Introduction

This travel time matrix records travel times and travel distances for routes between all centroids (N = 13231) 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 Yhdyskuntarakenteen seurantajärjestelmä (YKR) grid 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.

• `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 Zstandard algorithm, that many data science libraries, for instance, pandas, support transparently, directly, and automatically.
• `Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip`: an OGC GeoPackage standard 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., GDAL/OGR, QGIS, or geopandas, support natively.
• `Helsinki_Travel_Matrix_2023_travel_times.csv.zip`: a set of 13231 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.

Geometry, only:

• `Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip`: an OGC GeoPackage standard 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., GDAL/OGR, QGIS, or geopandas, support natively.
• `Helsinki_Travel_Time_Matrix_2023_grid.shp.zip`: an ESRI Shapefile 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.

Table structure

from_id

to_id

walk_avg

walk_slo

bike_avg

bike_fst

bike_slo

pt_r_avg

pt_r_slo

pt_m_avg

pt_m_slo

pt_n_avg

pt_n_slo

car_r

car_m

car_n

walk_d

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 DOI:10.5281/zenodo.3247563.

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 r5py, a Python library making use of the R⁵ engine by Conveyal, 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 Willberg et al., 2023, 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 Strava data, 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 Helsinki City Bike bike-share system, and the mean between the two.

Further, in line with the values observed by Jäppinen (2012), 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

Extreme value analysis of CCHaPS, the Coupled Coastal Hazard Prediction System, which is a 2D hydrodynamic and wave model (Semi-implicit Cross-scale Hydroscience Integrated System Model and Wind Wave Model III, SCHISM-WWMIII), configured around the Australian coastline including the Great Barrier Reef, and extending out to deep offshore waters or neighbouring landmasses (https://data.csiro.au/collection/csiro:65669).

The model simulates water levels due to astronomical tides, weather, waves and aspects of wave-flow interaction over multiple decades, allowing for full consideration of the dynamics of extreme sea levels and waves in the Australian region, at high spatial resolution from ~7 km offshore down to ~250 m at the coast, and ~100 m in major river mouths. The model is run on an unstructured (triangular) grid comprising over 1.4 million nodes, which extends overland up to a 12 m elevation contour, to enable modelling of inundation events and sea level rise.

The CCHaPS hindcast covers the 40-year period 1981 to 2020.

Extreme value analysis has been performed on 1) annual maximum{yr_max} significant wave heights {hs}, and 2) detrended annual maximum {yr_max-detrended} water levels {zos}, with the linear trend in mean water levels removed.

Three types of extreme value distributions (EVDs) are fitted to the annual maxima: - two-parameter Gumbel - three-parameter Generalised Extreme Value (GEV) - four-parameter mixed Gumbel distribution (https://doi.org/10.1038/s41598-022-08382-y)

Datasets are provided for 1) the commonly used GEV {fgev}, and 2) the best of the three EVD {bestEVD} types (with positive shape parameters), selected using the Akaike Information Criterion (AIC). Lower values of AIC indicate better models, in the sense that the model fit is better relative to the number of model parameters. Note: GEV fits with a negative shape parameter (i.e. bounded upper tail) can yield lower estimated extremes and are therefore not always preferred (e.g. Haigh et al., 2014; https://doi.org/10.1007/s00382-012-1652-1).

The dataset comprises 1, 2, 5, 10, 20, and 63% Annual Exceedance Probabilities (AEPs) with {upper} and {lower} 95% confidence intervals, stored as netCDF data on an unstructured grid, compliant with CF, ACDD and UGRID metadata conventions. Additional processing is also applied to the confidence intervals. A notebook demonstrating how to interact with the data will be attached under Supporting Documentation, and also available via GitHub (see Related Links, LINK TBA).

The netCDF files can be viewed in QGIS by importing them as a mesh through the Data Source Manager. (https://docs.qgis.org/3.40/en/docs/user_manual/working_with_mesh/mesh.html?utm_source=chatgpt.com)

The data is also stored on CSIRO infrastructure in the {ev-acs-wp3-cchaps} volume, and at NCI in /g/data/ia39/WP3/release/CCHaPS.

A manuscript describing this dataset is in preparation and will be linked to this metadata record in due course. Lineage: Extreme value analysis of hourly CCHaPS data (https://data.csiro.au/collection/csiro:65669) was performed using the R CRAN package loopevd (https://doi.org/10.32614/CRAN.package.loopevd), June 2025. File converted to metadata standards compliant netCDF via Python notebook, June 2025. Final dataset copied from NCI to CSIRO Bowen storage in preparation for publication via CSIRO DAP.

This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

■ 상품 설명 및 특징 - 공간 범위 : 전국 (2019년 기준 데이터 제공) - 파일형식 : ALL.shp(shp, shx, dbf, prj) - 제공파일형식 : zip파일 - 파일타입 : point - 좌표계 : 단일평면직각좌표계 UTM-K (100000, 200000), EPSG:5179 * 사방댐은 토사석력의 이동이 현저한 황폐한 계천의 종침식 및 횡침식을 방지하여 계상물매의 완화, 유출 토석류의 저류 및 조절, 계상을 높여 산각을 고정하고 난류구역에서의 유로 정리 등을 목적으로 함 * 산사태나 땅밀림 등으로 인한 토석류 재해를 저지하여 하류지역을 보전하기 위하여 황폐한 계천을 횡단하여 구축하는 사방공작물 ■ 컬럼 정보 - 사방댐관리번호 : 사방댐의 관리번호 - 사방댐국가지점번호 : 사방댐이 위치한 국가지점번호(예:라바51179677) - 산사태관리기관코드 : 산사태 관리기관코드 - 사방댐지역코드 : 법정동 코드 - 사방댐관리상세주소 : 지번 및 상세주소 - 사방댐위치위도값 : 사방댐 위도값(Y) - 사방댐위치경도값 : 사방댐 경도값(X) ※ 해당파일(SHP)을 확인하시려면 QGIS프로그램을 설치하셔야 확인가능하며. 아래의 링크로 들어가 QGIS 무료오픈소스 프로그램을 다운받으시면 됩니다. https://www.qgis.org/ko/site/ → 무료 QGIS프로그램 다운로드 https://docs.qgis.org/3.10/ko/docs/user_manual/ → QGIS 사용자 지침서 확인 원본 데이터는 로그인 후 구매하여 다운로드 하십시오.

■ 상품 설명 및 특징 - 공간 범위 : 시군구별 제공 (2020년 기준 데이터 제공) - 파일형식 : 시군구코드.shp(shp, shx, dbf, prj) - 제공파일형식 : zip파일 - 파일타입 : point - 좌표계 : 단일평면직각좌표계 UTM-K (100000, 200000), EPSG:5179 * 전국 산의 위치정보 및 등산로 시설물 위치정보 * 산림의 이용촉진과 이용자의 안정을 도모하기 위하여 필요한 등산로를 조성하고 이를 보전 · 관리한다. 등산로의 조성 · 보전 및 관리를 효율적으로 추진하기 위하여 10년마다 관할 산림의 등산로에 대한 실태를 조사하고, 관리계획을 수립하여 등산로를 관리 * 등산로 주요지점에 대한 조회는 산림빅데이터플랫폼 시각화포털을 통해 확인가능 ■ 컬럼정보 - 안전지점구분명1 : 안전지점구분내용 (예 : 분기점, 안내판, 음수대 등) - 관리지점구분명2 : 관리지점구분내용 (예 : 체육시설, 약수터 등) - 기타사항 : 기타 참고사항 - 산명 : 등산로 산명 ■ 샘플데이터 해당파일(SHP)을 확인하시려면 QGIS프로그램을 설치하셔야 확인가능하며. 아래의 링크로 들어가 QGIS 무료오픈소스 프로그램을 다운받으시면 됩니다. https://www.qgis.org/ko/site/ → 무료 QGIS프로그램 다운로드 https://docs.qgis.org/3.10/ko/docs/user_manual/ → QGIS 사용자 지침서 확인 원본 데이터는 로그인 후 구매하여 다운로드 하십시오.

■ 상품 설명 및 특징 - 공간 범위 : 전국 (2020년 기준 데이터 제공) - 파일형식 : ALL.shp(shp, shx, dbf, prj) - 제공파일형식 : zip파일 - 파일타입 : point - 좌표계 : 단일평면직각좌표계 UTM-K (100000, 200000), EPSG:5179 * 전국 산의 위치정보 및 등산로 시설물 위치정보 * 산림의 이용촉진과 이용자의 안정을 도모하기 위하여 필요한 등산로를 조성하고 이를 보전 · 관리한다. 등산로의 조성 · 보전 및 관리를 효율적으로 추진하기 위하여 10년마다 관할 산림의 등산로에 대한 실태를 조사하고, 관리계획을 수립하여 등산로를 관리 * 등산로 주요지점에 대한 조회는 산림빅데이터플랫폼 시각화포털을 통해 확인가능 ■ 컬럼정보 - 안전지점구분명1 : 안전지점구분내용 (예 : 분기점, 안내판, 음수대 등) - 관리지점구분명2 : 관리지점구분내용 (예 : 체육시설, 약수터 등) - 기타사항 : 기타 참고사항 - 산명 : 등산로 산명 ■ 샘플데이터 해당파일(SHP)을 확인하시려면 QGIS프로그램을 설치하셔야 확인가능하며. 아래의 링크로 들어가 QGIS 무료오픈소스 프로그램을 다운받으시면 됩니다. https://www.qgis.org/ko/site/ → 무료 QGIS프로그램 다운로드 https://docs.qgis.org/3.10/ko/docs/user_manual/ → QGIS 사용자 지침서 확인 원본 데이터는 로그인 후 구매하여 다운로드 하십시오.