Longitudinal spatial dataset on travel times and distances by different travel modes in Helsinki Region

A travel time matrix data set for the Helsinki region 2023 that is sensitive to time, mode and interpersonal differences, and uses open data and novel open-source software

Travel times between different locations form the basis for most contemporary measures of spatial accessibility. Travel times allow to estimate the potential for interaction between people and places, and is therefore a vital measure for understanding the functioning, sustainability, and equity of cities. Here, we provide an open travel time matrix dataset that describes travel times between the centroids of all cells in a grid (N = 13,132) covering the metropolitan area of Helsinki, Finland. The travel times recorded in the dataset follow a door-to-door approach that provides comparable travel times for walking, cycling, public transport and car journeys, including all legs of each trip by each mode, such as the walk to a bus stop, or the search for a parking spot. We used the r5py Python package, that we developed specifically for this computation. The data are sensitive to diurnal variations and to variations between people (e.g. slow and fast walking speed). We validated the data against the Google Directions API and present use cases from a planning practice. The five key principles that guided the data set design and production - comparability, simplicity, reproducibility, transferability, and sensitivity to temporal and interpersonal variations - ensure that urban and transport planners, business and researchers alike can use the data in a wide range of applications.

Public transport accessibility indicators to urban and regional services in Great Britain

Public transport accessibility to urban and regional services has been found to relate to various social and economic processes, such as unemployment, transport mode choice, property prices, and public health. A frequent type of measures representing accessibility are location-based. While these offer advantages, like flexibility and ease of interpretation, their estimation usually requires specialized skills and substantial computational resources. To lower these barriers, we have prepared a suite of accessibility indicators for key services across Great Britain at a spatially disaggregated level. The dataset includes ready-to-use public transport accessibility indicators for employment, general practitioners (GP, or family physician), hospitals, grocery stores, supermarkets, primary and secondary schools, and urban centres. It also includes the raw travel time matrix from each origin to every potential destination, a primary input for such indicator estimation. Altogether, this resource offers various levels of application, from direct input into a range of research topics to the foundation for creating comprehensive custom indicators.

What are patterns of rise and decline?

The notions of change, such as birth, death, growth, evolution and longevity, extend across reality, including biological, cultural and societal phenomena. Patterns of change describe how success and composition of every entity, from species to societies, vary across time. Languages develop into new languages, music and fashion continuously evolve, economies rise and decline, ecological and societal crises come and go. A common way to perceive and analyse change processes is through patterns of rise and decline, the ubiquitous, often distinctively unimodal trajectories describing life histories of various entities. These patterns come in different shapes and are measured according to varying definitions. Depending on how they are measured, patterns of rise and decline can reveal, emphasize, mask or obscure important dynamics in natural and cultural phenomena. Importantly, the variations of how dynamics are measured can be vast, making it impossible to directly compare patterns of rise and decline across fields of science. Standardized analysis of these patterns has the potential to uncover important but overlooked commonalities across natural phenomena and potentially help us catch the onset of dramatic shifts in entities' state, from catastrophic crashes in success to gradual emergence of new entities. We provide a framework for standardized recognizing, characterizing and comparing patterns of change by combining understanding of dynamics across fields of science. Our toolkit aims at enhancing understanding of the most general tendencies of change, through two complementary perspectives: dynamics of emergence and dynamics of success. We gather comparable cases and data from different research fields and summarize open research questions that can help us understand the universal principles, perception-biases and field-specific tendencies in patterns of rise and decline of entities in nature.

An approach for accessibility assessment and vulnerability analysis of national multimodal transport systems

A series of ambitious accessibility-oriented policies have been launched in the world, which center around improving accessibility via the national multimodal transport systems including road, airline, and railway. The national multimodal transport accessibility assessment is one of the "basic" analyses for the design and implementation of these policies, whereas existing national-scale accessibility studies either focus on single-mode or two-mode transport or ignore the schedule-dependent nature of railway and airline. This article models the integrated road, railway, airline, and walking transport as a four-layer network with the consideration of their interdependencies. An algorithm is then developed to accurately assess the travel time-based accessibility on the four-layer network with the consideration of the daily schedule of trains and flights. The proposed approach is applied to map accessibility to 363 cities in mainland China and analyze the optimal travel modes. In addition, this article investigates the travel time-based vulnerability of the national multimodal transport system in mainland China under the extreme storm recently occurred in Zhengzhou (July 2021). The findings in this work provide insightful suggestions for transport planners to design the national multimodal transport systems and for stakeholders to schedule travels.

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine learning models. To tackle the challenges in small-sample POP, we propose an approach named Adaptation and Learning to Learn (ALL) by adopting the capability of advanced deep learning and federated learning. ALL integrates two novel ideas: (1) Adaptation: by leveraging the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning technique, an auto-selector module is implemented, which can group and select data-scarce parks automatically as supporting sources to enable the knowledge adaptation in model training; and (2) Learning to learn: by applying federated meta-learning on selected supporting sources, a meta-learner module is designed, which can train a high-performance local prediction model in a collaborative and privacy-preserving manner. Results of an evaluation with 42 parking lots in two Chinese cities (Shenzhen and Guangzhou) show that, compared to state-of-the-art baselines: (1) the auto-selector can reduce the model variance by about 17.8%; (2) the meta-learner can train a converged model 102× faster; and (3) finally, ALL can boost the forecasting performance by about 29.8%. Through the integration of advanced machine learning methods, i.e., reinforcement learning, meta-learning, and federated learning, the proposed approach ALL represents a significant step forward in solving small-sample issues in parking occupancy prediction.

A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland

In this article, 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. An hourly population distribution dataset is 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 dataset is validated by comparing population register data from Statistics Finland for night 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 spatial accessibility analyses, crisis management, planning and beyond.

Measurement(s)	population distribution
Technology Type(s)	mobile phone • digital curation
Factor Type(s)	geographic location • hour of the day • day of the week
Sample Characteristic - Environment	city
Sample Characteristic - Location	Capital Region • Helsinki

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.17168978

Is a new office a blessing in disguise? The strategic importance of relocation

Purpose

Corporate relocation is a rare event in the history of an individual company. The choices related to location, building and workplace constitute major long-term strategic decisions that determine the company’s future operating environment. However, business decision-makers often do not evaluate all the aspects of relocation before making relocation decisions. Thus, the purpose of this paper is to systemise the knowledge behind corporate relocation and the strategic qualities and impacts of these choices.

Design/methodology/approach

This conceptual paper is based on a comprehensive literature review of 74 articles on the strategic qualities of short-distance corporate relocation of knowledge-intensive firms. Based on the review insights, a conceptual model of the strategic operational qualities for work environment selection is developed.

Findings

This paper identifies three strategic layers of physical environment change, namely, location, building and physical work environment, which need to be considered when deciding to relocate. Corporate relocation affects a company through five operational qualities, namely, staff productivity, costs, employee retention and availability, operational changes and organisational culture.

Practical implications

Relocation is a complex process for an individual company. Justifying choices based on direct costs can lead to unexpected changes in indirect costs for the company. This paper helps decision-makers understand the strategic importance of corporate relocation, identify relocation goals and plan successful relocation.

Originality/value

This paper uses a strategy and organisation lens to provide a systematic overview and synthesis of the strategic qualities of short-distance corporate relocation of knowledge-intensive firms.

A fine resolution dataset of accessibility under different traffic conditions in European cities

Urban accessibility and congestion indicators allow us to benchmark cities. If these indicators are also available at a fine resolution, we can compare different neighbourhoods within a city. We present a dataset of different accessibility indicators for all urban areas with more than 250 thousand people in the EU27, the UK, Switzerland and Norway. Each city is analysed by means of a population grid of 500 m by 500 m and represented by a wider area covering both the densely populated urban centre and the commuting zone. To capture congestion, we measure accessibility for each grid cell at different times of the day that correspond to different traffic conditions using the detailed network and congestion information provided by TomTom.

A Review of Models Used for Investigating Barriers to Healthcare Access in Australia

Understanding barriers to healthcare access is a multifaceted challenge, which is often highly diverse depending on location and the prevalent surroundings. The barriers can range from transport accessibility to socio-economic conditions, ethnicity and various patient characteristics. Australia has one of the best healthcare systems in the world; however, there are several concerns surrounding its accessibility, primarily due to the vast geographical area it encompasses. This review study is an attempt to understand the various modeling approaches used by researchers to analyze diverse barriers related to specific disease types and the various areal distributions in the country. In terms of barriers, the most affected people are those living in rural and remote parts, and the situation is even worse for indigenous people. These models have mostly focused on the use of statistical models and spatial modeling. The review reveals that most of the focus has been on cancer-related studies and understanding accessibility among the rural and urban population. Future work should focus on further categorizing the population based on indigeneity, migration status and the use of advanced computational models. This article should not be considered an exhaustive review of every aspect as each section deserves a separate review of its own. However, it highlights all the key points, covered under several facets which can be used by researchers and policymakers to understand the current limitations and the steps that need to be taken to improve health accessibility.

The green view dataset for the capital of Finland, Helsinki

Recent studies have incorporated human perspective methods like making use of street view images and measuring green view in addition to more traditional ways of mapping city greenery [1]. Green view describes the relative amount of green vegetation visible at street level and is often measured with the green view index (GVI), which describes the percentage of green vegetation in a street view image or images of a certain location [2]. The green view dataset of Helsinki was created as part of the master's thesis of Akseli Toikka at the University of Helsinki [3].

We calculated the GVI values for a set of locations on the streets of Helsinki using Google Street View (GSV) 360° panorama images from summer months (May through September) between 2009 and 2017. From the available images, a total of 94 454 matched the selection criteria. These were downloaded using the Google application programming interface (API). We calculated the GVI values from the panoramas based on the spectral characteristics of green vegetation in RGB images. The result was a set of points along the street network with GVI values.

By combining the point data with the street network data of the area, we generated a dataset for GVI values along the street centre lines. Streets with GVI points within a threshold distance of 30 meters were given the average of the GVI values of the points. For the streets with no points in the vicinity (∼67%), the land cover data from the area was used to estimate the GVI, as suggested in the thesis [3]. The point and street-wise data are stored in georeferenced tables that can be utilized for further analyses with geographical information systems.