New maps: Cross-country relationships between life expectancy, intertemporal choice and age at first birth

In one of my previous posts, I presented some interactive maps, made using Google Fusion Tables, to support a paper on Cross-country relationships between life expectancy, intertemporal choice and age at first birth, written with my collaborator, Adam Bulley. However, Google have since … Continue reading

Looking to convert various statistics to correlation coefficients? Here’s a script I made earlier

In the process of producing a recent meta-analysis on telomeres as markers of exposure to stress and adversity, we needed to convert various effect types (e.g. standardised betas, F-ratios and T-statistics) to correlation coefficients, ready to be meta-analysed. The script is fairly straightforward, but it took a little while to write the script and format the data entry to work smoothly with the script. So, to save others some time and energy, I’ve made a generic version of the script, along with a template input file–just to make things easy to use. You can download the files here, along with a guide on how to correctly enter your data into the template input file, so that the correlation converter script can read it.

Quick-start guide:
1. Enter your data into this template csv, using the table in this document as a guide.
2. Make sure the template csv is in the same folder as this R script.
3. Run the script in R. If you don’t have R, you can learn more and download it here.
4. Once you’ve run the script, a new file, entitled “MetaAnalyisCommonAssociations.csv” should appear in the same folder. It should contain all the data in your original csv, plus two new columns containing the “CommonEffect” (the correlation coefficient) and the “CommonEffectVariance” (the measure of variance).

What is the script doing? The script contains comments explaining what each piece of code does, but for those who find diagrams and equations easier to follow, this flow chart shows the operations it performs:


Mapping Census data from Newcastle commuters

In my current Research Associate role at the Institute for Health and Society, I’m working with a lot of data on travel behaviour, traffic flow and traffic accidents. As part of this, I’ve been having fun mapping Census data on how people travel to work. Since the data are publicly available, it would be a shame not to share some of the interactive maps I’ve built. So here they are. You can open the publicly available interactive maps by clicking on the screenshots below.

The data are organised by Lower Layer Super Output Area (LSOAs – read more about ONS geographies here) and the maps were built using Google Fusion Tables, by uploading a CSV of the 2011 Census data on Travel to Work and combining it with a KML file of the boundaries of the LSOAs in Newcastle Upon Tyne, which I created myself. For an introduction to how to create maps using Fusion Tables, go here.

Map 1 – people travelling to work by bus

Perhaps unsurprisingly, those living within a few miles of Newcastle city centre, but not in it, were most likely to travel to work by bus. The proportion of people travelling by bus also tends to be higher in the more economically deprived parts of the city.

Percent travelling by bus


Map 2 – people travelling to work by underground, metro, light rail or tram

No big surprises here. A greater proportion of people reported travelling to work by underground, metro, light rail or tram in the LSOAs with, or near, metro stations.

Percent travelling by metro

Map 3 – people travelling to work by train

As with the other maps, the raw numbers are small, but the greater proportions of the available commuter populations are travelling by train in those LSOAs near central station.

Percent travelling by train

Map 4 – people travelling to work by taxi

Once more, the numbers are small, but I was surprised to see that anyone at all travels to work by taxi. Furthermore, there was a higher proportion of self-reported taxi commuters in the more economically deprived areas of Newcastle. Perhaps these people are taxi drivers?

Percent travelling by taxi

Map 5 – people travelling to work by motorbike

Bikers don’t appear to be numerous in Newcastle, nor does their distribution take on a discernible pattern. However, the biker stronghold of Newcastle seems to be in Coxlodge, where the proportion of people who report getting to work by motorbike reaches its zenith at 1.3%.

Percent travelling by motorbike

Map 6 – people travelling to work by car

The car commute is, of course, the most common. The numbers are high and increase with distance from the city centre, while driving sees its nadir at 17.07%, in the LSOA next to Newcastle Central Station.

Percent travelling by car

Map 7 – people travelling to work as passengers in cars

People getting to work in other peoples’ cars are fewer than those driving themselves.  Interestingly, the patterns also appear to be different, with a higher proportion of car passengers coming from LSOAs at intermediate distances from the city centre.

Percent travelling as car passengers

Map 8 – people who get to work by bicycle

As with many other modes of transport, bar the car, the numbers of people reporting bicycle use are low. However, the higher proportions do seem to be in inner-city LSOAs with thriving student populations.

Percent travelling by bicycle

Map 9 – those who walk to work

Unsurprisingly, the proportions of people walking to work are greatest in the city centre and decrease with distance from the city. However, peak walking proportions are at 49.48% and can be seen in the LSOA adjacent to my place of work.

Percent walking to work

Map 10 – people travelling by other means

These people travelling by “other means” are somewhat of a mystery. The 2011 Census Analysis on Method of Travel to Work in England and Wales Report reported that, “all of the English regions and Wales had less than 1 per cent of people commuting by other means (such as by ferry).” So the 2.88% travelling by other means in Newcastle upon Tyne 024B are an intriguing group. This LSOA is mainly park land, north of the city centre. There is no large body of water in the area, so the ONS assertion that, “ferry and hovercraft services are likely to make up the majority of the other means of commuting to work”, doesn’t explain this particular group of commuters. Perhaps they’re commuting by skateboard, by zip wire, on horseback, or by mobility scooter? Answers on a postcard please.

Percent travelling by other means