Thèse Everyday Mobility And Social Segregation Around The Clock In Dutch Cities H/F - Doctorat.Gouv.Fr
- CDD
- Doctorat.Gouv.Fr
Les missions du poste
Établissement : Université Toulouse II Jean Jaurès École doctorale : TESC - Temps, Espaces, Sociétés, Cultures Laboratoire de recherche : LISST - Laboratoire Interdisciplinaire Solidarités, Sociétés, Territoires Direction de la thèse : Julie VALLEE ORCID 0000000187013047 Début de la thèse : 2027-09-01 Date limite de candidature : 2026-11-23T23:59:59 This PhD research project aims to measure and analyse social segregation 'around the clock' in four major Dutch cities: Amsterdam, Rotterdam, The Hague and Utrecht. Addressing a critical gap in urban research, the study shifts the focus from the traditional residential perspective to the hourly changes in neighbourhood social composition, which evolve as people move across urban areas throughout the day. Although a growing number of studies have begun to examine segregation from temporal and activity-based perspectives, they tend to focus on a single city region or a single social variable, such as income, whilst neglecting key factors such as gender and age. The open Mobiliscope platform (mobiliscope.cnrs.fr) has been developed to overcome these limitations by displaying hourly variations in neighbourhood social composition and segregation indices for a range of sociodemographic variables in 58 city regions (49 French cities, six Canadian cities and three Latin American cities).
In the Netherlands, 24-hour segregation research is scarce despite extensive residential studies. This PhD project will explore hourly segregation patterns in the four major Dutch cities. The methodology relies on origin-destination surveys, specifically the Dutch National Travel Survey (OdiN), conducted annually by Statistics Netherlands (CBS). In 2024, it surveyed 65,000 respondents with metropolitan oversampling for neighborhood-scale analysis. Data collection concerns people (age, gender, level of education, income and occupational status) and their daily trips (departure and arrival times, origins and destinations, reasons for travelling and modes of transport). The methodological approach will follow five key steps: converting trip data into hourly location datasets; calculating hourly segregation indices for a set of sociodemographic variables; comparing hourly segregation patterns across Dutch cities; integrating Dutch cities into Mobiliscope platform; and comparing Dutch everyday segregation patterns with other Mobiliscope cities (France, Canada, Latin America).
This thesis will advance knowledge on daily segregation dynamics in the Netherlands, share insights with stakeholders via Mobiliscope, and contribute to open science by releasing data, code, and results. By combining advanced computational methods, robust data, and an international comparative approach, it will provide insights into social segregation as a dynamic, temporal, and spatial phenomenon, enhancing tools for urban inequality analysis.
People's daily mobility in cities has been largely explored with particular attention to differences in trips number, distances travelled and transportation modes according to gender, age and social class. However, it remains much less common to examine how people's daily mobility leads to hourly variations in spatial concentration of gender, age and social subgroups within cities. Such a blind spot is unfortunate because spatial and temporal constrains that demographic and social subgroups unequally encounter in their daily life do not only translate into differences in the ways they move around but generate also divergent spatial patterns in their co-locations and in segregation of cities around the clock. It affects their exposure to diverse local contexts, which in turn shapes the formation and persistence of social networks, norms and opportunities.
Social segregation - i.e. the pattern of (geographical) separation between people of different social groups- is a large component of the scientific program of urban sciences (geography, sociology, demography and economics). Social segregation may produce social inequalities through contextual effects, since neighbourhood mixing or concentration plays a role in shaping individuals' opinions and behaviours in multiple life domains.
While social segregation remains largely studied from a residential perspective, it is only recently that authors have started to explore segregation from an activity-based perspective. However, the rare studies of everyday segregation are generally limited in their geographical scope focusing on a single city region. Furthermore, these studies tend to focus on a single axis of difference (e.g. income), without considering everyday exclusion processes impacting gender, age and other dimension of socio-economic position. To address these limitations, the Mobiliscope' platform (https://mobiliscope.cnrs.fr/en) displays hour-by-hour maps of neighbourhood-level social profiles and urban segregation indices on a typical weekday (Monday-Friday) for a vast range of cities (49 French cities, 6 Canadian cities and 3 Latin American cities in the actual version v4.3 of the Mobiliscope). It also provides access to the underlying data for research purposes. As an example, segregation was found to increase in all French cities between night-time and daytime for people with a very low level of education. Some interesting patterns in everyday segregation within French cities also appear for attributes such as gender and age: gender-based segregation, segregation of the youngest (aged 25-34) and segregation of the oldest people (65+) was found to increase during the day compared to night-time residential measurements.
In the Netherlands, literature investigating social segregation throughout the day remains very limited. This is in contrast to the large number of studies that have examined the measurement, description and evolution of social segregation in Dutch cities from a residential perspective, albeit with a long-time temporal perspective. This PhD research project aims to explore social segregation 'around the clock' in the four major Dutch cities (Amsterdam, Rotterdam, The Hague and Utrecht), add these cities to the Mobiliscope platform and compare their hourly segregation patterns with those of the wide range of cities still available on the platform. This PhD research aims to measure and analyse social segregation 'around the clock' in the four major Dutch cities (Amsterdam, Rotterdam, The Hague and Utrecht). The study seeks to address a critical gap in urban research by shifting the focus from traditional residential segregation to the dynamic and temporal dimensions of social separation, which evolve throughout the day as people move across urban spaces. This approach recognizes that social inequalities are not only shaped by where individuals live but also by how and when they occupy different areas of the city, reflecting the complex interplay between mobility, time, and space. Similarly to what has been done for cities included in the Mobiliscope platform, this PhD research will use data from origin-destination surveys to measure changes in neighbourhood social composition in Dutch cities over a 24-hour period. Such surveys provide actually some precise space-time data including a large range of person-based data (e.g. sex, age, education level, income, occupational status) from a representative population. Often seen as less fashionable that digital traces, with smaller sample of respondents, over a more limited time span, these traditional data sources are yet valuable especially when they concern a large number of cities and ensure that observations made in one city remain valid for other cities. Moreover, such surveys provide information about the mode of transport used and the activities carried out during the day, which are often missing from digital traces. More specifically, this PhD research will use data from the Dutch National Travel Survey (Onderweg in Nederland - OdiN), a representative survey conducted annually by Statistics Netherlands (CBS). In 2024, the sample size was about 65,000 respondents with oversampling in metropolitan areas to enable analysis at a neighbourhood scale within cities. Information on respondents include age, gender, education level and household income. Information on their daily trips include departure and arrival times, departure and arrival postcodes, purpose and modes of transportation. Data can be accessed from DANS Data Station Social Sciences and Humanities Support (https://ssh.datastations.nl/).
The PhD research will comprise distinct methodological steps: (i) Convert the trip dataset into an hourly location dataset for the four major Dutch cities (Amsterdam, Rotterdam, The Hague and Utrecht); (ii) Calculate hourly segregation indices (e.g. Duncan's dissimilarity index and Moran's autocorrelation index) for various socio-demographic variables (gender, age group, level of education, household income); (iii) Compare hourly segregation patterns across Dutch cities; (iv) Integrate the four Dutch cities into the Mobiliscope platform (with the data and code freely available) ; (v) Compare hourly segregation patterns across Dutch cities with those of other cities in France, Canada and South America.
The PhD student could build upon (and also improve) the algorithms developed by the Mobiliscope team, which are freely available in Git repositories: https://gitlab.huma-num.fr/mobiliscope/data-process for the pre-processing of raw data, and https://gitlab.huma-num.fr/mobiliscope/www
for the Geoviz platform.
In the Netherlands, 24-hour segregation research is scarce despite extensive residential studies. This PhD project will explore hourly segregation patterns in the four major Dutch cities. The methodology relies on origin-destination surveys, specifically the Dutch National Travel Survey (OdiN), conducted annually by Statistics Netherlands (CBS). In 2024, it surveyed 65,000 respondents with metropolitan oversampling for neighborhood-scale analysis. Data collection concerns people (age, gender, level of education, income and occupational status) and their daily trips (departure and arrival times, origins and destinations, reasons for travelling and modes of transport). The methodological approach will follow five key steps: converting trip data into hourly location datasets; calculating hourly segregation indices for a set of sociodemographic variables; comparing hourly segregation patterns across Dutch cities; integrating Dutch cities into Mobiliscope platform; and comparing Dutch everyday segregation patterns with other Mobiliscope cities (France, Canada, Latin America).
This thesis will advance knowledge on daily segregation dynamics in the Netherlands, share insights with stakeholders via Mobiliscope, and contribute to open science by releasing data, code, and results. By combining advanced computational methods, robust data, and an international comparative approach, it will provide insights into social segregation as a dynamic, temporal, and spatial phenomenon, enhancing tools for urban inequality analysis.
People's daily mobility in cities has been largely explored with particular attention to differences in trips number, distances travelled and transportation modes according to gender, age and social class. However, it remains much less common to examine how people's daily mobility leads to hourly variations in spatial concentration of gender, age and social subgroups within cities. Such a blind spot is unfortunate because spatial and temporal constrains that demographic and social subgroups unequally encounter in their daily life do not only translate into differences in the ways they move around but generate also divergent spatial patterns in their co-locations and in segregation of cities around the clock. It affects their exposure to diverse local contexts, which in turn shapes the formation and persistence of social networks, norms and opportunities.
Social segregation - i.e. the pattern of (geographical) separation between people of different social groups- is a large component of the scientific program of urban sciences (geography, sociology, demography and economics). Social segregation may produce social inequalities through contextual effects, since neighbourhood mixing or concentration plays a role in shaping individuals' opinions and behaviours in multiple life domains.
While social segregation remains largely studied from a residential perspective, it is only recently that authors have started to explore segregation from an activity-based perspective. However, the rare studies of everyday segregation are generally limited in their geographical scope focusing on a single city region. Furthermore, these studies tend to focus on a single axis of difference (e.g. income), without considering everyday exclusion processes impacting gender, age and other dimension of socio-economic position. To address these limitations, the Mobiliscope' platform (https://mobiliscope.cnrs.fr/en) displays hour-by-hour maps of neighbourhood-level social profiles and urban segregation indices on a typical weekday (Monday-Friday) for a vast range of cities (49 French cities, 6 Canadian cities and 3 Latin American cities in the actual version v4.3 of the Mobiliscope). It also provides access to the underlying data for research purposes. As an example, segregation was found to increase in all French cities between night-time and daytime for people with a very low level of education. Some interesting patterns in everyday segregation within French cities also appear for attributes such as gender and age: gender-based segregation, segregation of the youngest (aged 25-34) and segregation of the oldest people (65+) was found to increase during the day compared to night-time residential measurements.
In the Netherlands, literature investigating social segregation throughout the day remains very limited. This is in contrast to the large number of studies that have examined the measurement, description and evolution of social segregation in Dutch cities from a residential perspective, albeit with a long-time temporal perspective. This PhD research project aims to explore social segregation 'around the clock' in the four major Dutch cities (Amsterdam, Rotterdam, The Hague and Utrecht), add these cities to the Mobiliscope platform and compare their hourly segregation patterns with those of the wide range of cities still available on the platform. This PhD research aims to measure and analyse social segregation 'around the clock' in the four major Dutch cities (Amsterdam, Rotterdam, The Hague and Utrecht). The study seeks to address a critical gap in urban research by shifting the focus from traditional residential segregation to the dynamic and temporal dimensions of social separation, which evolve throughout the day as people move across urban spaces. This approach recognizes that social inequalities are not only shaped by where individuals live but also by how and when they occupy different areas of the city, reflecting the complex interplay between mobility, time, and space. Similarly to what has been done for cities included in the Mobiliscope platform, this PhD research will use data from origin-destination surveys to measure changes in neighbourhood social composition in Dutch cities over a 24-hour period. Such surveys provide actually some precise space-time data including a large range of person-based data (e.g. sex, age, education level, income, occupational status) from a representative population. Often seen as less fashionable that digital traces, with smaller sample of respondents, over a more limited time span, these traditional data sources are yet valuable especially when they concern a large number of cities and ensure that observations made in one city remain valid for other cities. Moreover, such surveys provide information about the mode of transport used and the activities carried out during the day, which are often missing from digital traces. More specifically, this PhD research will use data from the Dutch National Travel Survey (Onderweg in Nederland - OdiN), a representative survey conducted annually by Statistics Netherlands (CBS). In 2024, the sample size was about 65,000 respondents with oversampling in metropolitan areas to enable analysis at a neighbourhood scale within cities. Information on respondents include age, gender, education level and household income. Information on their daily trips include departure and arrival times, departure and arrival postcodes, purpose and modes of transportation. Data can be accessed from DANS Data Station Social Sciences and Humanities Support (https://ssh.datastations.nl/).
The PhD research will comprise distinct methodological steps: (i) Convert the trip dataset into an hourly location dataset for the four major Dutch cities (Amsterdam, Rotterdam, The Hague and Utrecht); (ii) Calculate hourly segregation indices (e.g. Duncan's dissimilarity index and Moran's autocorrelation index) for various socio-demographic variables (gender, age group, level of education, household income); (iii) Compare hourly segregation patterns across Dutch cities; (iv) Integrate the four Dutch cities into the Mobiliscope platform (with the data and code freely available) ; (v) Compare hourly segregation patterns across Dutch cities with those of other cities in France, Canada and South America.
The PhD student could build upon (and also improve) the algorithms developed by the Mobiliscope team, which are freely available in Git repositories: https://gitlab.huma-num.fr/mobiliscope/data-process for the pre-processing of raw data, and https://gitlab.huma-num.fr/mobiliscope/www
for the Geoviz platform.
Le profil recherché
We are seeking a proactive, research-driven candidate with a strong dual interest in:
- Social inequalities in geographical space
- Advanced computational methods for spatial data analysis
Education
- Master's degree (or equivalent) in Computer Science, Data Science, Geoinformatics (GIS), Spatial Econometrics, or a highly quantitative field in Geography, Sociology, or Urban Planning.
Programming & Tools
- Strong proficiency in R and experience with data science libraries.
- Version control tools (Git/GitLab).
Data Expertise
- Proven experience in processing large-scale, heterogeneous spatial datasets (e.g., geolocated data, travel surveys, census data, GIS vectors).
- Strong skills in statistical and spatial analysis.
Thematic Knowledge
- Deep understanding of urban issues, sociology, and geography.
- Familiarity with segregation measures and their applications.
Language Skills
- English: Excellent written and spoken proficiency (required).
- Dutch and French: Basic knowledge or a willingness to learn is a plus.
Application link: https://edd-projets.utoulouse.fr/
- Social inequalities in geographical space
- Advanced computational methods for spatial data analysis
Education
- Master's degree (or equivalent) in Computer Science, Data Science, Geoinformatics (GIS), Spatial Econometrics, or a highly quantitative field in Geography, Sociology, or Urban Planning.
Programming & Tools
- Strong proficiency in R and experience with data science libraries.
- Version control tools (Git/GitLab).
Data Expertise
- Proven experience in processing large-scale, heterogeneous spatial datasets (e.g., geolocated data, travel surveys, census data, GIS vectors).
- Strong skills in statistical and spatial analysis.
Thematic Knowledge
- Deep understanding of urban issues, sociology, and geography.
- Familiarity with segregation measures and their applications.
Language Skills
- English: Excellent written and spoken proficiency (required).
- Dutch and French: Basic knowledge or a willingness to learn is a plus.
Application link: https://edd-projets.utoulouse.fr/
Compétences requises
- Anglais
- Pro-activité
- Git
- Français