urban adventures to (re)present our habitat
In late February, Dan took up my offer to produce some maps for his series of urban story walks, an exploration of London using open government datasets to plan his routes. The aim of these walks was to see how the physical environment related to the highs and lows of government statistics. Naturally, like all the best projects, the request was made at the last minute.
The first walk was to explore areas with high levels of violent crime. From the online London mapper, Dan had observed that, apart from the West End, Kingston had the highest rate of reported violence. The journey was to start in Kingston and to travel across south West London to the west end.
Although by far the highest rates of violent crime occurs in the West End, this is due to it's low residential population, where as Kingston has a relatively average population level yet comes third across London.
Due to the data relating to violence, it felt that red was the right colour to use. Looking back now, my first foray into data mapping for exploration seems very simple. But then as you will see through the series, I may have got carried away.
Dan's next journey was to explore the least connected areas of London. Given that he would be taking people to some remote areas of London, it was also nice to see how those areas rated. The final map is a combination of TfL's Public Transport Accessibility Data (PTAL) and the Environment Deprivation Rank (ED) of the Indices of Multiple Deprivation (IMD).
To highlight the PTAL levels in highest and lowest ranked areas of Environmental Deprivation, I chose to change the point style in those areas. To achieve that a blur was used on those in middle ED areas.
Two things amuse me most about this map, the comment that it looks bruised in the areas at the bottom end of the ED ranks, and that the spots look really cool. The bruising wasn't planned just how the colours came out. I used dots instead of contours as I didn't have time to contour the PTAL data in time for the walks, we'd been away for the weekend with birthday celebrations.
Sometimes the best things come out of adversity.
Following on from the comments on the second map, I really started thinking about how I could colour the maps to represent the data I was showing. Luckily enough the next map in the series was to be depression. Looking at the data available, the best grain I could find was for measured depression based on +2 results from the NHS clinical interview schedule. This showed the rate of what was clinically determined depression at ward level. Given the obvious connotation of blue as depressed and yellow as happy it was mealy an examination of tones to find something I liked.
Whilst researching this map I also found data on anti depressants, I find it quite concerning that prescriptions have been shooting up in recent years; there's more profit in the sale of branded drugs than proper counselling and outreach. The sickening nature of this fact made me go for slightly puke colour on this bit of the map.</p>
Next up was an exploration of unemployment, given no natural colour choices for this I had a bit more experimentation to do. On the other hand, as these were based on actual numbers rather than a comparative indicator there was a lot more scope for detailing the extremes.
For areas of low unemployment, I went for a very soft looking field effect. As things get worse the colours darken and I think I have what looks like a smog effect. As the plan for the walk was to look at some of the highest areas of JSA claimant rates I felt it best to look at the top 10% in more detail. For this I attempted to make the areas look a lot harder. I highlighted the top five areas by using a cracked effect and individual colours.
There are also a few areas highlighted where there is a high gradient between wards or small areas of low relative Employment Deprivation (from the IMDs again) within wards with a high rates of JSA claimant.
The final walk of the series was a look at life expectancy. This initial problem this presented was that it varies between men and women. In order to express this I have chosen to express the average life expectancy in an areas, and then provide a variance between men and women; in the low numbers of areas where man outlive women, I changed the text to yellow.
Unlike the other maps I had not used quantiles as I decided to colour the areas at year intervals of life expectancy. In order to explain this I chose to key the map using a graph which shows the percentage of the population which live over a certain number of years.
Dan was also looking to contour along borders between high and low life expectancy whilst out exploring. Five of these are shown but unfortunately, because I found these by looking at the differences between ward centroids, one or two high boundaries are not picked up where wards are large so the distance between centroid was unusually high. More time would have allowed me to identify these areas, but as always this was not available.
I've been lucky enough to show these maps to a designer who was not shy to criticise. With future maps I plan to take on board these points, specifically in relation to the info-graphic style keys.
If you would like to know more about the maps or see them in more detail, we plan to add them to our website once we have a hi-res image widget set up on our server. They will be available at www.city-farmers.co.uk, I'll let you know when they are.
If you'd like copies of the maps then feel free to contact us through the site, I'm happy to send a link to download them whilst they are unavailable.
If you'd like to commission some maps of your own then I'm happy to discus this. The analysis for these was relatively straight forward but would take on more complex sets of information analysis, whilst still aiming working towards something that looks good and draws the eye.
Thanks for reading reading, I hope you like what you see.
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