Browsing by Subject "human mobility"

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  • Willberg, Elias; Järv, Olle; Väisänen, Tuomas Lauri Aleksanteri; Toivonen, Tuuli (2021)
    The coronavirus disease 2019 (COVID-19) crisis resulted in unprecedented changes in the spatial mobility of people across societies due to the restrictions imposed. This also resulted in unexpected mobility and population dynamics that created a challenge for crisis preparedness, including the mobility from cities during the crisis due to the underlying phenomenon of multi-local living. People changing their residences can spread the virus between regions and create situations in which health and emergency services are not prepared for the population increase. Here, our focus is on urban–rural mobility and the influence of multi-local living on population dynamics in Finland during the COVID-19 crisis in 2020. Results, based on three mobile phone datasets, showed a significant drop in inter-municipal mobility and a shift in the presence of people—a population decline in urban centres and an increase in rural areas, which is strongly correlated to secondary housing. This study highlights the need to improve crisis preparedness by: (1) acknowledging the growing importance of multi-local living, and (2) improving the use of novel data sources for monitoring population dynamics and mobility. Mobile phone data products have enormous potential, but attention should be paid to the varying methodologies and their possible impact on analysis.
  • Faghihi, Farbod (Helsingfors universitet, 2017)
    The ubiquity of mobile devices with positioning sensors make it possible to derive user's location at any time. However, constantly sensing the position in order to track the user's movement is not feasible, either due to the unavailability of sensors, or computational and storage burdens. In this thesis, we present and evaluate a novel approach for efficiently tracking user's movement trajectories using decomposition and prediction of trajectories. We facilitate tracking by taking advantage of regularity within the movement trajectories. The evaluation of our approach is done using three large-scale spatio-temporal datasets, from three different cities: San Francisco, Porto, and Beijing. Two of these datasets contain only cab traces and one contains all modes of transportation. Therefore, our approach is solely dependent on the inherent regularity within the trajectories regardless of the city or transportation mode.
  • Massinen, Samuli (Helsingin yliopisto, 2019)
    The Greater Region of Luxembourg is the largest cross-border labor market in the European Union with the greatest number of cross-border workers in the area. European integration, the Schengen Area, and socio-economical divergences have been the main factors facilitating human cross-border movements in the area and thus the birth and expansion of the borderland community. Despite the freedom of movement, country borders have not been erased and socio-economic divergences have not been levelled. In addition, the spatial extent of the daily movements is not well known. Thus, it is important to study cross-border dynamics and try to separate daily movements from infrequent mobility patterns. Thus far, cross-border mobility studies have mainly leaned on national registers and census data. These datasets have mostly been too scarce in trying to understand the complexities of cross-border mobility. Many studies have only focused on aggregate-level movement patterns, and the viewpoint of individuals has been missing. Hence, there has been a growing need for individual-level data to be applied in cross-border mobility research. In this study, a person-based approach is employed using geotagged Twitter Big Data to study spatio-temporal cross-border mobility patterns in the Greater Region of Luxembourg. The aim is to examine how to implement social media in cross-border research as well as how to separate daily cross-border movers from infrequent border crossers and consequently move beyond aggregate-level inspections. Being one of the first studies of its kind, a heuristic programmatic approach is utilized. To the writer’s knowledge, social media data sources have not been applied previously to distinguish different cross-border mobility types. All developed scripts in this study are openly available on Digital Geography Lab’s GitHub -pages (https://github.com/DigitalGeographyLab/cross-border-mobilitytwitter) to promote open science and to introduce new quantitative method tools for cross-border mobility research. The results show that social media can be implemented in cross-border mobility research, and social media Big Data can provide a relatively good proxy for daily cross-border mobility of people on a regional level. Aggregate-level cross-border mobility patterns and activity location densities correspond closely with previous studies, and outcomes from temporal variation inspections indicate a valid cross-border mover type identification; Twitter users classified as daily cross-border movers seem to be more mobile on weekdays whereas infrequent border crossers on weekends. Daily cross-border mobility patterns also provided new information about the spatial extent of the movements. In addition, heuristic approach resulted in high accuracy in home detection; the “unique weeks” algorithm introduced in this study produced an accuracy of 88.6 % with respect to the ground truth. Although the results are promising on a regional level, they should be considered in relation to population densities and Twitter use activity; attributes that both vary spatio-temporally and thus can cause bias. Further studies and method development are also needed to draw global conclusions about cross-border mobility; other geographical areas and study settings could result in varied outcomes. In addition, some solutions with data and methods should be considered with a critical stance due to scarcity of valid references. Yet, this study has identified that the coverage of geotagged Twitter data is dependent on data acquisition processes and that Twitter can provide valuable information for cross-border mobility research. In future studies, multi-level data acquisition processes are recommended jointly with person-based approach combining spatio-temporal and content analysis methodologies.
  • Järv, Olle; Tominga, Ago; Müürisepp, Kerli; Silm, Siiri (2021)
    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.
  • Järv, Olle; Masso, Anu; Silm, Siiri; Ahas, Rein (2021)
    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.