Urban Complexity
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Constructing cities, deconstructing scaling laws

Cities can be characterized and modelled through different urban measures. Consistency within these observables is crucial in order to advance towards a science of cities. Bettencourt et al . have proposed that many of these urban measures can be predicted through universal scaling laws. We develop a framework to consistently define cities, using commuting to work and population density thresholds, and construct thousands of realizations of systems of cities with different boundaries for England and Wales. These serve as a laboratory for the scaling analysis of a large set of urban indicators. The analysis shows that population size alone does not provide us enough information to describe or predict the state of a city as previously proposed, indicating that the expected scaling laws are not corroborated. We found that most urban indicators scale linearly with city size, regardless of the definition of the urban boundaries. However, when nonlinear correlations are present, the exponent fluctuates considerably.


Constructing cities, deconstructing scaling laws 

Elsa Arcaute, Erez Hatna, Peter Ferguson, Hyejin Youn, Anders Johansson, Michael Batty

Interface Volume: 12 , issue: 102, 2014 

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Is this scaling nonlinear?

One of the most celebrated findings in complex systems in the last decade is that different indexes y (e.g., patents) scale nonlinearly with the population~x of the cities in which they appear, i.e., y∼xβ,β≠1. More recently, the generality of this finding has been questioned in studies using new databases and different definitions of city boundaries. In this paper we investigate the existence of nonlinear scaling using a probabilistic framework in which fluctuations are accounted explicitly. In particular, we show that this allows not only to (a) estimate β and confidence intervals, but also to (b) quantify the evidence in favor of β≠1 and (c) test the hypothesis that the observations are compatible with the nonlinear scaling. We employ this framework to compare 5 different models to 15 different datasets and we find that the answers to points (a)-(c) crucially depend on the fluctuations contained in the data, on how they are modeled, and on the fact that the city sizes are heavy-tailed distributed.

Is this scaling nonlinear?
J. C. Leitao, J.M. Miotto, M. Gerlach, E. G. Altmann
arXiv:1604.02872 
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Predicting traffic volumes and estimating the effects of shocks in massive transportation systems

We propose a new approach to analyzing massive transportation systems that leverages traffic information about individual travelers. The goals of the analysis are to quantify the effects of shocks in the system, such as line and station closures, and to predict traffic volumes. We conduct an in-depth statistical analysis of the Transport for London railway traffic system. The proposed methodology is unique in the way that past disruptions are used to predict unseen scenarios, by relying on simple physical assumptions of passenger flow and a system-wide model for origin–destination movement. The method is scalable, more accurate than blackbox approaches, and generalizable to other complex transportation systems. It therefore offers important insights to inform policies on urban transportation.

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A Method to Ascertain Rapid Transit Systems’ throughput Distribution Using Network Analysis

We present a method of predicting the distribution of passenger throughput across stations and lines of a city rapid transit system by calculating the normalized betweenness centrality of the nodes (stations) and edges of the rail network. The method is evaluated by correlating the distribution of betweenness centrality against throughput distribution which is calculated using actual passenger ridership data. Our ticketing data is from the rail transport system of Singapore that comprises more than 14 million journeys over a span of one week. We demonstrate that removal of outliers representing about 10% of the stations produces a statistically significant correlation above 0.7. Interestingly, these outliers coincide with stations that opened six months before the time the ridership data was collected, hinting that travel routines along these stations have not yet settled to its equilibrium. The correlation is improved significantly when the data points are split according to their separate lines, illustrating differences in the intrinsic characteristics of each line. The simple procedure established here shows that static network analysis of the structure of a transport network can allow transport planners to predict with sufficient accuracy the passenger ridership, without requiring dynamic and complex simulation methods.


A Method to Ascertain Rapid Transit Systems’ throughput Distribution Using Network Analysis
Muhamad Azfar Ramli, Christopher Pineda Monterola, Gary Lee Kee Khoon, Terence Hung Gih Guang

Procedia Computer Science Vol 29, 2014, Pages 1621–1630


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Cluster Statistics and Quasisoliton Dynamics in Microscopic Car-following Models

Using the optimal velocity (OV) model as an example, we show that in the non-linear regime there is an emergent quantity that gives the extremum headways in the cluster formation, as well as the coexistence curve separating the absolute stable phase from the metastable phase. This emergent quantity is independent of the density of the traffic lane, and determines an intrinsic scale that characterizes the dynamics of localized quasisoliton structures given by the time derivative of the headways. The intrinsic scale is analogous to the "charge" of quasisolitons that controls the strength of interaction between multiple clusters, leading to non-trivial cluster statistics from random perturbations to initial uniform traffic. The cluster statistics depend both on the charge and the density of the traffic lane; the relationship is qualitatively universal for general car-following models.

 

Cluster Statistics and Quasisoliton Dynamics in Microscopic Car-following Models

Bo Yang, Xihua Xu, John Z.F. Pang, Christopher Monterola

 

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Simulating Congestion Dynamics of Train Rapid Transit Using Smart Card Data

Investigating congestion in train rapid transit systems (RTS) in today's urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent-based model of the Singapore RTS to provide a more comprehensive understanding of the congestion dynamics than can be obtained through analytical modelling alone. Our model is empirically validated, and allows for close inspection of the dynamics including station crowdedness, average travel duration, and frequency of missed trains—all highly pertinent factors in service quality. Using current data, the crowdedness in all 121 stations appears to be distributed log-normally. In our preliminary scenarios, we investigate the effect of population growth on service quality. We find that the current population (2 million) lies below a critical point; and increasing it beyond a factor of approximately 10% leads to an exponential deterioration in service quality. We also predict that incentivizing commuters to avoid the most congested hours can bring modest improvements to the service quality provided the population remains under the critical point. Finally, our model can be used to generate simulated data for statistical analysis when such data are not empirically available, as is often the case.

 

http://dx.doi.org/10.1016/j.procs.2014.05.146

Simulating Congestion Dynamics of Train Rapid Transit Using Smart Card Data
N Othman, EF Legara, V Selvam, and C Monterola

Procedia Computer Science Vol. 29, 2014, Pages 1610–1620


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A functional approach to monitor and recognize patterns of daily traffic profiles

A functional approach to monitor and recognize patterns of daily traffic profiles | Urban Complexity | Scoop.it

• We analyze traffic flow using functional data analysis and use multivariate control charts for long-term monitoring.
• It is an analysis to test for stability in traffic behavior, to detect drifts, changes of level, and shape of traffic flow.
• Our method provides a reduction of data dimensionality and computational expensiveness.
• Our monitoring methodology was tested using simulation techniques to detect change in traffic flow, shape, and volume.
• We illustrate the methodology through the use of real-world data.

 

DOI 10.1016/j.trb.2014.04.006

A functional approach to monitor and recognize patterns of daily traffic profiles
I.G. Guardiola, T. Leon, and F. Mallor

Transportation Research Part B: Methodological, Volume 65, July 2014, Pages 119–136

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Exploring the evolution of London's street network in the information space: A dual approach

Exploring the evolution of London's street network in the information space: A dual approach | Urban Complexity | Scoop.it

We study the growth of London's street network in its dual representation, as the city has evolved over the past 224 years. The dual representation of a planar graph is a content-based network, where each node is a set of edges of the planar graph and represents a transportation unit in the so-called information space, i.e., the space where information is handled in order to navigate through the city. First, we discuss a novel hybrid technique to extract dual graphs from planar graphs, called the hierarchical intersection continuity negotiation principle. Then we show that the growth of the network can be analytically described by logistic laws and that the topological properties of the network are governed by robust log-normal distributions characterizing the network's connectivity and small-world properties that are consistent over time. Moreover, we find that the double-Pareto-like distributions for the connectivity emerge for major roads and can be modeled via a stochastic content-based network model using simple space-filling principles.

 

DOI 10.1103/PhysRevE.89.012805
Exploring the evolution of London's street network in the information space: A dual approach
A. Paolo Masucci, Kiril Stanilov, and Michael Batty

Phys. Rev. E 89, 012805 – Published 13 January 2014

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Critical capacity, travel time delays, and travel time distribution of rapid mass transit systems

Critical capacity, travel time delays, and travel time distribution of rapid mass transit systems | Urban Complexity | Scoop.it

We set up a mechanistic agent-based model of a rapid mass transit system. Using empirical data from Singapore’s unidentifiable smart fare card, we validate our model by reconstructing actual travel demand and duration of travel statistics. We subsequently use this model to investigate two phenomena that are known to significantly affect the dynamics within the RTS: (1) overloading in trains and (2) overcrowding in the RTS platform. We demonstrate that by varying the loading capacity of trains, a tipping point emerges at which an exponential increase in the duration of travel time delays is observed. We also probe the impact on the rail system dynamics of three types of passenger growth distribution across stations: (i) Dirac delta, (ii) uniform and (iii) geometric, which is reminiscent of the effect of land use on transport. Under the assumption of a fixed loading capacity, we demonstrate the dependence of a given origin–destination (OD) pair on the flow volume of commuters in station platforms.

 

Critical capacity, travel time delays and travel time distribution of rapid mass transit systems
EF Legara, C Monterola, KK Lee, GG Hung

Physica A, 406, 15 July 2014, Pages 100–106

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Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows

We test the recently introduced radiation model against the gravity model for the system composed of England and Wales, both for commuting patterns and for public transportation flows. The analysis is performed both at macroscopic scales, i.e., at the national scale, and at microscopic scales, i.e., at the city level. It is shown that the thermodynamic limit assumption for the original radiation model significantly underestimates the commuting flows for large cities. We then generalize the radiation model, introducing the correct normalization factor for finite systems. We show that even if the gravity model has a better overall performance the parameter-free radiation model gives competitive results, especially for large scales.

 

 

DOI: http://dx.doi.org/10.1103/PhysRevE.88.022812
Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows
Phys. Rev. E 88, 022812 – Published 22 August 2013
A. Paolo Masucci, Joan Serras, Anders Johansson, and Michael Batty

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Vulnerability Analysis and Passenger Source Prediction in Urban Rail Transit Networks

Vulnerability Analysis and Passenger Source Prediction in Urban Rail Transit Networks | Urban Complexity | Scoop.it

Based on large-scale human mobility data collected in San Francisco and Boston, the morning peak urban rail transit (URT) ODs (origin-destination matrix) were estimated and the most vulnerable URT segments, those capable of causing the largest service interruptions, were identified. In both URT networks, a few highly vulnerable segments were observed. For this small group of vital segments, the impact of failure must be carefully evaluated. A bipartite URT usage network was developed and used to determine the inherent connections between urban rail transits and their passengers' travel demands. Although passengers' origins and destinations were easy to locate for a large number of URT segments, a few show very complicated spatial distributions. Based on the bipartite URT usage network, a new layer of the understanding of a URT segment's vulnerability can be achieved by taking the difficulty of addressing the failure of a given segment into account. Two proof-of-concept cases are described here: Possible transfer of passenger flow to the road network is here predicted in the cases of failures of two representative URT segments in San Francisco.

 

Vulnerability Analysis and Passenger Source Prediction in Urban Rail Transit Networks

Wang J, Li Y, Liu J, He K, Wang P

PLoS ONE 8(11): e80178 (2013)

DOI:10.1371/journal.pone.0080178

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Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization

Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data.

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A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels

A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels | Urban Complexity | Scoop.it

The daily trip to work is ubiquitous, yet its characteristics differ widely from person to person and place to place. This is manifested in statistics on mode and distance of travel, which vary depending on a range of factors that operate at different scales. This heterogeneity is problematic for decision makers tasked with encouraging more sustainable commuter patterns. Numerical models, based on real commuting data, have great potential to aid the decision making process. However, we contend that new approaches are needed to advance knowledge about the social and geographical factors that relate to the diversity of commuter patterns, if policies targeted to specific individuals or places are to be effective. To this end, the paper presents a spatial microsimulation approach, which combines individual-level survey data with geographically aggregated census results to tackle the problem. This method overcomes the limitations imposed by the lack of available geocoded micro-data. Further, it allows a range of scales of analysis to be pursued in parallel and provides insights into both the types of area and individual that would benefit most from specific interventions.

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A survey of results on mobile phone datasets analysis



In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.

A survey of results on mobile phone datasets analysis 
Vincent D Blondel, Adeline Decuyper and Gautier Krings
EPJ Data Science20154:10




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Patterns of Residential Segregation

The spatial distribution of income shapes the structure and organisation of cities and its understanding has broad societal implications. Despite an abundant literature, many issues remain unclear. In particular, all definitions of segregation are implicitely tied to a single indicator, usually rely on an ambiguous definition of income classes, without any consensus on how to define neighbourhoods and to deal with the polycentric organization of large cities. In this paper, we address all these questions within a unique conceptual framework. We avoid the challenge of providing a direct definition of segregation and instead start from a definition of what segregation is not. This naturally leads to the measure of representation that is able to identify locations where categories are over- or underrepresented. From there, we provide a new measure of exposure that discriminates between situations where categories co-locate or repel one another. We then use this feature to provide an unambiguous, parameter-free method to find meaningful breaks in the income distribution, thus defining classes. Applied to the 2014 American Community Survey, we find 3 emerging classes—low, middle and higher income—out of the original 16 income categories. The higher-income households are proportionally more present in larger cities, while lower-income households are not, invalidating the idea of an increased social polarisation. Finally, using the density—and not the distance to a center which is meaningless in polycentric cities—we find that the richer class is overrepresented in high density zones, especially for larger cities. This suggests that density is a relevant factor for understanding the income structure of cities and might explain some of the differences observed between US and European cities.


Patterns of Residential Segregation 

Rémi Louf, Marc Barthelemy 

PLoS ONE 11(6): e0157476. doi:10.1371/journal.pone.0157476

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Quantifying the benefits of vehicle pooling with shareability networks

Recent advances in information technologies have increased our participation in “sharing economies,” where applications that allow networked, real-time data exchange facilitate the sharing of living spaces, equipment, or vehicles with others. However, the impact of large-scale sharing on sustainability is not clear, and a framework to assess its benefits quantitatively is missing. For this purpose, we propose the method of shareability networks, which translates spatio-temporal sharing problems into a graph-theoretic framework that provides efficient solutions. Applying this method to a dataset of 150 million taxi trips in New York City, our simulations reveal the vast potential of a new taxi system in which trips are routinely shareable while keeping passenger discomfort low in terms of prolonged travel time.

 

Quantifying the benefits of vehicle pooling with shareability networks
Paolo Santi, Giovanni Resta, Michael Szell, Stanislav Sobolevsky, Steven H. Strogatz, and Carlo Ratti

http://dx.doi.org/10.1073/pnas.1403657111


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Efficiency and robustness of different bus network designs

We compare the efficiencies and robustness of four transport networks that can be possibly formed as a result of deliberate city planning. The networks are constructed based on their spatial resemblance to the cities of Manhattan (lattice), Sudan (random), Beijing (single-blob) and Greater Cairo (dual-blob). For a given type, a genetic algorithm is employed to obtain an optimized set of the bus routes. We then simulate how commuter travels using Yen's algorithms for k shortest paths on an adjacency matrix. The cost of traveling such as walking between stations is captured by varying the weighted sums of matrices. We also consider the number of transfers a posteriori by looking at the computed shortest paths. With consideration to distances via radius of gyration, redundancies of travel and number of bus transfers, our simulations indicate that random and dual-blob are more efficient than single-blob and lattice networks. Moreover, dual-blob type is least robust when node removals are targeted but is most resilient when node failures are random. The work hopes to guide and provide technical perspectives on how geospatial distribution of a city limits the optimality of transport designs.

 

Efficiency and robustness of different bus network designs
John Zhen Fu Pang, Nasri Bin Othman, Keng Meng Ng, Christopher Monterola

Int. J. Mod. Phys. C (2014) DOI: 10.1142/S0129183115500242

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Professional diversity and the productivity of cities

Professional diversity and the productivity of cities | Urban Complexity | Scoop.it

Attempts to understand the relationship between diversity, productivity and scale have remained limited due to the scheme-dependent nature of the taxonomies describing complex systems. We analyze the diversity of US metropolitan areas in terms of profession diversity and employment to show how this frequency distribution takes a universal scale-invariant form, common to all cities, in the limit of infinite resolution of occupational taxonomies. We show that this limit is obtained under general conditions that follow from the analysis of the variation of the occupational frequency across taxonomies at different resolutions in a way analogous to finite-size scaling in statistical physical systems. We propose a theoretical framework that derives the form and parameters of the limiting distribution of professions based on the appearance, in urban social networks, of new occupations as the result of specialization and coordination of labor. By deriving classification scheme-independent measures of functional diversity and modeling cities as social networks embedded in infrastructural space, these results show how standard economic arguments of division and coordination of labor can be articulated in detail in cities and provide a microscopic basis for explaining increasing returns to population scale observed at the level of entire metropolitan areas.

 

Professional diversity and the productivity of cities
Luís M. A. Bettencourt, Horacio Samaniego, and Hyejin Youn  

Scientific Reports 4, Article number 5393 (2014)
doi:10.1038/srep05393

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A new schedule-based transit assignment model with travel strategies and supply uncertainties

A new schedule-based transit assignment model with travel strategies and supply uncertainties | Urban Complexity | Scoop.it

• Propose a schedule-based transit assignment model.
• Consider both supply uncertainties and optimal strategies.
• Rely on the usage of Bellman’s recursion principle for transit network loading.
• Avoid path enumeration or column generation in the solution procedure.

 

DOI 10.1016/j.trb.2014.05.002

Transportation Research Part B: Methodological Volume 67, September 2014, Pages 35–67

Younes Hamdouch, W.Y. Szeto, Y. Jiang



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Estimating human trajectories and hotspots through mobile phone data

Estimating human trajectories and hotspots through mobile phone data | Urban Complexity | Scoop.it

Nowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and applications. Recently, mobile data-based research reached important conclusions about various aspects of human mobility patterns. But how accurately do these conclusions reflect the reality? To evaluate the difference between reality and approximation methods, we study in this paper the error between real human trajectory and the one obtained through mobile phone data using different interpolation methods (linear, cubic, nearest interpolations) taking into consideration mobility parameters. Moreover, we evaluate the error between real and estimated load using the proposed interpolation methods. From extensive evaluations based on real cellular network activity data of the state of Massachusetts, we show that, with respect to human trajectories, the linear interpolation offers the best estimation for sedentary people while the cubic one for commuters. Another important experimental finding is that trajectory estimation methods show different error regimes whether used within or outside the “territory” of the user defined by the radius of gyration. Regarding the load estimation error, we show that by using linear and cubic interpolation methods, we can find the positions of the most crowded regions (“hotspots”) with a median error lower than 7%.

 

Estimating human trajectories and hotspots through mobile phone data
S Hoteit, S Secci, S Sobolevsky, C Ratti, G Pujollea
Computer Networks, Volume 64, 8 May 2014, Pages 296–307

http://dx.doi.org/10.1016/j.comnet.2014.02.011

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Smart cities of the future

Smart cities of the future | Urban Complexity | Scoop.it

Here we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for urban populations. 

 

Smart cities of the future
M. Batty, K.W. Axhausen, F. Giannotti, A. Pozdnoukhov, et al.

Eur. Phys. J. Special Topics 214, 481-518 (2012)
http://dx.doi.org/10.1140/epjst/e2012-01703-3

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The Emergence of Urban Land Use Patterns Driven by Dispersion and Aggregation Mechanisms

The Emergence of Urban Land Use Patterns Driven by Dispersion and Aggregation Mechanisms | Urban Complexity | Scoop.it

We employ a cellular-automata to reconstruct the land use patterns of cities that we characterize by two measures of spatial heterogeneity: (a) a variant of spatial entropy, which measures the spread of residential, business, and industrial activity sectors, and (b) an index of dissimilarity, which quantifies the degree of spatial mixing of these land use activity parcels. A minimalist and bottom-up approach is adopted that utilizes a limited set of three parameters which represent the forces which determine the extent to which each of these sectors spatially aggregate into clusters. The dispersion degrees of the land uses are governed by a fixed pre-specified power-law distribution based on empirical observations in other cities. Our method is then used to reconstruct land use patterns for the city state of Singapore and a selection of North American cities. We demonstrate the emergence of land use patterns that exhibit comparable visual features to the actual city maps defining our case studies whilst sharing similar spatial characteristics. Our work provides a complementary approach to other measures of urban spatial structure that differentiate cities by their land use patterns resulting from bottom-up dispersion and aggregation processes.

 

DOI:10.1371/journal.pone.0080309

he Emergence of Urban Land Use Patterns Driven by Dispersion and Aggregation Mechanisms

Decraene J, Monterola C, Lee GKK, Hung TGG, Batty M

PLoS ONE 8(12): e80309 (2013)

 

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Network Science To Aid Disaster Relief Operations

Network Science To Aid Disaster Relief Operations | Urban Complexity | Scoop.it
Scientists from Singapore have modeled the flow of goods and relief efforts to obtain better logistical contingency plans.
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Resiliently evolving supply-demand networks

The ability to design a transport network such that commodities are brought from suppliers to consumers in a steady, optimal, and stable way is of great importance for distribution systems nowadays. In this work, by using the circuit laws of Kirchhoff and Ohm, we provide the exact capacities of the edges that an optimal supply-demand network should have to operate stably under perturbations, i.e., without overloading. The perturbations we consider are the evolution of the connecting topology, the decentralization of hub sources or sinks, and the intermittence of supplier and consumer characteristics. We analyze these conditions and the impact of our results, both on the current United Kingdom power-grid structure and on numerically generated evolving archetypal network topologies.

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Inferring human mobility using communication patterns

Understanding the patterns of mobility of individuals is crucial for a number of reasons, from city planning to disaster management. There are two common ways of quantifying the amount of travel between locations: by direct observations that often involve privacy issues, e.g., tracking mobile phone locations, or by estimations from models. Typically, such models build on accurate knowledge of the population size at each location. However, when this information is not readily available, their applicability is rather limited. As mobile phones are ubiquitous, our aim is to investigate if mobility patterns can be inferred from aggregated mobile phone call data. Using data released by Orange for Ivory Coast, we show that human mobility is well predicted by a simple model based on the frequency of mobile phone calls between two locations and their geographical distance. We argue that the strength of the model comes from directly incorporating the social dimension of mobility. Furthermore, as only aggregated call data is required, the model helps to avoid potential privacy problems.

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