The global response to the COVID-19 pandemic has brought with it significant changes to human mobility patterns and working environments. We aimed to explore how social distancing measures affected ...recreational use of urban green space during the partial lockdown in Oslo, Norway. Mobile tracking data from thousands of recreationists were used to analyze high resolution spatio-temporal changes in activity. We estimated that outdoor recreational activity increased by 291% during lockdown relative to a 3 yr average for the same days. This increase was significantly greater than expected after adjusting for the prevailing weather and time of year and equates to approx. 86 000 extra activities per day over the municipality (population of 690 000). Both pedestrians (walking, running, hiking) and cyclists appeared to intensify activity on trails with higher greenviews and tree canopy cover, but with differences in response modulated by trail accessibility and social distancing preferences. The magnitude of increase was positively associated with trail remoteness, suggesting that green spaces facilitated social distancing and indirectly mitigated the spread of COVID-19. Finally, pedestrian activity increased in city parks, peri-urban forest, as well as protected areas, highlighting the importance of access to green open spaces that are interwoven within the built-up matrix. These findings shed new light on the value of urban nature as resilience infrastructure during a time of crisis. The current pandemic also reveals some important dilemmas we might face regarding green justice on the path towards urban planning for future sustainable cities.
•Increases in recreational activity during COVID-19 pandemic in Oslo, Norway.•Increases sustained up to 6 months post-lockdown.•Largest increases were in protected areas and cultural ...landscapes.•Teenager demographic exhibited greatest relative increase.
In Oslo, as elsewhere, the COVID-19 outbreak and the following lockdown measures during spring 2020 led to increased use of urban green infrastructure. Whether this has led to more durable changes in recreation patterns remains an open question. We used mobile tracking data from 53,000 STRAVA users to explore the longevity of increases in recreational activity. We compared 2020 activity levels relative to a weather benchmark (i.e. baseline), defined as the activity one would have expected given the time of year and prevailing weather conditions. Recreational activity increased by 240% during the five weeks of comprehensive lockdown and were maintained until the summer vacation period in June/July when they dropped to baseline levels. Yet, during August they increased again to 89% above baseline. Although activity increased across all city land use zones, after lockdown there was a shift away from residential and commercial zones toward city green spaces including forests and protected areas. Cultural landscapes and protected areas received disproportionately high activity levels relative to the length of recreational trails available within them. Recreationists in the teenager demographic (13–19) exhibited a four-fold increase in their share of the STRAVA user-base at the start of lockdown. The COVID-19 pandemic and its disruptions to the status quo has had lasting effects over the short-term on the way Norwegian citizens recreate. Our findings reinforce the value of urban nature and open spaces for societal pandemic preparedness, particularly youths, during and after times of crisis.
The increasing number of cyclists in cities around the world results in a greater focus on bicycle traffic. Next to traffic volume, the main characteristic of traffic used in road safety analysis, ...infrastructure planning, design, etc. is its speed. Bicycle speed is strongly affected by the type of bicycle facility, motor vehicle traffic parameters (volume, speed, share of heavy vehicles), trip motivation, weather conditions, etc., and therefore it is difficult to estimate. Traditionally, bicycle speed is determined directly using speed radar or indirectly, as a quotient of measurement base length and travel time calculated using a stopwatch or video technique. There are also researches where bicycle speed was estimated based on GPS sources, mainly mobile apps. However, depending on the GPS source and the group of cyclists, bicycle speed gained from GPS data can be different from the speed of regular cyclists (due to different levels of experience or types of bicycle). In the paper, the relationships between bicycle speed obtained from empirical measurements and two different GPS sources, which were bikesharing system (Wavelo) and Strava app, were analysed. In total 18 research sites were selected different in terms of bicycle facility (bicycle path, shared pedestrian/bicycle path, contraflow lane) and element of road network (road segment, bicycle crossing with or without traffic signals). Two-tailed test for two means was conducted to analyse the statistical significance of differences in bicycle speed estimated based on GPS data and empirical measurements using video technique. It showed that Wavelo and Strava speeds are by 17.4% lower are by 23.1% higher than the speeds of regular cyclists respectively. Two linear regression models describing relationships between bicycle speeds from empirical measurements and GPS data were developed. The results show that the variance of bicycle speed is almost 80% described by the variance of Wavelo speed and 60% described by the variance of Strava speed, which suggests that bicycle free-flow speed can be estimated based on GPS data either from bikeshare system or dedicated app.
The increasing number of cyclists in cities around the world results in a greater focus on bicycle traffic. Next to traffic volume, the main characteristic of traffic used in road safety analysis, ...infrastructure planning, design, etc. is its speed. Bicycle speed is strongly affected by the type of bicycle facility, motor vehicle traffic parameters (volume, speed, share of heavy vehicles), trip motivation, weather conditions, etc., and therefore it is difficult to estimate. Traditionally, bicycle speed is determined directly using speed radar or indirectly, as a quotient of measurement base length and travel time calculated using a stopwatch or video technique. There are also researches where bicycle speed was estimated based on GPS sources, mainly mobile apps. However, depending on the GPS source and the group of cyclists, bicycle speed gained from GPS data can be different from the speed of regular cyclists (due to different levels of experience or types of bicycle). In the paper, the relationships between bicycle speed obtained from empirical measurements and two different GPS sources, which were bikesharing system (Wavelo) and Strava app, were analysed. In total 18 research sites were selected different in terms of bicycle facility (bicycle path, shared pedestrian/bicycle path, contraflow lane) and element of road network (road segment, bicycle crossing with or without traffic signals). Two-tailed test for two means was conducted to analyse the statistical significance of differences in bicycle speed estimated based on GPS data and empirical measurements using video technique. It showed that Wavelo and Strava speeds are by 17.4% lower are by 23.1% higher than the speeds of regular cyclists respectively. Two linear regression models describing relationships between bicycle speeds from empirical measurements and GPS data were developed. The results show that the variance of bicycle speed is almost 80% described by the variance of Wavelo speed and 60% described by the variance of Strava speed, which suggests that bicycle free-flow speed can be estimated based on GPS data either from bikeshare system or dedicated app.
Lately, the sub two-hour marathon attempt in Monza was still based on the belief that constant speed is the best way of running. This idea is relayed by marathon organizers who offer pace-group ...leaders to help the runners to maintain a target race speed. The purposes of this study are to verify the hypotheses that 1. The mass runners try to maintain a constant speed without succeeding. 2. Marathoners run in an asymmetric way and this turns out to be visible in the speed time series. Those two points are independent of the gender, the level of performance (2h30–3h40) and the profile of the race (Paris vs Berlin). Before considering a predictive running strategy for optimizing personal marathon running performance, here we shed light on some significant statistical features by analyzing speed time series data recorded by 273 runners’ GPS. We started with looking for a trend in the speed time series. By means of Kendall’s non-parametric rank correlation coefficient we exhibited a decreasing trend in speed data, whichever the level of performance, gender (Male and Female) and race profile (Berlin and Paris marathons). Going deeper in the study we applied a systematic analysis of the asymmetry of speed via classical statistical measures of skewness. Among them the quantiles of the average speed, i.e. the proportion of the race run above or below the final average The combination of the trend and the asymmetry lead to building up a statistical signature for the speed time series which is identical regardless the level of performance, gender and race profile.
•70% of the runners tend towards having a strong asymmetric behavior.•Runners are spending more time above their marathon average speed than below.•The statistical signature of a marathon keeps the same order (−−) (−＋) (＋＋) (＋−).
•All bicycling ridership can be modeled with fitness app and geographic data.•Variables needed to predict all ridership with Strava: income and safety.•Model accuracy improves when official counts ...represent all bicycling conditions.
Fitness apps, such as Strava, are a growing source of data for mapping bicycling ridership, due to large samples and high resolution. To overcome bias introduced by data generated from only fitness app users, researchers build statistical models that predict total bicycling by integrating Strava data with official counts and geographic data. However, studies conducted on single cities provide limited insight on best practices for modeling bicycling with Strava as generalizability is difficult to assess. Our goal is to develop a generalized approach to modeling bicycling ridership using Strava data. In doing so we enable detailed mapping that is more inclusive of all bicyclists and will support more equitable decision-making across cities. We used Strava data, official counts, and geographic data to model Average Annual Daily Bicycling (AADB) in five cities: Boulder, Ottawa, Phoenix, San Francisco, and Victoria. Using a machine learning approach, LASSO, we identify variables important for predicting ridership in all cities, and independently in each city. Using the LASSO-selected variables as predictors in Poisson regression, we built generalized and city-specific models and compared accuracy. Our results indicate generalized prediction of bicycling ridership on a road segment in concert with Strava data should include the following variables: number of Strava riders, percentage of Strava trips categorized as commuting, bicycling safety, and income. Inclusion of city-specific variables increased model performance, as the R2 for generalized and city-specific models ranged from 0.08–0.80 and 0.68–0.92, respectively. However, model accuracy was influenced most by the official count data used for model training. For best results, official count data should capture diverse street conditions, including low ridership areas. Counts collected continuously over a long time period, rather than at peak periods, may also improve modeling. Modeling bicycling from Strava and geographic data enables mapping of bicycling ridership that is more inclusive of all bicyclists and better able to support decision-making.
Humans profoundly affect animal distributions by directly competing for space, not only transforming, but actively using their habitat. Anthropogenic disturbance is usually measured via structural ...proxies such as infrastructure and land use that overlook the impact of human presence, or functional disturbance. In this study, we propose a methodology unifying two paradigms, human mobility and animal movement, to fill this gap. We developed a novel spatially-explicit index of anthropic disturbance, the Cumulative Outdoor activity Index (COI), and validated it with ground truth observations derived from camera trapping (r = +0.63, p < 0.001). Building on previous work from Peters et al. (2015, Biol. Cons. 186, 123–133) on a Critically Endangered brown bear population in the Alps, we used Resource Selection Analysis to assess the influence of different forms of anthropogenic disturbance on the relative probability of habitat selection. The intensity of COI provided an effective measure of functional anthropogenic disturbance, and it outperformed all alternative and commonly-used proxies of structural disturbance in predicting bear habitat use. Our predictions suggest that brown bear shrinks its ecological niche as a consequence of intense human use of otherwise suitable habitat. These constraints may limit the potential range expansion of bears to establish a viable Alpine-Dinaric metapopulation. Conclusive conservation and future land use planning towards human-wildlife coexistence should account for the functional presence of humans on the landscape. The proposed COI could help determine where mitigation measures should be enforced.
•Structural anthropic disturbance underestimates human-wildlife competition for space.•We developed the Cumulative Outdoor activity Index (COI) based on human mobility data.•COI best predicted brown bear space use in a reintroduced endangered population.•Predictions indicate limits for the establishment of an Alpine bear metapopulation.•COI can identify where to enforce human mobility restrictions for conservation purposes.
Cycling participation is context-sensitive and weather condition is reportedly a significant factor. How weather affects cyclists with different demographics, trip purposes, and in the context of ...cycling infrastructure, built environment and geographic factors is less well understood by existing literature. This paper applies autoregressive models to explain difference in Strava cycling volume from the same hour of the previous day as a function of change in weather conditions, and day of the week; the contextual effect of cycling infrastructure, built environment and geographic factors is accounted for using interaction terms. We use Strava crowdsourced cycling data in Sydney, Australia, as a case study; commute and leisure cyclists, male and female, young and older cyclists are modeled separately. We find weather conditions have a statistically significant effect on cycling participation; rain, rainfall in the last 2 hours and wind are general deterrents to cycling. Physically separated cycling lanes reduce the adverse effect of precipitation on leisure cyclists and male cyclists but have little effect in retaining commute cyclists and female cyclists. The adverse effect of precipitation and wind on commute cycling is amplified in areas with good access to jobs, possibly due to the availability of better alternative modes of transport. Inland locations generally attenuate effects of windy conditions, except for young adults. This paper sheds light on factors attenuating adverse weather effects on cycling participation and provides useful guidance for future cycling infrastructure.
Encouraging more cycling is increasingly seen as an important way to create more sustainable cities and to improve public health. Understanding how cyclists travel and how to encourage cycling ...requires data; something which has traditionally been lacking. New sources of data are emerging which promise to reveal new insights. In this paper, we use data from the activity tracking app Strava to examine where people in Glasgow cycle and how new forms of data could be utilised to better understand cycling patterns. We propose a method for augmenting the data by comparing the observed link flows to the link flows which would have resulted if people took the shortest route. Comparing these flows gives some expected results, for example, that people like to cycle along the river, as well as some unexpected results, for example, that some routes with cycling infrastructure are avoided by cyclists. This study proposes a practical approach that planners can use for cycling plans with new/emerging cycling data.
Monitoring bicycle trips is no longer limited to traditional sources, such as travel surveys and counts. Strava, a popular fitness tracker, continuously collects human movement trajectories, and its ...commercial data service, Strava Metro, has enriched bicycle research opportunities over the last five years. Accrued knowledge from colleagues who have already utilised Strava Metro data can be valuable for those seeking expanded monitoring options. To convey such knowledge, this paper synthesises a data overview, extensive literature review on how the data have been applied to deal with drivers' bicycle-related issues, and implications for future work. The review results indicate that Strava Metro data have the potential-although finite-to be used to identify various travel patterns, estimate travel demand, analyse route choice, control for exposure in crash models, and assess air pollution exposure. However, several challenges, such as the under-representativeness of the general population, bias towards and away from certain groups, and lack of demographic and trip details at the individual level, prevent researchers from depending entirely on the new data source. Cross-use with other sources and validation of reliability with official data could enhance the potentiality.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
PDF
