Understanding Road Usage Patterns in Urban Areas

In this paper, we combine the most complete record of daily mobility, based on large-scale mobile phone data, with detailed Geographic Information System (GIS) data, uncovering previously hidden patterns in urban road usage. We find that the major usage of each road segment can be traced to its own - surprisingly few - driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach.NNSFC of China (51208520

The Collaborative Image of The City: Mapping the Inequality of Urban Perception

A traveler visiting Rio, Manila or Caracas does not need a report to learn that these cities are unequal; she can see it directly from the taxicab window. This is because in most cities inequality is conspicuous, but also, because cities express different forms of inequality that are evident to casual observers. Cities are highly heterogeneous and often unequal with respect to the income of their residents, but also with respect to the cleanliness of their neighborhoods, the beauty of their architecture, and the liveliness of their streets, among many other evaluative dimensions. Until now, however, our ability to understand the effect of a city's built environment on social and economic outcomes has been limited by the lack of quantitative data on urban perception. Here, we build on the intuition that inequality is partly conspicuous to create quantitative measure of a city's contrasts. Using thousands of geo-tagged images, we measure the perception of safety, class and uniqueness; in the cities of Boston and New York in the United States, and Linz and Salzburg in Austria, finding that the range of perceptions elicited by the images of New York and Boston is larger than the range of perceptions elicited by images from Linz and Salzburg. We interpret this as evidence that the cityscapes of Boston and New York are more contrasting, or unequal, than those of Linz and Salzburg. Finally, we validate our measures by exploring the connection between them and homicides, finding a significant correlation between the perceptions of safety and class and the number of homicides in a NYC zip code, after controlling for the effects of income, population, area and age. Our results show that online images can be used to create reproducible quantitative measures of urban perception and characterize the inequality of different cities.MIT Media Lab Consortiu

Bridging the Adoption Gap for Smart City Technologies: An Interview with Rob Kitchin

Rob Kitchin talks about how we can bridge the adoption gap between city administrations and developers of smart city technologies. This interview is part of a special issue on smart cities

Mobile Pedestrian Navigation Systems: Wayfinding Based on Localisation Technologies

The first part of this paper gives a survey of the state of the art of research on human spatio-temporal behaviour in connection with the development of pedestrian navigation systems. The second part of the paper deals with the problem of pedestrian route choice behaviour. It is in particular concerned with localisation technologies and their adaptation to location-based information systems. The third part of the paper outlines three projects performed at arsenal research and the Vienna University of Technology in these areas. Firstly, it describes a research project on the requirements with regard to the development of ubiquitous cartography for pedestrians in indoor and outdoor environments. secondly, it describes a self-learning travel guide for city tourists based on mobile phones and GPS. Lastly, it describes an audio-guide system which provides landmark-based navigation instruction

Data Collection Methods.

<p><b>A.</b> The website used to collect votes. Participants were presented a random pair of images and voted by clicking on one in response to the question. <b>B</b>. Robustness of the urban perception metric (Q). <i>B</i> is the square of the Pearson correlation between two disjoint subsets of votes of size <i>v</i> containing the same number of images.</p

Identifying places associated with different urban perceptions.

<p><b>A.</b> High and low scoring images for safety <b>B</b>. social-class and <b>C</b>. uniqueness. <b>D</b>. Scatter plot of Q-scores for safety and social-class with four examples illustrating images with different combinations of evaluative criteria. <b>E</b>. Same as <b>D</b>, but for safety and uniqueness. <b>G</b>. Same as <b>D</b>, but for social-class and uniqueness.</p

Urban perception and violent crime.

<p><b>A</b> Comparison between the location of crimes in NYC and the predictions of urban perception, area and population (model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068400#pone.0068400-Robinson1" target="_blank">[4]</a>). <b>B</b>. Demographics (model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068400#pone.0068400-Lynch1" target="_blank">[1]</a>). <b>C</b>. All variables (model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068400#pone.0068400-Howard1" target="_blank">[5]</a>).</p

Comparison between the means and standard deviations of the urban perception recorded for each city and question.

<p>Comparison between the means and standard deviations of the urban perception recorded for each city and question.</p

Getis Spatially Filtered Regression including variables for demographic and urban perception.

<p>Getis Spatially Filtered Regression including variables for demographic and urban perception.</p