Here’s a very quick and dirty attempt at taking some data logged (on an iPhone) at a track day I did a while back and using CartoDB’s excellent web mapping tools to create an interactive visualisation of the data. Hover over the points to view speed, lateral g (cornering) and lineal g (acceleration/braking). Click, drag and scroll as usual to pan and zoom around the map.
Note that the iPhone can only gather GPS information at 1Hz, which is why the data is pretty coarse. I’m at Snetterton in a few weeks so I’ll do a bit more data logging and analysis then.
Here’s the snow map mentioned in my last post. The map shows geotagged, snow-related tweets collected over the weekend of 11th-14th January, normalised by the number of tweets per county taken in a separate random sample.
As usual, you can click, zoom and pan around the map in the usual way
As part of the background learning and research I’ve been doing for work, I’ve been looking into various forms of spatially referenced data and ways of presenting it over the web. In particular, I’ve been developing a tool in Java that will collect geotagged tweets from Twitter over a period of time, filtered by things such as keywords, hashtags or geographic bounding boxes.
Earlier in January, I collected a few days worth of tweets filtered on keywords related to snow, whilst the UK was experiencing its first snowfall of the year. The idea was to see how effective this might be as a means of gathering data on location-specific events. Over the period of a weekend, around 20,000 snow-related, geotagged tweets were collected and plotted onto a UK map.
Immediately it was obvious that the map was pretty useless as far as representing snowfall in the UK was concerned. The hotspots around the country fell firmly around large cities and areas with large numbers of Twitter users. To produce a sensible map, the data would need to be normalised against a control sample; a random collection of tweets collected over long period that could be used to highlight and therefore eliminate the effect of Twitter hotspots around the UK.
The corrected snow map will follow shortly (edit: here it is), but for now, here’s a map based on my control sample showing Twitter population density for counties across the UK and Ireland (plus a few other regions that accidentally fell inside my bounding box). You can zoom and pan the map as normal and click on each county to get further information.
Did a big old bike ride last week. Cambridge to London via some nice sweeping country lanes and then back to Cambridge again along the official London to Cambridge route. Apart from a few short rides for an hour or two here and there, it was the first ‘proper’ ride I’ve done on a road bike.
Surprisingly, legs were fine pretty much all the way, although I was starting to run out of steam for the last few miles. What I struggled with most was the pain in my arms and hands; I’m pretty sure my bars could do with being closer and a touch higher, although for the first few hours they felt absolutely fine.
Covered 104 miles in total, here’s a little map of the route.