Invited seminar - College of Architecture and urban planning, Tongji U.

 

Clémentine Cottineau

Research Associate at CNRS

UMR 8097 - Centre Maurice Halbwachs

RA from Universtiy College London CASA

 

Shanghai, CN. Tuesday 16th April 2019

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.

My project at CNRS

Investigate the link between economic inequality and spatial segregation

Describe inequality at various geographic scales (global, national, regional, urban, local)

Come up with new measures of inequality to incorporate spatial distribution

Model and simulate inequality including dynamics in time and space

Test public policy scenario and compare effects at different geographical scales

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

  1. From individuals to cell towers
  2. From cell towers to municipalities
  3. 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

Results

Results

Restrictive definitions of dense city cores

Results

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)

Results

Poverty and segregation levels and (to a lesser extent) inequality account for a significant share of variations in size and diversity of the social network called and the spatial network of cell towers

Poverty correlates with a decrease in size and diversity of networks, regardless of city definition

 

 

Poverty and segregation levels and (to a lesser extent) inequality account for a significant share of variations in size and diversity of the social network called and the spatial network of cell towers

Results

It correlates with an intensification of interaction per contact and an increase in calls from home

Results

Poverty and segregation levels and (to a lesser extent) inequality account for a significant share of variations in size and diversity of the social network called and the spatial network of cell towers

Poverty correlates with a decrease in size and diversity of networks, regardless of city definition

Results

Poverty and segregation levels and (to a lesser extent) inequality account for a significant share of variations in size and diversity of the social network called and the spatial network of cell towers

Segregation correlates with a decrease in size and diversity of social networks. For spatial networks, statistical relations depends heavily on city definition

Results

Poverty and segregation levels and (to a lesser extent) inequality account for a significant share of variations in size and diversity of the social network called and the spatial network of cell towers

Inequality correlates with a increase in diversity and intensity of networks, regardless of city definition

Results

Poverty and segregation levels and (to a lesser extent) inequality account for a significant share of variations in size and diversity of the social network called and the spatial network of cell towers

This is even more true for definitions close to metropolitan areas (dense city core and integrated commuting periphery)

Results

Poverty and segregation levels and (to a lesser extent) inequality account for a significant share of variations in size and diversity of the social network called and the spatial network of cell towers

This is less true for definitions of compact cities (very dense city core and no commuting periphery)

Results

Some mobile phone indicators vary independently from the socio-economic structure of cities (length of calls, share of nocturnal calls)

Conclusions

Economic inequalities are sensitive to geographical space

 

Economic inequalities are sensitive to city size and morphology

 

 

The link between income, inequality and segregation is to be explored further, at the level of cities and citizens

Thank you.

谢谢.

 

 

 

clementine.cottineau@ens.fr

@clementinecttn

 

 

 

 

 

Campus ENS/PSE Jourdan

48 boulevard Jourdan

75014 Paris, France

 

 

 

 

 

 

 

 

 

Tongji University

16 April 2019

Shanghai, China

Seminar Tongji Uni Shanghai - April 2019

By Clémentine Cottineau

Seminar Tongji Uni Shanghai - April 2019

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