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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.

Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer individuals' activities from their mobile phone traces. Using Metro Boston as an example, we develop an activity detection model with travel diary surveys to reveal the common laws governing individuals' activity participation, and apply the modeling results to mobile phone traces to extract the embedded activity information. The proposed approach enables us to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new "big data" such as mobile phone traces and traditional travel surveys to improve transportation planning and urban planning and management.

The technology for determining the geographic location of cell phones and other handheld devices is becoming increasingly available. It is opening the way to a wide range of applications, collectively referred to as location-based services (LBS), that are primarily aimed at individual users. However, if deployed to retrieve aggregated data in cities, LBS could become a powerful tool for urban analysis. In this paper we aim to review and introduce the potential of this technology to the urban planning community. In addition, we present the 'Mobile Landscapes' project: an application in the metropolitan area of Milan, Italy, based on the geographical mapping of cell phone usage at different times of the day. The results enable a graphic representation of the intensity of urban activities and their evolution through space and time. Finally, a number of future applications are discussed and their potential for urban studies and planning is assessed.

As is often believed that the more centrally located a shop, the higher its sales volume, this paper analyzed relationships between the spatial clustering of retail stores, their respective transaction volumes, and the urban street networks to determine whether, and to what extent, the accessibility and density of a store's location was correlated with its transaction volume. While this hypothesis is widely accepted, its veracity is underexplored and rarely validated using large-scale empirical datasets, possibly owing to the lack of access. Therefore, transaction datasets and accessibility indicators were first examined; a clear, positive correlation between density and revenue was found for specialty stores wherein people do "comparison shopping," and for stores that complemented each other for activities such as "one-trip shopping," the revenues were positively correlated when the stores were clustered. Generally, daily-use stores' revenues were more sensitive to local access and those of non-daily-use stores were more sensitive to global access. In conclusion, these findings would not have been found using conventional methodology focused on the retail sector as a whole, because aggregate market mechanisms would have hidden the observed effects on specific store categories. Therefore, upon disaggregating the data, we found a distinct heterogeneity across the different store types for what concerns the relationship between revenue and location.