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"Ratti, Carlo"
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Predicting neighborhoods’ socioeconomic attributes using restaurant data
Accessing high-resolution, timely socioeconomic data such as data on population, employment, and enterprise activity at the neighborhood level is critical for social scientists and policy makers to design and implement location-based policies. However, in many developing countries or cities, reliable local-scale socioeconomic data remain scarce. Here, we show an easily accessible and timely updated location attribute—restaurant—can be used to accurately predict a range of socioeconomic attributes of urban neighborhoods. We merge restaurant data from an online platform with 3 microdatasets for 9 Chinese cities. Using features extracted from restaurants, we train machine-learning models to estimate daytime and nighttime population, number of firms, and consumption level at various spatial resolutions. The trained model can explain 90 to 95% of the variation of those attributes across neighborhoods in the test dataset. We analyze the tradeoff between accuracy, spatial resolution, and number of training samples, as well as the heterogeneity of the predicted results across different spatial locations, demographics, and firm industries. Finally, we demonstrate the cross-city generality of this method by training the model in one city and then applying it directly to other cities. The transferability of this restaurant model can help bridge data gaps between cities, allowing all cities to enjoy big data and algorithm dividends.
Journal Article
The city of tomorrow : sensors, networks, hackers, and the future of urban life
\"Since cities emerged ten thousand years ago, they have become one of the most impressive artifacts of humanity. But their evolution has been anything but linearcities have gone through moments of radical change, turning points that redefine their very essence. In this book, a renowned architect and urban planner who studies the intersection of cities and technology argues that we are in such a moment. The authors explain some of the forces behind urban change and offer new visions of the many possibilities for tomorrows city. Pervasive digital systems that layer our cities are transforming urban life. The authors provide a front-row seat to this change. Their work at the MIT Senseable City Laboratory allows experimentation and implementation of a variety of urban initiatives and concepts, from assistive condition-monitoring bicycles to trash with embedded tracking sensors, from mobility to energy, from participation to production. They call for a new approach to envisioning cities: futurecraft, a symbiotic development of urban ideas by designers and the public. With such participation, we can collectively imagine, examine, choose, and shape the most desirable future of our cities.\" -- Provided by publisher.
Book
Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data
Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)-despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors-as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors.
Journal Article
Revisiting Street Intersections Using Slot-Based Systems
Since their appearance at the end of the 19th century, traffic lights have been the primary mode of granting access to road intersections. Today, this centuries-old technology is challenged by advances in intelligent transportation, which are opening the way to new solutions built upon slot-based systems similar to those commonly used in aerial traffic: what we call Slot-based Intersections (SIs). Despite simulation-based evidence of the potential benefits of SIs, a comprehensive, analytical framework to compare their relative performance with traffic lights is still lacking. Here, we develop such a framework. We approach the problem in a novel way, by generalizing classical queuing theory. Having defined safety conditions, we characterize capacity and delay of SIs. In the 2-road crossing configuration, we provide a capacity-optimal SI management system. For arbitrary intersection configurations, near-optimal solutions are developed. Results theoretically show that transitioning from a traffic light system to SI has the potential of doubling capacity and significantly reducing delays. This suggests a reduction of non-linear dynamics induced by intersection bottlenecks, with positive impact on the road network. Such findings can provide transportation engineers and planners with crucial insights as they prepare to manage the transition towards a more intelligent transportation infrastructure in cities.
Journal Article
Network science for museums
This paper introduces network science to museum studies. The spatial structure of the museum and the exhibit display largely determine what visitors see and in which order, thereby shaping their visit experience. Despite the importance of spatial properties in museum studies, few scientific tools have been developed to analyze and compare the results across museums. This paper introduces the six habitually used network science indices and assesses their applicability to museum studies. Network science is an empirical research field that focuses on analyzing the relationships between components in an attempt to understand how individual behaviors can be converted into collective behaviors. By taking the museum and the visitors as the network, this methodology could reveal unknown aspects of museum functions and visitor behavior, which could enhance exhibition knowledge and lead to better methods for creating museum narratives along the routes.
Journal Article
Urban sensing using existing fiber-optic networks
The analysis of urban seismic signals offers valuable insights into urban environments and society. Yet, accurate detection and localization of seismic sources on a city-wide scale with conventional seismographic network is unavailable due to the prohibitive costs of ultra-dense seismic arrays required for imaging high-frequency anthropogenic sources. Here, we leverage existing fiber-optic networks as a distributed acoustic sensing system to accurately locate urban seismic sources and estimate how their intensity varies over time. By repurposing a 50-kilometer telecommunication fiber into an ultra-dense seismic array, we generate spatiotemporal maps of seismic source power (SSP) across San Jose, California. Our approach overcomes the proximity limitations of urban seismic sensing, enabling accurate localization of remote seismic sources generated by urban activities, such as traffic, construction, and school operations. We also show strong correlations between SSP values and environmental noise levels, as well as various persistent urban features, including land use patterns and demographics.
This study leverages existing fiber-optic networks for urban sensing. By mapping Seismic Source Power, it reveals urban activities, land use patterns, and demographic trends, enabling scalable urban monitoring without additional sensor deployment.
Journal Article
Revealing centrality in the spatial structure of cities from human activity patterns
Identifying changes in the spatial structure of cities is a prerequisite for the development and validation of adequate planning strategies. Nevertheless, current methods of measurement are becoming ever more challenged by the highly diverse and intertwined ways of how people actually make use of urban space. Here, we propose a new quantitative measure for the centrality of locations, taking into account not only the numbers of people attracted to different locations, but also the diversity of the activities they are engaged in. This 'centrality index' allows for the identification of functional urban centres and for a systematic tracking of their relative importance over time, thus contributing to our understanding of polycentricity. We demonstrate the proposed index using travel survey data in Singapore for different years between 1997 and 2012. It is shown that, on the one hand, the city-state has been developing rapidly towards a polycentric urban form that compares rather closely with the official urban development plan. On the other hand, however, the downtown core has strongly gained in its importance, and this can be partly attributed to the recent extension of the public transit system.
Journal Article
Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries
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.
Journal Article
Mapping facade materials utilizing zero-shot segmentation for applications in urban microclimate research
To address the Urban Heat Island (UHI) effect—a significant urban climate challenge—detailed urban microclimate modeling is essential. Such modeling typically requires data on urban surface properties and morphologies from street canyons and buildings. Most urban surveying efforts have focused on morphological attributes such as sky view factor, vegetation or building surface ratio, while the mass-collection of facade materials has been hindered by the complexity of the segmentation task and the need for large and diverse labeled datasets. Recognizing the importance of mapping facade materials for urban thermal comfort, envelope heat emissions, and building energy studies, we employ computer vision-based state-of-the-art zero-shot learning paradigms for high-fidelity facade material extraction. Our approach circumvents the traditional need for extensive labeled training data, allowing for adaptation to a variety of urban contexts and material types. Tested in Dubai, Amsterdam, and Boston (three architecturally diverse cities), our algorithm successfully detects the predominant facade material in 68% of cases and identifies the top three present material classes in 85% of cases. Additionally, we show how material coverage identification is crucial for assessing outdoor thermal comfort, as evident in shifts in annual cold and heat stress hours across the climates of the three cities in a sample urban canyon.
Journal Article