Researchers create new Urban Network Analysis toolbox

MIT researchers have created a new Urban Network Analysis (UNA) toolbox that enables urban designers and planners to describe the spatial patterns of cities using mathematical network analysis methods. Such tools can support better informed and more resilient urban design and planning in a context of rapid urbanization.

"Network centrality measures are useful predictors for a number of interesting urban phenomena," explains Andres Sevtsuk, the principal investigator of the City Form Research Group at MIT that produced the toolbox. "They help explain, for instance, on which streets or buildings one is most likely to find local commerce, where foot or vehicular traffic is expected to be highest, and why city land values vary from one location to another."

Network analysis is widely used in the study of social networks, such as Facebook friends or phonebook connections, but so far fairly little in the spatial analysis of cities. While the study of spatial networks goes back to Euler and his famous puzzle of Königsberg's seven bridges in the 18th century, there were, until recently, no freely accessible tools available for city planners to calculate computation-intensive spatial centrality measures on dense networks of city streets and buildings. The new toolbox, which is distributed as free and open-source plugin-in for ArcGIS, allows urban designers and planners to compute five types of graph analysis measures on spatial networks: Reach; Gravity; Betweenness; Closeness; and Straightness. "The Reach measure, for instance, can be used to estimate how many destinations of a particular type -- buildings, residents, jobs, transit stations etc. -- can be reached within a given walking radius from each building along the actual circulation routes in the area," said Michael Mekonnen, a course six sophomore who worked on the project. "The Betweenness measure, on the other hand, can be used to quantify the number of potential passersby at each building."

The tools incorporate three important features that make network analysis particularly suited for urban street networks. First, they account for geometry and distances in the input networks, distinguishing shorter links from longer links as part of the analysis computations. Second, unlike previous software tools that operate with two network elements (nodes and edges), the UNA tools include a third network element -- buildings -- which are used as the spatial units of analysis for all measures. Two neighboring buildings on the same street segments can therefore obtain different accessibility results. And third, the UNA tools optionally allow buildings to be weighted according to their particular characteristics -- more voluminous, more populated, or otherwise more important buildings can be specified to have a proportionately stronger effect on the analysis outcomes, yielding more accurate and reliable results to any of the specified measures.

The toolbox offers a powerful set of analysis options to quantify how centrally each building is positioned in an urban environment and how easily a user can access different amenities from each location. It introduces a novel methodology for tracking the growth and change of cities in the rapidly urbanizing world and offers analytic support for their designers and policymakers.

The UNA toolbox can be downloaded from the group's website at:http://cityform.mit.edu/projects/urban-network-analysis.html


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The above post is reprinted from materials provided by Massachusetts Institute of Technology. Note: Materials may be edited for content and length.

Article source: Science Daily

This map visualizes human activity in cities around the World

By Aleks Buczkowski




Last year the number of active mobile phones crossed the level 7.22 billion, while the number of people is estimated at the level of 7.2 billion. It basically means that there are more mobile devices than human beings and that statistically everyone in the world owns a mobile phone.

What we are not fully aware of, is that each phone call, text message, and each movement of our device creates a huge amount of data. With the development of Big Data tools researches and companies have understood the potential of this data and started to look closer into what can be done with it.

One of the key research centres pushing developments in that area is MIT’s Senseable City Labs. The Lab partnered with Ericsson in order to explore an unprecedented dataset which includes spatio-temporal traces of calls, SMS, data requests (initiated either by the users or background applications), and data traffic from mobile phones in several cities around the world.

The project called ManyCities visualizes this dataset on a cool interactive map for London, New York, Los Angeles and Hong Kong. The map allows to you to switch between three views. The first one shows how phone usage varies over time, revealing clear daily and weekly patterns as well as longer term trends. The second view shows activity clusters in different parts of cities and by analyzing these clusters, it classifies them per land use (core business, commercial, mixed, residential). The last one allows to view density maps per different mobile phone activities.

This sort of location-based big data, although already commercially used, is under heavy investigation of researchers and companies. We are still lacking of powerful computational tools that can gather, crunch, and present the data in meaningful ways. But with theManaCities project we’re definitely one step closer.

source: MIT Technology Review