1. New Journal: Spatial Demography

    Thu 06 June 2013
    cfarmer

    I have recently joined the editorial team at Spatial Demography — a new journal outlet for demographers and others who use spatial data, methods, and theory.

    A bit more about the journal:

    Spatial Demography [ISSN: 2164-7070 (online)] focuses on the spatial analysis of demographic processes. This cross-disciplinary work involves modern demographic data visualization, enhanced geo-referenced data availability, and spatial statistics, facilitated through full color graphics, motion video tools, and a quick time-to-publication. The journal publishes research articles, essays, research reports, data sources, computing software, teaching notes, and book reviews on a wide range of topics of interest to the social demographer.

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  2. Making the switch to Pelican

    Sun 12 May 2013
    cfarmer

    Welcome to the new and improved carsonfarmer.com! If you are reading this, then you are enjoying my new, responsive static website/blog. The new site is powered by Pelican — a static website generator written in Python — and is hosted on GitHub using GitHub Pages. Most of the content on the site is written in Markdown, which makes it really easy to add headings, anchors, and all sorts of goodies to simplify writing blog posts and web-pages.

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  3. Humans as systems

    Thu 14 February 2013
    cfarmer

    From xkcd comes another brilliant insight into the human condition:

    A human is a system for converting dust billions of years ago into dust billions of years from now via a roundabout process which involves checking email a lot.

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  4. Will it Python?

    Tue 12 February 2013
    cfarmer

    Over the past few weeks, I’ve been following a really great blog by Carl Vogel. This blog has an excellent (growing) collection of Python examples based on porting code and examples from R to Python. In general, it is useful for those “interested in the Python data analysis toolkit and its viability as an alternative to R”. Carl draws on examples from Machine Learning for Hackers by Drew Conway and John Miles White, as well as Gelman and Hill’s Data Analysis Using Regression and Multilevel/Hierarchical Models.

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