Category Archives: Open Source

OSGeo BC Meet-Up July 27 (during GeoWeb)

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Martin recently announced that we will be holding an OSGeo British Columbia meet-up in Vancouver on July 27. We have timed this to coincide with GeoWeb; it falls on the Tuesday evening after the workshops are over and before the conference sessions start.

This session is open to all, so if you’re going to be at GeoWeb, or are in the area and want to hang out and eat pizza with some Cool GeoGeeks (oxymoron?) RSVP on the wiki!

We’re also looking for some presentation ideas; if you’ve got a burning desire, then please propose a topic on the wiki.

-J

StackOverflow For Geo!

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If you’re not familiar with StackOverflow, it’s a collaboratively edited questions and answers site for developers. With its wiki-like editing and community voting and reputation system, answers at StackOverflow (and its sister sites like ServerFault) are more complete, accurate, and accessible than any other technical resources.

With this understanding, I was excited to see that George Silva was involved in StackExchange’s incubator, and had put together a proposal for a StackOverflow for Geographic Information Systems.

GIS has long been more of a diaspora than an online community, with information stored across dozens of mailing lists, forums, blogs and other locations. Each open source project and proprietary application has its own set of resources, as do academic communities. Answers have been hard to find, and expert participation in these communities can quickly lead to burn-out. I believe that having a StackOverflow for GIS will help to solve these problems, and increase our individual efficiency working with GIS.

If you agree, please take the time to sign up for George’s proposal, and commit to being involved in StackOverflow for GIS!

-J

MapGuide Maestro 2.0: now with more Maestro!

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Kenneth Skovhede recently announced the official release of MapGuide Maestro 2.0, the culmination of over a year of feature development and usability enhancements to the open source MapGuide authoring tool. Here are my picks for the top 10 features of MapGuide Maestro 2.0:

1. Theming, with ColorBrewer Suport

Being able to theme maps based on data distribution is a basic mapping function, and this release of Maestro delivers. Adding support for ColorBrewer means that you can be confident that your colour scheme is visually distinct and cartographically appropriate for the message that you are conveying. The Maestro UI automatically constrains the colour choices based on the underlying data categories:

2. Expression Editor

MapGuide and the underlying FDO data providers support a powerful expression language, and previously you were on your own to write valid expressions. Thanks to Jackie Ng, MapGuide Maestro was able to use the same expression editor that FDO Toolbox is using, giving expression-completion, valid value extraction, and more:

Check out Jackie’s posts on the FDO Expression Editor and the follow-up where he talks about the addition of value auto-completion.

3. Resource Validation

The Maestro resource validator walks from application to map to layers to data, warning if it detects any common errors such as broken references or potential performance issues like unmatched projections. This is an invaluable tool for troubleshooting problems in your maps:

Resource Validator Init

Resource Validator Results

4. Improved XML Editor

While Maestro’s GUI is great for most purposes, there are times when you need to access the full power of MapGuide XML configuration syntax, like when you’re editing complex XML-based stylization elements. Maestro makes it easy for you to edit any resource as XML simply by right-clicking on it and choosing “Edit as xml”. Maestro 2.0 comes with many improvements to the XML editor, including validation against the schema, cursor position (important when tracking down errors), and the ability to attach arbitrary files to the current resource, which is critical when making bitmap-based symbols:

5. Profiling

Profiling allows you to easily find the performance bottlenecks on a given map, or quickly determine whether changes to theming or other items are having an impact on performance:

6. Package Management

MapGuide packages are a zipped export of the DBXML repository including XML resources, associated binary files, and a manifest. Typically these are managed on the server, but Maestro allows you to create, edit, and load these files via the GUI. This can be really handy when you’re migrating changes between servers. One of my favourite tricks is to export both test and prod as packages, unzip them, and compare using Beyond Compare (not free, but worth every penny).

7. Custom Resource Templates

Any time you are creating a new data source, layer definition, map definition, etc, you are basically just creating a new XML document. If you find that you are always performing certain steps as part of creating these resources, you can create your own custom resource types with customized versions of these XML documents. For instance, I like to start layer creation using the version 1.3 of the LayerDefinition schema, with a default scale range of 1:1->1:500,000, and none of the feature types being displayed. All I had to do was create a new LayerDefinition with these settings, save it to the MapGuideMaestro\Templates directory with a name like “Nanaimo Layer.xml”, and it shows up when I want to create a new resource:

8. Duplicate Resource

This might not seem like a big deal, but when creating dozens of similar layers it can be a huge timesaver. Simply right-click on any resource and choose “Duplicate” and a copy of that resource is created and ready for you to customize:

9. Colour-Coded Resource Tree

When editing many resources at once, things can get a bit confusing. Crispin Hoult from 1Spatial contributed a feature that colour-codes currently open resources in green, and resources that have unsaved changes in pink. The currently active resource comes up in a darker shade. This feature makes it much easier to keep track of what’s going on in your work area, and is surprisingly useful:

10. General Usability

OK, maybe this doesn’t count as a feature, but a LOT of thought has been put into how various user interactions work, and countless small refinements have been made. A few examples:

  • inserting a new layer into a map definition when you have a group selected inserts the layer into that group, and places the layer into the overall draw order right after the bottom-most layer in that group
  • you can now right-click on any resource and copy its ID (like Library://Nanaimo/Data/MyFile.FeatureSource) to the clipboard, which can be incredibly useful when writing code to access resources
  • you can multi-select many layers for insertion into a MapDefinition at once
  • maestro keeps track of references when you rename or move resources, prompting you for whether you want dependent resources updated

All of the little enhancements in this release added together have saved me hours of work (I’ve been using the pre-release versions for a few months).

All-in-all this is a very impressive release, with countless new features and enhancements to existing functionality. Give it a spin, and I’m sure you’ll turn up your own favourites!

Thanks Kenneth, and great work.

-J

Nanaimo meet OpenID. OpenID meet Nanaimo.

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How’s that for protocol?

If you’re anything like me, you probably use the password reset function on websites more often than the login function. This is a huge problem, both for security and for user experience.

The City of Nanaimo recognized that as useful as the city’s web applications are, requiring citizens to remember yet another password is not reasonable. Early this year the city did an initial analysis of OpenID, and Jeff Jacob–one of my colleagues–took on the task of developing the infrastructure to support OpenID and one of the first applications to take advantage of it. You can read the everyman’s description of Nanaimo’s OpenID initiative along with links to the OpenID-enabled services.

While the majority of users probably have an OpenID account already, it would not be responsible to require citizens to sign up for an external login service. A mix of forms-based and OpenID login capabilities may have been easier, but it just made more sense for Jeff to implement a city-specific OpenID provider using the DotNetOpenID open source library. This allows Nanaimo’s application login class to be more streamlined while presenting a consistent user experience, but more importantly it allows the city to act as a provider for third party / COTS web applications as these start supporting OpenID. Eventually Nanaimo citizens will be able to log into all city services using a single ID of their choice.

It is gratifying to see that during the City’s implementation phase many other organisations, such as the US Federal Government, have been embracing OpenID. Allowing citizens to access services using their own credentials is a key part of Nanaimo’s longstanding policy of providing easy access to the information residents and businesses need to live and do business here.

If you work for a local government and are interested in sharing information and/or code, please get in touch with Nanaimo’s IT department!

-J

P.S. As always, I am writing from a personal perspective. Opinions here are my own, and are not necessarily shared by my employer.

FWTools FTW … because GDAL FTW didn’t sound as cool!

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I’ve received a bunch of compliments on the performance of the NanaimoMap MapGuide / Fusion application that the City of Nanaimo launched in beta last week.

There is a lot involved in making a web map perform, especially if you are not leveraging tile caching. One part of the story is hardware, and I’m lucky enough to share space on a dual quad-core machine with 4GB RAM and relatively fast disk. Another part is proper generalization of the vector data for display; no point in carrying sub-micron precision on a map that will generally be displayed at 1:500 or smaller. And of course, there’s MapGuide’s inherent speed when properly configured. This leaves out one of the most important parts though: raster data.

Raster data is big, brutish and hard to work with, and optimizing raster access is often one of the most important parts of delivering a successful web map. Users have come to expect “satellite” imagery on their web maps, and complain when it doesn’t perform as well as Google Maps. One of the best ways that I have found of flipping and folding raster data is Frank Warmerdam’s FWTools, which wraps GDAL and some other utilities in a single easy-to-use package.

My starting point consisted of:

  • 79 TIFF + Worldfile images, 10cm resolution, about 1.1GB each
  • 14 TIFF + Worldfile images, 30cm resolution, about 600MB each

So, I was working with about 100GB of images, none of which were optimized for web-based display, and which did not contain the spatial reference information that the FDO Raster Provider (also based on GDAL!) works best with.

The first thing I did was set up a batch process to optimize the individual images. This involved three steps:

1. Obtain a correct .prj file containing the WKT spatial reference information for my images. The easiest place for me to get this was SpatialReference.org, but you might just have one hanging around.

http://spatialreference.org/ref/epsg/26910/

2. Reprocess the image into a Tiled GeoTIFF, with no compression and a relatively large internal block size, and specifying the projection file obtained above. The caret (^) is the DOS line continuation character:

gdal_translate ^
-co "TILED=YES" ^
-co "PROFILE=GEOTIFF" ^
-co "INTERLEAVE=BAND" ^
-co "BLOCKXSIZE=512" ^
-co "BLOCKYSIZE=512" ^
-a_srs utm83-10.prj ^
infile.tif ^
outfile.tif

You can obtain more information on gdal_translate and the GeoTIFF options on the GDAL website. Depending on your source data and intended use, other values could be more appropriate, and you really should experiment.

3. Create internal pyramids in each image so that the entire image does not need to be fetched when zoomed out. This is one of the easiest performance gains you can get if you can afford the extra disk space.

gdaladdo -r gauss output.tif 2 4 8 16 32 64 128

Once this was done, I had a really decent set of fast images to work with, but these would only be appropriate to load at large scales when only one or a very few of the images need to be opened on each map view. For smaller scales, I needed to reduce the size of the images being processed, and also reduce the number of files being accessed on each fetch. I decided to go with a simple two-tier approach: Load the individual images at scales larger than some fixed value, and load a single overview image at scales smaller than that value.

The only problem was that I did not have an appropriate overview image. I wanted something that was relatively small, highly optimized, and which had white fill in its nodata areas. Fortunately GDAL and the awesome folks in the #gdal channel at freenode came to the rescue again, this time with four steps.

1. The first thing I needed to do was build a list of all of the images I wanted to have as part of the overview and feed these into the gdalbuildvrt command to build a single virtual image. You could do this manually, but I have the awesome GnuWin32 utilities installed so used these instead; they’re almost enough to make me not miss the days when I spent most of my time in Unix:

find images/ -name "*.tif" | xargs gdalbuildvrt -resolution highest all_images.vrt

2. Because I wanted a white background on my overviews, I then edited the all_images.vrt, adding a <NoDataValue/> section at the top of each of the three <VRTRasterBand /> sections:

<VRTRasterBand dataType="Byte" band="1">
<NoDataValue>255</NoDataValue>

3. The gdalinfo command gave me the dimensions of the virtual image, each of which I then divided iteratively to give me reasonable overview dimensions which I could feed into gdal_translate.

gdal_translate ^
-outsize 53120 14000 ^
-co "TILED=YES" ^
-co "PROFILE=GEOTIFF" ^
-co "INTERLEAVE=BAND" ^
-co "BLOCKXSIZE=512" ^
-co "BLOCKYSIZE=512" ^
all_images.vrt ^
all_images.tif

When this completed, I deleted the all_images.tif.aux.xml file because I did not want to carry the additional metadata that GDAL maintains in that file.

Careful with sizes here. If you’re using an application that supports it, you can specify the -CO “BIGTIFF=YES” option to generate files larger than 4GB, but you’re likely better off generating an intermediate level of aggregated and resampled tiles instead.

4. The final step was to once again generate internal pyramids to allow for better performance at small scales:

gdaladdo -r gauss all_images.tif 2 4 8 16 32 64 128

Once these two data sets were processed, I simply used MapGuide Maestro to make two raster data connections. For the first data connection, I added all of the individual TIFF images to a composite raster type, and Maestro generated a configuration document which allows MapGuide to know which image to access for a given extent. For the second layer, I just pointed to the overview GeoTiff. I then created layers for these, experimented until I found the scale where the overview image started looking pixelated, and set the layers’ view scale properties accordingly. There are some notes on working with rasters in the Maestro documentation.

More performance could probably be gained by having an intermediate level where the coverage area was aggregated into larger tiles before being combined into one large overview image, but for the initial launch this was deemed to have high enough performance.

On my production server, I’m lucky enough to have a fast, high-spindle-count RAID shelf dedicated to storing these uncompressed TIFFs, and they scream off the disk. My test server is VMWare-based, and disk performance and space are both at a premium. In this case, I still used the TIFF overview map, but at large scales I access a set of tiled MrSID files instead. This seemed like a decent compromise given the constraints, but did seem to thrash the CPU a bit.

GDAL was one of the first open source geospatial applications I tried (not counting GRASS and MOSS) and is constantly coming in handy, whether I’m reprojecting, adding spatial reference information to images, or converting between formats.

Thanks to hobu (Howard Butler), FrankW (Frank Warmerdam) and EvenR (Even Rouault) from the #gdal IRC channel on freenode for helping me work my way to this solution. Amazing support!

-J