Our innovative approach uses a three-dimensional "income triade" framework to visualize social mixing among high, medium, and low-income groups.
This method reveals not only the extent of segregation but also its specific composition across the urban landscape.
Exploring how social mixing patterns change across neighborhoods and time in one of Italy's most vibrant cities through high-resolution location data
Learn MoreTraditional approaches to urban income segregation focus on static residential patterns, missing the complex rhythms of how different socioeconomic groups interact throughout the day. Our research breaks new ground by analyzing high-resolution location data from 94,000 mobile phone users in Milan over a ten-month period.
Key innovation: The three income groups define a novel three-dimensional space embedded in the temporal dynamics of urban activities, which we propose as a framework to analyse social mixing.
By tracking mobility patterns of high, medium, and low-income residents, we've discovered that Milan's social landscape transforms dramatically as people move through the city for work and leisure activities.
This research offers crucial insights for urban planners and policymakers seeking to create more inclusive cities through evidence-based design interventions that can reduce socioeconomic divides.
We analyze high-resolution mobility data from 100,000 users in Milan to capture the real-time dynamics of income mixing across the city.
Ten months of anonymized data tracking home, work, and third-place activities.
Rental prices near residences provide estimates of income levels.
300m hexagons categorized by Accessibility, Liveability, and Attractivity metrics.
During nighttime, we observe high levels of segregation as people remain at home. This reveals the underlying residential divisions in Milan:
The histograms below each triangle show segregation levels measured by the Gini coefficient, with values closer to 1 indicating higher segregation.
This residential pattern forms the baseline from which daily mobility will temporarily alter the segregation landscape during other time periods.
During work hours, segregation decreases dramatically as people commute to different parts of the city. Central areas become hubs for cross-income interactions:
Work environments serve as temporary bridges connecting different socioeconomic groups.
Our research reveals that high-ALA neighborhoods exhibit 'dual functionality' - they become highly inclusive during working hours but often revert to higher levels of segregation during leisure periods. This pattern underscores how urban spaces can serve different social roles throughout the day.
During evening leisure hours, voluntary mixing patterns emerge. We observe distinct patterns across neighborhood types:
This phase highlights how free-time choices and urban amenities influence social integration.
Notably, medium-ALA neighborhoods with greater price diversity function as important 'transition zones' that maintain higher inclusivity during evening hours than might be expected from their infrastructure quality alone. This challenges conventional urban planning assumptions that focus primarily on high-end central development.
Note: The interactive map focuses specifically on third places (leisure and social activity locations), allowing you to explore how income mixing changes during stops made not at home or work.
This interactive map visualizes income segregation patterns in Milan, Italy. The hexagons represent neighborhoods classified by their Accessibility, Liveability, and Attractivity (ALA) metrics.
When you click on a hexagon, you'll see a triangular plot called the "income triade." This visualization shows:
Click on any hexagon to explore detailed income trajectory data for weekdays and weekends. The colors indicate the quality of neighborhoods:
This study analyzes how income segregation patterns vary throughout the day in Milan. We use Location-Based Services data from approximately 100,000 users and examine the interplay between neighborhood characteristics and social mixing.
This research uses high-resolution location data from mobile phones to reveal how social mixing patterns change throughout the day across Milan's diverse neighborhoods. By examining the spatial and temporal dynamics of income segregation, we provide insights into how urban design and neighborhood characteristics influence socioeconomic integration.
