Cyclists' exposure to air pollution, noise, and greenery: a population-level spatial analysis approach.

Urban travel exposes people to a range of environmental qualities with significant health and wellbeing impacts. Nevertheless, the understanding of travel-related environmental exposure has remained limited. Here, we present a novel approach for population-level assessment of multiple environmental exposure for active travel. It enables analyses of (1) urban scale exposure variation, (2) alternative routes' potential to improve exposure levels per exposure type, and (3) by combining multiple exposures. We demonstrate the approach's feasibility by analysing cyclists' air pollution, noise, and greenery exposure in Helsinki, Finland. We apply an in-house developed route-planning and exposure assessment software and integrate to the analysis 3.1 million cycling trips from the local bike-sharing system. We show that especially noise exposure from cycling exceeds healthy thresholds, but that cyclists can influence their exposure by route choice. The proposed approach enables planners and individual citizens to identify (un)healthy travel environments from the exposure perspective, and to compare areas in respect to how well their environmental quality supports active travel. Transferable open tools and data further support the implementation of the approach in other cities.

Exploring human-nature interactions in national parks with social media photographs and computer vision.

Understanding the activities and preferences of visitors is crucial for managing protected areas and planning conservation strategies. Conservation culturomics promotes the use of user-generated online content in conservation science. Geotagged social media content is a unique source of in situ information on human presence and activities in nature. Photographs posted on social media platforms are a promising source of information, but analyzing large volumes of photographs manually remains laborious. We examined the application of state-of-the-art computer-vision methods to studying human-nature interactions. We used semantic clustering, scene classification, and object detection to automatically analyze photographs taken in Finnish national parks by domestic and international visitors. Our results showed that human-nature interactions can be extracted from user-generated photographs with computer vision. The different methods complemented each other by revealing broad visual themes related to level of the data set, landscape photogeneity, and human activities. Geotagged photographs revealed distinct regional profiles for national parks (e.g., preferences in landscapes and activities), which are potentially useful in park management. Photographic content differed between domestic and international visitors, which indicates differences in activities and preferences. Information extracted automatically from photographs can help identify preferences among diverse visitor groups, which can be used to create profiles of national parks for conservation marketing and to support conservation strategies that rely on public acceptance. The application of computer-vision methods to automatic content analysis of photographs should be explored further in conservation culturomics, particularly in combination with rich metadata available on social media platforms.

Exploración de las Interacciones Humano-Naturaleza en los Parques Nacionales por Medio de Fotografías en Redes Sociales y Visión por Computadora Resumen La comprensión de las actividades y preferencias de los visitantes es crucial para el manejo de las áreas protegidas y la planeación de las estrategias de conservación. La culturomia de la conservación promueve el uso del contenido en línea generado por usuarios en las ciencias de la conservación. El contenido de redes sociales etiquetado geográficamente es una fuente única de información in situ sobre la presencia humana y sus actividades en la naturaleza. Las fotografías publicadas en las redes sociales son una fuente prometedora de información, aunque el análisis manual de grandes volúmenes de fotografías sigue siendo laborioso. Evaluamos la aplicación de algunos métodos de punta de visión por computadora para estudiar las interacciones humano-naturaleza. Usamos agrupaciones semánticas, clasificación de escenas y detección de objetos para analizar automáticamente las fotografías tomadas por visitantes domésticos e internacionales dentro de los parques nacionales finlandeses. Nuestros resultados mostraron que las interacciones humano-naturaleza pueden extraerse de fotografías generadas por usuarios mediante la visión por computadora. Los diferentes métodos se complementaron unos a otros al revelar temas visuales generalizados relacionados con el nivel del conjunto de datos, fotogeneidad del paisaje y las actividades humanas. Las fotografías geoetiquetadas revelaron unos perfiles regionales distintos para los parques nacionales (p. ej.: preferencias en los paisajes y las actividades), que son potencialmente útiles para el manejo de los parques. El contenido fotográfico difirió entre los visitantes domésticos y los internacionales, lo cual indica diferencias en sus actividades y preferencias. La información extraída automáticamente de las fotografías puede ayudar a identificar las preferencias entre los grupos diversos de visitantes, lo cual puede usarse para crear un perfil de cada parque nacional para su uso en el mercadeo de la conservación y para apoyar a las estrategias de conservación que dependen de la aceptación pública. La aplicación de los métodos de visión por computadora al análisis automático de contenido de las fotografías debería explorarse mucho más en la culturomia de la conservación, particularmente en combinación con la riqueza de metadatos disponibles en las plataformas sociales.

Using Wikipedia to measure public interest in biodiversity and conservation.

The recent growth of online big data offers opportunities for rapid and inexpensive measurement of public interest. Conservation culturomics is an emerging research area that uses online data to study human-nature relationships for conservation. Methods for conservation culturomics, though promising, are still being developed and refined. We considered the potential of Wikipedia, the online encyclopedia, as a resource for conservation culturomics and outlined methods for using Wikipedia data in conservation. Wikipedia's large size, widespread use, underlying data structure, and open access to both its content and usage analytics make it well suited to conservation culturomics research. Limitations of Wikipedia data include the lack of location information associated with some metadata and limited information on the motivations of many users. Seven methodological steps to consider when using Wikipedia data in conservation include metadata selection, temporality, taxonomy, language representation, Wikipedia geography, physical and biological geography, and comparative metrics. Each of these methodological decisions can affect measures of online interest. As a case study, we explored these themes by analyzing 757 million Wikipedia page views associated with the Wikipedia pages for 10,099 species of birds across 251 Wikipedia language editions. We found that Wikipedia data have the potential to generate insight for conservation and are particularly useful for quantifying patterns of public interest at large scales.

La Wikipedia como Instrumento de Medición del Interés Público por la Biodiversidad y la Conservación Resumen El crecimiento reciente de los datos masivos en línea ofrece oportunidades para la medición rápida y asequible del interés público. La culturomia de la conservación es un área emergente de investigación que utiliza la información en línea para estudiar las relaciones entre el humano y la naturaleza y usarlas para la conservación. Los métodos de conservación basados en culturomia, aunque prometedores, todavía están siendo desarrollados y refinados. Consideramos el potencial de Wikipedia, la enciclopedia en línea, como recurso para la culturomia de la conservación y los métodos para usar sus datos en la conservación. El gran tamaño de Wikipedia, su uso extenso, estructura subyacente de datos y acceso abierto tanto a su contenido como a sus análisis de uso hacen que sea muy adecuada para usarse en la investigación de culturomia de la conservación. Las limitantes de usar la información de Wikipedia incluyen la falta de ubicación de la información asociada con algunos metadatos y la información limitada sobre los motivos de muchos usuarios. Hay siete pasos metodológicos a considerar cuando se usa la información de Wikipedia para la conservación: la selección de metadatos, temporalidad, taxonomía, representación del idioma, geografía de la Wikipedia, geografía física y biológica y medidas comparativas. Cada una de estas decisiones metodológicas puede afectar a las medidas del interés en línea. Como estudio de caso, exploramos estos temas analizando 757 millones de vistas de páginas en Wikipedia para las páginas sobre 10, 099 especies de aves a través de 251 ediciones de Wikipedia en idiomas diferentes. Encontramos que la información de Wikipedia fue particularmente útil para cuantificar los patrones de interés público a grandes escalas y tiene el potencial para generar conocimiento para la conservación.

Global protected area expansion is compromised by projected land-use and parochialism.

Protected areas are one of the main tools for halting the continuing global biodiversity crisis caused by habitat loss, fragmentation and other anthropogenic pressures. According to the Aichi Biodiversity Target 11 adopted by the Convention on Biological Diversity, the protected area network should be expanded to at least 17% of the terrestrial world by 2020 (http://www.cbd.int/sp/targets). To maximize conservation outcomes, it is crucial to identify the best expansion areas. Here we show that there is a very high potential to increase protection of ecoregions and vertebrate species by expanding the protected area network, but also identify considerable risk of ineffective outcomes due to land-use change and uncoordinated actions between countries. We use distribution data for 24,757 terrestrial vertebrates assessed under the International Union for the Conservation of Nature (IUCN) 'red list of threatened species', and terrestrial ecoregions (827), modified by land-use models for the present and 2040, and introduce techniques for global and balanced spatial conservation prioritization. First, we show that with a coordinated global protected area network expansion to 17% of terrestrial land, average protection of species ranges and ecoregions could triple. Second, if projected land-use change by 2040 (ref. 11) takes place, it becomes infeasible to reach the currently possible protection levels, and over 1,000 threatened species would lose more than 50% of their present effective ranges worldwide. Third, we demonstrate a major efficiency gap between national and global conservation priorities. Strong evidence is shown that further biodiversity loss is unavoidable unless international action is quickly taken to balance land-use and biodiversity conservation. The approach used here can serve as a framework for repeatable and quantitative assessment of efficiency, gaps and expansion of the global protected area network globally, regionally and nationally, considering current and projected land-use pressures.

Health research needs more comprehensive accessibility measures: integrating time and transport modes from open data.

BACKGROUND: In this paper, we demonstrate why and how both temporality and multimodality should be integrated in health related studies that include accessibility perspective, in this case healthy food accessibility. We provide evidence regarding the importance of using multimodal spatio-temporal accessibility measures when conducting research in urban contexts and propose a methodological approach for integrating different travel modes and temporality to spatial accessibility analyses. We use the Helsinki metropolitan area (Finland) as our case study region to demonstrate the effects of temporality and modality on the results. METHODS: Spatial analyses were carried out on 250 m statistical grid squares. We measured travel times between the home location of inhabitants and open grocery stores providing healthy food at 5 p.m., 10 p.m., and 1 a.m. using public transportation and private cars. We applied the so-called door-to-door approach for the travel time measurements to obtain more realistic and comparable results between travel modes. The analyses are based on open access data and publicly available open-source tools, thus similar analyses can be conducted in urban regions worldwide. RESULTS: Our results show that both time and mode of transport have a prominent impact on the outcome of the analyses; thus, understanding the realities of accessibility in a city may be very different according to the setting of the analysis used. In terms of travel time, there is clear variation in the results at different times of the day. In terms of travel mode, our results show that when analyzed in a comparable manner, public transport can be an even faster mode than a private car to access healthy food, especially in central areas of the city where the service network is dense and public transportation system is effective. CONCLUSIONS: This study demonstrates that time and transport modes are essential components when modeling health-related accessibility in urban environments. Neglecting them from spatial analyses may lead to overly simplified or even erroneous images of the realities of accessibility. Hence, there is a risk that health related planning and decisions based on simplistic accessibility measures might cause unwanted outcomes in terms of inequality among different groups of people.

Evaluating the impact of distance measures on deforestation simulations in the fluvial landscapes of amazonia.

Land use and land cover change (LUCC) models frequently employ different accessibility measures as a proxy for human influence on land change processes. Here, we simulate deforestation in Peruvian Amazonia and evaluate different accessibility measures as LUCC model inputs. We demonstrate how the selection, and different combinations, of accessibility measures impact simulation results. Out of the individual measures, time distance to market center catches the essential aspects of accessibility in our study area. The most accurate simulation is achieved when time distance to market center is used in association with distance to transport network and additional landscape variables. Although traditional Euclidean measures result in clearly lower simulation accuracy when used separately, the combination of two complementary Euclidean measures enhances simulation accuracy significantly. Our results highlight the need for site and context sensitive selection of accessibility variables. More sophisticated accessibility measures can potentially improve LUCC models' spatial accuracy, which often remains low.

Quantitative conservation geography.

Ongoing biodiversity loss represents the erosion of intrinsic value of living nature, reduces the contributions nature provides to people, and undermines efforts to move towards sustainability. We propose the recognition of quantitative conservation geography as a subfield of conservation science that studies where, when, and what conservation actions could be implemented in order to mitigate threats and promote sustainable people-nature interactions. We outline relevant methods and data needed in quantitative conservation geography. We also discuss the importance of filling information gaps, for example by using emerging technologies and digital data sources, for the further advancement of this subfield. Quantitative conservation geography can help inform the implementation of national and international conservation actions and policy to help stem the global biodiversity crisis.

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.

Environmental exposure during travel: A research review and suggestions forward.

Daily travel through the urban fabric exposes urban dwellers to a range of environmental conditions that may have an impact on their health and wellbeing. Knowledge about exposures during travel, their associations with travel behavior, and their social and health outcomes are still limited. In our review, we aim to explain how the current environmental exposure research addresses the interactions between human and environmental systems during travel through their spatial, temporal and contextual dimensions. Based on the 104 selected studies, we identify significant recent advances in addressing the spatiotemporal dynamics of exposure during travel. However, the conceptual and methodological framework for understanding the role of multiple environmental exposures in travel environments is still in an early phase, and the health and wellbeing impacts at individual or population level are not well known. Further research with greater geographical balance is needed to fill the gaps in the empirical evidence, and linking environmental exposures during travel with the causal health and wellbeing outcomes. These advancements can enable evidence-based urban and transport planning to take the next step in advancing urban livability.

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

Comparable data on spatial accessibility by different travel modes are frequently needed to understand how city regions function. Here, we present a spatial dataset called the Helsinki Region Travel Time Matrix that has been calculated for 2013, 2015 and 2018. This longitudinal dataset contains travel time and distance information between all 250 metres statistical grid cell centroids in the Capital Region of Helsinki, Finland. The dataset is multimodal and multitemporal by nature: all typical transport modes (walking, cycling, public transport, and private car) are included and calculated separately for the morning rush hour and midday for an average working day. We followed a so-called door-to-door principle, making the information between travel modes comparable. The analyses were based primarily on open data sources, and all the tools that were used to produce the data are openly available. The matrices form a time-series that can reveal the accessibility conditions within the city and allow comparisons of the changes in accessibility in the region, which support spatial planning and decision-making.

COVID-19 is spatial: Ensuring that mobile Big Data is used for social good.

The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises.

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.

Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement.

Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, location-based, and artificial intelligence technologies to investigate the long-term effects of environmental, behavioural, psychosocial, and biological factors on health-related states and events and the underlying mechanisms. With the growing number of studies reporting findings from this field and the critical need for public health and policy decisions to be based on the strongest science possible, transparency and clarity in reporting in spatial lifecourse epidemiologic studies is essential. A task force supported by the International Initiative on Spatial Lifecourse Epidemiology (ISLE) identified a need for guidance in this area and developed a Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) Statement. The aim is to provide a checklist of recommendations to improve and make more consistent reporting of spatial lifecourse epidemiologic studies. The STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement for cohort studies was identified as an appropriate starting point to provide initial items to consider for inclusion. Reporting standards for spatial data and methods were then integrated to form a single comprehensive checklist of reporting recommendations. The strength of our approach has been our international and multidisciplinary team of content experts and contributors who represent a wide range of relevant scientific conventions, and our adherence to international norms for the development of reporting guidelines. As spatial, location-based, and artificial intelligence technologies used in spatial lifecourse epidemiology continue to evolve at a rapid pace, it will be necessary to revisit and adapt the ISLE-ReSt at least every 2-3 years from its release.

Tropical timber rush in Peruvian Amazonia: spatial allocation of forest concessions in an uninventoried frontier.

Land-use allocation has important implications for the conservation and management of tropical forests. Peru's forestry regime has recently been reformed and more than 7 million ha has been assigned as forest concessions. This potentially has a drastic impact on the land-use practices and species composition of the assigned areas. Nevertheless, the environmental variation found within the concessions and the process applied to delimit them are poorly studied and documented. Thus, it is difficult to estimate the biological impacts of forestry activities in concessions or plan them sustainably. This paper reveals the characteristics of the current concession allocation in Loreto, Peruvian Amazonia, using environmental and access-related variables and compares the concessions to other major land-use assignments. The work draws on a number of data sets describing land-use, ecosystem diversity, and fluvial network in the region. According to our data, certain environment types such as relatively fertile Pebas soils are overrepresented in the concessions, while others, like floodplain forests, are underrepresented in comparison to other land-use assignments. Concessions also have less anthropogenic disturbance than other areas. Furthermore, concessions are located on average further from the river network than the other land-use assignments studied. We claim that forest classification based on productivity, soil fertility, accessibility, and biodiversity patterns is an achievable long-term goal for forest authorities in Peru, and in many other tropical countries. We present a rough design of a geographic information system incorporating environmental, logging, and access-related data that could be applied to approach this goal in Peru.

Assessing global popularity and threats to Important Bird and Biodiversity Areas using social media data.

Understanding worldwide patterns of human use of sites of international significance for biodiversity conservation is crucial for meeting global conservation targets. However, robust global datasets are scarce. In this study, we used social media data, mined from Flickr and Twitter, geolocated in Important Bird and Biodiversity Areas (IBAs) to assess i) patterns of popularity; ii) relationships of this popularity with geographical and biological variables; and iii) identify sites under high pressure from visitors. IBAs located in Europe and Asia, and in temperate biomes, had the highest density of users. Sites of importance for congregatory species, which were also more accessible, more densely populated and provided more tourism facilities, received higher visitation than did sites richer in bird species. We found 17% of all IBAs assessed to be under very high threat also received high visitation. Our results show in which IBAs enhanced monitoring should be implemented to reduce potential visitation risks to sites of conservation concern for birds, and to harness the potential benefits of tourism for conservation.

Identifying global centers of unsustainable commercial harvesting of species.

Overexploitation is one of the main threats to biodiversity, but the intensity of this threat varies geographically. We identified global concentrations, on land and at sea, of 4543 species threatened by unsustainable commercial harvesting. Regions under high-intensity threat (based on accessibility on land and on fishing catch at sea) cover 4.3% of the land and 6.1% of the seas and contain 82% of all species threatened by unsustainable harvesting and >80% of the ranges of Critically Endangered species threatened by unsustainable harvesting. Currently, only 16% of these regions are covered by protected areas on land and just 6% at sea. Urgent actions are needed in these centers of unsustainable harvesting to ensure that use of species is sustainable and to prevent further species' extinctions.