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Browsing posts in: Mapping

Quantified Self & Mapping

The quantified self (QS) movement is intrinsically about knowing more about your most important asset in order to make better decisions. There are many applications out there that allow you to record daily activities – be it mood, food consumed or movement. For the great majority of these they probably, in truth, gain little more actual utility than simply writing these down with pen and paper would provide.

When can these quantified self applications be effective? Some applications such as ‘OptimizeMe‘ use integrated approach of these measurements and give intelligent suggestions such as ‘If you get more sleep on Sunday you will have a better mood on Monday’ ; with these technologies emerging, it feels as if the next step, integration of self-data with real data will be combined – such as the application suggesting : ‘Today the sunniest morning of the week – maybe you should walk to work’. Can having these kind of suggestions available make people, en masse, happier, healthier or more active than they already are?

As part of our MRes coursework we are working on creating visual representations of ‘the active city’. One element my group deduced was physical activity and we are currently exploring different QS datasets and how these might be involved.

At the moment I am testing out the ‘Moves‘ application. This application seamlessly records your movement throughout the day which can then be extracted and visualised using the Moves API. So far it has been impressive in its ability to deduce when I am walking by foot, using transport and which venues I spend hours on the computer.


There is also a GitHub example of visualising this data through Processing available!

screenshot_1 More information about this viz.

Gentrification in London continued…


Observational analysis of the alterations of the social structure and housing markets of areas in inner-city London led Glass (1964) to coin the term ‘gentrification’, described as a phenomenon in which the social character of a district is transformed through the displacement of its working class inhabitants. Further research on gentrification has identified it to include factors such as the physical regeneration of housing stock and industrial areas.

Above is an example of the output of a function I have been developing in R to support quantitative research in urban planning. This function provides a tool to analyse weighted combinations of different variables and how they may have changed over time – suitable for identifying scenarios such as gentrification which have no clear single identifier.

The map aims to show concentrations of areas that have experienced a significantly high change in class, in dwelling stock and property value (dark blue) over the ten year period between 2001 and 2011. A number of clear areas have been identified in the inner-east and outer-east of the city.

Further information about this function and analysis of results to come.

Visualising change over time of multi-variate urban planning phenomena

As part of my Master of Research coursework I have been developing a function in R named ‘spot-the-difference’ to support quantitative research in urban planning. This particularly supports the analysis of multivariate phenomenon (such as gentrification, segregation, migration) over time which are well-covered in urban planning literature, but their narratives often fall short in the incorporation of modern GIS technology in their analyses.

R is an open-source programming language and environment that enables statistical computing and the creation of graphics supported by a wide range of geospatial packages.

Early stages of the development of this function led me to discover some great maps already about the similar topic:

‘Gentrification Map’ produced by Savills, using an unknown weighting on social classification data. Source: Economist
Gentrification Map Savills

An Urban Renaissance Achieved? Mapping a Decade of Densification in UK Cities by Duncan Smith


My own analysis of gentrification in London will combine change in the professional class, social housing stock and housing prices as a weighted index. Below is a sample from the function which is still under development – showing change in upper professional managerial class. Data Source: NS-SeC UK Census 2001 and 2011