280 research outputs found
A 24-hour dynamic population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland
No full textRelated article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.
In this dataset:
We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.
Please cite this dataset as:
Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4
Organization of data
The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:
HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.
HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.
HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.
target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.
Column names
YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.
H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59.
The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)
In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.
License
Creative Commons Attribution 4.0 International.
Related datasets
Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612
Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.324756
Paikkatietojärjestelmät opetuksessa : kirja-arvostelu
No full textGlobus Gis -paikkatietojärjestelmä, Markku Löytönen, Tuuli Toivonen & Ilta-Kanerva Kankaanrinta, Porvoo (2003
On the journey of transforming transport systems for human scale cities
No full textThis chapter concludes the book Transport in Human Scale Cities. The chapter includes four parts, with the first three parts including highlights and reflections on findings from each of the sections of the book. The first part focuses on human scale mobility in urban transport systems. The second part focuses on lessons for responsible innovation practices for human scale cities. The third part focuses on potentials for developing planning processes for human scale cities. The last part of this chapter includes reflections on the diverse aspects of systemic transformation. First, we argue for changes in actants responsible for envisioning futures, their actions, as well as emotions and virtues associated with those actions. Second, we argue for development of governance networks for further knowledge co-creation, organizational activities over time, and rethinking of our collective agenda-setting desires and visions. Third, we argue for transformation of our anchoring conceptualizations, centred around the concept of human scale city. © Miloš N. Mladenović, Tuuli Toivonen, Elias Willberg and Karst T. Geurs 2021.Peer reviewe
Setting the stage for transport in human scale cities
No full textThis chapter sets the stage for the book Transport in Human Scale Cities. First, we highlight the multitude of current challenges facing urban transport systems, spanning from questions of climate crisis to distributive injustices. Second, we argue that if we are to have a systemic transition out of unsustainable lifestyles, we need to question our understanding of human on the move in the city. Here, the central point of paradigm change is a move away from narrowly defined homo economicus imaginary. As the third point, we argue for the move away from a simplistically rationalist image of our future-making actant networks and processes shaping cities and their transport systems. Thus, we need to recognize the dynamically complex nature of both people in cities and in organizations. For furthering our understanding, we underline the potential that the concept of human scale cities has for unpacking the multidimensional challenge of sustainability transformation. Rescaling our focus towards human scale in cities can be a turning point for us collectively making cities and transport systems better for all life on this planet, now and in the far future. © Miloš N. Mladenović, Tuuli Toivonen, Elias Willberg and Karst T. Geurs 2021.Peer reviewe
Challenge Based Innovation gala
No full text
Challenge Based Innovation gala
There’s a new experiment starting in CERN called IdeaLab where we work together with detector R&D researchers to help them to bridge their knowledge into a more human, societally oriented context. Currently we are located in B153, but will move our activities to a new facility next to the Globe in May 2014.
One of our first pilot projects is a 5 month course CBI (Challenge Based Innovation) where two multidisciplinary student teams join forces with Edusafe & TALENT projects at CERN. Their goal is to discover what kind of tools for learning could be created in collaboration with the two groups. After months of user interviews and low resolution prototyping they are ready to share the results with us in the form of an afternoon gala.
We warmly welcome you to join us to see the students' results and experience the prototypes they have conceived.
The event is in three parts, you are welcome to visit all of them, or just the one(s) that your personal schedule allows. For the remote participants, the presentations (part 1) wil be available through a CERN webcast (webcast.cern.ch) 14.30 - 16.45 (GMT+1).
Part I 14.30 Project presentations at 222 Filtration plant
Part II 17:00 Prototype demonstrations at B153
Part III 19:00 The afterparty at B153
For more information
Challenge Based Innovation course blog
CBI introduction video
CBI contact Tuuli Utriainen ([email protected]) or Lauri Repokari ([email protected])
IdeaLab contact Harri Toivonen ([email protected])
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DigitalGeographyLab/some-parkbaskets: Exploring human-nature interactions in national parks using social media photographs and computer vision
No full textThis release contains the scripts used in a forthcoming article Väisänen, T., V. Heikinheimo, T. Hiippala & T. Toivonen (2020) Exploring human-nature interactions in national parks using social media photographs and computer vision accepted in Conservation Biology to be published in a special issue on conservation culturomics
Multi-temporal function-based dasymetric interpolation tool for mobile phone data
No full text<p>This release provides a Python implementation of "Multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data" that is represented in an article:</p>
<ul>
<li>Järv, Tenkanen & Toivonen (2017). <em>Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation.</em> Submitted to <strong>International Journal of Geographical Information Science</strong>".</li>
</ul>
<p>This release demonstrates in practice how MFD interpolation method can be used to interpolate mobile phone data distributed in spatially varying coverage areas of mobile phone base stations into a desired layout of predefined spatial units using ancillary data sources.</p>
Experiences in sharing research data and methods in conservation science
No full textOpenness, transparency, and reproducibility are hallmarks of scientific methods as they enable the peer-evaluation of the quality and accuracy of research. In practice, however, most research carried out still today cannot be reproduced or replicated by others and thus evaluated in detail. This is due to limitations in access to original data, vague or insufficient method descriptions or simply difficulties in accessing the publications that describe the research work. In fields like conservation, where real-life decisions may be based on scientific work, this is a challenge also for transparent decision-making.
During the past years, the Open Science movement has gained popularity among scientists, research, national science policies and everyday practice of many journals. Despite the advancements, a lot of work is still needed to make scientific publications, research data and methods openly available for others to evaluate and develop further. The scientific merit system is gradually changing to support openness, and technological advancements are making it easier in practice. Ultimately, however, it is still up to individual researchers or research groups to decide if and how to share and open the outputs of their research. Hence, personal level experiences are important determinants of the adoption of open science practices.
In our Digital Geography Lab, we have attempted to follow the practices of open science for the past years. We have aimed to use open data sources whenever possible as the source of our research, publish our methods online, share the output data and apply storytelling to make our research more approachable and accessible.
In my short presentation, I will share some of the practical experiences we have gained while doing so: What has worked, has it been worth the effort and where we have failed and why.peerReviewedunknown accessibilityei tietoa saavutettavuudest
Escaping from Cities during the COVID-19 Crisis : Using Mobile Phone Data to Trace Mobility in Finland
No full textThe 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
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