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In this study, we set out to understand how the changes in daily mobility of people during the first wave of the COVID-19 pandemic in spring 2020 influenced daytime spatial segregation. Rather than focusing on spatial separation, we approached this task from the perspective of daytime socio-spatial diversity – the degree to which people from socially different neighbourhoods share urban space during the day. By applying mobile phone data from Greater Stockholm, Sweden, the study examines weekly changes in 1) daytime social diversity across different types of neighbourhoods, and 2) population groups' exposure to diversity in their main daytime activity locations. Our findings show a decline in daytime diversity in neighbourhoods when the pandemic broke out in mid-March 2020. The decrease in diversity was marked in urban centres, and significantly different in neighbourhoods with different socio-economic and ethnic compositions. Moreover, the decrease in people's exposure to diversity in their daytime activity locations was even more profound and long-lasting. In particular, isolation from diversity increased more among residents of high-income majority neighbourhoods than of low-income minority neighbourhoods. We conclude that while some COVID-19-induced changes might have been temporary, the increased flexibility in where people work and live might ultimately reinforce both residential and daytime segregation.

The extent to which ethnic segregation results from differences in socio-economic factors remains a seminal topic of debate. The growing literature demonstrating the multifaceted phenomenon of segregation urges more focus on individuals' spatial and social interactions. We applied an activity space approach and considered ethnic differences in individuals' activity spaces as an indicator of spatial segregation. We used mobile phone and survey datasets in Estonia. We show that place-based segregation indices derived from both datasets indicate similar levels of ethnic segregation. From an activity space perspective, the results show that the main socio-economic factor affecting the extensity of activity spaces is self-estimated social status rather than education and income. Results show that ethnic inequality in spatial behaviour is not straightforward, but rather that it is linked to how individuals position themselves in society. We argue that socio-economic factors need to be controlled to examine ethnic segregation from activity space perspective.

Globalization, urbanization and international mobility have led to increasingly diverse urban populations. Compared to traditional traits for measuring urban diversity, such as ethnicity and country of origin, the role of language remains underexplored in understanding diversity, interactions between different groups and socio-spatial segregation. In this article, we analyse language use in the Helsinki Metropolitan Area by combining individual-level register data, socio-economic grid database, mobile phone and social media data to understand spatio-temporal patterns of linguistic diversity better. We measured linguistic diversity using metrics developed in the fields of ecology and information theory, and performed spatial clustering and regression analyses to explore the spatio-temporal patterns of linguistic diversity. We found spatial and temporal differences between register and social media data, show that linguistic diversity is influenced by the physical and socio-economic environment, and identified areas where different linguistic groups are likely to interact. Our results provide insights for urban planning and understanding urban diversity through linguistic information. As global urbanization, international migration and refugee flows and climate change drive diverse populations into cities, understanding urban diversity and its implications for urban planning and sustainability become increasingly important.

The concept of accessibility – the potential of opportunities for interaction – binds together the key physical components of urban structure: people, transport and social activity locations. Most often these components are dynamic in nature and hence the accessibility landscape changes in space and time based on people's mobilities and the temporality of the transport network and activity locations (e.g. services). Person-based accessibility approaches have been successful in incorporating time and space in the analyses and models. Still, the more broadly applied location-based accessibility modelling approaches have, on the other hand, often been static/atemporal in their nature. Here, we present a conceptual framework of dynamic location-based accessibility modelling that captures the dynamic temporality of all three accessibility components. Furthermore, we empirically test the proposed framework using novel data sources and tools. We demonstrate the impact of temporal aspects in accessibility modelling with two examples: by investigating food accessibility and its spatial equity. Our case study demonstrates how the conventional static location-based accessibility models tend to overestimate the access of people to potential opportunities. The proposed framework is universally applicable beyond the urban context, from local to global scale and on different temporal scales and multimodal transport systems. It also bridges the gap between location-based accessibility and person-based accessibility research. The concept of accessibility- the potential of opportunities for interaction- binds together the key physical components of urban structure: people, transport and social activity locations. Most often these components are dynamic in nature and hence the accessibility landscape changes in space and time based on people's mobilities and the temporality of the transport network and activity locations (e.g. services). Person-based accessibility approaches have been successful in incorporating time and space in the analyses and models. Still, the more broadly applied location-based accessibility modelling approaches have, on the other hand, often been static/atemporal in their nature. Here, we present a conceptual framework of dynamic location-based accessibility modelling that captures the dynamic temporality of all three accessibility components. Furthermore, we empirically test the proposed framework using novel data sources and tools. We demonstrate the impact of temporal aspects in accessibility modelling with two examples: by investigating food accessibility and its spatial equity. Our case study demonstrates how the conventional static location-based accessibility models tend to overestimate the access of people to potential opportunities. The proposed framework is universally applicable beyond the urban context, from local to global scale and on different temporal scales and multimodal transport systems. It also bridges the gap between location-based accessibility and person-based accessibility research.

Novel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.