Uploaded on 2017-01-02 by Bror Muller

As I get further into this course it feels like there is a strong bias towards technical results in city planning: the right number of buses, the most efficient layout for schools, hospitals and residential areas etc. However I am more interested in city planning as a social modification tool. I suspect some people will read 'modification' with negative implications but I simply want to understand how to develop and use a tool (future city planning) that can make people 'better'. What we choose to do with that tool, what is meant by 'better' are much bigger discussions :-) Another point I want to make reflects an earlier point in this course: data is one thing but it is the relationship between data that gives information and then it is only special information that offers knowledge. So we can make mllions of invisible things visible and even try to map a relationship between them but getting valuable knowledge seems like a very ambitious goal. I like it! In my picture I see hundreds of invisible data points that include obvious things like traffic patterns and pollution levels but true to my points above I am interested in measuring things that: 1. Affect social behaviour and 2. Can be compared with other data points to illicit information So I propose measuring tangible things like 1. The surface area of windows (extrapolating the amount of natural light in a building) 2. The amount of greenery that can be seen from any point in the environment (inside, outside), i.e. give a 'green' index to every intersecting point of longitude, latitude and altitude 3. The amount of CO2 at intersecting points of longitude, latitude and altitude 4. The level of education of the people 5. The net worth of people 6. The amount of debt a person maintains and some less tangible data 7. The degree to which a person is concerned with the future 8. The level of motivation for a person to apply themselves to their family, their work, their community There will certainly be information resulting from the interesection of these data points but the objective is knowledge. I am not convinced I know what invisible date to make visible and how to combine it into the right information that gives us insightful knowledge. I think this is the classic modelling problem... what is it you need to measure to identify patterns that offer knowledge. This is true in all aspects of life from the stock market predictions to genetic modification. It is ironic that a profession as old as architecture seems so new at this.