add attributes or color the polygons as you desire.

Adding contours from Surfer into ESRI ArcMap

Import the polygon file and you can see the contour fill and marker symbol polygons. 

As a comparison, if you did not check the option to have the marker symbols rendered in the Export Options dialog, then Surfer would have generated three SHP files (one for the polylines, one for the polygons and one for the points). If you imported all three into ArcMap, you could see the same polylines, but the polygon file would not contain the marker symbols. The points would be saved in a point SHP file and when that file is imported into ArcMap, the points are displayed using the default symbol.

Adding contours from Surfer into ESRI ArcMap

Do not render marker symbols during export, and points in a post map are exported as points instead of rendered as polygons.

There are a couple other scenarios that might interest you. You can also export contours to a shapefile by clicking the Map | Export Contours command. What is the difference between File | Export and Map | Export Contours?  The Export Contours command only exports the contour lines themselves. The fill polygons, points, and any other map objects are not exported to the file.   In addition, you have the option to export 2D SHP files (where the Z value of the contour lines is saved in the DBF file, equivalent to File | Export) or as 3D SHP files. The 3D SHP file option stores the Z values for each vertex in each polyline.  Some rules of thumb:

  1. If you need a true 3D SHP file, then you must use Map | Export Contours and choose to save the contour lines as a 3D SHP.
  2. If you just want the Z values of the contour lines stored in the DBF file as an attribute for the polyline object, you could use either of the commands.
  3. If you want to export contour fill polygons, points, other layer objects, text or marker symbols in addition to the contour lines, then you must use File | Export.

One other option for using Surfer in conjunction with ArcMap is to generate the grid in Surfer, save it from Surfer as an ESRI Arc grid file, import it as a raster into ArcMap, and recreate the contours directly in ArcMap. ArcMap does accept Surfer grid files (*.GRD), but for some reason it does not recognize PRJ files associated with the GRD, so you lose the coordinate system information. To keep the coordinate system information, it is best to convert to one of ESRI’s grid formats directly before use in ArcMap. For example:

  1. Click Grid | Convert.
  2. Select the Surfer grid file and click Open.
  3. Choose to save the grid as an ADF Arc/Info Binary Grid (*.adf) and click Save.
  4. When prompted to save the spatial reference information, make sure ESRI .PRJ file is selected and click OK.
  5. Now you can use this raster in ArcMap. Simply click File | Add Data | Add Data, select the ADF file and click Add. The raster is imported and a color fill map is automatically created, similar to Surfer's image map type.
  6. Depending on the tools you have available, you could generate contours directly in ArcMap (Spatial Analyst | Surface | Contour).

Using grids and contours from Surfer in ESRI's ArcMap and ArcGIS

Create ESRI grid files in Surfer and load them directly into ArcMap as rasters.

Golden Software makes every effort to ensure Surfer is compatible with other popular software packages like ArcMap. If for any reason, the export isn’t working the way you expect it to, please email the Surfer *.SRF file to, report what version of Surfer you are using, and describe what is wrong when you export it to a SHP file (or whatever file format you select).  


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