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implementation as the clustering algorithm. There are obvious implications for hardware procure-
ment in running parallel k -means clustering algorithms, as these need a powerful and appropriately
specified graphics card installed on the machine. However, we argue that with the decrease in com-
putation prices in recent years, this implication is acceptable.
Classification algorithms are only one area where parallel computing has a potentially large
benefit. In 2010, the Committee on Strategic Directions for the Geographical Sciences in the Next
Decade and National Research Council presented 11 fundamental challenges for the geographical
sciences ranging from dealing with an increased population to the impact of climate change on the
human-environment system to food security issues. If there is any hope of tackling these complex,
global problems, then parallel computing will need to become more commonly used in the future. If
geographers do not recognise the need to embrace and master this technology, then there is a danger
that we will be left behind, continuing to develop sequentially based models that address only the
simplest of problems.
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