Summer has came

It's getting hot over there, so hot, I want to see some UFOs, I am getting to hot, I want to see some UFOs   

— Not quite Nelly

Summer is here (well in the US — in Australia, it's winter). And along with the rise in the temperature, comes a rise in reported UFO sightings (Figure 1). Just like our 'day of the week' analysis, this finding is statistically significant at any reasonable level.

A keen observer would note that Figure 1 is missing some key information. Namely, that the 'summer effect' could be due to one particularly active summer, rather than a consistent reoccurring pattern each year. That is why we need to look at the time series of the number of reported UFO sightings (Figure 2). Glossing over this series shows clear evidence of seasonality. Decomposing it, reaffirms this finding (see technical stuff). 

The time series also tells us the the number of reports has been trending up since the early 90s. And the growth in the upward trend has been faster than growth in US population (Figure 3).

 

Tying all the results together reaffirms earlier findings (apologies for some repetition). People are more likely to stay out late and drink more in the summer. While the upward trend likely relates to an increase in the ease of reporting sightings, a fall in the stigma related to reporting sightings, or biases in how the data are collected. There are also some large movements that, provided the demand for UFO information remains high, I will look at later.

Next post, “Ma... Ma… there's big ole shiny fing in the sky. Will there be 'nough time to get the camera and shotgun? Or would it be best if I just got the shotgun?” — We look at how long UFO sightings tend to last for.

Technical Stuff

Ideally, I would seasonally adjust the data using x13 from the US Census. This is basically an ARIMA specially designed for seasonal adjustment. But I can't get this working with Ubuntu — this didn't help. So for the time being to look at the seasonal components you'll just need to make do with a decomposition (Figure 4). Simply put, this process splits the data into three components, a trend, a seasonal, and an irregular.

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