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.

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.

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.

A framework for investigating illegal wildlife trade on social media with machine learning.

Article impact statement: Machine learning can be used to automatically monitor and assess illegal wildlife trade on social media platforms.

Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas.

Social media data is increasingly used as a proxy for human activity in different environments, including protected areas, where collecting visitor information is often laborious and expensive, but important for management and marketing. Here, we compared data from Instagram, Twitter and Flickr, and assessed systematically how park popularity and temporal visitor counts derived from social media data perform against high-precision visitor statistics in 56 national parks in Finland and South Africa in 2014. We show that social media activity is highly associated with park popularity, and social media-based monthly visitation patterns match relatively well with the official visitor counts. However, there were considerable differences between platforms as Instagram clearly outperformed Twitter and Flickr. Furthermore, we show that social media data tend to perform better in more visited parks, and should always be used with caution. Based on stakeholder discussions we identified potential reasons why social media data and visitor statistics might not match: the geography and profile of the park, the visitor profile, and sudden events. Overall the results are encouraging in broader terms: Over 60% of the national parks globally have Twitter or Instagram activity, which could potentially inform global nature conservation.

Social media reveal that charismatic species are not the main attractor of ecotourists to sub-Saharan protected areas.

Charismatic megafauna are arguably considered the primary attractor of ecotourists to sub-Saharan African protected areas. However, the lack of visitation data across the whole continent has thus far prevented the investigation of whether charismatic species are indeed a key attractor of ecotourists to protected areas. Social media data can now be used for this purpose. We mined data from Instagram, and used generalized linear models with site- and country-level deviations to explore which socio-economic, geographical and biological factors explain social media use in sub-Saharan African protected areas. We found that charismatic species richness did not explain social media usage. On the other hand, protected areas that were more accessible, had sparser vegetation, where human population density was higher, and that were located in wealthier countries, had higher social media use. Interestingly, protected areas with lower richness in non-charismatic species had more users. Overall, our results suggest that more factors than simply charismatic species might explain attractiveness of protected areas, and call for more in-depth content analysis of the posts. With African countries projected to develop further in the near-future, more social media data will become available, and could be used to inform protected area management and marketing.

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.

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.