The latest outputs from researchers, alumni and friends at the UCL Centre for Advanced Spatial Analysis (CASA).
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Where do atheists live? Maps show the ‘godless’ cities of England and Wales
The Guardian (blog) DataShine, a new census data visualisation tool from Oliver O’Brien at UCL’s Centre for Advanced Spatial Analysis, shows ‘no religion’ hotspots in Brighton, Bristol and Norwich; while Bradford and Leeds are clearly split and Liverpool keeps the faith. |
Where do atheists live? Maps show the ‘godless’ cities of England and Wales The GuardianDataShine, a new census data visualisation tool from Oliver O’Brien at UCL’s Centre for Advanced Spatial Analysis, shows ‘no religion’ hotspots in some c…
Continue reading »Preamble In the last few years, Objective-C has become enormously DRYer. For example: in the past, adding a property to a class meant adding several of: an ivar, a @property, a @synthesize, a getter/setter, and a release call in dealloc. Worse, renaming or deleting the property meant updating all these. This was error-prone busywork, and […]
Continue reading »Preamble In the last few years, Objective-C has become enormously DRYer. For example: in the past, adding a property to a class meant adding several of: an ivar, a @property, a @synthesize, a getter/setter, and a release call in dealloc. Worse, renaming or deleting the property meant updating all these. This was error-prone busywork, and […]
Continue reading »Recently a couple of colleagues (Ying Jin and Steve Denman) and I have been exploring the potential of geo-located tweets in understanding spatiotemporal urban dynamics. The analysis is based on a passively generated dataset containing 25 millions geo-…
Continue reading »
Recently a couple of colleagues (Ying Jin and Steve Denman) and I have been exploring the potential of geo-located tweets in understanding spatiotemporal urban dynamics. The analysis is based on a passively generated dataset containing 25 millions geo-…
Continue reading »
Recently a couple of colleagues (Ying Jin and Steve Denman) and I have been exploring the potential of geo-located tweets in understanding spatiotemporal urban dynamics. The analysis is based on a passively generated dataset containing 25 millions geo-…
Continue reading »