Using the excellent SANET extension for ArcGIS 9.3 I was able to take some of my data for Southwark that I had geocoded to address level, and estimate the population density using the OS Mastermap ITN product. The procedure is essentially a Kernel Density Estimation that takes place on a given network rather than across 2D space, this effectively controls for the effect of spatial structure, such as urban form, of which the data relates to residential locations. The estimation is made for c.300,000 people in Southwark on a network with around 30,000 road segments so it is to be expected that the calculation takes several hours to run. The KDE process is parameterised in much the same way as the straightforward density estimation procedures in the ARCGIS Spatial Analyst toolboxes, bandwidth and cell size are specified. In this case though cell size relates to the length of segments into which the network has to be cut in order to represent the output. Additionally, SANET allows you to control how you handle road intersections, either by using a continuous or discontinuous approach to the bifurcation, i arbitrarily chose the continuous approach, essentially meaning that the density estimation can turn corners. A straightforward representation can be made in 2D as below.

The interesting aspect to this image that is obscured in 2D smoothed representations is the relative usage of different streets, clearly visible are the residential streets as distinct from the more commercial area on Southwark’s Bankside, and along major roads, and the effect of open space and water features in reducing network density (i.e. if only one side of a road has residences on it). I’ve attempted to explore this further by using ArcScene’s 3D visualisation capabilities, but the complexity of the data make this an incredibly arduous process. The result i was able to obtain outside of ArcScene simply crashing are below.

In this example, Southwark is presented in a kind of 2.5D perspective in which the streets have been extruded so that their height represents the population density at that point. I’ve included some contextual elements, the Thames, and parks, wooded areas, and other water features. Whether or not this image is in anyway an improvement over a simple 2D representation is open to debate, but the selections below do present an interesting cross section of the data.

Amongst other things, I’m beginning to tap into a data source I have acquired for my research known as Hospital Episode Statistics (HES). These are datasets which record the particulars of hospital service by patients. Generally they have a bit of a learning curve, and require the gathering of several additional datasets in order to make them useful. Having gathered all this data and put in all within a MySQL database I decided to conduct a basic analysis, using my study site of Southwark as a guinea pig. Essentially I wanted to known whether more people from Southwark were using hospitals of outpatient appointments than we would expect from national (England) figures. There are many reasons why any given area might be using health care services at a greater or lesser rate than other areas, but for the moment I simply wanted to see whether there was any interesting patterns.

In the HES data it is simple to calculate the total number of people using outpatient care, what is more complex is deriving an expected score from the national data. I went about it in the following way:

Firstly, I took the ONS experimental population projections from mid-2008 and calculated the number of people in each Southwark LSOA, and at the national (England) level, for each of the available age bands by men and women. The population projection age bands are quite coarse, giving totals for 5 population groups: 0-15, 16-29, 30-44, 45-64 (for men) or 45-59 (for women) and 65+ (for men) and 60+ for women. This isn’t ideal, but the age bands do roughly correlate with the different groups of mortality causes in the Grim Reaper’s road map (Shaw, Thomas, Smith and Dorling, 2008). Then I calculated the admission totals for all of the age-sex bands nationally (England), with this I could create a ratio of admissions against popualtion nationally. By applying this ratio to the Southwark LSOA population projects I could create an expected value for number of admissions per areas. Finally it is simply a case of dividing the observed admissions by the expected and multipling by 100 to get a score.

I mapped the results as follows, a score of 100 suggests that the area is not different from the national picture, whereas a value higher than 100 suggests that the area has more people using hospitals than we would expect and a value lower than 100 suggests the converse.

In the case of Southwark, the pattern seems to follow those that are often observed in my work on Southwark, in that the Bankside areas, and the southern part of the borough, in addition with the north-eastern former docklands area have levels of admissions that are equivilant too, or lower than what we would expect nationally, whereas the central areas have admission numbers higher than the national level.

One of the best things about having address-geocoded an entire popualtion dataset it that you can finally get away from non-uniform areal representation (OAs, Postcodes) and present something that is uniformly disaggregate. Academics such as David Martin have long expounded the value of gridded representation of population data as it is regular and hence spatial unbiased. In fact the current work his group are doing is really interesting stuff, looking at daytime (as opposed to residential) population.

Anyhow, using the address-geocoded patient register for Southwark I was able to create a population density visualisation on a 100m x 100m grid that still preserves patient anonymity to an appropriate level. Of course there are some issues with Patient Registers, notably that they are only complete for people that register with a GP. Nonetheless, they provides a uniquely fine grain view of population in Southwark without resorting to the statistical uncertaintyof a smoothing surface-based density estimation, or an irregular, space-filling administrative/postal areal unit solution.

I was caused to consider the problem of generalisable human behaviour by a presentation on evacuation modelling. In my eyes the ability to model or predict anything, and make the implementation transferable across different contexts is contingent on the assumption that you know how people will act. The fallacy here is that you know how people will act, but you don’t necessarily know how individuals will act. It is easy (relatively) to aggregate across social characteristics and say: young people move at x speed, but old people move at y speed, and use it as a way of building socially stratified and perhaps logically more realistic models (this is often known as disaggregation). This approach seems to work well for regional systems, and large groups of people, however as the system of interest becomes more and more localised, individuals whose observable characteristics diverge enough from a generalisable norm can actually have an important role in the outcome of the model. I tend to think of this group as the 10% who change everything, although the actually percentage is likely to vary contextually.

In my work I have been looking at uptake and registration with GPs (doctors’ surgeries) with a view to isolating the demographic qualifiers of choice that create these different spatial patternings of uptake. Essentially attempting to find interesting social disaggregations within the data. In this work, which is at the level of the surgery, but for a PCT system (i.e. administrative health area), it is clear that some people, a small minority, act unexpectedly and differently to others with the exact same socio-economic characteristics, and that the problem is exacerbated when you look at small and smaller problems. For example, the largest surgeries in Southwark have between 10,000 and 20,000 registered patients, for these it is far easier to model patient registration as a function of distance than it is for a surgery of only 2,000 people. This is because larger surgeries can aggregate out the small number of ‘deviants’ better than a small surgery can. This is a defacto small numbers problem – the effect of a small number of outlying cases has a larger effect on smaller units of aggregation (surgeries) because they make up a larger proportion of the total population.

What does this mean for small-scale agent based simulations then? Well, as far as I can see it is very difficult to predict who out of a population is likely to diverge from their socially-stratified peers and be the outlying individual, and since the scale of simulation is so localised this uncertainty is liable to dramatically change the predicted outcomes. Thus in any case estimating the likely outcome within a margin for error is plausible – x people of y population were subject to some disadvantageous outcome (i.e. death/injury), but assessing where deaths/injuries occured, or the characteristics of who died/was injured may be a bit ambitious, or open to a quantitatively unacceptable uncertainty.

Naturally, understanding local level social systems should be a priority, and deriving generalisable rule-bases to give the best possible outcome for the incidence of a given phenomena is important. However, I think we always have to accept that in these circumstances a small number of people can have a large effect on the outcome in a way which is largely incalculable.

As I mentioned in a previous post, I was looking to link up the London Population data made easily available by the London Datastore with some sort of dynamic cartogram representation. I’ve now done that if you click on the picture below. It’s a fairly rudimentary little applet, but I think it tells an interesting story.

The slider bar at the bottom allows you to move between the London Geography (the first position) and the cartogram of any year by clicking between them, the years you have chosen and the direction of transition are represented by the text on the right. You can replay any transition by clicking on London. Clicking on the play button slowly cycles through all cartograms.

The initial state shows that from 1801 – 1851 the relative popualtion change in London was very minor, there was some expansion of the inner London boroughs, with the City getting proportionally smaller as a result, but generally London is best seen as what we now think of as the City of London. This trend increases with growing pace during the Victorian period which sees much more impressive growth of the Inner London boroughs up until WWI. From WWI until the 1939 census, the focus of London’s growth seems to shift to suburbanisation, and although fairly even at first, by 1939/1951 there is a clear preference for west London which diminishes the size of east London and shifts the position of the city to the east as a result. Perhaps this is due to the destruction of vast parts of East London during the blitz. From 1981 to 1991 it is clear the process of suburbanisation is slowing and the trend for growth in west London is diminished in favour of a small realtive growth in population in East London, and a slight general movement towards a recentralisation of population in inner London.

I think this is an interesting application of data, although I am slightly skeptical about linking cartograms in this way. The cartograms themselves are proportional to themselves, but not proportional across the whole timescale. If this were true then London would grow from a small image, representing a small total popualtion, to a much larger one by the 20th century as it becomes a global city with a large population. However, by holding the size temporally constant, the actual compositional changes of the different London Authorities do seem to show interesting and expected patterns.

If anyone is interested you can see the code, which is messy, by clicking source on the applet page, I haven’t really commented it, but am happy to discuss if anyone wants to.

NB: The applet will run at a speed that depends on your CPU, essentially it is trying to juggle loads of numbers which are then displayed in the applet window, the slower the CPU the longer it takes to handle the numbers, there doesn’t seem to be a way arouind this. Sorry if it is slow.

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.

]]> http://danieljlewis.org/2010/03/03/analysis-of-surnames-from-southwark-patient-register/feed/ 0 http://danieljlewis.org/2009/07/06/popfest-09-impressions-part-2/ http://danieljlewis.org/2009/07/06/popfest-09-impressions-part-2/#comments Mon, 06 Jul 2009 17:56:51 +0000 Daniel Lewis http://danieljlewis.org/?p=32

This post details the presentations from the second day of PopFest 09 at LSE that I found most interesting.

Garret Maher – National University of Ireland, Galway – “Labour migration and migrant remittances: Brazilians in Ireland”

Commonly held knowledge on Irish migration dictates that Ireland is seen as a great exporter of people, particularly to the United States, however more recently, and in particular due to the expansion of the EU, Ireland has seen increasing levels of in migration. Garrett however doesn’t focus on the EU, his case study concerns the rather rarer, and more interesting, phenomenon of Brazilians working in a Galway town called ‘Gort’. Since the turn of the millenium, Brazilian’s have been actively recruited to work in Ireland, predominantly through social networks, initally more formal relationships persisted with companies with ties to Brazil recruiting workers, but later through Brazilian friendship networks funded by worker remittances. The growth in Irish population was notably leading to an official 4720 Brazialians in Ireland, although Garrett speculates there may be more than this in practice. Despite having done his fieldwork prior to the gloabl economic recession, Garrett was able to offer some insights into the state of play currently having maintained links to the areas in Brazil and Ireland that he studied.

Stephen Jivraj - University of Manchester – “Factors influencing low income migration between reighbourhood areas in England”

Stephen’s research is concerned with the nature of migration and whether it is possible to demonstrate increasing levels of residential inequality in poor areas. To do this he used the Pupil Level Annual School Census (PLASC)  dataset, which is longditudinal, with a fine temporal scale (yearly) and good spatial resolution (postcode), it also allowed for the use of the free school meals indicator to create an icome variable to represent low income pupils. Stephen built a multilevel model in MLwiN to accoutn for movements of individual pupils between areas with characteristics at the individual and neighbourhood levels. Interestingly the conclusion that Stephen came up with showed that areas which are poorer to begin with have a lower change in concentration of income poor pupils than areas which are less poor. This is slightly counterintuitive and there may be a bit of a small-numbers problem at work here, certainly the effect of poor moving into less poor areas is not substantial, however it is still possible to generalise and say: migration does increase the concentration of poor in poor areas. Of course the results only really holds for school pupils.

Jenna Truder – University of Brighton – “Re-theorising long-distance family migration in a global-city region”

Jenna presented some of what is clearly an extensive piece of research on processes of family migration at coastal margins, her particular study site being ‘Old Town’ a particularly affluent part of Hastings which is apparently naturally ebclaved by the physical landscape of the southern England coastline. Jenna characterises it as a “pocket of privillege” which is distinct in its gentrification and and cafe culture. In this presentation Jenna specifically focused on a demographic group called ‘DFLs’ or ‘down from Londons’ a group of people settling on the seaside  to fulfil desires and aspiration pertaining to quality of life that were unobtainable in London. This group has a lengthy history in Old Town, stemming from post-war East Londoners, but now encompassing most of London who shape Old town in terms of its community, environment and the opportunity for an alternative lifestyle. The atmosphere is described as somewhat sanitised from the influnce of the local fishing community. The most interesting discussion within this work is the ‘geography of loss and gain’, wherein inmigrants incur as economic loss in moving to Old Town in exchange for a perceived non economic (perhaps famillial or place specific) gain. This geography of loss and gain is seemingly magnified in Old Town due to the extent to which in migrants actively cut ties with London in moving, a practice not experienced in other cases such as Bristol and East Anglia.

Celia Fernandez Carro – Centre d’Estudis Demographics, Barcelona – “Residential context of the Spanish elderly in the early 21st century”

In her presentation Celia outlined the pressures on Spain due to the aging popualtion – with an additional 10 million elderly people to support by 2060, and the strain this puts on family and housing stock. In Spain there is a tendancy for the elderly to want to ‘stay in place’, rather than live in a care home in their old age, instead relying on their family as their main care provider. Celia attempted to model the likelihood of elderly people remaining in their residences using a logistic regression, the dependent variable being the binary of whether the home is rented or owned and characteristics regarding the home explaining this choice. Independent variables included household size, years of occupancy, number of rooms etc. All data was derived from the Spanish Living Conditions Survey, which was a sample of c.65,000 of which the sample of elderly people totals c.3870. Celia demonstrated that the the size of the house and the number of years of occupancy suggested a higher chance of being a homeowner, and linked this to the Spanish tendency of investing by buying property.

Jo Sage – University of Brighton – “The complexities of studentification”

In one of my favourite presentations from the conference, Jo elaborated on her findings regarding ‘studentification’ : the socio-economic changes caused by very large numbers of students living in particular areas of towns or cities. Jo was investigating the economic, social, cultural and environmental dimensions of a transition of an area from owner occupied family housign to having a transient student popualtion. Her main argument is that the geographies of studentification have diversified massively and the current theoretical construction is unsuitable to represent the diversity of the situation, in particularly she stresses the recent shift from a student housing market driven by landlords to the increasing agency of student in decision making , noting the link between studentication and later gentrification.

The remaining sessions dipped quite substantially out of my area of perceived expertise into something I know comparatively little about – HIV/AIDS and reproductive/sexual behaviour in developing countries. Needless to say the research presented was interesting (who knew, for instance, that contraceptives could be injectable?!) and it took be back to the development modules I studies during my undergraduate in Geography at LSE.

Lucia Knight – London School of Hygenie and Tropical Medicine – “Changing Rural Livelihoods HIV and AIDS-related illness and death in KwaZulu-Natal, South Africa.”

An interesting insight into the effect of the gap insocial welfare caused by no umemployment benefit on AIDS-related illness in South Africa. Incredibly the Umkhanyakade area of KwaZulu Natal has over a 50% rate of AIDS related deaths and accounts for 3,500 individuals using anti-retroviral drugs. This study used a household survey to capture people’s experiences of illness and its effect on the livelihood of the family. The loss of economic contrivution to a household due to illness, coupled with the increased burden the illness puts on other members of the houshold and dependents economically, and in terms of care is huge. Lucia elaborated on this, and the success of anti-retro virl drugs in restoring household order.

Sarah Keogh – London School of Hygenie and Tropical Medicine – “Reproductive behaviour and HIV status in pregnant women in Mwanza region, Tanzania”

Sarah focused on the issue of high HIV prevalance in women of reproductive age, an incredible 97% of whom attend Tanzanian ante-natal clinics. She used a regression model to assess the uptake of different contraceptive methods amongst women with different characteristics, such as number of children and HIV status, as well as the likelihood that they would have another child. She found that women with HIV were less likely to want another child than those without, and that likelihood of being HIV positive was affected by engagement in family planning, longer birth intervals and remarriage. Sarahs main point was the need to have tighter links between anti-natal clinics and family planning.

Billie de Haas – University of Gronigen – “Sex and relationships in Uganda – Adolescents’ views embedded within their cultural and socio-economic context”

A case study of school students in Kampala, Uganda, set in a day (i.e. non-boarding) mixed secondary school. The students were aged 15-19 and Billie researched the decisions made by these student in choosing sex or abstinence. One notable outcome was the inflated perception of risk caused by HIV/AIDS and pregnancy likelihood.

Dewi Ismajani Puradiredja – London School of Economics – “Contextualising Condom (non-) use by rural and urban female sex workers in Indonesia – A mixed methods study”

Jani was investigating the low condom uptake amongst sex workers in indonesia which occurs in spite of overwhelming evidence of their effectiveness in preventing infection, both HIV/AIDS and STIs. What is more, the sex-workers themselves were found to possess the knowledge of the condom’s effectiveness and still often not use them. Jani’s interesting and creative research methodology used mixed-methods, taking samples from 310 sex workers that she identified. One interesting finding was the extent to which rural sex workers are less informed about condom use than urban sex workers.

Paul Mathews – London School of Economics – “Family and Fertility: The influence of social network composition on the probability of childlessness”

Coming at his research from the somewhat questionable angle of evolutionary psychology (that it is our genes that are governing our more basic, primal, behaviours not rationality) Paul looked at the kin-effect of childbirth. The basic position being that family members will want other members of their family to have children because genes are shared, thus you don’t necessarily have to reproduce yourself in order to pass on some of your genes to the next generation. This is the evolutionary imperative of lineage extended to the social network. Using event history analysis, a form of regression, on the longitudinal British Household Panel Survey (BHPS) dataset Paul showed that mothers and partners had a large effect on the likelihood of first birth. This finding seems somewhat tenuous to put down to a genetic predisposition however, I certainly think it will be very difficult to unpick the specific attitudes and behaviours at play in a question such as this within a social science context.

Here are a selection of the presentation from Day 1 of Popfest 09 that I have enjoyed, or have interested me so far:

Thomas Clemens – University of St. Andrews – “Unemployment, mortality and overcoming the problem of health-related selection: Evidence from the Scottish Longitudinal Study”

Tom gave a really interesting talk, based on a paper he has written that challenges commonly held assumptions in the construction of a robust methodology for longitudinal research. Starting with the idea that there is a correlation between unemployment and health, namely that there is a strong relationship between not having a job and being more prone to illness and inevitably death, Tom enlightened us of the issue of reverse causality in longitudinal research. This issue is this: the researcher has a window within which the status of employment, and health characteristics of a person are evident, within this window there is the possibility of observing a relationship between poor health and unemployment, however it is impossible to say whether this is causal, i.e. that poor health derived from not having a job, or reverse causal, i.e. poor health was the cause of the unemployment. The net result of this reserve causality is the idea that you therefore overestimate the correlation between unemployment and mortality/morbidity. The common tactics here is to enforce a ‘wear-off period’, commonly 5 years, in which observed deaths are ignored in order to account for  the likely effects of short-term health problems (i.e. death or recovery and readmission to the labour force). In his analysis Tom found that the data from the Scottish Longitudinal Study didn’t support the need for a wear off period in the assessment of unemployed status leading to poor health, and that instead adjusting for limiting longterm illness may be a crude but useful way of accounting for the effect of the ‘wear-off period’ in other contexts.

Michael Grayer – QMUL – “Estimation of life expectancy for small areas”

Michael, in a delightfully geographical study, showed how he was able to compute the likely life expectancy for London wards. Life expectancy is a good summary indicator of population mortality, however its appropriateness for small area studies has been, upto this point, challenge by the inherent small numbers problem of small areas, that in a small area with few people a single person can have a large effect on the result. Michael demonstrated this with some examples, showing that small variations in the numbers of elderly people created an uncharacteristic ‘long-tail’ in the survivorship curve for a ward when compared to the expected shape, this artificially raised the estiamted life expectancy for the area. In order to adjust for this Michael introduced ‘Silcock’s Bias correction method’ a mathematical function which seems to have the effect of smoothing the tail of the survivorship curve and creating a survivorship curve of the expected shape. The next set for Michael is to look at applying confience intervals and doing some ward-by-ward comparisons.

Ignacio Pardo Rodriguez – Universidad Complutense de Madrid – “Mixed methods and demography: why, when and how”

I wanted to flag this talk, not because I particularly agreed with the message per se, there seemed to be an advocation of ‘all mixed method, all the time’, but because I think it is an important and fascinating discussion. No one can claim to be unaware of the increasing buzz surrounding mixed-methods, certainly in the world of GIS we have increasing seen it. I particularly like Pavlovskaya (2006) on this very issue, as well as the myriad examples in Public Participation GIS (PPGIS) and Critical GIS (CGIS) and am personally quite interested in the emerging world of ‘critical quantitative geography’ as posited by Mei-Po Kwan and Tim Schwanen (2009 and forthcoming) which elaborate on and break down the quant-qual divide that has been seen to persist since perhaps the cultural turn. Personally I favour an approach which uses the most appropriate tool for the job, be in quant, qual or a bit of both informing the other, certainly I favour the inter/intra – disciplinary working approach which engages the expertise of others, with differing skills to yourself, in order to formulate an approach to methodology. This kind of discussion reminds me of a paper by Bell and Reed (2004) entitled “adapting to the machine” in which the possibilities of mixed-methodologies are expounded in essentially a kind of socratic dialogue.

James Cheshire – UCL – “Surnames as Indicators of cultural regions in the UK”

I work with James, so perhaps it is unfair to expound the virtues of his work too greatly, but I am fascinated by the methods he has used to manipulate and transform surname data and create something actually very interesting and perhaps culturally significant. James uses several different techniques, cluster analysis and Multi-dimensional scaling (MDS) in order to represent the existence of distinct ethno-linguistic regions in the UK. What really fascinates is the time frame, James has data for the 1881 census and the 2001 electoral register, basically millions of names. He seems to demonstrate with this that actually, we don’t move around all that much. In an interesting case study of Corby, James showed how his 1881 data suggested the town of Corby to be ‘English’ and yet in 2001 it was consistently clustered with Scotland, qualitative research on the matter demonstrated that this wasn’t an error, but that a factory had moved into town employing only Scottish workers which had had a long-lasting impact on the town as shown by his analysis. The map visualisation throughout this presentation were excellent.

See James’ blog here.

Fabian Neuhaus – UCL – “Urban Diary – The spatial extension of everyday life”

Fabian’s presentation was perhaps the richest of all in the first two sessions, dealing with the many-fold overlays of people and place, and their activities within the city as a way of understanding and documenting urban rhythms and behaviours. Fabian used the space-time cube, or space-time ‘aquarium’ technique very well to visualise his participants activities and is able to draw on a strong graphic design background to really elicit the maximum aesthetic from his experiments. I was also particularly take by his considertion of different transport modes and the urban experience, as well as his use of mental maps in conjunction with GPS traces. Fabian also keeps a rather good blog, access here.

The references from the brief discussion of mixed methods were:

Bell, S., and Reed, M. (2004) “Adapting to the machine: Integrating GIS into qualitative research” Cartographica, 39(1), 55-66.

Kwan, M-P and Schwanen, T. (2009) “Quantitative revolution 2: the critical turn”, The Professional Geographer, forthcoming

Kwan, M-P and Schwanen, T. (2009) “Critical Quantitative Geographies”, Environment and Planning A, Vol. 41(2), 261-264

Pavlovskaya, M. E. (2006) “Theorizing with GIS: A tool for critical geographies?”  Environment and Planning A, Vol. 38(11), 2003-2020