Data City: the fourth utility city problem.
Data City design is based on the fourth utility: data grid circulatory systems. At the beginning of the 20th century General Motors leaded city design trends promoting cities for cars. At the beginning of 21st century Software Industry is leading city design making cities be designed for data.
Nicola Tesla as soon as in 1926 predicted that when Wi-Fi available at the whole planet, the planet will became a huge brain making all things particles of a real and rhythmic whole.
The “3 utilities problem” is a classical problem called also “the water, gas, and electricity problem”. It is based on the idea of how for a 3 houses located on the same plane hoy the 3 companies can make the proper connections to each house without interfering or crossing each other, not being allowed connection through the houses or in 3d. This problem had a broad usage as the very basis of early 20th century city planning.
Data city add a fourth variable to this classical question. Other approaches were far from a positive view of the Data City predicting a more apocalyptic future.
Yale’s Prof. Gelernter proposed his idea of “Mirror worlds” in 1991. He argued that through the technology-based obsession to control everything, humanity would reach technology slavery.
Mirror Worlds are not only scientific viewing tools through which humans will try to control the parts and whole of the city as if it was an AI machine, but also, an all seeing eye that will give the humans the ability to do so.
Gelernter presents a future in which humans will become dependent on Mirror Worlds becoming technology slaves, a new kind of slavery.
Through the obsession of visualizing, recording and analyzing all in detail, social implications will become so astonishing that society will be destabilized.
At the end of the 90’s, Simon Herbert, in his book “The sciences of the artificial”, argued also that if they are important systems in the world complex without being hierarchic, they may to a considerable extent scape our observation and understanding, as analysis of their behavior would involve such detailed knowledge and calculation of the interactions of their elementary parts that it will be beyond of our capacities of memory of computation.
As Anthony Townsend remarks, the first big data collection city related was, handmade and with punch cards afterwards, the first USA census that took place between 1790 and 1890.
The first attempt to use computers to manage cities occurred after the cold war, using military software that was trying to make profitable after war. That type of software was based on cybernetics. It was based on the idea of the city as a system definable as a set of equations and which behavior could be predicted.
The fist trials done in the 60’s tried to run predictive software Pittsburg. Being based on the cybernetic idea of systems of systems are perfectly reducible the failure was notable.
Basing the city on cybernetics was defending the idea of a city as a set of equation perfectly solvable and so that, perfectly predictive in its future behaviors.
In his “Urban Dynamics” book, Jay Forrester presented in 1969 the idea of a computer model able to describe and control the major forces of the city. He defended the viability of prediction through it and, so that, the decision making.
New York fire stations network planning and development was initially based on his urban-policy decision method.
MIT Urban Systems Laboratory closed in 1974 settling the end of an Urban Theory era that will remain closed for the next 25 years.
It was not until 2011 that IBM resurrected Urban Dynamics in Portland, Oregon.
IBM Smarter Cities Challenge started in 2010 with seven cities being planned to rapidly grow to 1000 cities
Using past based NASA software it was then the first time that the city was conceived a whole which systems and services can be managed through the gathering, analysis and action on city data.
This second Urban Dynamics phase has a strong link with the new disciple of Data Visualization as the first viable visible results of research. Famous examples are the Manhattan Tweets several existing representations representation or the Zurich based Interactive Things Studio Ville Vivante, representation of cell calls through Geneva visualizing the daily flow.
Cities Data visualizations are these days remarkable and common and are intensively trying to point attention on the importance on the fourth utility.