Distribution of the population over the city is neither random nor completely regular. The attributes of the population, such as age distribution and educational level are important for city planners in order to make correct decisions where to place for example school, hospitals etc. It is also important to know how city's neighbourhoods are distributed accordingly to demographic and economic variables. Urban planers, as well as politicians, are urged to understand those processes and variables in order them to do proper planing.
In paper 72 neighbourhoods, which consists 554 500 inhabitants, in Mexico City were analysed in terms of economic, demographic, mobility, air quality and several other variables in years 2000 and 2010. Data was gathered from public data which is more or less reliable. For example they did destination survey of how people move and used free map software to gather data of all streets. Older data gathered from library which was apparently hard work.
Neme spoke how they have two major interests in studying gathered data. First, they sought to identify neighbourhoods with similar urban features and similarity of certain urban regions. Second, they intended to visualise the evolution of neighbourhoods from urban point of view. Cities are constantly changing. Citizen get older, new ones are born, new streets are build, new schools buildings are build. So they compared data from years 2000 and 2010 and analysed how each neighbourhood has modified its own variables by time. Ten years is a short period of time to observe significant changes in a city. In general, observed neighbourhoods tend to stay more or less the same. There were, however, some neighbourhoods that clearly changed. One of neighbourhoods, a small residential area, shifted its position towards a cluster of neighbourhoods with higher standards.
When analysing cities there is many parameters. To make sense of several aspects of cities, such as traffic flow, mobility, social welfare, social exclusion, and commodities, data mining may be an appropriate technique. In research basic SOM was used. They defined a neighbourhood as a region of blocks that share same administrative instance. Which is of course an artificial division, but defines the city well enough. Each neighbourhood is defined by an attribute vector. This allows the use of multiple variables.
So, what could be seen from maps?
The visual information obtained by self-organizing map shows interesting and previously unseen relations. For example, households with at least one car lived in richer neighbourhood. This is quite logical. People who have money can afford a car. But it was also noticed that they lived in places that were badly connected to other neighbourhoods. So they have a lot of cars but less streets to leave and enter to neighbourhood. This of course causes traffic. So do people buy cars because they have money or because there they tend to live in neighbourhoods that have bad connections?
Another interesting thing was noticed. Neighbourhoods with the highest percentage of educational level are not the ones whose inhabitants earn the highest salaries. And in neighbourhoods with lower level of education people tend to travel more outside from their own neighbourhood (for work, social reasons). This may mean that people with higher level of education tend to live near their jobs.
After session some question were asked. One from audience commented that in his country highly educated people earn money by selling drugs. So they seem to be earning less than they really are. Neme answered that he doubts that it is the case in Mexico City. In his opinion it is because highly educated and students usually live near the universities and don't really get high salary.
So conclusion was that Self-organizing maps are a suitable tool for planners who seek correlations in cities. It might help to discover relevant information. So self-organizing maps are a good alternative at least in the visualisation and data inspection task to make sense of the city.
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