1. Syntax highlighting with PyQt

    Thu 02 July 2009
    cfarmer

    A few months ago I decided to add syntax highlighting capabilities to a piece of software that I have been working on. Since it is a PyQt based application, the obvious choice for implementing syntax highlighting was to use Qt’s QSyntaxHighlighter. Unfortunately, there weren’t many examples around that implemented syntax highlighting in Python, so I decided to post my own.

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  2. R featured in New York Times

    Wed 28 January 2009
    cfarmer

    I’m sure everyone has seen this already, but I’m going to post it anyway, as I think the more exposure open-source tools get, the better off we’ll all be!

    Check out this New York Times article which features R, the open-source statistical programming language. R now has quite an extensive range of spatial analysis options, and is the software of choice for researchers using spatial statistics and geographic information analysis.

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  3. Open up your online maps with OpenStreetMap

    Mon 26 January 2009
    cfarmer

    OpenStreetMap (OSM) is a project designed to create and provide free spatial data (street maps) to anyone and everyone who wants them. It is based on an open-source philosophy, and combines wiki-like user generated data, with free access, allowing users to create, edit, download, and use OSM data to their hearts content. According to the OSM website, “the project was started because most maps you think of as free actually have legal or technical restrictions on their use, holding back people from using them in creative, productive or unexpected ways.” There are now tones of websites and open-source software projects that incorporate OSM data, and the growing popularity of the site means that the data is only going to get better (more accurate) and bigger (more data).

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  4. Understanding spatial reference systems

    Mon 12 January 2009
    cfarmer

    For those of you who are still unclear about what exactly a spatial reference system is, how it is used, and what it means for your data, I found a pretty good quick guide to spatial references, coordinate systems, projections, datums and ellipsoids. This article was written by Morten Nielsen (who works for ESRI), and it does a good job of quickly and simply describing what makes up a spatial reference system, and some of the errors that people make when talking about their spatial data.

    Having a good grasp of this stuff is important when working with spatial data ...

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  5. gedit: The ultimate LaTeX editor

    Fri 12 December 2008
    cfarmer

    Out of the box gedit is a basic text editor, but it comes equipped with about 12 standard plugins, and another 9 readily available. In addition to this, there are a range of ‘third-party’ plugins developed to do various specific tasks, such as assist you in writing and exporting LaTeX documents! First, get all the basic plugins:

    sudo apt-get install gedit-plugins`

    and enable them in gedit by going to Edit > Preferences > Plugins, and checking the ones that you want.

    Second, make sure you have all the required dependencies for the actual \(\LaTeX\) plugin: 1. The plugin is written in Python ...

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  6. Quick guide to setting up a PostGIS database

    Fri 28 November 2008
    cfarmer

    Recently I decided to seriously start using PostGIS to manage my spatial data. As I have several projects on the go, organizing and managing my data effectively has become extremely important, and PostGIS is by far the most convenient way to do this. There is lots of documentation out there that explains in detail how to set up PostGIS, but by far the best reference I’ve found is from Tim Sutton’s blog, mainly because he uses Ubuntu, and sudo-apt gets everything you need to have PostGIS working in minutes.

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  7. View spatial data attribute tables in R

    Tue 14 October 2008
    cfarmer

    Many GIS offer the ability to view the attribute table of a vector layer. While this is perhaps less obvious in the R environment, it is not impossible. The following command allows you to visually inspect, and change any data.frame (or other vector, matrix, etc.), including Spatial*DataFrames.

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  8. R spatial indentify tool

    Tue 23 September 2008
    cfarmer

    This is useful for visually exploring R spatial data such as SpatialPointDataFrames or SpatialGridDataFrames. By clicking on various features, the value at that point will be displayed.

    library(rgdal)
y = readGDAL(system.file("pictures/Rlogo.jpg", package="rgdal")[1], band=1)
y.grid = y@grid
y.coords = coordinates(y.grid) 
image(y)
identify(x=y.coords, y=NULL, n=1)

    where x and y refer to coordinates (in this case because y.coords contains both x and y coordinates, y can be set to NULL), and n is the number of features to identify.

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