The time geography of segregation during working hours

Dannemann, Teodoro; Sotomayor-Gómez, Boris; Samaniego, Horacio

doi:10.1098/rsos.180749

Cited by 37 publications

(40 citation statements)

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“…Within the urban sensing literature, mobile phone data play a prominent role as they form a source of passively collected information (users do not need to make an explicit action to share their locations as would be the case in, for example, location-based services or social networks), for large shares of populations (a large proportion of the world population now owns a mobile device of any sort), captured at a rather high spatial resolution (in general, the density of cell towers is high in urban areas). Mobile phone data research in an urban context has been applied to a diversity of individual cities, or to international comparison of cities: Paris [12]; Maputo [7], Dhaka [8], Santiago [13], Boston and Singapore [14], London, Singapore and Beijing [2]. Research with a focus on a single city, or a set of single cities, bears the advantage that it can easily tap into local knowledge when questioning the quantitative results obtained.…”

Section: The Single-city Focus Of Urban Sensingmentioning

confidence: 99%

Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities

Cottineau

Vanhoof

2019

IJGI

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Thanks to the use of geolocated big data in computational social science research, the spatial and temporal heterogeneity of human activities is increasingly being revealed. Paired with smaller and more traditional data, this opens new ways of understanding how people act and move, and how these movements crystallise into the structural patterns observed by censuses. In this article we explore the convergence between mobile phone data and more traditional socioeconomic data from the national census in French cities. We extract mobile phone indicators from six months worth of Call Detail Records (CDR) data, while census and administrative data are used to characterize the socioeconomic organisation of French cities. We address various definitions of cities and investigate how they impact the statistical relationships between mobile phone indicators, such as the number of calls or the entropy of visited cell towers, and measures of economic organisation based on census data, such as the level of deprivation, inequality and segregation. Our findings show that some mobile phone indicators relate significantly with different socioeconomic organisation of cities. However, we show that relations are sensitive to the way cities are defined and delineated. In several cases, changing the city delineation rule can change the significance and even the sign of the correlation. In general, cities delineated in a restricted way (central cores only) exhibit traces of human activity which are less related to their socioeconomic organisation than cities delineated as metropolitan areas and dispersed urban regions.

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Section: The Single-city Focus Of Urban Sensingmentioning

confidence: 99%

Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities

Cottineau

Vanhoof

2019

IJGI

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“…There is a need for segregation studies to expand from placebased measures to people-based measures, and to put more efforts to advancing temporally integrated analysis [16,18]. To fill these gaps, scholars started to incorporate travel behaviour and activities of individuals into their analytical frameworks through the usage of travel surveys and spatial trajectories [19][20][21][22][23][24][25][26][27]. Leveraging the notion and measurements of human activity space, these studies substantially improved the understanding of segregation beyond residential spaces, and also enabling individual-oriented and time-space views of social segregation.…”

Section: Introductionmentioning

confidence: 99%

Quantifying segregation in an integrated urban physical-social space

Belyi

Santi

et al. 2019

J. R. Soc. Interface.

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Our knowledge of how cities bring together different social classes is still limited. Much effort has been devoted to investigating residential segregation, mostly over well-defined social groups (e.g. race). Little is known of how mobility and human communications affect urban social integration. The dynamics of spatial and social-network segregation and individual variations along these two dimensions are largely untapped. In this article, we put forward a computational framework based on coupling large-scale information on human mobility, social-network connections and people’s socio-economic status (SES), to provide a breakthrough in our understanding of the dynamics of spatio-temporal and social-network segregation in cities. Building on top of a social similarity measure, the framework can be used to depict segregation dynamics down to the individual level, and also provide aggregate measurements at the scale of places and cities, and their evolution over time. By applying the methodology in Singapore using large-scale mobile phone and socio-economic datasets, we find a relatively higher level of segregation among relatively wealthier classes, a finding that holds for both social and physical space. We also highlight the interplay between the effect of distance decay and homophily as forces that determine communication intensity, defining a notion of characteristic ‘homophily distance’ that can be used to measure social segregation across cities. The time-resolved analysis reveals the changing landscape of urban segregation and the time-varying roles of places. Segregations in physical and social space are weakly correlated at the individual level but highly correlated when grouped across at least hundreds of individuals. The methodology and analysis presented in this paper enable a deeper understanding of the dynamics of human segregation in social and physical space, which can assist social scientists, planners and city authorities in the design of more integrated cities.

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“…Lorenz curves in Figure 2 represent 130 the different degrees of agglomeration driven by housing (G origins = 0.32) and daily 131 activities (G destinations = 0.56). As is true for large cities [16,17], inferred trips were 132 largely directed towards a few, hyper-affluent areas. In our model, these areas receive 133 25% of all daily trips but occupy less than 5% of the metropolis; meanwhile, housing 134 places 25% of the population throughout 13.5% of the city's most densely populated 135 regions.…”

mentioning

confidence: 86%

“…between the residents of distant neighborhoods [16,17]. In what follows, we quantify the 23 agglomeration of urban mobility using two Gini coefficients 24 (0 ≤ G origins , G destinations ≤ 1) that use neighborhood-level census data to measure 25 spatial inequalities in the area-density of housing and activities throughout a city.…”

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confidence: 99%

Understanding the role of urban design in disease spreading

Brizuela

García-Chan

Pulido

et al. 2019

Preprint

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Cities are complex systems whose characteristics impact the health of people who live in them. Nonetheless, urban determinants of health often vary within spatial scales smaller than the resolution of epidemiological datasets. Thus, as cities expand and their inequalities grow, the development of theoretical frameworks that explain health at the neighborhood level is becoming increasingly critical. To this end, we developed a methodology that uses census data to introduce urban geography as a leading-order predictor in the spread of influenza-like pathogens. Here, we demonstrate our framework using neighborhood-level census data for Guadalajara (GDL, Western Mexico). Our simulations were calibrated using weekly hospitalization data from the 2009 A/H1N1 influenza pandemic and show that daily mobility patterns drive neighborhood-level variations in the basic reproduction number R 0 , which in turn give rise to robust spatiotemporal patterns in the spread of disease. To generalize our results, we ran simulations in hypothetical cities with the same population, area, schools and businesses as GDL but different land use zoning. Our results demonstrate that the agglomeration of daily activities can largely influence the growth rate, size and timing of urban epidemics. Overall, these findings support the view that cities can be redesigned to limit the geographic scope of influenza-like outbreaks and provide a general mathematical framework to study the mechanisms by which local and remote health consequences result from characteristics of the physical environment. Introduction 1 Empirical studies have identified inter-city variations in the timing, intensity and 2 severity of influenza-like outbreaks [1-4]. Aiming to understand the mechanisms 3 through which city characteristics yield such health consequences, epidemiologists have 4 resourced to a variety of methods. Epidemiological data reveal compelling statistical 5 correlations, but do not resolve intra-city variations in health that are driven by lifestyle 6 inequalities at the neighborhood level [2, 4-6]. In contrast, agent-based computational 7 models use massive mobility datasets to recreate the behavior of individuals as they 8 September 11, 2019 1/15interact and spread infections [7][8][9][10][11][12]. Even though this approach allows for 9 household-level analyses, its elevated complexity obscures relationships between model 10 parameters and their health impacts, making it inadequate for the development of 11 general strategies in public health [13]. Furthermore, agent-based simulations are costly 12 and the information necessary to calibrate them is not openly available for most of the 13 world's cities. Thus, despite the fact that epidemics have the potential to be seeded 14 anywhere, these intricate models have been overwhelmingly applied to populations in 15 the developed world. Meanwhile, the fundamental mechanisms that drive health 16 inequalities within metropolitan areas remain elusive. 17Urban design determines the densities and relative locations o...

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The time geography of segregation during working hours

Cited by 37 publications

References 50 publications

Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities

Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities

Quantifying segregation in an integrated urban physical-social space

Understanding the role of urban design in disease spreading

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