129 research outputs found
Mapping mobile service usage diversity in cities
The growing accessibility to mobile phone data, including Internet traffic
information, has enabled us over the past ten years to explore human behaviors
and cities' structures and functions at high spatio-temporal resolutions. In
this article, we explore and map the diversity of mobile service usages in 20
French cities by focusing on the hourly traffic volume information of six
social network services at a high spatial resolution. We relied on diversity
and similarity metrics to investigate the diversities of mobile service usage
in space and time at different scales with an emphasis on the difference
between regular and holiday weeks. In particular, we show that the diversity is
globally lower during holiday than during regular weeks. We also identified a
significant difference in mobile service usage diversity between cities located
in the southern and northern half of France. We finally demonstrate that based
on the similarity in mobile service usage it is possible to divide each city in
three regions whose spatial structure varies in time.Comment: 8 pages, 6 figures + Appendix [as submitted to the NetMob 2023 Data
Challenge
Systematic comparison of trip distribution laws and models
Trip distribution laws are basic for the travel demand characterization
needed in transport and urban planning. Several approaches have been considered
in the last years. One of them is the so-called gravity law, in which the
number of trips is assumed to be related to the population at origin and
destination and to decrease with the distance. The mathematical expression of
this law resembles Newton's law of gravity, which explains its name. Another
popular approach is inspired by the theory of intervening opportunities which
argues that the distance has no effect on the destination choice, playing only
the role of a surrogate for the number of intervening opportunities between
them. In this paper, we perform a thorough comparison between these two
approaches in their ability at estimating commuting flows by testing them
against empirical trip data at different scales and coming from different
countries. Different versions of the gravity and the intervening opportunities
laws, including the recently proposed radiation law, are used to estimate the
probability that an individual has to commute from one unit to another, called
trip distribution law. Based on these probability distribution laws, the
commuting networks are simulated with different trip distribution models. We
show that the gravity law performs better than the intervening opportunities
laws to estimate the commuting flows, to preserve the structure of the network
and to fit the commuting distance distribution although it fails at predicting
commuting flows at large distances. Finally, we show that the different
approaches can be used in the absence of detailed data for calibration since
their only parameter depends only on the scale of the geographic unit.Comment: 15 pages, 10 figure
A Universal Model of Commuting Networks
We test a recently proposed model of commuting networks on 80 case studies
from different regions of the world (Europe and United-States) and with
geographic units of different sizes (municipality, county, region). The model
takes as input the number of commuters coming in and out of each geographic
unit and generates the matrix of commuting flows betwen the geographic units.
We show that the single parameter of the model, which rules the compromise
between the influence of the distance and job opportunities, follows a
universal law that depends only on the average surface of the geographic units.
We verified that the law derived from a part of the case studies yields
accurate results on other case studies. We also show that our model
significantly outperforms the two other approaches proposing a universal
commuting model (Balcan et al. (2009); Simini et al. (2012)), particularly when
the geographic units are small (e.g. municipalities).Comment: 11 pages, 5 figure
Tweets on the road
The pervasiveness of mobile devices, which is increasing daily, is generating
a vast amount of geo-located data allowing us to gain further insights into
human behaviors. In particular, this new technology enables users to
communicate through mobile social media applications, such as Twitter, anytime
and anywhere. Thus, geo-located tweets offer the possibility to carry out
in-depth studies on human mobility. In this paper, we study the use of Twitter
in transportation by identifying tweets posted from roads and rails in Europe
between September 2012 and November 2013. We compute the percentage of highway
and railway segments covered by tweets in 39 countries. The coverages are very
different from country to country and their variability can be partially
explained by differences in Twitter penetration rates. Still, some of these
differences might be related to cultural factors regarding mobility habits and
interacting socially online. Analyzing particular road sectors, our results
show a positive correlation between the number of tweets on the road and the
Average Annual Daily Traffic on highways in France and in the UK. Transport
modality can be studied with these data as well, for which we discover very
heterogeneous usage patterns across the continent.Comment: 15 pages, 17 figure
Is spatial information in ICT data reliable?
An increasing number of human activities are studied using data produced by
individuals' ICT devices. In particular, when ICT data contain spatial
information, they represent an invaluable source for analyzing urban dynamics.
However, there have been relatively few contributions investigating the
robustness of this type of results against fluctuations of data
characteristics. Here, we present a stability analysis of higher-level
information extracted from mobile phone data passively produced during an
entire year by 9 million individuals in Senegal. We focus on two
information-retrieval tasks: (a) the identification of land use in the region
of Dakar from the temporal rhythms of the communication activity; (b) the
identification of home and work locations of anonymized individuals, which
enable to construct Origin-Destination (OD) matrices of commuting flows. Our
analysis reveal that the uncertainty of results highly depends on the sample
size, the scale and the period of the year at which the data were gathered.
Nevertheless, the spatial distributions of land use computed for different
samples are remarkably robust: on average, we observe more than 75% of shared
surface area between the different spatial partitions when considering activity
of at least 100,000 users whatever the scale. The OD matrix is less stable and
depends on the scale with a share of at least 75% of commuters in common when
considering all types of flows constructed from the home-work locations of
100,000 users. For both tasks, better results can be obtained at larger levels
of aggregation or by considering more users. These results confirm that ICT
data are very useful sources for the spatial analysis of urban systems, but
that their reliability should in general be tested more thoroughly.Comment: 11 pages, 9 figures + Appendix, Extended version of the conference
paper published in the proceedings of the 2016 Spatial Accuracy Conference, p
9-17, Montpellier, Franc
Intersectional approach of everyday geography
Hour-by-hour variations in spatial distribution of gender, age and social
class within cities remain poorly explored and combined in the segregation
literature mainly centered on home places from a single social dimension.
Taking advantage of 49 mobility surveys compiled together (385,000 respondents
and 1,711,000 trips) and covering 60% of France's population, we consider
variations in hourly populations of 2,572 districts after disaggregating
population across gender, age and education level. We first isolate five
district hourly profiles (two 'daytime attractive', two 'nighttime attractive'
and one more 'stable') with very unequal distributions according to urban
gradient but also to social groups. We then explore the intersectional forms of
these everyday geographies. Taking as reference the dominant groups (men,
middle-age and high educated people) known as concentrating hegemonic power and
capital, we analyze specifically whether district hourly profiles of dominant
groups diverge from those of the others groups. It is especially in the areas
exhibiting strong increase or strong decrease of ambient population during the
day that district hourly profiles not only combine the largest dissimilarities
all together across gender, age and education level but are also widely more
synchronous between dominant groups than between non-dominant groups (women,
elderly and low educated people). These intersectional patterns shed new light
on areas where peers are synchronously located over the 24-hour period and thus
potentially in better position to interact and to defend their common
interests.Comment: 13 pages, 5 figures + Appendi
A commuting network model: going to the bulk
The influence of commuting in socio-economic dynamics increases constantly.
Analysing and modelling the networks formed by commuters to help
decision-making regarding the land-use has become crucial. This paper presents
a simple spatial interaction simulated model with only one parameter. The
proposed algorithm considers each individual who wants to commute, starting
from their living place to all their workplaces. It decides where the location
of the workplace following the classical rule inspired from the gravity law
consisting in a compromise between the job offers and the distance to the jobs.
The further away the job offer is, the more important it must be in order to be
considered. Inversely, only the quantity of offers is important for the
decision when these offers are close. The paper also presents a comparative
analysis of the structure of the commuting networks of the four European
regions to which we apply our model. The model is calibrated and validated on
these regions. Results from the analysis shows that the model is very efficient
in reproducing most of the statistical properties of the network given by the
data sources.Comment: submitted to JASS
Crowdsourcing the Robin Hood effect in cities
Socioeconomic inequalities in cities are embedded in space and result in
neighborhood effects, whose harmful consequences have proved very hard to
counterbalance efficiently by planning policies alone. Considering
redistribution of money flows as a first step toward improved spatial equity,
we study a bottom-up approach that would rely on a slight evolution of shopping
mobility practices. Building on a database of anonymized credit card
transactions in Madrid and Barcelona, we quantify the mobility effort required
to reach a reference situation where commercial income is evenly shared among
neighborhoods. The redirections of shopping trips preserve key properties of
human mobility, including travel distances. Surprisingly, for both cities only
a small fraction () of trips need to be altered to reach equity
situations, improving even other sustainability indicators. The method could be
implemented in mobile applications that would assist individuals in reshaping
their shopping practices, to promote the spatial redistribution of
opportunities in the city.Comment: 9 pages, 4 figures + Appendi
Generating French virtual commuting network at municipality level
We aim to generate virtual commuting networks in the French rural regions in
order to study the dynamics of their municipalities. Since we have to model
small commuting flows between municipalities with a few hundreds or thousands
inhabitants, we opt for a stochastic model presented by Gargiulo et al. 2012.
It reproduces the various possible complete networks using an iterative
process, stochastically choosing a workplace in the region for each commuter
living in the municipality of a region. The choice is made considering the job
offers in each municipality of the region and the distance to all the possible
destinations. This paper presents how to adapt and implement this model to
generate French regions commuting networks between municipalities. We address
three different questions: How to generate a reliable virtual commuting network
for a region highly dependant of other regions for the satisfaction of its
resident's demand for employment? What about a convenient deterrence function?
How to calibrate the model when detailed data is not available? We answer
proposing an extended job search geographical base for commuters living in the
municipalities, we compare two different deterrence functions and we show that
the parameter is a constant for network linking French municipalities.Comment: 11 pages, 7 figure
Human diffusion and city influence
International audienceCities are characterized by concentrating population, economic activity and services. However, not all cities are equal and a natural hierarchy at local, regional or global scales spontaneously emerges. In this work, we introduce a method to quantify city influence using geolocated tweets to characterize human mobility. Rome and Paris appear consistently as the cities attracting most diverse visitors. The ratio between locals and non-local visitors turns out to be fundamental for a city to truly be global. Focusing only on urban residents' mobility flows, a city to city network can be constructed. This network allows us to analyze centrality measures at different scales. New York and London play a predominant role at the global scale, while urban rankings suffer substantial changes if the focus is set at a regional level
- …