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Sea level rise (SLR) and storm surge inundation are major concerns along the coast of the San Francisco Bay (the Bay Area), impacting both coastal communities and critical infrastructure networks. The oil industry comprises a complex and critical infrastructure network located in the Bay Area. There is an urgent need to assess consequences and identify risk-based solutions to increase the resilience of this industrial network in the Bay Area to SLR and storm surge. In this study, a comprehensive multi-modal network model representing the fuel supply system was built. A total of 120 coastal flooding scenarios, including four General Circulation Models, two Representative Concentration Pathways, three percentiles of future SLR estimates, and five planning horizons (20 year intervals from 2000 to 2100) were considered. The impact of coastal flooding on fuel transportation networks was studied at two different scales: regional and local. At the regional scale, basic network properties and network efficiency were analyzed across multiple flooding scenarios. At the local scale, cascading effects of individual node disruptions were simulated. Based on this research, smarter and more holistic risk-based adaptation strategies can be established which could lead to a more resilient fuel transportation network system.
Yiyi He; Sarah Lindbergh; Yang Ju; Marta Gonzalez; John Radke. Towards Resilient Critical Infrastructures: Understanding the Impact of Coastal Flooding on the Fuel Transportation Network in the San Francisco Bay. ISPRS International Journal of Geo-Information 2021, 10, 573 .
AMA StyleYiyi He, Sarah Lindbergh, Yang Ju, Marta Gonzalez, John Radke. Towards Resilient Critical Infrastructures: Understanding the Impact of Coastal Flooding on the Fuel Transportation Network in the San Francisco Bay. ISPRS International Journal of Geo-Information. 2021; 10 (9):573.
Chicago/Turabian StyleYiyi He; Sarah Lindbergh; Yang Ju; Marta Gonzalez; John Radke. 2021. "Towards Resilient Critical Infrastructures: Understanding the Impact of Coastal Flooding on the Fuel Transportation Network in the San Francisco Bay." ISPRS International Journal of Geo-Information 10, no. 9: 573.
Over the last decades, severe haze pollution constitutes a major source of far-reaching environmental and human health problems. The formation, accumulation and diffusion of pollution particles occurs under complex temporal scales and expands throughout a wide spatial coverage. Seeking to understand the transport patterns of haze pollutants in China, we review a proposed framework of time-evolving directed and weighted air quality correlation networks. In this work, we evaluate monitoring stations’ time-series data from China and California, to test the sensitivity of the framework to region size, climate and pollution magnitude across multiple years (2014–2020). We learn that the use of hourly $$\hbox {PM}_{2.5}$$ PM 2.5 concentration data is needed to detect periodicities in the positive and negative correlations of the concentrations. In addition, we show that the standardization of the correlation function method is required to obtain networks with more meaningful links when evaluating the dispersion of a severe haze event at the North China Plain or a wildfire event in California during December 2017. Post COVID-19 outbreak in China, we observe a significant drop in the magnitude of the assigned weights, indicating the improved air quality and the slowed transport of $$\hbox {PM}_{2.5}$$ PM 2.5 due to the lockdown. To identify regions where pollution transport is persistent, we extend the framework, partitioning the dynamic networks and reducing the networks’ complexity through node subsampling. The end result separates the temporal series of $$\hbox {PM}_{2.5}$$ PM 2.5 in set of regions that are similarly affected through the year.
Dimitrios M. Vlachogiannis; Yanyan Xu; Ling Jin; Marta C. González. Correlation networks of air particulate matter ($$\hbox {PM}_{2.5}$$): a comparative study. Applied Network Science 2021, 6, 1 -18.
AMA StyleDimitrios M. Vlachogiannis, Yanyan Xu, Ling Jin, Marta C. González. Correlation networks of air particulate matter ($$\hbox {PM}_{2.5}$$): a comparative study. Applied Network Science. 2021; 6 (1):1-18.
Chicago/Turabian StyleDimitrios M. Vlachogiannis; Yanyan Xu; Ling Jin; Marta C. González. 2021. "Correlation networks of air particulate matter ($$\hbox {PM}_{2.5}$$): a comparative study." Applied Network Science 6, no. 1: 1-18.
Seamless access to destinations of value such as workplaces, schools, parks or hospitals, influences the quality of life of people all over the world. The first step to planning and improving proximity to services is to estimate the number of trips being made from different parts of a city. A challenge has been representative data available for that purpose. Relying on expensive and infrequently collected travel surveys for modeling trip distributions to facilities has slowed down the decision-making process. The growing abundance of data already collected, if analyzed with the right methods, can help us with planning and understanding cities. In this chapter, we examine human mobility patterns extracted from data passively collected. We present results on the use of points of interest (POIs) registered on Google Places to approximate trip attraction in a city. We compare the result of trip distribution models that utilize only POIs with those utilizing conventional data sets, based on surveys. We show that an extended radiation model provides very good estimates when compared with the official origin–destination matrices from the latest census in Mexico City.
Pierre Melikov; Jeremy A. Kho; Vincent Fighiera; Fahad Alhasoun; Jorge Audiffred; José L. Mateos; Marta C. González. Characterizing Urban Mobility Patterns: A Case Study of Mexico City. The Life and Afterlife of Gay Neighborhoods 2021, 153 -170.
AMA StylePierre Melikov, Jeremy A. Kho, Vincent Fighiera, Fahad Alhasoun, Jorge Audiffred, José L. Mateos, Marta C. González. Characterizing Urban Mobility Patterns: A Case Study of Mexico City. The Life and Afterlife of Gay Neighborhoods. 2021; ():153-170.
Chicago/Turabian StylePierre Melikov; Jeremy A. Kho; Vincent Fighiera; Fahad Alhasoun; Jorge Audiffred; José L. Mateos; Marta C. González. 2021. "Characterizing Urban Mobility Patterns: A Case Study of Mexico City." The Life and Afterlife of Gay Neighborhoods , no. : 153-170.
The urban–rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. Although both model versions can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales in all model specifications.
Balázs Lengyel; Eszter Bokányi; Riccardo Di Clemente; János Kertész; Marta C. González. The role of geography in the complex diffusion of innovations. Scientific Reports 2020, 10, 1 -11.
AMA StyleBalázs Lengyel, Eszter Bokányi, Riccardo Di Clemente, János Kertész, Marta C. González. The role of geography in the complex diffusion of innovations. Scientific Reports. 2020; 10 (1):1-11.
Chicago/Turabian StyleBalázs Lengyel; Eszter Bokányi; Riccardo Di Clemente; János Kertész; Marta C. González. 2020. "The role of geography in the complex diffusion of innovations." Scientific Reports 10, no. 1: 1-11.
The era of the automobile has seriously degraded the quality of urban life through costly travel and visible environmental effects. A new urban planning paradigm must be at the heart of our road map for the years to come, the one where, within minutes, inhabitants can access their basic living needs by bike or by foot. In this work, we present novel insights of the interplay between the distributions of facilities and population that maximize accessibility over the existing road networks. Results in six cities reveal that travel costs could be reduced in half through redistributing facilities. In the optimal scenario, the average travel distance can be modeled as a functional form of the number of facilities and the population density. As an application of this finding, it is possible to estimate the number of facilities needed for reaching a desired average travel distance given the population distribution in a city.
Yanyan Xu; Luis E. Olmos; Sofiane Abbar; Marta C. González. Deconstructing laws of accessibility and facility distribution in cities. Science Advances 2020, 6, eabb4112 .
AMA StyleYanyan Xu, Luis E. Olmos, Sofiane Abbar, Marta C. González. Deconstructing laws of accessibility and facility distribution in cities. Science Advances. 2020; 6 (37):eabb4112.
Chicago/Turabian StyleYanyan Xu; Luis E. Olmos; Sofiane Abbar; Marta C. González. 2020. "Deconstructing laws of accessibility and facility distribution in cities." Science Advances 6, no. 37: eabb4112.
Energy storage is a key solution to supply renewable electricity on demand and in particular batteries are becoming attractive for consumers who install PV panels. In order to minimize their electricity bill and keep the grid stable, batteries can combine applications. The daily match between PV supply and the electricity load profile is often considered as a determinant for the attractiveness of residential PV-coupled battery systems, however, the previous literature has so far mainly focused on the annual energy balance. In this paper, we analyze the techno-economic impact of adding a battery system to a new PV system that would otherwise be installed on its own, for different residential electricity load profiles in Geneva (Switzerland) and Austin (U.S.) using lithium-ion batteries performing various consumer applications, namely PV selfconsumption, demand load-shifting, avoidance of PV curtailment, and demand peak shaving, individually and jointly. We employ clustering of the household’s load profile (with 15-minute resolution) for households with low, medium, and high annual electricity consumption in the two locations using a 1:1:1 sizing ratio. Our results show that with this simple sizing rule-of-thumb, the shape of the load profile has a small impact on the net present value of batteries. Overall, our analysis suggests that the effect of the load profile is small and differs across locations, whereas the combination of applications significantly increases profitability while marginally decreasing the share of self-consumption. Moreover, without the combination of applications, batteries are far from being economically viable.
Alejandro Pena-Bello; Edward Barbour; Marta C. Gonzalez; Selin Yilmaz; Martin K. Patel; David Parra. How does the Electricity Demand Profile Impact the Attractiveness of PV-coupled Battery Systems Combining Applications? Energies 2020, 13, 4038 .
AMA StyleAlejandro Pena-Bello, Edward Barbour, Marta C. Gonzalez, Selin Yilmaz, Martin K. Patel, David Parra. How does the Electricity Demand Profile Impact the Attractiveness of PV-coupled Battery Systems Combining Applications? Energies. 2020; 13 (15):4038.
Chicago/Turabian StyleAlejandro Pena-Bello; Edward Barbour; Marta C. Gonzalez; Selin Yilmaz; Martin K. Patel; David Parra. 2020. "How does the Electricity Demand Profile Impact the Attractiveness of PV-coupled Battery Systems Combining Applications?" Energies 13, no. 15: 4038.
The present work proposes a global framework to estimate all MFD model parameters using mobile phone data. The three major components that are estimated in the present context are MFD shapes, regional trip lengths and path flow distribution. A trip enrichment scheme based on the map matching process is proposed for the trips that have sparser records. Time dependent penetration rates are estimated by fusing the OD matrix and the Loop Detector Data (LDD). Two different types of penetration rates of vehicles are proposed based on the OD flow and the trips starting within an origin, respectively. The estimated MFDs based on two types of penetration rates are stable with very low scatter. In the following step, macro-paths and their corresponding trip lengths are estimated. This work is the first to present empirical evidences of the dynamic evolution of mean trip lengths over the day, which is very difficult to capture with other types of data sources. The last component is the time dependent path flow distributions between the different macro-paths for a given OD pair. The manuscript is concluded by presenting the time evolution of the User Equilibrium (UE) gap for different macroscopic OD pairs. It is noticed that UE principle holds true most of the time, except for OD pairs that have macro-paths transversing through congested reservoirs, especially during peak hours.
Mahendra Paipuri; Yanyan Xu; Marta C. González; Ludovic Leclercq. Estimating MFDs, trip lengths and path flow distributions in a multi-region setting using mobile phone data. Transportation Research Part C: Emerging Technologies 2020, 118, 102709 .
AMA StyleMahendra Paipuri, Yanyan Xu, Marta C. González, Ludovic Leclercq. Estimating MFDs, trip lengths and path flow distributions in a multi-region setting using mobile phone data. Transportation Research Part C: Emerging Technologies. 2020; 118 ():102709.
Chicago/Turabian StyleMahendra Paipuri; Yanyan Xu; Marta C. González; Ludovic Leclercq. 2020. "Estimating MFDs, trip lengths and path flow distributions in a multi-region setting using mobile phone data." Transportation Research Part C: Emerging Technologies 118, no. : 102709.
The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two macroscopic characteristics for network traffic dynamics, namely congestion propagation rate β and congestion dissipation rate μ. We describe the dynamics of congestion spread using these new parameters embedded within a system of ordinary differential equations, similar to the well-known susceptible-infected-recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.
Meead Saberi; Homayoun Hamedmoghadam; Mudabber Ashfaq; Seyed Amir Hosseini; Ziyuan Gu; Sajjad Shafiei; Divya J Nair; Vinayak Dixit; Lauren Gardner; S. Travis Waller; Marta C. González. A simple contagion process describes spreading of traffic jams in urban networks. Nature Communications 2020, 11, 1 -9.
AMA StyleMeead Saberi, Homayoun Hamedmoghadam, Mudabber Ashfaq, Seyed Amir Hosseini, Ziyuan Gu, Sajjad Shafiei, Divya J Nair, Vinayak Dixit, Lauren Gardner, S. Travis Waller, Marta C. González. A simple contagion process describes spreading of traffic jams in urban networks. Nature Communications. 2020; 11 (1):1-9.
Chicago/Turabian StyleMeead Saberi; Homayoun Hamedmoghadam; Mudabber Ashfaq; Seyed Amir Hosseini; Ziyuan Gu; Sajjad Shafiei; Divya J Nair; Vinayak Dixit; Lauren Gardner; S. Travis Waller; Marta C. González. 2020. "A simple contagion process describes spreading of traffic jams in urban networks." Nature Communications 11, no. 1: 1-9.
The risk for a global transmission of flu‐type viruses is strengthened by the physical contact between humans and accelerated through individual mobility patterns. The Air Transportation System plays a critical role in such transmissions because it is responsible for fast and long‐range human travel, while its building components—the airports—are crowded, confined areas with usually poor hygiene. Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) consider hand hygiene as the most efficient and cost‐effective way to limit disease propagation. Results from clinical studies reveal the effect of hand washing on individual transmissibility of infectious diseases. However, its potential as a mitigation strategy against the global risk for a pandemic has not been fully explored. Here, we use epidemiological modeling and data‐driven simulations to elucidate the role of individual engagement with hand hygiene inside airports in conjunction with human travel on the global spread of epidemics. We find that, by increasing travelers engagement with hand hygiene at all airports, a potential pandemic can be inhibited by 24% to 69%. In addition, we identify 10 airports at the core of a cost‐optimal deployment of the hand‐washing mitigation strategy. Increasing hand‐washing rate at only those 10 influential locations, the risk of a pandemic could potentially drop by up to 37%. Our results provide evidence for the effectiveness of hand hygiene in airports on the global spread of infections that could shape the way public‐health policy is implemented with respect to the overall objective of mitigating potential population health crises.
Christos Nicolaides; Demetris Avraam; Luis Cueto‐Felgueroso; Marta C. González; Ruben Juanes. Hand‐Hygiene Mitigation Strategies Against Global Disease Spreading through the Air Transportation Network. Risk Analysis 2019, 40, 723 -740.
AMA StyleChristos Nicolaides, Demetris Avraam, Luis Cueto‐Felgueroso, Marta C. González, Ruben Juanes. Hand‐Hygiene Mitigation Strategies Against Global Disease Spreading through the Air Transportation Network. Risk Analysis. 2019; 40 (4):723-740.
Chicago/Turabian StyleChristos Nicolaides; Demetris Avraam; Luis Cueto‐Felgueroso; Marta C. González; Ruben Juanes. 2019. "Hand‐Hygiene Mitigation Strategies Against Global Disease Spreading through the Air Transportation Network." Risk Analysis 40, no. 4: 723-740.
The classification of streets on road networks has been focused on the vehicular transportational features of streets such as arterials, major roads, minor roads and so forth based on their transportational use. City authorities on the other hand have been shifting to more urban inclusive planning of streets, encompassing the side use of a street combined with the transportational features of a street. In such classification schemes, streets are labeled for example as commercial throughway, residential neighborhood, park etc. This modern approach to urban planning has been adopted by major cities such as the city of San Francisco, the states of Florida and Pennsylvania among many others. Currently, the process of labeling streets according to their contexts is manual and hence is tedious and time consuming. In this paper, we propose an approach to collect and label imagery data then deploy advancements in computer vision towards modern urban planning. We collect and label street imagery then train deep convolutional neural networks (CNN) to perform the classification of street context. We show that CNN models can perform well achieving accuracies in the 81% to 87%, we then visualize samples from the embedding space of streets using the t-SNE method and apply class activation mapping methods to interpret the features in street imagery contributing to output classification from a model.
Fahad Alhasoun; Marta Gonzalez. Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development. 2019, 1 .
AMA StyleFahad Alhasoun, Marta Gonzalez. Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development. . 2019; ():1.
Chicago/Turabian StyleFahad Alhasoun; Marta Gonzalez. 2019. "Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development." , no. : 1.
Accurate occupancy is crucial for planning for sustainable buildings. Using massive, passively-collected mobile phone data, we introduce a novel framework to estimate building occupancy at unprecedented scale. We show that, at urban-scale, occupancy differs widely from current estimates based on building types. For commercial buildings, we find typical occupancy rates are 5 times lower than current assumptions imply, while for residential buildings occupancy rates vary widely by neighborhood. Our mobile phone based occupancy estimates are integrated with a state-of-the-art urban building energy model to understand their impact on energy use predictions. Depending on the assumed relationship between occupancy and internal building loads, we find energy consumption which differs by +1% to −15% for residential buildings and by −4% to −21% for commercial buildings, compared to standard methods. This highlights a need for new occupancy-to-load models which can be applied at urban-scale to the diverse set of city building types. Building retrofits offer enormous potential for energy reduction and must be designed with occupancy in mind. Here, the authors developed a method for estimating building occupancy at urban scale using mobile phone traces and they find that energy saving estimates differ by +1 to −15% for residential buildings and by −4 to −21% for commercial buildings.
Edward Barbour; Carlos Cerezo Davila; Siddharth Gupta; Christoph Reinhart; Jasleen Kaur; Marta C. González. Planning for sustainable cities by estimating building occupancy with mobile phones. Nature Communications 2019, 10, 1 -10.
AMA StyleEdward Barbour, Carlos Cerezo Davila, Siddharth Gupta, Christoph Reinhart, Jasleen Kaur, Marta C. González. Planning for sustainable cities by estimating building occupancy with mobile phones. Nature Communications. 2019; 10 (1):1-10.
Chicago/Turabian StyleEdward Barbour; Carlos Cerezo Davila; Siddharth Gupta; Christoph Reinhart; Jasleen Kaur; Marta C. González. 2019. "Planning for sustainable cities by estimating building occupancy with mobile phones." Nature Communications 10, no. 1: 1-10.
Accurately forecasting urban development and its environmental and climate impacts critically depends on realistic models of the spatial structure of the built environment, and of its dependence on key factors such as population and economic development. Scenario simulation and sensitivity analysis, i.e., predicting how changes in underlying factors at a given location affect urbanization outcomes at other locations, is currently not achievable at a large scale with traditional urban growth models, which are either too simplistic, or depend on detailed locally-collected socioeconomic data that is not available in most places. Here we develop a framework to estimate, purely from globally-available remote-sensing data and without parametric assumptions, the spatial sensitivity of the (\textit{static}) rate of change of urban sprawl to key macroeconomic development indicators. We formulate this spatial regression problem as an image-to-image translation task using conditional generative adversarial networks (GANs), where the gradients necessary for comparative static analysis are provided by the backpropagation algorithm used to train the model. This framework allows to naturally incorporate physical constraints, e.g., the inability to build over water bodies. To validate the spatial structure of model-generated built environment distributions, we use spatial statistics commonly used in urban form analysis. We apply our method to a novel dataset comprising of layers on the built environment, nightlighs measurements (a proxy for economic development and energy use), and population density for the world's most populous 15,000 cities.
Adrian Albert; Jasleen Kaur; Emanuele Strano; Marta Gonzalez. Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks. 2019, 1 .
AMA StyleAdrian Albert, Jasleen Kaur, Emanuele Strano, Marta Gonzalez. Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks. . 2019; ():1.
Chicago/Turabian StyleAdrian Albert; Jasleen Kaur; Emanuele Strano; Marta Gonzalez. 2019. "Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks." , no. : 1.
Hand hygiene is considered as an efficient and cost-effective way to limit the spread of diseases and, as such, it is recommended by both the World Health Organization (WHO) and the Centres for Disease Control and Prevention (CDC). While the effect of hand washing on individual transmissibility of a disease has been studied through medical and public-health research, its potential as a mitigation strategy against a global pandemic has not been fully explored yet. In this study, we investigate contagion dynamics through the world air transportation network and analyze the impact of hand-hygiene behavioural changes of airport population against the spread of infectious diseases worldwide. Using a granular dataset of the world air transportation traffic, we build a detailed individual mobility model that controls for the correlated and recurrent nature of human travel and the waiting-time distributions of individuals at different locations. We perform a Monte-Carlo simulation study to assess the impact of different hand-washing mitigation strategies at the early stages of a global epidemic. From the simulation results we find that increasing the hand cleanliness homogeneously at all airports in the world can inhibit the impact of a potential pandemic by 24 to 69%. By quantifying and ranking the contribution of the different airports to the mitigation of an epidemic outbreak, we identify ten key airports at the core of a cost-optimal deployment of the hand-washing strategy: increasing the engagement rate at those locations alone could potentially reduce a world pandemic by 8 to 37%. This research provides evidence of the effectiveness of hand hygiene in airports on the global spread of infectious diseases, and has important implications for the way public-health policymakers may design new effective strategies to enhance hand hygiene in airports through behavioral changes.
Christos Nicolaides; Demetris Avraam; Luis Cueto-Felgueroso; Marta C. González; Ruben Juanes. Hand-hygiene mitigation strategies against global disease spreading through the air transportation network. 2019, 530618 .
AMA StyleChristos Nicolaides, Demetris Avraam, Luis Cueto-Felgueroso, Marta C. González, Ruben Juanes. Hand-hygiene mitigation strategies against global disease spreading through the air transportation network. . 2019; ():530618.
Chicago/Turabian StyleChristos Nicolaides; Demetris Avraam; Luis Cueto-Felgueroso; Marta C. González; Ruben Juanes. 2019. "Hand-hygiene mitigation strategies against global disease spreading through the air transportation network." , no. : 530618.
Air pollution imposes significant environmental and health risks worldwide and is expected to deteriorate in the coming decade as cities expand. Measuring population exposure to air pollution is crucial to quantifying risks to public health. In this work, we introduce a big data analytics framework to model residents' stay and commuters' travel exposure to outdoor PM2.5 and evaluate their environmental justice, with Beijing as an example. Using mobile phone and census data, we first infer travel demand of the population to derive residents' stay activities in each analysis zone, and then focus on commuters and estimate their travel routes with a traffic assignment model. Based on air quality observations from monitoring stations and a spatial interpolation model, we estimate the outdoor PM2.5 concentrations at a 500-m grid level and map them to road networks. We then estimate the travel exposure for each road segment by multiplying the PM2.5 concentration and travel time spent on the road. By combining the estimated PM2.5 exposure and housing price harnessed from online housing transaction platforms, we discover that in the winter, Beijing commuters with low wealth level are exposed to 13% more PM2.5 per hour than those with high wealth level when staying at home, but exposed to less PM2.5 by 5% when commuting the same distance (due to lighter traffic congestion in suburban areas). We also find that the residents from the southern suburbs of Beijing have both lower level of wealth and higher stay- and travel- exposure to PM2.5, especially in the winter. These findings inform more equitable environmental mitigation policies for future sustainable development in Beijing. Finally, or the first time in the literature, we compare the results of exposure estimated from passive data with subjective measures of perceived air quality (PAQ) from a survey. The PAQ data was collected via a mobile-app. The comparison confirms consistencies in results and the advantages of the big data for air pollution exposure assessments.
Yanyan Xu; Shan Jiang; Ruiqi Li; Jiang Zhang; Jinhua Zhao; Sofiane Abbar; Marta C. González. Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach. Computers, Environment and Urban Systems 2019, 75, 12 -21.
AMA StyleYanyan Xu, Shan Jiang, Ruiqi Li, Jiang Zhang, Jinhua Zhao, Sofiane Abbar, Marta C. González. Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach. Computers, Environment and Urban Systems. 2019; 75 ():12-21.
Chicago/Turabian StyleYanyan Xu; Shan Jiang; Ruiqi Li; Jiang Zhang; Jinhua Zhao; Sofiane Abbar; Marta C. González. 2019. "Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach." Computers, Environment and Urban Systems 75, no. : 12-21.
Stories of mega-jams that last tens of hours or even days appear not only in fiction but also in reality. In this context, it is important to characterize the collapse of the network, defined as the transition from a characteristic travel time to orders of magnitude longer for the same distance traveled. In this multicity study, we unravel this complex phenomenon under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time τ that takes a representative group of commuters to arrive at their destinations once their maximum density has been reached. While this time differs from city to city, it can be explained by Γ, defined as the ratio of the vehicle miles traveled to the total vehicle distance the road network can support per hour. Modifying Γ can improve τ and directly inform planning and infrastructure interventions. In this study we focus on measuring the vulnerability of the system by increasing the volume of cars in the network, keeping the road capacity and the empirical spatial dynamics from origins to destinations unchanged. We identify three states of urban traffic, separated by two distinctive transitions. The first one describes the appearance of the first bottlenecks and the second one the collapse of the system. This collapse is marked by a given number of commuters in each city and it is formally characterized by a nonequilibrium phase transition.
Luis E. Olmos; Serdar Çolak; Sajjad Shafiei; Meead Saberi; Marta C. González. Macroscopic dynamics and the collapse of urban traffic. Proceedings of the National Academy of Sciences 2018, 115, 12654 -12661.
AMA StyleLuis E. Olmos, Serdar Çolak, Sajjad Shafiei, Meead Saberi, Marta C. González. Macroscopic dynamics and the collapse of urban traffic. Proceedings of the National Academy of Sciences. 2018; 115 (50):12654-12661.
Chicago/Turabian StyleLuis E. Olmos; Serdar Çolak; Sajjad Shafiei; Meead Saberi; Marta C. González. 2018. "Macroscopic dynamics and the collapse of urban traffic." Proceedings of the National Academy of Sciences 115, no. 50: 12654-12661.
Humans are intrinsically social creatures and our mobility is central to understanding how our societies grow and function. Movement allows us to congregate with our peers, access things we need, and exchange information. Human mobility has huge impacts on topics like urban and transportation planning, social and biologic spreading, and economic outcomes. Modeling these processes has however been hindered so far by a lack of data. This is radically changing with the rise of ubiquitous devices. In this chapter, we discuss recent progress deriving insights from the massive, high resolution data sets collected from mobile phone and other devices. We begin with individual mobility, where empirical evidence and statistical models have shown important intrinsic and universal characteristics about our movement: we as human are fundamentally slow to explore new places, relatively predictable, and mostly unique. We then explore methods of modeling aggregate movement of people from place to place and discuss how these estimates can be used to understand and optimize transportation infrastructure. Finally, we highlight applications of these findings to the dynamics of disease spread, social networks, and economic outcomes.
Jameson L. Toole; Yves-Alexandre De Montjoye; Marta C. González; Alex (Sandy) Pentland. Modeling and Understanding Intrinsic Characteristics of Human Mobility. Handbook of Mobile Data Privacy 2018, 13 -34.
AMA StyleJameson L. Toole, Yves-Alexandre De Montjoye, Marta C. González, Alex (Sandy) Pentland. Modeling and Understanding Intrinsic Characteristics of Human Mobility. Handbook of Mobile Data Privacy. 2018; ():13-34.
Chicago/Turabian StyleJameson L. Toole; Yves-Alexandre De Montjoye; Marta C. González; Alex (Sandy) Pentland. 2018. "Modeling and Understanding Intrinsic Characteristics of Human Mobility." Handbook of Mobile Data Privacy , no. : 13-34.
Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics, and social sciences. In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchase sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted from their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior. Digital traces of our lives have the potential to allow insights into collective behaviors. Here, the authors cluster consumers by their credit card purchase sequences and discover five distinct groups, within which individuals also share similar mobility and demographic attributes.
Riccardo Di Clemente; Miguel Luengo-Oroz; Matias Travizano; Sharon Xu; Bapu Vaitla; Marta C. González. Sequences of purchases in credit card data reveal lifestyles in urban populations. Nature Communications 2018, 9, 1 -8.
AMA StyleRiccardo Di Clemente, Miguel Luengo-Oroz, Matias Travizano, Sharon Xu, Bapu Vaitla, Marta C. González. Sequences of purchases in credit card data reveal lifestyles in urban populations. Nature Communications. 2018; 9 (1):1-8.
Chicago/Turabian StyleRiccardo Di Clemente; Miguel Luengo-Oroz; Matias Travizano; Sharon Xu; Bapu Vaitla; Marta C. González. 2018. "Sequences of purchases in credit card data reveal lifestyles in urban populations." Nature Communications 9, no. 1: 1-8.
This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.
Fahad Alhasoun; Faisal AlEissa; May Alhazzani; Luis G. Moyano; Claudio Pinhanez; Marta C. González. Age density patterns in patients medical conditions: A clustering approach. PLOS Computational Biology 2018, 14, e1006115 .
AMA StyleFahad Alhasoun, Faisal AlEissa, May Alhazzani, Luis G. Moyano, Claudio Pinhanez, Marta C. González. Age density patterns in patients medical conditions: A clustering approach. PLOS Computational Biology. 2018; 14 (6):e1006115.
Chicago/Turabian StyleFahad Alhasoun; Faisal AlEissa; May Alhazzani; Luis G. Moyano; Claudio Pinhanez; Marta C. González. 2018. "Age density patterns in patients medical conditions: A clustering approach." PLOS Computational Biology 14, no. 6: e1006115.
Yanyan Xu; Serdar Çolak; Emre C. Kara; Scott J. Moura; Marta C. Gonzalez. Planning for electric vehicle needs by coupling charging profiles with urban mobility. Nature Energy 2018, 3, 484 -493.
AMA StyleYanyan Xu, Serdar Çolak, Emre C. Kara, Scott J. Moura, Marta C. Gonzalez. Planning for electric vehicle needs by coupling charging profiles with urban mobility. Nature Energy. 2018; 3 (6):484-493.
Chicago/Turabian StyleYanyan Xu; Serdar Çolak; Emre C. Kara; Scott J. Moura; Marta C. Gonzalez. 2018. "Planning for electric vehicle needs by coupling charging profiles with urban mobility." Nature Energy 3, no. 6: 484-493.
Edward Barbour; Marta C. Gonzalez. Projecting Battery Adoption In The Prosumer Era. Science Trends 2018, 1 .
AMA StyleEdward Barbour, Marta C. Gonzalez. Projecting Battery Adoption In The Prosumer Era. Science Trends. 2018; ():1.
Chicago/Turabian StyleEdward Barbour; Marta C. Gonzalez. 2018. "Projecting Battery Adoption In The Prosumer Era." Science Trends , no. : 1.