Christoph Fink

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.

Despite increased interest in the development of smart cities and urban spaces that cater to the needs of their inhabitants, there is a significant lack of information and experience when it comes to working with older people. URBANAGE is a European H2020 project focused on supporting urban planners and policymakers in the decision-making process for age-friendly cities by developing new technologies for evidence-based decision-making. Older adults over 60 years of age, public servants and other relevant stakeholders were invited to co-create and test the solution to ensure that their needs and challenges were being addressed by the project. Decision-makers are facing major challenges in terms of understanding and addressing the needs of vulnerable population groups, such as older people, because of the lack of large enough datasets of disaggregated anonymised data. In this article, we report on the main challenges encountered during the implementation of the URBANAGE project, and the development of the components for big data analytics, visualisations, predictive algorithms and simulation. Using examples from three European locations – Helsinki, Flanders and Santander – we describe and discuss how we can gather personal data related to the daily lives of older people in terms of the existing privacy and data protection laws in the EU. The use of new technologies, such as location-based information devices, can provide up-to-date and precise information regarding problems that older people face while moving around the city, but they pose privacy concerns at the same time.

Riding a bicycle is increasingly encouraged as a sustainable transport solution, especially in urban areas. However, safety concerns, both perceived and actual, can significantly lower the willingness to ride among the population. To support cycling planning and policy in the double task of increasing the levels of cycling while mitigating crash risk, there is a need to better understand the behaviours and attitudes of bicycle riders. In this manuscript, we study a cohort of Finnish bicycle riders through four questionnaires, the Cycling Behaviour Questionnaire (CBQ), Cyclist Risk Perception and Regulation Scale (RPRS), Cyclist Anger Scale (CAS), and Cyclist Aggression Expression Inventory (CAX). Our findings show low self-reported errors and violations, and high levels of knowledge regarding traffic rules among Finnish bicycle riders. Most participants report low levels of aggression, which is generally dealt with in constructive ways, while anger was most commonly a result of interactions with motor vehicles and less with other road users such as pedestrians. To further reduce the crash risk in cycling, our results point to the need for further separation between bicycle riders and motorised vehicles, and for the development of risk perception and positive behaviours among riders, particularly those engaging in risky behaviours.

The construction of railroad infrastructure in East Siberia and the Russian Far East was a key aspect of Soviet industrialization during the 1970s and 1980s. Although built primarily for freight transportation, the Baikal-Amur Mainline (BAM) and the Amur-Yakutsk Mainline (AYaM) have also been used for passenger transport and have thus contributed to increased mobility and heightened local expectations about future mobility. This article presents the results of an extensive survey carried out in the BAM/AYaM region, which maps experiences of individual mobility, including usage-related needs, practices, and expectations. The findings show low levels of satisfaction differing across the region’s social and spatial diversity. The paper argues that hierarchies of mobility prevail at two related levels in the BAM/AYaM region: 1) the state’s regional development policies favor industrial development, focusing on freight transportation while neglecting local passengers’ needs for improved individual mobility; and 2) intersectional structural conditions along lines of diversity, such as gender, age, ethnicity, and place of residence, result in mobility disadvantage and lower mobility satisfaction. These hierarchies are embedded in the broader social and spatial inequality structures in the Russian Federation.

Social media data are being increasingly used in conservation science to study human–nature interactions. User‐generated content, such as images, video, text, and audio, and the associated metadata can be used to assess such interactions. A number of social media platforms provide free access to user‐generated social media content. However, similar to any research involving people, scientific investigations based on social media data require compliance with highest standards of data privacy and data protection, even when data are publicly available. Should social media data be misused, the risks to individual users' privacy and well‐being can be substantial. We investigated the legal basis for using social media data while ensuring data subjects’ rights through a case study based on the European Union's General Data Protection Regulation. The risks associated with using social media data in research include accidental and purposeful misidentification that has the potential to cause psychological or physical harm to an identified person. To collect, store, protect, share, and manage social media data in a way that prevents potential risks to users involved, one should minimize data, anonymize data, and follow strict data management procedure. Risk‐based approaches, such as a data privacy impact assessment, can be used to identify and minimize privacy risks to social media users, to demonstrate accountability and to comply with data protection legislation. We recommend that conservation scientists carefully consider our recommendations in devising their research objectives so as to facilitate responsible use of social media data in conservation science research, for example, in conservation culturomics and investigations of illegal wildlife trade online.

Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long‐term success of biodiversity conservation depends on a thorough understanding of human–nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, the Information Age also provides new opportunities to better understand human–nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human–nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scales. Nevertheless, technical challenges associated with the identification, access, and analysis of relevant data hamper the wider adoption of culturomics methods. To help overcome these barriers, we propose a conservation culturomics research framework that addresses data acquisition, analysis, and inherent biases. The main sources of culturomic data include web pages, social media, and other digital platforms from which metrics of content and engagement can be obtained. Obtaining raw data from these platforms is usually desirable but requires careful consideration of how to access, store, and prepare the data for analysis. Methods for data analysis include network approaches to explore connections between topics, time‐series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications.

Wildlife trade, when unsustainable, can be an important threat to biodiversity conservation. In this contribution, we explored the use of digital data to investigate the online market for songbirds in Indonesia, where keeping pet songbirds is a deeply rooted cultural practice. We examined the spatial characteristics of three dimensions of the songbird trade using data from online sources: birdwatchers’ sightings as a proxy for the supply of the songbird market, small ads from an online marketplace platform, representing the trade itself and its transactions, and videos by pet songbird owners to represent the demand side of the songbird market. We found that, geographically, these three stages of the songbird supply chain did not overlap, which potentially hints at the roles extended transport networks and commercial captive breeding play for the songbird trade. The trade was not confined to major cities but spread out through the country, indicating both a possible democratisation of the trade (i.e. a larger group of sellers, and consumers selling to consumers) and an opportunity to observe previously covert parts of the trade. We further found that the asking prices on online marketplaces were significantly higher than the prices stated in an independently carried out consumer survey, and discuss possible reasons. Data from digital sources can give rich insights into the spatial, temporal and taxonomic structure of wildlife trade, can help understand the motivations of buyers and sellers, and can help direct wildlife trade towards a more sustainable fashion. Our methodology toolbox that allows automatic and continuous monitoring of online marketplaces and includes data preparation and cleaning, and follows the highest standards of data privacy principles, is openly available.