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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.

Twitter is a popular social media platform for scholarly research, because the user-generated content on the platform can also include geographic and temporal information. We collect a corpus of 38 million Twitter messages with two million geographical coordinates to map the languages used across Finland at the level of regions and municipalities. To cope with the high volume of social media data, we use automatic language identification and place of residence detection. We estimate the linguistic richness and diversity of users and locations using measures developed within ecology and information sciences. The analyses reveal a rich, multilingual environment that varies geographically and temporally, particularly between coastal, rural and urban areas. The results, which underline the mutual benefits of collaboration between linguists and geographers, provide a more fine-grained, accurate and comprehensive view of the languages used on Twitter in Finland than previously available.

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.

Improved understanding of human-nature interactions is crucial to conservation science and practice, but collecting relevant data remains challenging. Recently, social media have become an increasingly important source of information on human-nature interactions. However, the use of advanced methods for analysing social media is still limited, and social media data are not used to their full potential. In this article, we present available sources of social media data and approaches to mining and analysing these data for conservation science. Specifically, we (i) describe what kind of relevant information can be retrieved from social media platforms, (ii) provide a detailed overview of advanced methods for spatio-temporal, content and network analyses, (iii) exemplify the potential of these approaches for real-world conservation challenges, and (iv) discuss the limitations of social media data analysis in conservation science. Combined with other data sources and carefully considering the biases and ethical issues, social media data can provide a complementary and cost-efficient information source for addressing the grand challenges of biodiversity conservation in the Anthropocene epoch.

Global crises such as the COVID-19 pandemic affect both the functioning of our societies and the daily lives of people. Yet the impact of the crisis and its mitigation measures have exerted disproportionate influence on different population groups. In March – May 2020, COVID-19 mitigation measures such as closures of national borders affected transnational people who cross borders frequently for work, shopping, services, family reasons and socialising. We have examined the influence of the COVID-19 pandemic on the daily lives of transnational Estonians residing in Finland, based on a unique longitudinal smartphone tracking survey. Findings show that besides a drastic but expected decrease in trans-nationals’ spatial mobility, the pandemic has especially affected their cross-border mobility patterns to and time spent in Estonia. Interestingly, during the lockdown, some transnationals decided to stay not in their primary home in Finland, but in Estonia. Mobile phone communication activity followed moderately the downward trend of spatial mobility, but the crisis changed the division of communication partners by country: Finnish contacts diminished, whereas Estonian partners remained active. We reflect on our findings for future research and discuss the applicability of the smart-phone tracking approach for capturing the socio-spatial interactions of transnational people.

Parks and other green spaces are an important part of sustainable, healthy and socially equal urban environment. Urban planning and green space management benefit from information about green space use and values, but such data are often scarce and laborious to collect. Temporally dynamic geographic information generated by different mobile devices and social media platforms are a promising source of data for studying green spaces. User-generated data have, however, platform specific characteristics that limit their potential use. In this article, we compare the ability of different user-generated data sets to provide information on where, when and how people use and value urban green spaces. We compare four types of data: social media, sports tracking, mobile phone operator and public participation geographic information systems (PPGIS) data in a case study from Helsinki, Finland. Our results show that user-generated geographic information sources provide useful insights about being in, moving through and perceiving urban green spaces, as long as evident limitations and sample biases are acknowledged. Social media data highlight patterns of leisure time activities and allow further content analysis. Sports tracking data and mobile phone data capture green space use at different times of the day, including commuting through the parks. PPGIS studies allow asking specific questions from active participants, but might be limited in spatial and temporal extent. Combining information from multiple user-generated data sets complements traditional data sources and provides a more comprehensive understanding of green space use and preferences.

The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises.

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.

Given ongoing developments altering social and spatial cohesion in urban societies, a more comprehensive understanding of segregation is needed. Taking the ‘mobilities turn’ at heart, we move beyond place-based segregation approaches and focus on the practised urban experiences of individuals through a more comprehensive assessment of their activity spaces. This study contributes to people-based segregation research by mapping the activity spaces of individuals on the basis of mobile phone data in Tallinn (Estonia) and relating these activity spaces to (mainly) the users’ ethnic background (i.e. Estonian versus Russian). Significant ethnic differences in terms of (1) the number of activity locations, (2) the geographical distribution of these locations, and (3) the overall spatial extent of activity spaces are found. We also find that these differences tend to deepen as the temporal framework is extended. We discuss the main implications for segregation research and highlight some avenues for further research.

Human activity-travel behaviour (ATB) is a complex pattern of paths and activities in space and time. Studies indicate that ATB is the construction of daily habitual, weekly, monthly and seasonal routines together with strong variety seeking behaviour. Daily habitual travel patterns are usually taken as a basis, but for transportation planners more knowledge is needed on longitudinal trends in human ATB. Empirical data on prolonged perspective are hard to come by while mobile phone based call detail records could be one means of narrowing this research gap. By implementing this method, the present study attempts to provide new insights on individual monthly spatial travel behaviour. Using call detail records obtained from a set of anonymous mobile phone users, we examined their activity locations and activity spaces for 12 consecutive months. We found modest monthly variation in the number of activity locations, whereas there were great variations in the sizes of individual activity spaces. The monthly variation in individual spatial behaviour is explained up to 17% by seasonality, although the variance is predominantly attributed to individual factors and results indicate significant intrapersonal monthly variability. Findings suggest new avenues for future work on ATB from a longitudinal perspective.

Background In this paper, we demonstrate why and how both temporality and multimodality should be integrated in health related studies that include accessibility perspective, in this case healthy food accessibility. We provide evidence regarding the importance of using multimodal spatio-temporal accessibility measures when conducting research in urban contexts and propose a methodological approach for integrating different travel modes and temporality to spatial accessibility analyses. We use the Helsinki metropolitan area (Finland) as our case study region to demonstrate the effects of temporality and modality on the results. Methods Spatial analyses were carried out on 250 m statistical grid squares. We measured travel times between the home location of inhabitants and open grocery stores providing healthy food at 5 p.m., 10 p.m., and 1 a.m. using public transportation and private cars. We applied the so-called door-to-door approach for the travel time measurements to obtain more realistic and comparable results between travel modes. The analyses are based on open access data and publicly available open-source tools, thus similar analyses can be conducted in urban regions worldwide. Results Our results show that both time and mode of transport have a prominent impact on the outcome of the analyses; thus, understanding the realities of accessibility in a city may be very different according to the setting of the analysis used. In terms of travel time, there is clear variation in the results at different times of the day. In terms of travel mode, our results show that when analyzed in a comparable manner, public transport can be an even faster mode than a private car to access healthy food, especially in central areas of the city where the service network is dense and public transportation system is effective. Conclusions This study demonstrates that time and transport modes are essential components when modeling health-related accessibility in urban environments. Neglecting them from spatial analyses may lead to overly simplified or even erroneous images of the realities of accessibility. Hence, there is a risk that health related planning and decisions based on simplistic accessibility measures might cause unwanted outcomes in terms of inequality among different groups of people.

The article introduces a model for the location of meaningful places for mobile telephone users, such as home and work anchor points, using passive mobile positioning data. Passive mobile positioning data is secondary data concerning the location of call activities or handovers in network cells that is automatically stored in the memory of service providers. This data source offers good potential for the monitoring of the geography and mobility of the population, since mobile phones are widespread, and similar standardized data can be used around the globe. We developed the model and tested it with 12 months' data collected by EMT, Estonia's largest mobile service provider, covering more than 0.5 million anonymous respondents. Modeling results were compared with population register data; this revealed that the developed model described the geography of the population relatively well, and can hence be used in geographical and urban studies. This approach also has potential for the development of location-b...

The article introduces a model that uses passive mobile positioning data to determine respondents’ home and work anchor point locations. Passive mobile positioning data is secondary data concerning the location of call activities or hand-overs in network cells, which is automatically stored in the log files of service providers. This data source offers good potential for monitoring of the short-term mobility of populations, since mobile phones are widespread, and similar standardised data can be used around the globe. We developed the model and tested it with 12 months’ data collected by Estonia’s largest mobile service provider EMT, covering more than 0.5 million anonymous respondents. Modelling results were compared with population register data; this revealed that the developed model described the geography of the population relatively well, and can hence be used as a quantitative source in the study of population geography or for developing location-based services.

This paper introduces the mobile phone positioning-based social positioning method (SPM) in studying activity spaces of commuters and residents of the city center of Tallinn. The SPM studies space-time behavior by analyzing the location coordinates of mobile phones and the social characteristics of the people carrying them. Our study experiments took place in the Tallinn and Tartu region in Estonia from 2003–2006. The results demonstrate that mobile positioning-based tracing is applicable in geographical studies, as an analysis of temporal movement patterns and activity spaces. This is good reference for analyzing travel diaries, especially for analyzing daily activity spaces and space consumption which is a key issue in sustainable planning of the sprawling cities. The biggest advantage of mobile positioning-based methods is that mobile phones are wide-spread, positioning works inside buildings, and the collection of movement data is done by a third party at regular intervals. The disadvantage of mobile positioning today is relatively low preciseness and surveillance fears. The boom in the generation of phones with A-GPS will raise positioning accuracy in the near future.

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