Geographer, Economist

Who am I?

Pumain D., Swerts E., Cottineau C., Vacchiani-Marcuzzo, Ignazzi A., Bretagnolle A., Delisle F., Cura R., Lizzi L., Baffi S., 2015, « Multilevel comparison of large urban systems », Cybergeo, document 706, http://cybergeo.revues.org/26730

Cottineau C., Chapron P., Reuillon R., 2015, “Growing models from the bottom up. An evaluation-based incremental modelling method (EBIMM) applied to the simulation of systems of cities”, JASSS, Vol. 18, No. 4, 9. DOI:10.18564/jasss.2828

PhD on genericity of urbanisation of the post-Soviet space, 1840-2010

Data collection on 2000 cities over historical censuses

Modelling (ABM, stats) of urbanisation

Cottineau C., Slepukhina I., 2018, “The Russian urban system : evolution under transition”, in D. Pumain, C. Rozenblat & E. Velasquez, International and Transnational Perspectives on Urban Systems, Springer

Cura, R., Cottineau, C., Swerts, E., Antonio Ignazzi, C., Bretagnolle, A., Vacchiani‐Marcuzzo, C., & Pumain, D. (2017). The old and the new: qualifying city systems in the world with classical models and new data. Geographical Analysis, 49(4), 363-386.

Cottineau C., Reuillon R., Chapron P., Rey-Coyrehourcq S., Pumain D., 2015 “A modular modelling framework for hypotheses testing in the simulation of urbanisation”, Systems, 3, pp. 348-377, DOI: 10.3390/systems3040348

Chérel G., Cottineau C., Reuillon R., 2015, « Beyond corroboration: strengthening model validation by looking for unexpected patterns. », PLoS One, Vol. 10, No. 9, e0138212.

Spatial analyst à CASA, University College London

Who am I?

Post-doc on urban scaling laws with E. Arcaute and M. Batty

Generation of city definitions

Sensitivity analysis of power law relationships between city attributes and population

Cottineau, C., & Vanhoof, M. (2019). Mobile phone indicators and their relation to the socioeconomic organisation of cities. ISPRS International Journal of Geo-Information, 8(1), 19.

Finance, O., & Cottineau, C. (2018). Are the absent always wrong? Dealing with zero values in urban scaling. Environment and Planning B: Urban Analytics and City Science, 2399808318785634.

Cottineau, C., Finance, O., Hatna, E., Arcaute, E., & Batty, M. (2018). Defining urban clusters to detect agglomeration economies. Environment and Planning B: Urban Analytics and City Science, 2399808318755146.

Cottineau, C. (2017). MetaZipf. A dynamic meta-analysis of city size distributions. PloS one, 12(8), e0183919.

Cottineau C., Arcaute E., Hatna E., Batty, M., 2017, “Diverse cities or the Systematic Paradox of Urban Scaling Laws”, Computer, Environment and Urban Systems (CEUS), Vol. 59, DOI: 10.1016/j.compenvurbsys.2016.04.006

Agent-based modeler

Who am I?

Projects on urban density and segregation effects on dynamic mechanisms

Various scenarios of spatio-temporal patterns of segregation in Paris region and effect on diffusion of dietary behaviours

Sensitivity analysis of Schelling and Sugarscape results to variation of initial spatial conditions

Raimbault, J., Cottineau, C., Texier, M. L., Néchet, F. L., & Reuillon, R. (2018). Space Matters: extending sensitivity analysis to initial spatial conditions in geosimulation models. arXiv preprint arXiv:1812.06008.

Cottineau, C., Perret, J., Reuillon, R., Rey-Coyrehourcq, S., & Vallée, J. (2018, March). An agent-based model to investigate the effects of social segregation around the clock on social disparities in dietary behaviour. In CIST2018-Représenter les territoires/Representing territories.

Project at CASA - UCL

https://www.mdpi.com/2220-9964/8/1/19

Research question

Is there a relationship between social and spatial practices sensed by mobile phone data and the socioeconomic structure of cities?

Are they uniformly robust to city definition?

or are there differences depending on the indicator of mobile phone chosen?
or some delineations of cities are more illustrative of statistical relations than others?

Data sources

Mobile Phone

Socio

economic

All mobile subscribers to Orange

from 13th May 2007 to 15th October 2007

Continental France

CDR (Call Detailed Records)

- timestamp of call

- call length

- Cell tower ID of caller

- Cell tower ID of called contact

Identification of cell tower of residence

Creation of individual indicators of spatial mobility and social network characterstics

Bandicoot library / Python

Maarten Vanhoof at Orange Labs 2014-2017

Census and administrative data

2008 / 2011

Indicator definition

[Cottineau, Vanhoof, 2019]

Poverty

Inequality

Segregation

European Deprivation Index (EDI) defined by Pornet et al. (2012) from survey on subjective and objective poverty

Gini index from aggregated distribution of earnings (#jobs by earning category, CLAP 2008)

Ordinal segregation index (Reardon, 2009) from aggregated distribution of earnings (#jobs by earning category, CLAP 2008)

[Cottineau et al., 2018]

Mobile Phone

Socio

economic

Spatial distribution of indicators

[Cottineau, Vanhoof, 2019]

Mobile Phone

Socio

economic

Poverty

Inequality

Segregation

Spatial harmonisation of data

From individuals to cell towers
From cell towers to municipalities
From municipalities to cities

Which definition of cities?

- municipalities?

- morphological agglomerations?

- metro areas?

Spatial harmonisation of data

Which definition(s) of cities?

Variation of density D

Variation of commuting flow F

Variation of population P

> 1 aggregation per combination

Credits: Y. Jiang, T. Russell, C. Cottineau, E. Arcaute

Cottineau, Hatna, Arcaute, Batty, 2017, Computer Environment and Urban Systems

Systematic

Delineation

of cities

39 values of D

x 21 values of F

x 6 values of P

= 4914 representations of the system of cities

From definitions of cities to sensitivity analysis of relations

39 values of D

x 21 values of F

x 6 values of P

= 4914 representations of the system of cities

x N (var mobile)

x M (Var census)

> millions regressions

From 4914 cases

to 6 cluster medoids

p = 10 000 residents

k-medoids method

Which definition(s) of cities?

How do mobile phone indicators vary with measures of socioeconomic organisation of cities?

Do these relations vary when the definition of cities change?

Results

The different definitions

Negative Correlation

Positive Correlation

The different indicators

Results

Where cities are poorer on average,

their residents have been less active, their calls shorter,

their mobility shorter and less diversified

Results

Where cities are poorer on average,

their residents have called more calls from home,

the intensity of their interactions with contacts was stronger

Results

The correlation with the total number of calls and

the share of nocturnal calls depends on city definition

Results

Where cities are more unequal on average,

their residents have been in more calls, have been more mobile and diversified in terms of social and spatial networks

Results

Temporal indicators do not correlate much

with city levels of inequality

Results

Spatial segregation by earnings in cities is associated with less calls and shorter ones, less contacts and less active days.

Results

But most correlations can go both directions

depending on the city delineation chosen.

Results

Restrictive definitions of dense city cores

Results

Loose definitions of commuting periphery

< 40% (INSEE)

When segregation increases,

diversity of contacts called decreases

Results

Restrictive definitions of dense city core

> 15 inhab./ha

When segregation increases,

diversity of contacts called increases

or

no correlation

Results

Aires

urbaines

(metro areas)

Results

When segregation increases,

diversity of contacts called decreases

(for large cities)

or

no correlation

(p < 30,000)