Mastering Clojure Data Analysis: If you'd like to apply your Clojure skills to performing data analysis, this is the book for you. The example based approach aids fast learning and covers basic to advanced topics. Get deeper into your data.

While the preceding description is accurate, it doesn't really help us much. As befits a field concerned with the lay of the land, GIS really begins in the field. Data is gathered using aerial and satellite photography, and it is also gathered from people on the ground using GPS, laser range finders, and surveying tools. GIS can also make use of existing maps, especially for historical research and to compare time periods. For example, this may involve studying how a city has evolved over time or national boundaries have changed. A lot of time and energy in GIS goes into gathering this data and entering it into the computer.

Once the data is in the computer, GIS can perform a wide range and variety of analyses on the data, depending on the questions being asked and the task at hand. For example, the following are some of the many things you can do with GIS:

So, let's roll up our sleeves and perform some geospatially informed data analysis.

For our problem, we'll look at how the climate change affects the continental United States over the last century or so. Specifically, we'll look at how the average maximum temperature for July has changed. For North America, this should give us a good snapshot of the hottest temperatures.

One nice thing about working with the weather data is that there's a lot of it, and it's easily available. US National Oceanic and Atmospheric Administration (NOAA) collects it and maintains archives of it.

For this project, we'll use the Global Summary of the Day (http://www.ncdc.noaa.gov/cgi-bin/res40.pl). This includes daily summaries from each active weather station. We'll filter out any weather stations that aren't in the US, and we'll filter out any data that is not in use for the month of July.

Climate is typically defined on thirty-year periods. For example, the climate for a location would...

Have you looked at a world wall map and noticed how big Greenland is? It's huge. It's larger than China, the United States, and Australia, and is about as big as Africa. Too bad it's so cold, or we could fit a lot of people up there. Or could we?

Actually, Australia is about three and a half times as big as Greenland, China is almost four and a half times as big, and Africa is almost fourteen times as large!

What's going on? The Mercator projection is what's going on. It was developed by the Flemish cartographer Gerardus Mercator in 1569. Over time, it's become very popular, at least partially so because it fits nicely onto a rectangular page without wasting a lot of space around the edges, the way some projections do.

A map projection is a transformation of locations on a sphere or ellipsoid onto locations on a plane. You can think of it as a function that transforms latitudes and longitudes of the earth into the x and y coordinates on a sheet of paper. This allows...

Working with projections and base maps can be fiddly and prone to errors. While there are Java libraries that can help us with this, let's use the major software package in this domain, ArcGIS, for the purposes of this demonstration. While it's awesome to be able to program solutions in a powerful, flexible language like Clojure, sometimes, it's nicer to get pretty pictures quickly.

We're going to start this by getting the base layer. ESRI maintains a set of topological maps, and this map of the United States is perfect for this:

Now we'll add our data. This was created using the functions that we defined earlier as well as a few more that are available in this chapter's code download:

This has been a fun little experiment. Looking at the data, however, suggests caution. Some of the stations have been in operation long enough to have only a few of the sliding windows defined. Others have been operational for much longer. This makes it difficult to compare the aggregated numbers from the different different stations, which is what we're doing by creating the heat map.

Nevertheless, this does point to some interesting areas of future enquiry, and it provides a brief glimpse of what geographical information systems can provide and how to use them. They can add a geospatially informed edge to the modeling and analysis, which isn't possible with the data, tools, and techniques they bring to the table.

In this next chapter, we'll turn our attention to sifting through free-form textual data using topic modeling.