M. van Ham
Please Note
166 records found
1
Co-creation with carbon data
Reframing the designer’s role in the decarbonization of the built environment
Uneven Digital Visibility of Urban Places
Evidence From TikTok Hotspots
The Problem of Uncertain Contextual Characteristic (PUCC)
Does it matter how contextual poverty is measured for the neighbourhood effect estimation?
The Economic Urban Divide
A Detailed Study of Income Inequality and Segregation in Dutch Urban Areas (2011–2022)
Do you see it how I see it?
Differences in neighborhood perceptions explained by individuals’ socioeconomic characteristics and trust attitudes
When Ageing Meets Neighbourhood Demolition
Negotiating Time, Space, and Kinship in State-Led Urban Redevelopment in China
Regression Toward the Mean in Neighborhood Effects Research
A Geographic Perspective
Although urban form and thermal behavior are inherently interrelated, similar urban forms can exhibit different thermal responses depending on factors like vegetation cover, impervious surfaces, and building materials. To better represent real-world variability, separating morphological classifications from thermal characteristics allows for an analysis that accounts for these differences.
To address these challenges, we develope an approach that generates empirically derived urban morhophological types while maintaining connections to LCZ categories. Our tool systematically classifies urban morphological types for fine-grained, nationwide assessments, enabling consistent comparisons across diverse Dutch urban residential areas. This approach uses readily available geospatial data and applies unsupervised machine learning techniques to identify urban morphological typologies. By standardizing the classification process into 100 x 100 m grid cells from Statistics Netherlands, our method provides a consistent spatial and temporal framework that transcends changing administrative boundaries.
Our approach helps streamline vulnerability analysis by facilitating the intersection of multiple environmental and social dimensions. We demonstrate the tool's utility through an explorative analysis that identifies which socio-economic groups reside in neighborhoods with high heat exposure, considering both morphological types and additional factors influencing heat exposure. This tool provides urban planners and researchers with an empirically-grounded framework for identifying priority areas in existing settlements for scalable adaptation interventions across different urban contexts. ...
Although urban form and thermal behavior are inherently interrelated, similar urban forms can exhibit different thermal responses depending on factors like vegetation cover, impervious surfaces, and building materials. To better represent real-world variability, separating morphological classifications from thermal characteristics allows for an analysis that accounts for these differences.
To address these challenges, we develope an approach that generates empirically derived urban morhophological types while maintaining connections to LCZ categories. Our tool systematically classifies urban morphological types for fine-grained, nationwide assessments, enabling consistent comparisons across diverse Dutch urban residential areas. This approach uses readily available geospatial data and applies unsupervised machine learning techniques to identify urban morphological typologies. By standardizing the classification process into 100 x 100 m grid cells from Statistics Netherlands, our method provides a consistent spatial and temporal framework that transcends changing administrative boundaries.
Our approach helps streamline vulnerability analysis by facilitating the intersection of multiple environmental and social dimensions. We demonstrate the tool's utility through an explorative analysis that identifies which socio-economic groups reside in neighborhoods with high heat exposure, considering both morphological types and additional factors influencing heat exposure. This tool provides urban planners and researchers with an empirically-grounded framework for identifying priority areas in existing settlements for scalable adaptation interventions across different urban contexts.
The spatio-temporal evolution of social inequalities in cities
A multidimensional, multiscalar and longitudinal approach for neighbourhood classification
The conflicting geographies of social frontiers
Exploring the asymmetric impacts of social frontiers on household mobility in Rotterdam
The neighbourhood
Where Wilson, Schelling and Hägerstrand meet
Unravelling the Roles of Active Residents in a Politically Challenging Context
An Exploration in Cairo
Capital cities struggle with population growth that challenges existing infrastructure and affects the quality of urban life. The failure of local governments to manage urban deterioration motivates active resident groups to improve their neighborhoods, but they struggle to play a role in neighborhood governance in contexts where citizens’ engagement in public affairs is restricted. In this article we aim to understand active residents’ roles in the neighborhood governance process and how these roles unfold in a context that challenges citizen engagement in public life. We adopted a case study methodology and interviewed active residents and local officials from selected districts in Cairo, which revealed that active residents’ influence is limited mostly to neighborhood management and implementation activities. In this limited space, the role of active residents is confined to either that of the ‘fixer’ who restores existing services, or that of the struggling and intermittent ‘self-provider’, neither of whom can influence policy formulation. This study provides a structured and zoomed-out view of local activism in Cairo, offering a starting point for scholars and decision makers seeking to enhance active residents’ roles in Cairo.
1. What are the current levels of residential socio-economic segregation in European cities? Do levels of segregation continue to increase?
2. How have the patterns of segregation changed over the past 20 years, and are these patterns and their trends of change similar between European cities?
3. What are the key factors contributing to the observed levels and spatial changes of residential segregation in European cities?
The paper includes the following case studies associated with the author teams with in-depth local knowledge and access to data: Amsterdam, Barcelona, Bratislava, Dublin, Helsinki, Lisbon, London, Oslo, Paris, Prague, Riga, Rome, Stockholm, Tallinn, Vilnius and Warsaw. To ensure comparability, researchers adopted a consistent definition of functional urban areas and used small spatial units to analyse segregation levels and spatial patterns, following a pre-established and unified methodology. The empirical analysis draws on census or register-based data, from approximately 2001, 2011, and 2021. Socio-economic groups are distinguished based on occupational status and classified into Top, Middle, and Bottom categories. The study is currently in advanced progress. ...
1. What are the current levels of residential socio-economic segregation in European cities? Do levels of segregation continue to increase?
2. How have the patterns of segregation changed over the past 20 years, and are these patterns and their trends of change similar between European cities?
3. What are the key factors contributing to the observed levels and spatial changes of residential segregation in European cities?
The paper includes the following case studies associated with the author teams with in-depth local knowledge and access to data: Amsterdam, Barcelona, Bratislava, Dublin, Helsinki, Lisbon, London, Oslo, Paris, Prague, Riga, Rome, Stockholm, Tallinn, Vilnius and Warsaw. To ensure comparability, researchers adopted a consistent definition of functional urban areas and used small spatial units to analyse segregation levels and spatial patterns, following a pre-established and unified methodology. The empirical analysis draws on census or register-based data, from approximately 2001, 2011, and 2021. Socio-economic groups are distinguished based on occupational status and classified into Top, Middle, and Bottom categories. The study is currently in advanced progress.