We were tasked with recommending the government about where to initiate various employment programmes, and where to improve infrastructure, based purely on spatial data we were given. Through programs like ArcMap, which I had no prior experience with, we first had to work out the best way of representing the facts. Through analysis of this map, we could in turn provide a recommendation, and offer insight into any implications our recommendation may have on the area.
A large focus of the Urban Regeneration MSc, and indeed the entire School of Education, Environment and Development of which it is a part, is the imperative of spatial rebalancing in the UK. Compared with federal states such as the U.S. or Germany, the UK lacks economic spatial balance. This is manifested in the relative wealth of London and the South East in contrast to regions typically further North. In order to address this, a number of devolution and localism policies have been initiated, but as time goes on without too visible an effect, more extreme forms of devolution have begun to surface. University institutions typically favour these kinds of strategy, as it will result in a fairer slice of the pie for each region.
In applied spatial analysis then, this imbalance is the first thing you are bound to notice when working with the Indices of Multiple Deprivation datasets, and also most other employment or economic data. Therefore, when working on these projects, I was able to imagine myself as someone using map-making software with the aim of improving lives in more deprived areas. I learnt this morning on BBC News that around 40% of children in Manchester grow up in poverty. Using GIS to confront shocking figures like this can improve the efficiency of government programmes enormously.
Well, supposedly. With this assignment, I've been using census data from 2001 from local authorities. It can be really difficult to present data fairly when using such arbitrary geographical boundaries. These days, 'Lower Super Output Areas' or 'Medium Super Output Areas' are more commonly used as they divide the country into fairer divisions of equal population, with a lot of detail and attention applied to protect individual's privacy. For example, if there is clearly only one family from an ethnic minority background in an LSOA, the data will be strategically shuffled around to protect that persons details, without affecting the overall statistics of the overall area.
But we weren't permitted to use this methodology. Furthermore, each time I tried to represent certain facts on the map, certain 'health hazards' would emerge which would add a vicious sort of subjectivity to my map. It helped me realise that graphs, charts and representations can really be manipulated to tell or sell a certain story.
The reason I'm writing about this is just to remind myself of the complexities that can arise when you are trying to create a map that - when completed - can appear so simple. I was dubious when approaching this course as the extent of possible manipulation had not been entirely clear to me. Now I have learnt that I should perhaps consider a number of options first, and choose the least deceptive of those options.