Skip to content

Some Surname-based Rank-Size thoughts

By Dan
— March 5, 2010 at 3:24 pm

Yesterday Professor Mike Batty introduced me to the rank-size rule, an idea popularised by George Kingsley Zipf as the relationship between the frequency of an observed phenomenon against the phenomenon’s rank in its group. This is best exemplified by the example of city sizes: essentially Zipf shows that for every really large city, there exist many smaller ones; however these smaller cities aren’t just a bit smaller than the large city, they are considerably smaller, in fact the difference in city size from the biggest cities to the smallest can be explained by a power law, this can be represented as:

Where Pn is the frequency of occurance of a phenomenon ranked nth, and the exponent alpha is usually roughly equal to 1.

The power law thus produces a plot where the 2nd item is 1/2 the size of the 1st, the 3rd item is a 1/3 the size of the 1st etc. This can be represented by a plot of surname frequency in Southwark by rank.

Plot of Surname Frequency against Rank in Southwark for all observed surname (using R)

It is clear from the graph that there are very few surnames which are popular and many which are relatively unique. Another interesting characteristic of a power law, such as the relationship between surname frequency and rank are self similar: if we examine any portion of the curve we should get the same curve, albeit at a different scale.

Plot of Surname frequency for Rank 300 - 6000

It is clear from the above graph that a subset of the full data gives a power law relationship. We can attempt to linearise this relationship by taking the log of the frequency and rank:

The fact that the line is not straight indicates that the relationship is not a true power law. The long tail is accentuated by the stepped line, frequencies are integers so when we get to increasingly unique surnames the ranks tend to cluster. In the rank-size distribution of cities, the characteristic fall in the long tail when linearised like this indicates that city size distributions are really log-normal, however this is not the case in terms of surnames. If we exclude some of the long tail, the relationship can look a bit more linear as this plot demonstrates:

No Comments

Categories: Southwark, Thoughts, Uncategorized.

Tags: , , , , ,

Analysis of Surnames from Southwark Patient Register

By Dan
— March 3, 2010 at 4:32 pm

My colleague James Cheshire’s research deals with understanding and classifying spatial patterns in surnames. He has been able to show, through various techniques, that there exists in the UK a regional geography of surnames. This in mind, I thought I’d interogate my database of NHS patient registrations for Southwark and see what was going on in surname terms there. This first table shows the top 20 most popular surnames in Southwark, ranked by occurance.

Figure 1: Top 20 Surnames in Southwark, by occurance.

Unsurprisingly perhaps, the top places are dominated by surnames native to the UK, classically Smith, Williams, Jones etc. However, in line with Southwark’s reputation as a diverse borough and in light of it’s high inmigration figures, it is also clear that of these top 20 surnames some of them would be connected to inmigrant names: Kamara, Ahmed, Ali, Patel and Khan are all surnames that are increasingly associated with a previous period of migration to the UK. Interestingly the Vietnamese population is very small, less than 1% of the population of Southwark, but around 23% of these have the surname ‘Nguyen’. The ethnicity of the surnames is derived from Onomap.

The frequency distribution of Southwark surnames looks like this:

Figure 2: Surname Frequency Distribution for Southwark, 2009

Note the characteristic long tail, there are a huge number of unique, or almost unique surnames, and considerably fewer surnames which are possessed by a large number of people. Such a distribution seems to obey a power law of some sort.

We can dig deeper into this phenomenon by looking at the number of surnames that comprise a given percentage of the population:

Figure 3: Surnames comprising given percentages of the Southwark Population

As we can see from the above figure, only 56 names account for 10% of the Southwark Population, but that in total there are 88,124 distinct surnames in Southwark. Again there is a characteristic decay to the curve.

Finally, let us consider just the charactersitics of the long-tail of the distribution:

Figure 4: Focus on the long-tail - percentage population for given surname frequencies.

From figure 4 it is clear that almost 25% of the Southwark population have a surname that is share by fewer that 11 people, indeed just over 16% of the Southwark population have a surname unique to the Southwark patient register. The shape of the curve in figure 4 demonstrate the effect of the long tail seen in figure 2.

For more information on surnames research check out James Cheshire’s blog, working paper or Pablo Mateos’ working paper.

No Comments

Categories: Southwark, Thoughts.

Tags: , , , ,

London 2001 Cartogram Visualisation in Processing

By Dan
— February 26, 2010 at 7:10 pm

Recently I’ve been messing around with processing as a way of visualising cartograms dynamically. The link below is a fairly striped down ’sketch’ that shows the geographic representation of London Boroughs morphing to a cartogram representation. Thus cartogram describes a situation in which each London Borough’s area is represented not by its physical land area, but by its relative 2001 population size.

Cartogram for London 2001 Census Population

If you click the above image you will be linked to a page which contains the Processing Java applet. The only interaction I’ve employed is the mouseclick, which resets the animation, allowing you to watch it morph again and again, and really get a sense of how many people are crammed into inner London. (NB You will need Java installed).

Actually making this animation was surprisingly simple. The Cartogram is pre-rendered using the ArcGIS cartogram extension, and the coordinates for the cartogram and the original London Shapefile are exported and read into Processing as an array. The array is visualised using Processing’s built in ‘Shape’ class, so you simply position your list of vertices between a ‘beginShape()’ and ‘endShape()’ tag and Processing draws the rest for you. Naturally, some manipulation is required – the British National Grid (BNG) coordinates need to be converted to screen coordinates, to do this I’ve used Processing’s built in ‘map()’ function.

The ‘morphing’ process that takes place is actually called ‘tweening’, which stands for ‘inbetweening’ – essentially this means rendering a smooth transition between known points. In order to effect this I used the ‘megamu.shapetween‘ library for processing which is very good, their website is worth checking if only for the ancillary resources they have included. Integrating this library is very simple, as is the entirety of the code, if you click on the image and then on the source code link you’ll be able to view it.

I’m hoping to be able to integrate more of the population data into the visualisation and make a dynamic visualisation across a number of years. The data is freely available from the london.data.gov site, the geographical data is not unfortunately and is a simplified version of the boundaries available from UKborders.

1 Comment

Categories: Representation.

Tags: , , , ,

Unlocking UFOs from the National Archives

By Dan
— February 18, 2010 at 3:04 pm

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.

No Comments

Categories: GIS, News.

Tags: , , , , ,

Health Inequalities – the Marmot Review – Some thoughts.

By Dan
— February 17, 2010 at 1:34 pm

The Marmot Review on health inequalities was released last week and is available at: http://www.ucl.ac.uk/marmotreview/ named ‘Fair Society, Healthy Lives’. I attended a lecture on it given by Prof. Peter Goldblatt which sought to details some of the process, evidence and outcomes that arose from the review. I will aim to briefly outline the nature of the report before sharing a few thoughts I had that arose from the session.

The report develops from a previous Marmot report called “Closing the Gap in a Generation” commissioned by the WHO. This report details the neccessity for Social Justice; Material, psychosocial and political empowerment; and, critically, the need to create conditions for people to have control over their lives. Health inequality is the situation in which different social classes can expect to have different health outcomes, the following graph from the Marmot Review demonstrates just this; that there is a social gradient in health from most deprived to most advantaged and that there is evident stratification between those that are healthy and those with Limiting Long Term Illness.

Life expectancy and disability-free life expectancy (DFLE) at birth, persons by neighbourhood income level, England, 1999–2003

It is interesting to note that the report is using the Index of Multiple Deprivation (I assume 2004) at MSOA (not LSOA) to define the axis ‘Neighbourhood Income Deprivation’. It is notable that the social gradient of health inequality in terms of life expectancy is greater for those with disabilities than without. Other graphs in the Marmot review confirm that the social gradient in health inequalities is not closing. Additionally there is also evidence for regional gradients. It is not just within the quantitative data that these gradients exist, but there is further evidence that social gradients in health inequalities are evident even in subjective questions in the Health Survey for England (HSE).

Having made health inequalities most evident, the review sought to collate as much evidence as possible, investigate measurement, indicators and targets that would be useful to this end, and finally suggest a strategy for implementing a reduction in health inequality. To this end it is important to note that the review was written under a philosophy of ‘progressive universalism’ – the authors believe in intervention across the social gradient, but put a greater focus on the least well-off areas. The review’s impetus is to create a greater sense of social justice and lower health inequalities by creating an enabling society; one in which there exists a sense of ‘Health Equity’ in all policy and in which benefit systems are used properly so as to maximise links between health and social protection and hence provide an adequate minimum income for healthy living. The 6 major recommendations, aimed at intervening along the entire life course, but specifically targetted at earlier stages where more good can be done, are:

  • Give every child the best start in life
  • Enable all children, young people and adults to maximise their capabilities and have control over their lives
  • Create fair employment and good work for all
  • Ensure a healthy standard of living for all.
  • Create and develop healthy and sustainable places and communities
  • Strengthen the role and impact of ill-health prevention.

It is clear from even a basic summary of the review that it is far reaching, this is the nature of my first observation: health no longer simply means an absense of disease as dictated by a Doctor, rather it is an incredibly complex, multi-dimensional concept. Health has seen a shift from something that is solely related to the body and the mind in terms of illness and mental health, to something that is far more pervasive. We now talk of the health of society- a collective health, the ‘wellbeing’ of people – a broader social aspect of health, the presence of healthy places – places that promote health, and the differential effects of health needs and access to healthcare. The Marmot review deals more with social conditions that with health per se, as such the report is reminiscent of the attitudes of Victorian philanthropists and social reformers advocating the clearing of slums and publishing on the state of the poor and the need for social uplift. The breadth of the question of health really calls into question how such recommendations as noted above could be adequately implemented, despite the fact that the Department of Health is one of the most well funded government departments, many of the recommendations of the Marmot review are multi-disciplinary in the sense that they seek to act on aspects of society as a whole through diverse channels at multiple levels.

I’ve previously commented on the nature of policy to develop interventions at the local/community/neighbourhood/place level without having an adequate definitions of what these levels constitute. I think it is clear that, again, this review picks up these current tendencies in government policy without really stopping to ask what they actually are, or why they are important. Health geographers talk a lot about health and place, and often the meaning behind this is very nuanced. This is something which is is very difficult to account for in policy which is quantitatively driven, or requires concrete indicators of performance and success, and a universal toolkit applicable nationally, even regionally.

Finally, I pick up the comment about ‘Health Equity’. Asthana and Gibson (2008) define both health equity and healthcare equity. Health equity is the condition of equal “opportunity to be healthy” whereas healthcare equity is more targeted than health equity, specifying “equal opportunities of access to healthcare for equal needs” (2008 p.4). Health Equity is a deficient term because it supposes a uniformly distributed population across space, essentially it fails to account for the spatial dimensions of health. When one considers the one-dimensional space of healthcare inequalities in which inequalities are dictated by universally applicable socio-economic characterisitics such as social class or wealth is is easy to see how a logical conclusion to reach would be health equity, but this is because the inequalities of access to health and the patterns of environmental quality are not adequately assessed. Distribution of people over space is an implicit indicator of likely health inequalities that is most evident in the persistence of ‘postcode lotteries of care’.

Reference

Asthana S and Gibson A 2008 Health care equity, health equity & resource allocation: Towards a normative approach to achieving the core principles of the NHS. Radical Statistics 96: 4-28

1 Comment

Categories: Health Geography, Thoughts.

Tags: , , , ,

Southwark Households – A Preliminary

By Dan
— February 12, 2010 at 8:28 pm

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.

No Comments

Categories: Health GIS, Modeling, PhD Work, Southwark.

Tags: , , , ,

OAC quality using entropy scores

By Dan
— February 10, 2010 at 9:10 pm

The following map shows an entropy score by Great British Output Areas based on each OA’s ‘distance’ from each OAC supergroup cluster centre. Essentially I’m attempting to measure whether any given OA fits discretely into it’s cluster assignment or not. I’m using the cluster distance data from the University of Sheffield OAC datasite. To get a sense of fit I’m using entropy scores, given by the following equation:

Where pi is the distance of a given OA to a given supergroup cluster centre with respect to the other distance to centres. Essentially this is a measure of evenness, in terms of OAC we’d like the results to be less-even as this would suggest that one distance to centre is much smaller than the others indicating a good cluster assignment, OAs that are more-even are indicative of OAs which don’t fit as well into a single OAC class. In the map below a lower entropy score indicates less evenness and hence more a more discrete assignment of OAC class.

The pattern that seems to emerge is that urban areas, such as London, and extremely remote areas, such as the highlands of Scotland, do not fit the classification so well. I quickly tested this conclusion by summarising the entropy scores by the rural urban classification 2004 from the ONS.

This graph seems to confirm the visual reading of the map to some extent, the fit is worst for Urban areas, better for town and fringe, best for villages and slightly worse again for Hamlets and Isolated Dwellings. This graph was created only from data pertaining to OAs in England and Wales though, as Scotland has a different classification as is its want. The effect of Scottish OAs may lift the value for Hamlets though, as Scotland has more remote areas than England and Wales in general. I’ve also created a graph for the Rural and Urban Classification 2004 using the combination classification that takes into account ’sparseness’ as well. Ostensibly sparcity relates to the number of housholds in the surrounding 30km of a grid which has been aggregated to OA level. From this a distinction of sparse and less sparse is created, I’ve got no idea what this means and it seems useless and confusing, however it does back up the earlier poitn for what it’s worth:

Areas that are ’sparse’ seem to be less well classified than areas that are ‘less sparse’ – I’ve no idea what that means though. Nevertheless the pattern is much the same.

Essentially OAC works better if you’re not classifying extremely urban, or extremely rural areas. I think someone should look at the rural urban classification though, or a least write a sensible description of what is actually meant by sparse or less sparse – a less sparse urban areas? I wish I knew what that meant!

No Comments

Categories: GIS, Modeling, Thoughts.

Tags: , , , ,

Data Uncertainty: Southwark’s Disappearing Estates

By Dan
— February 9, 2010 at 9:31 pm

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.

3 Comments

Categories: PhD Work, Southwark, Thoughts.

Tags: , , , ,

Apple’s iPad and Mobile/Field GIS

By Dan
— January 27, 2010 at 7:38 pm

It seems that everyone has been waiting for Apple’s latest invention, the iPad, however whilst others chose to speculate on the dimensions, aesthetic and performance (such as the Guardian’s relentless coverage), whilst giving little thought regarding why it might be useful, I thought I’d think about what it offers to the GIS community. We are of course assuming in has a GPS onboard, otherwise you’d need an add-on or hack.

Steve Jobs with an iPad (source: Gizmodo.com)

Essentially what we have is a tablet computer, these are not particularly new, a previous example having been the series of HP Compaq tablets. This technology is also not new to Apple who previously put out the ‘Newton’ platform for PDAs and had their OS X (illegally) modified by company Axiotron to run on a tablet-style computer based on reassembling a MacBook and christened the ‘Modbook‘.

Technologies such as smartphones are really pushing forward what can be done geospatially, the Apple iPhone, for instance, allows apps to connect to GPS and provide ‘location based’ services- a popular term a couple of years ago which has still yet to really take off. However it is the Android platform which really seems to be pushing forward mapping on smartphones, with Garmin and Nokia forging ahead with development. This seems logically connected to the fact that Google, officially an internet giant, is behind the android operating systems and has itself invested heavily in mapping, most recently launching the ‘Streetview’ components of it’s Google Maps resource. However, much of what currently exists on smartphones is navigationally based, using prerendered tiles and allowing little in the way of interaction, editting or analysis. Ostensibly this is because smartphone still lack the computing power to conduct GIS functions. This is where tablets come in, just a netbooks occupy the inter- smartphone/laptop territory, tablets do likewise but with a perspective different to the netbook, rather than simply being a tool for browsing the internet, a tablet computer offers laptop functionality with a more portable and user-friendly set up for people in the field or on the move.

iPad shot showing map and hinting at location and thus GPS capability - crucial for field GIS. (source: Gizmodo.com)

Currently, the most notable mobile-GIS is ESRIs ArcPad. ArcPad is software which complements their flagship ArcGIS Desktop product, which is currently on release 9.3, but look set to introduce 9.4 (now upgraded to ArcGIS 10) sometime soon. ArcPad is fundamentally designed as a GIS tool for experts, in the field. As such it focuses on likely requirements such as editting, digitisation, attribute tagging, and display. Unfortunately ArcPad, as with ArcGIS desktop, only works on Microsoft OSs (either mobile or PC) thus it is unlikely to be much use on the Apple iSlate without either a virtual machine or dual boot (and why would you want to do that to your new, long awaited Apple product?!). Users of ArcPad will note too that the appearance of ArcPad (see this ESRI pdf for some screenshots) is very much connected to Microsoft’s pre-7 lack of design aesthetic and many note instabilities in the software, either as a result of the platform, or due to ESRI software’s legendary instability.

So ArcPad is unlikely to revolutionise field GIS on the new Apple iPad anytime soon then. So what other solutions are there? Well, Fieldworker seems like a potential solution, but I was too bored by their website’s sales rhetoric to really figure out exactly what it is they wanted to sell me. Likewise Starpal’s HGIS (Handheld Geographic Information System) might be a good candidate if I could get over their mid 90s website. Others such as FieldSmart by Mapframe or PocketGIS look more promising. However the thing that would most excite with regard to the potential offered by new tablet PCs and mobile GIS would be a port for Quantum GIS (qGIS). Whilst some functionality is lacking, and perhaps it has been a bit rushed to 1.0 status, the elements that would be most applicable to a mobile GIS are there, such as digitisation and display. Similarly, members of the community seem to think that creating a mobile version of this open source software would not be a huge effort. I suppose with this in mind, it might not be inconceivable to look at openGeoda as something that could also be developed to run as a mobile GIS, although it will likely require more work than qGIS. Nevertheless, I’ve recently been running openGeoda and qGIS on my Macbook and they seem to complement each other well.

So, essentially what I’m suggesting is that Apple’s latest product offers the potential for some new innovations in mobile GIS and I for one would be excited to see them coming from an open source angle.

4 Comments

Categories: GIS, News, Thoughts.

Tags: , , , , ,

Royal College of Surgeons announces new ‘postcode lottery’

By Dan
— January 25, 2010 at 2:30 pm

One of the most popular media narratives regarding care in the NHS is based around the concept of a ‘postcode lottery’. It is however something that is also acknowledged by the Department of Health in some areas of health care, for instance in the NHS Cancer Plan it is noted in chapter 1 that:

“In addition to relatively poor survival rates, the NHS also suffers from unacceptable variations in access to high quality cancer services.” (DoH, 2000)

The term ‘postcode lottery’ thus refers to a situation in which there exists geographic variation in the quality and type of treatment that prospective patients receive. As such the care any given patient receives is connected to where they live, thus the term ‘postcode lottery’ arises. The existence of such a situation is attributable to any number of factors including NHS resource allocation, insufficient numbers of specialist staff in a given area, accessibility to key services, and the possible presence of another postcode lottery centring around prescribing and access to pharmacy services. Connected to this idea is the proven fact of the ‘inverse care law’ first described by Julian Tudor Hart in 1971 which shows that communities most at risk from bad health tend to have the worst levels of access to the required NHS services. Wealth is often a factor in this function, in that the areas most at risk from poor health are likely to be those areas which are more deprived (as per the IMD) or from neighbourhoods which are again less-desirable or well-off (as per OAC).

The particular findings of the Royal College of Surgeons (RCS) relates to access to surgery to combat obesity, a particularly popular topic within the NHS at the moment:

“Access to NHS weight-loss surgery is ‘inconsistent, unethical and completely dependent on geographical location’, say senior surgeons” (RCS, 2010)

The RCS goes on to make a somewhat sinister claim that in some areas where budgets and resources are stretched, NHS decision makers are ignoring guidelines and denying patients’ access to surgery. Whilst in others, patients who already meet the criteria are forced to wait until either they become more obese or develop life-threatening illnesses like diabetes.The RCS calls for a basic tenent of the NHS systems to be upheld – universal service and the values that surround it that are enshrined in the NHS Constitution (2009) regarding fairness:

“Surgeons want to see consistency and transparency across the NHS so that patients are clear about what they are entitled to and doctors can treat all patients equally.” (RCS, 2010)

The main findings from an anonymous survey of UK bariatric surgeons (surgeons with a specialism in obesity related surgery) reveals that:

  • Approximately two thirds of surgeons said patients who are eligible under guidelines are refused surgery in their centres.
  • Criteria for surgery varies dramatically depending on geographical location and within the same Strategic Health Authorities.
  • Some centres are treating patients with referrals from multiple Primary Care Trusts (PCTs) with different eligibility criteria meaning that patients with a BMI of 60 + are being refused surgery in the same hospitals that are treating patients with a BMI of 40 or less.
  • Some Primary Care Trusts are refusing to commission any obesity surgery.

English Strategic Health Authorities acts as containers for Primary Care Trusts. The RCS has reported that even within some SHAs there exist PCTs which have a different policy towards obesity care. Thus the postcode lottery exists at a number of scales.

Guidelines set out by the National Institute for Clinical Excellence (NICE) were intended to herald the end of postcode lotteries, but in this case it seems that the power of local commisioning has meant that the national guidelines haven’t been followed. This has led to a call for the Department of Health (DH) to invest further in a strategy that will uphold patients right to not be subject to unequal access to treatment.

Finally, one wonders about the merits of refusing access to treatment, when, as Dr David Haslam (Chair of The National Obesity Forum), states:

“Bariatric surgery is amongst the most clinically-effective and cost-effective specialities in any field of medicine, preventing premature death, and transforming lives, whilst saving vast amounts of money for the NHS and the economy. Even the most cynical taxpayer should support bariatric surgery, alongside clinicians, in opposing the unethical and immoral barriers to surgery imposed by NHS purse-string holders.” (RCS, 2010, emphasis added)

Acknowledgements

The post is derived from the RCS website here

The map image is from data subject to: Crown Copyright 2009 UKBorders, an Edina/JISC supplied service.

No Comments

Categories: Health Geography, News.

Tags: , , , , , , ,