The British National Archives has released a large number of files relating to UFO sightings between 1994 and 2000. These previous classified documents detail (often amusingly or excruciatingly) the reports made by members of the public to the MoD regarding the sighting of Unidentified Flying Objects (UFOs). As a geographer and a user of GIS, one of the overriding beliefs is that there exists an abundance of location information stored in documents waiting to be captured and analysed spatially. The records concerning UFO sightings are one such example, hundreds of reports of sightings that all give a location in addition to a lot of other information including numerous drawings of the ‘craft’ that were seen. Because I was interested in testing out the new “Unlock” geocoding service available though Edina Digimap for academic subscribers I decided to extract some of the place information from the first UFO file and use unlock to geocode the sightings.

For those who need clarification,geocoding refers to the process whereby a textual reference to a location, such as a place name, an address, a postcode etc. is given a spatial reference, i.e. a pair of coordinates that can be represented on a map. What “Unlock” does is take a given placename, look for it in a gazetteer – a dictionary of all known places- and when it finds it, returns the coordinate inforamtion associated with that placename in the gazeteer. The Unlock service can be found at: http://digimap.edina.ac.uk/unlock/

Using this service allowed me to create the following map:

I decided to classify the sightings fairly crudely by season as the impression I got from the data was that there were very few sighting in the summer, and more in the autumn and winter. These sightings are mostly from 1994, part of the earliest tranche of sightings released and are to be found in the first file released by the National Archive. As a result this only accounts for about a quarter of all sightings in this year. The MoD also like to make maps, however their techniques aren’t quite so well defined as we can now achieve with GIS and geocoding technologies:

Nevertheless the map is quite illustrative of the major patterns in UFO reporting (as this may be different from raw sightings).

The only downside to this whole process is that “Unlock” the site I used to geocode some of the data is actually quite poor. The site looks nice and the data is undoubtedly very strong, however it is a terrible user experience, there didn’t seem to be any accessible intructions on how to use the site and when I copied from the examples given to try and geocode my 50 places as a batch the output seemed to randomly drop some places in favour of several options for others, in spite of this individual searches showed that the dropped places did exist. An assessment of the sites usability is required – I can’t imagine it being used currently by anyone beyond real specialists, certainly no one will be unlocking much data unless they have a good familiarity with a number of web protocols and data structures. Hopefully in time though it will become more friendly.

I’ve spent a chunk of time recently address geocoding the Southwark PCT patient register to Ordnance Survey Address Layer 2 data. What this means is that I can start identifying and (later) classifying households, this will allow me to ask questions about how different households approach healthcare. More broadly it allows me an insight into the demographic character of Southwark.

The data actually extends past the Southwark boundary as people in Lambeth, Lewisham, Bromley and Croyden do also to some extent use Southwark primary healthcare services (GPs) this means that although Southwark’s population is only c.300,000 the datset I’m using is for just over 340,000 people. There is some uncertainty in the data naturally, this results from the two datasets used; on the one hand addresses recorded in the Southwark patient register are not all necessarily complete, for example there is sometimes a failure to record which particular subdivision of a house someone lives in, or which flat in a larger block of social housing. On the other hand the AddressLayer2 data, although very rich, is not necessarily complete, this could be due to the prescence of unacknowledged subdivisions in residential housing, and although most social housing estates seem well documented, some commercial developments are not necessarily registered beyond the building level. Similarly, there are a number of instances of social institutions, such as the Salvation Army and St. Mungos, or marinas and dormitories having a single registered address for a high number of residents. This may have the effect of skewing the data slightly. With this in mind I created the following graph from the dataset of Number of households against number of inhabitants per household:

This shows that there is still a major trend for single-person households, but equally that around a quarter of all households are co-habited. The long tail in the graph (which i have truncated here) is caused by a few special cases, some examples of which are acknowledged in the previous paragraph. The average household size of 3.10 is itself higher than the UK average household sizes reported after the 2001 census which was 2.36; at the time the borough of Newham in East London had the highest household occupancy rate at 2.64. Of course there are any number of reasons why these data are not comparable, to start with the census took place 8 years before the Southwark dataset was created, similarly the uncertainty in the Southwark dataset is higher as it was not created with the primary purpose that it be able to successfully locate all patients as more often than not patients go to the Doctor and not vice-versa, whereas the census is distributed at a household level to each individual. The Southwark dataset does also include particularly tranisient communities which are missed by the census, such as the homeless who don’t have a fixed address (and hence may be using shelter or hostel addresses) but still require medical treatment at times.

Nevertheless, an interesting first look. The next steps will involve evaluating and validating the dataset to the best of my ability and then moving on to look at ways of examining and classifying household structure.

I’ve spent some time recently working towards a situation in which the whole dataset for patients registered to General Practices in the London Borough of Southwark is coded to address level. Previously I had been working with the data at postcode level and I wanted to start investigating the effects of households on uptake of service, and well as profiling patients at a finer granularity and integrating geographically more sensitive analyses. The geocoding project obeyed the general rules set out for this kind of work; it was reasonably easy, in the end, to address match 92% of the data by scripting, somewhat frustrating to push that total up to 99% (through semi-automated methods of address matching) and all but impossible to match the last 0.5% of patients.

This last group, roughly equivilant to 1500 people who have given addresses which i cannot, even manually, match. This tends to be because, perhaps unwittingly, the postcode doesn’t exist, there is too much uncertainty meaning it could relate to several possible places or the house or the road simply does not exist. In some cases it was easy to clean up the data, for instance it became clear that in a number of cases the addresses actually related to boats moored in South Dock Marina, London’s largest marina. Obviously people that live on boats still need health care, but do not have an address as such, in this case I registered boats to the Dock Office. Similar issues occured with students registered as living in one of Southwark’s numerous student residences, the student’s transient nature meant that there were numerous different ways of recording their residences. In a similar vein it was interesting to deal with the fairly substantial group of people who were either registered as NFA (no fixed abode) and to the GP surgery’s postcode, or to one of several shelters or missions such as the Salvation Army or St. Mungos. This aspect of the data gives an insight that is otherwise quite hard to get at, naturally homeless people require health care from time to time, and it order to receive it they need to go into the system in some way, the fixed address structure of registration means these people occur as somewhat anomolous results within the database. This has the potential to give an insight into the homeless situation in Southwark. Finally there seemed to be some trouble matching patietns that were registered as living in care homes, again these were easy to address match, it was simply that the address information itself had been misreported, or simply read the name of the particular care home in question.

Having gone through the unmatched patients and weeded out cases such as those above that were valid patients, but who didn’t neatly fit into a database with an address-based structure I was left with what appeared to be whole sets of estates that were completely unmatched. I ran a series of wildcard searches on the AddressLayer2 database I have set up in order to try and find these estates, but kept returning empty sets of results. One of the estates that I couldn’t match was the “Sumner Estate”, this rang a bell as I used to live in Peckham and cycled through this estate everyday on the way to LSE, I vaguely remembered reading about its scheduled demolition in The Economist in about 2006-2007. I did a quick google search and found that it was in fact part of the Aylesbury Regeneration scheme, a £2.5bn regeneration by Southwark Council that aimed to clear and rebuild some of Southwark’s worst and most notorious social housing estates. This estate was bad from the beginning and in fact lasted fewer than 50 years, with the most recent 20 being acknowledged as in a state of critical decay.

Aylesbury Estate. Source: http://www.flickr.com/photos/se9

I conducted a number of further searches on google of the following places: Wood Dene; Alison House; Marchant House; Yeoman House; Saul House; Sharpness House; Rainswick Court; Lambourne House; Silwood Estate; Kingshill; Dobson House; Dufrey House; Ayton House; Habington House; Hordle Promenade South and North; North Peckham Estate. I found that all of these houses or estates had been demolished at some point in the mid 2000s. This accounted for around 600-700 patients in my dataset, the larger issue here is data uncertainty: if there exists people in the dataset that don’t actually exist in reality then we have an issue. Having said that, the 600 people that I uncovered as having a defunct registered address only accounts for 0.17% of the dataset, so maybe it’s not too bad. What I actually wanted to focus on here is the hidden nature of these regenerated places.

In conducting internet-based searches for information on the various housing estates listed above I found a very dark picture. To start with inforamtion is very scarce, there is little record on Southwark Council website express regarding regeneration and which blocks were torn down. Some information came from copies of local papers and bulletins. Sadly a great deal was also associated with news media that was reporting the regeneration of an estate as an aside to far graver news, most notably the murder of Damilola Taylor on the North Peckham Estate. Indeed several estates were conspicious in their absence from any online resource or comment other than court documents acknowledging that a defendant heralded from such an estate. In redeveloping large estates, whole roads were removed, the aforementioned Hordle Promenade North and South, as well as Clanfield Way or Walkford Way. The legacy these roads leave however is quite interesting, Hordle Promenade North is a Google maps POI despite no longer existing. Similarly the postcode for Clanfield Way – SE15 6EW remains a poi, allocated to a different stretch of road now, as well as SE16 6EY former postcode for the now demolished Walkford Way. Occasionally planning documents deal with house and estate clearing in a very matter of fact way. There is almost not voice online for any of the inhabitants of these places.

It is easy to view the city as a static entity, it changes so slowly compared to the pace of life, and yet when changes do occur they are easily assimilated into our internal map, as if a change never occured. However, these estates still linger, as hidden reminders of the palimpsestic nature of the city – slums torn down and regenerated, deprivation papered over, tragic events of the past lapsing into memory, slowly forgotten as the city turns over and adjusts its morphology.