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zenodo48/100

Supplementary data for analysing distributed temperature sensing (DTS) measurements from Helsinki, Finland

Supplementary data used in the analysis of distributed temperature sensing (DTS) measurements from Helsinki, Finland, as described in a journal article manuscript  "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland". Eddy covariance, radiation and precipitation data is provided from the SMEAR III station by the Institute for Atmospheric and Earth System Research at the University of Helsinki under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). The data can also be accessed programmatically via https://smear.avaa.csc.fi/. All SMEAR III data is time referenced to UTC+2. The 2-metre temperature data is provided by the Finnish Meteorological Institute under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). All Finnish Meteorological Institute data is referenced to UTC.

opencc-by-4.0Dec 2021View details →

zenodo48/100

Helsinki Region Travel Time Matrix 2018-2023

Introduction This travel time matrix records travel times and travel distances for routes between all centroids (N = 13132) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by walking, cycling, public transportation, and private car. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), and assuming different physical abilities (such as walking and cycling speeds), see details below. The grid follows the geometric properties and enumeration of the versatile <a href="https://ckan.ymparisto.fi/dataset/ykr-ruutuaineisto">Yhdyskuntarakenteen seurantajärjestelmä (YKR) grid</a> used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region.   Data formats The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably. <ul> <li>Helsinki_Travel_Time_Matrix_2023.csv.zst: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the <a href="https://facebook.github.io/zstd/">Zstandard algorithm</a>, that many data science libraries, for instance, pandas, <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">support transparently, directly, and automatically</a>.</li> <li>Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip: an <a href="https://www.ogc.org/standard/geopackage/">OGC GeoPackage standard</a> file containing all data columns and the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., <a href="https://gdal.org/drivers/vector/gpkg.html">GDAL/OGR</a>, <a href="https://docs.qgis.org/">QGIS</a>, or <a href="https://geopandas.org/en/stable/docs/reference/api/geopandas.read_file.html">geopandas</a>, support natively.</li> <li>Helsinki_Travel_Matrix_2023_travel_times.csv.zip: a set of 13132 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv where 5787545 is replaced by the to_id by which the rows in the file are grouped. Use the from_id column to join with the geometries from one of the files below.</li> </ul> Geometry, only: <ul> <li>Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip: an <a href="https://www.ogc.org/standard/geopackage/">OGC GeoPackage standard</a> file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the from_id and to_id columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., <a href="https://gdal.org/drivers/vector/gpkg.html">GDAL/OGR</a>, <a href="https://docs.qgis.org/">QGIS</a>, or <a href="https://geopandas.org/en/stable/docs/reference/api/geopandas.read_file.html">geopandas</a>, support natively.</li> <li>Helsinki_Travel_Time_Matrix_2023_grid.shp.zip: an <a href="https://gdal.org/drivers/vector/shapefile.html">ESRI Shapefile</a> archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the from_id and to_id columns of the data files.</li> </ul>   Table structure from_id: ID number of the origin grid cell  to_id: ID number of the destination grid cell  walk_avg: Travel time in minutes from origin to destination by walking at an average speed  walk_slo: Travel time in minutes from origin to destination by walking slowly  bike_avg: Travel time in minutes from origin to destination by cycling at an average speed bike_fst: Travel time in minutes from origin to destination by cycling fast bike_slo: Travel time in minutes from origin to destination by cycling slowly pt_r_avg: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at an average speed  pt_r_slo: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at a slower speed  pt_m_avg: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at an average speed  pt_m_slo: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at a slower speed  pt_n_avg: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at an average speed  pt_n_slo: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at a lower speed  car_r: Travel time in minutes from origin to destination by private car in rush hour traffic  car_m: Travel time in minutes from origin to destination by private car in midday traffic  car_n: Travel time in minutes from origin to destination by private car in nighttime traffic  walk_d: Distance from origin to destination, in meters, on foot   Data for 2013, 2015, and 2018 At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iterations' results. Not all input data sets are available to recompute the historical matrices with new methods, however, we were able to repeat the 2018 calculation using the same methods as the 2023 data set, please find the results below, in the same format. For the travel time matrices for 2013 and 2015, as well as for 2018 using an older methodology, please refer to <a href="https://doi.org/10.5281/zenodo.3247563">DOI:10.5281/zenodo.3247563</a>.   Methodology Computations were carried out for Wednesday, 15 February, 2023, and Monday, 29 January, 2018, respectively. 'Rush hour' refers to an 1-hour window between 8 and 9 am, 'midday' to 12 noon to 1 pm, and 'nighttime' to 2-3 am. All routes have been calculated using <a href="https://github.com/r5py/r5py">r5py</a>, a Python library making use of the <a href="https://github.com/conveyal/r5">R5</a> engine by <a href="https://conveyal.com">Conveyal</a>, with modifications to consider local characteristics of the Helsinki use case and to inform the computation models from local real-world data sets. In particular, we made the following modifications: Walking Walking speeds, and in turn walking times, are based on the findings of <a href="https://doi.org/10.1016/j.jtrangeo.2022.103521">Willberg et al., 2023,</a> in which we measured walking speeds of people of different age groups in varying road surface conditions in Helsinki. Specifically, we chose to use the average measured walking speed in summer conditions for walk_avg (as well as the respective pt_*_walk_avg), and the slowest quintile of all measured walker across all conditions for walk_slo (and the respective pt_*_walk_slo). Cycling Cycling speeds are derived from two input data sets. First, we averaged cycling speeds per network segment from <a href="https://metro.strava.com/">Strava data</a>, and computed a ratio between the speed ridden in each segment and the overall average speed. We then use these ratios to compute fast, slow, and average cycling speeds for each segment, based on the mean overall Strava speed, the mean speeds cycled in the <a href="https://www.hsl.fi/kaupunkipyorat">Helsinki City Bike bike-share system</a>, and the mean between the two. Further, in line with the values observed by <a href="https://blogs.helsinki.fi/saavutettavuus/files/2013/01/Py%C3%B6r%C3%A4ily_osana_joukkoliikennett%C3%A4_kaupunkipy%C3%B6rien_vaikutus_alueiden_saavutettavuuteen_p%C3%A4%C3%A4kaupunkiseudulla.pdf">Jäppinen (2012)</a>, we add a flat 30 seconds each for unlocking and locking the bicycle at the origin and destination. Public Transport We used public transport schedules in General Transit Feed Specification (GTFS) format published by the <a href="https://www.hsl.fi/hsl/avoin-data">Helsinki Regional Transport Authority</a>, and adjusted the walking speeds (for connections between vehicles, as well as for access and egress to and from public transport stops) using the same methods as described above for walking. Private motorcar To represent road speeds actually driven in the Helsinki metropolitan region, we used floating car data of a representative sample of the roads in the region to derive the differences between the speed limit and the driven speed on different road classes, and by speed limit, see <a href="https://hdl.handle.net/10138/358181">Perola (2023)</a> for a detailed description of the methodology. Because these per-segment speeds factor in potential waiting times at road crossings, we eliminated turn penalties from R5.   Our modifications were carried out in two ways: some changes can be controlled by preparing input data sets in a certain way, or by setting model parameters outside of R5 or r5py. Other modifications required more profound changes to the source code of the R5 engine. You can find a fully patched fork of the R5 engine in the Digital Geography Lab's GitHub repositories at <a href="https://github.com/DigitalGeographyLab/r5">github.com/DigitalGeographyLab/r5</a>. The code that handles input data mangling and model parameter estimations is kept together with the logic to read input parameters and to collate output data, in the repository at <a href="https://github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices/">github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices</a>.

opencc-by-4.0Sep 2023View details →

zenodo48/100

Data on public understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki

A public participatory GIS -survey dataset detailing public understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki, Finland.

opencc-by-4.0Feb 2023View details →

zenodo44/100

Helsinki Deblur Challenge 2021 test dataset

This dataset was primarily designed and captured to be used for the testing part of the Helsinki Deblur Challenge 2021 but it can be used for any testing and benchmarking purposes of image deblurring algorithms. The dataset contains photographs of random strings of text (including numbers), natural images and QR codes with varying levels of blur caused by misfocusing the camera. Each photo has both a blurred and sharp version. The images are split into 4 separate zip files each having 5 steps of different blur. Altogether there are 20 steps. Each one of the zip files contains two folders, one folder named CAM1_focused (Camera 1) with the sharp images and one named CAM2_blurred (Camera 2) with the blurred images. For each step there are 40 images of text character targets, 15 natural image targets, one QR-code target and 3 images of technical targets. Each one of the text target images is accompanied by a text file (same file name but .txt extension) containing the correct transcription of that particular text target. As a difference to the HDC training data (<a href="https://doi.org/10.5281/zenodo.4916176">https://doi.org/10.5281/zenodo.4916176</a>) the test data character targets include also numbers.   A detailed description of the training dataset that was acquired in the exact same way as this test dataset can be found here: <a href="http://arxiv.org/abs/2105.10233">http://arxiv.org/abs/2105.10233</a> Here is a link to the official webpage of the Helsinki Deblur Challenge 2021: <a href="https://www.fips.fi/HDC2021.php">https://www.fips.fi/HDC2021.php</a>

opencc-by-4.0Nov 2021View details →

zenodo44/100

Herbarium specimen image of Verrucaria conformis Nyl. f. rhypontoides Nyl., part of the collection of Finnish Museum of Natural History LUOMUS, University of Helsinki

Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published. Content of this deposition: - A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values. - A JPEG image file of the scanned herbarium sheet. - A lossless TIFF image from which the JPEG image has been derived.