The idealized modern city is orderly: activities are categorized and assigned to intentionally designed and thereby designated spaces. For Example, Commerce is planned to occur in spaces such as shops, stores, malls and markets. Similarly, activities of recreation are planned in the form of parks, gyms and sports fields. These modes of functioning are situated/dispersed in between the primary form of urban functioning – the dwelling.
So, how do we connect these seemingly separated functions of modernist planning and make these spaces more accessible and thereby allow for the effective functioning of these spaces. This is usually achieved through the streets, sidewalks and modes of transit – the effective use of public space – an interesting contradiction to the intent of modernist planning to define, frame and thereby produce exclusive or private spheres of functioning.
With mobility being the connect between the various modes of functioning in the city, I would like to explore on the ability of the map to form the basis of efficiency through mapping time, irrespective of the space it is functioning upon.
A static Isochrone (maps that measure distances in ‘Time’ rather than spatial geography). Here the geography is fixed and the time map takes an amoebic form of representation.
A Dynamic Isochrone (The possibility of mapping time irrespective of its geography): http://metropolitain.io/#
The Haussmann’s Model of Urban form may not be part of the modernist movement in planning and architecture, but it preceded and definitely set the tone for formal planning during the modernist era.
Info: One of the most intricate and dense underground networks in Europe, the metro is a central component in the daily life of millions of Parisians. As a result, the official metro map conditions the very way commuters approach time, and space, as they tend to select their journeys based on the perceived smallest distance between two points This visualization offers to challenge this conventional view. Metropolitain takes on an unexpected gamble: using cold, abstract figures to take the pulse of a hectic and feverish metropolis.
Time View: Here, the time is represented in the form of concentric rings with 10 minute intervals. Clicking on any of the stations modifies the physical geography of the city with respect to the time it takes to travel in the city, with the station of interest as the nodal point. We can also select the train line that we are interested in, or the lines that we would be transferring to as well to get an idea of how long the commute would take. We can get more specific with the time using the slider below, as the interval between the trains would change over the course of the day with respect to the passenger traffic that the line would be handling.
Crowd View: This represents the number of people who use the various stations on a daily basis. Here, we can read the geography of Paris through the high mounds as being areas of high concentration, usually defined through architecture, urban design, zoning and other land use policies. These high concentrations are usually regions of commercial/recreational interest that require the accumulation of labor/capital to allow for the most efficient appropriation of space for the production of surplus value. The smaller mounds and lower heats on the rest of the map shows primarily residential use, with the transportation network bridging the gap between the various land use ordinances.
An analysis over the time view and the crowd view of the map would give us an idea of the property values and thereby the median income of the neighborhood around the transit station – which further shows the effectiveness of time based mapping on the urban form, determining where public investments are made, where speculation can be relied upon as a mode of investment etc. Also, if this map were extended to include data over a period of time, we will be able to see patterns of gentrification and displacement over the entire city with respect to the transit system.
However, I am not familiar with Paris and may not be entirely correct upon making these inferences. Redundancies could arise if there were neighborhoods where a culture of walking to work is the norm or where a strong form of urban restructuring (such as a gated community) exists. Also, patterns of suburbanization would further prove me wrong.
The time view relies upon the itinerary calculation and travelers data from Open Street Maps under the DDBL license. The crowd view thrives on the open data of annual incoming traffic per station (2011) provided by by data.ratp.fr.
Other experiments of Mapping time based datasets:
A more global scale time map: flightradar24.com
My own experimentation with time based mapping:
What if we go back in time to an era where there were no wireless technologies? We are travelling on the subway and were reminded of an errand that we forgot about or a task that needs to be assigned to a co-worker. When faced with such a crisis, the public pay phones in the city would come in handy. This map tries to identify the most accessible public pay phones in the city, to communicate our newfound task.
The map tries to identify the most accessible public pay phones in the city with the subway stops as a measure of accessibility. The regions of influence (two-minute walking radii) multiply into each other to show us the most accessible phones from a subway station. The widget on the right corner of the screen shows the concentration of pay phones within a range of the council districts (1 to 51) that the map has been zoomed into at any given time.
The datasets used include public pay phones and subway station locations in New York City sourced from NYC Open Data. The legend shows the various phone companies and the areas of influence of the subway stations.