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A fascinating exploration of how the growth of digital mapping, spurred by sensing technologies, is affecting cities and daily lives. What have smart technologies taught us about cities? What lessons can we learn from today's urbanites to make better places to live? Antoine Picon and Carlo Ratti argue that the answers are in the maps we make. For centuries, we have relied on maps to navigate the enormity of the city. Now, as the physical world combines with the digital world, we need a new generation of maps to navigate the city of tomorrow. Pervasive sensors allow anyone to visualize cities in entirely new ways—ebbs and flows of pollution, traffic, and internet connectivity. This book explores how the growth of digital mapping, spurred by sensing technologies, is affecting cities and daily lives. It examines how new cartographic possibilities aid urban planners, technicians, politicians, and administrators; how digitally mapped cities could reveal ways to make cities smarter and more efficient; how monitoring urbanites has political and social repercussions; and how the proliferation of open-source maps and collaborative platforms can aid activists and vulnerable populations. With its beautiful, accessible presentation of cutting-edge research, this book makes it easy for readers to understand the stakes of the new information age—and appreciate the timeless power of the city.

Over the last 10 years, ride-hailing companies (such as Uber and Grab) have proliferated in cities around the world. While generally beneficial from an economic viewpoint, having a plurality of operators that serve a given demand for point-to-point trips might induce traffic inefficiencies due to the lack of coordination between operators when serving trips. In fact, the efficiency of vehicle fleet management depends, among other things, density of the demand in the city, and in this sense having multiple operators in the market can be seen as a disadvantage. There is thus a tension between having a plurality of operators in the market, and the overall traffic efficiency. To this date, there is no systematic analysis of this trade-off, which is fundamental to design the best future urban mobility landscape. In this paper, we present the first systematic, data-driven characterization of the cost of non-coordination in urban on-demand mobility markets by proposing a simple, yet realistic, model. This model uses trip density and average traffic speed in a city as its input, and provides an accurate estimate of the additional number of vehicles that should circulate due to the lack of coordination between operators—the cost of non-coordination. We plot such cost across different cities—Singapore, New York (limited to the borough of Manhattan in this work), San Francisco, Vienna and Curitiba—and show that due to non-coordination, each additional operator in the market can increase the total number of circulating vehicles by up to 67%. Our findings could support city policy makers to make data supported decisions when regulating urban on-demand mobility markets in their cities. At the same time, our results outline the need of a more proactive government participation and the need for new, innovative solutions that would enable a better coordination of on-demand mobility operators.

Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions. ; National Science Foundation (U.S.) ; Singapore-MIT Alliance for Research and Technology