The visualisation above shows the average relative duration of Boris Bikers’ weekday journeys over a 4 month period at hourly intervals. For each time step the average journey time (in seconds) from each docking station has been calculated.This information is interesting because it shows the preference for short journeys around the City of London, whilst people on the outskirts of the the scheme (especially to the west) take longer journeys. I also like the the fact that journey times around Soho and the West End are longest around 23:00- perhaps correlating with the number of after-work drinks consumed. In one visualisation you get to see the changes in the cyclists behaviour- from the early morning commuters through to the late night cruisers

The data come from Transport for London’s recent release of 1.4 million Barclays Cycle Hire journeys to their developers area (thanks to this FOI request). The data are said include all the journeys between 30 July 2010 and 3 November 2010, except those starting between midnight and 6am. In this analysis journeys taking more than one hour are not included (there are relatively few and many were actually the bikes being removed for maintenance) and docking stations with fewer than 10 journeys within each hour across the time period have also been ignored.

The maps can be improved in many ways- stay tuned for more developments and I will also post something a bit more technical about the methods I used etc to create the map (I used a strange cocktail of R and ArcGIS 10) .

I also recommend Ollie O’Brien’s (@oobr) brilliant interactive visualisations these data.

Overall I found the app to be very impressive. You are greeted with a world map that you can instantly interact with by panning and zooming in the usual way.

The map tiles loaded surprisingly quickly- it took approx 5 seconds to zoom from the above view to building level with only 1 bar of signal.

Users can search for places of interest…

…but this provided the only disappointment with the map appearing to lack the required data.

Zooming out a little resolved this problem, but I am not sure how many people would think to do this. I suspect the problem is relatively easily  addressed and may well be in future updates. Offering standard maps is not particularly innovative and not what the app sets out to achieve. The real innovation is the ability to view layers available from the ArcGIS.com website. You can, for example, use OpenStreetMap as the base map

or overlay additional information, in this case the Gulf Oil Spill Forecast:

Navigation to layers is straightforward:

I especially like the fact that users can add their own servers and also bookmark their favourite layers. On top of these features users can measure distances and calculate areas.

I found these tools to be extremely intuitive and I expect they will become an integral part to many field based introductory GIS courses. In a recent talk I attended, Jack Dangermond said that GIS software in the past was made to be complicated. With this application ESRI have demonstrated that GIS can be made to be easy. I think all who use this app both from within and beyond GIS with be genuinely impressed.

**I have just discovered a more in depth review worth reading from James Fee’s GIS blog.**

Over the last couple of days I have utilised some excellent free GIS resources. I have listed these and some others below.

Geospatial Analysis: This is the free online version of de Smith, Longley and Goodchild’s excellent book by the same title. It provides full coverage of current GIS methodologies. It also provides extensive information regarding the various GIS software available.

Analysing Spatial Point Patterns in R: 200 pages of workshop notes written by Adrian Baddeley. These provide extremely detailed and comprehensive overview of the spatstat in R.

GeoDa Center Tutorials: A range of tutorial material provided by creators of the GeoDa Software. I would focus on the R tutorials as the GeoDa tutorials are awaiting an update in line with the software’s latest release.

Spatial Stats. in ArcGIS: A preview chapter from the Springer’s Handbook of Applied Spatial Analysis.

CATMOGs: A hugely successful series of publications that cover the basics of spatial analysis, they have been written by many of the pioneers in the field. Topics include The Modifiable Areal Unit Problem (Openshaw), Voronoi (Thiessen) Polygons (Boots), Spatial Autocorrelation (Goodchild).

CASA Working Papers: A shameless plug for my fellow researchers. The nice thing about these is you don’t need to be part of an academic institution to access academic research.

I am sure there are many others and I welcome your suggestions…