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Browsing posts in: Programming

Processing Simulations Part I – ABM


This post will cover some of the examples I have worked on in terms of simulating urban phenomena in Processing as part of my MRes coursework.

Diffusion-Limited Aggregation
For a city to exist people must have proximity to one another. New entrants to the city will need to connect to those already there. However, individual values dictate their locational choice. This model holds these values as seeking as much personal space as possible – so that they wish to live as far away from others as possible but still remain connected and within the city’s area. Here a diffusion-limited aggregation (DLA) rule has the particles (here, individuals) undertaking a random walk until they connect with a static particle. The simulation begins with one static particle in the centre and you can see the pattern begins to form dendritic structure similar to those seen in some settlements and transportation networks.

Agent-based models
As touched on in my other post, agent-based models can be used to describe system behaviours from the bottom-up, where simple behavioural rules work together to create a phenomena more, or different, to the sum of their parts. Here the agents are performing basic behaviours of avoiding each other and moving towards a goal. One can see the effect the agents have on eachother reaching their goal. One can start to observe how these models can be used to simulate the effects of congestion and crowding.

Bus flows in Processing


This visualisation was created as part of a short exercise for a digital visualisation module of my current studies.

It was coded in the software/language Processing . The challenge of this task was making sure each bus was
treated as an individual object/agent and creating a global timer that dictates not only where but when the object appears there.

Visualising this movement was also a challenge – as without trails of the previous bus movement it just looked like a twinkling display of unrelated dots. Using unique colours to identify buses was an alternative idea, but resulted in a bit of cognitive overload. It has here ended up to include very slight differences in shade of blue for each bus. The technique used here seem to be particularly effective on the edges of the city, and it becomes less effective when there is a high density of buses clustered near each other.

Another interesting experiment was drawing uniquely coloured lines as the bus progresses along its route.

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.