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Modeling and Forecasting Percent Changes in National Park Visitation Using Social Media
Figure 1. Yellowstone National Park time series decomposition of National Park Service (NPS) counts. National parks have tremendous cultural, ecological, and economic value to societies. In order to manage and maintain these public spaces, decision-makers rely on detailed information about park use and park condition. Many parks, however, lack precise visitor counts because of challenges associated with monitoring large and inaccessible areas with porous boundaries. To facilitate better management, we propose a method to estimate percentage changes in park visitation without using any on-site visitor counts. Specifically, using 20 national parks in the United States, we develop a time series model for forecasting future monthly changes in visitation based on the volume of social media images shared by visitors to parks. Forecasts are generated from historic park-level and national-level photo-user-days (PUD) of images posted to Flickr, using singular spectrum analysis (SSA). We further propose an approach for augmenting existing on-site visitation data collected by the US National Park Service. Our model evaluations indicate that the proposed model that only uses social media data achieves competitive performance to the models which partially or fully utilizes on-site visitor counts.
Read moreSocial Media Data for Environmental Sustainability: A Critical Review of Opportunities, Threats, and Ethical Use
Figure 1. A virtuous cycle for social media (SM) data andsustainability through transparency, inclusivity, and responsibledata use Social media data are transforming sustainability science. However, challenges from restrictions in data accessibility and ethical concerns regarding potential data misuse have threatened this nascent field. Here, we review the literature on the use of social media data in environmental and sustainability research. We find that they can play a novel and irreplaceable role in achieving the UN Sustainable Development Goals by allowing a nuanced understanding of human-nature interactions at scale, observing the dynamics of social-ecological change, and investigating the co-construction of nature values. We reveal threats to data access and highlight scientific responsibility to address trade-offs between research transparency and privacy protection, while promoting inclusivity. This contributes to a wider societal debate of social media data for sustainability science and for the common good.
Read moreWhere Wilderness is Found: Evidence From 70,000 Trip Reports
Figure 1. The study area, showing the boundaries of the Mt. Baker-Snoqualmie National Forest, wilderness areas within it and the trailheads for the 470 hikes in the Washington Trails Association hiking guide that we included in the sample. Map created with the R programming language using the sf, ggspatial and cowplot packages (Dunnington, 2022; Pebesma & Bivand, 2023; R Core Team, 2022; Wilke, 2019). Data from USDA Forest Service, Washington Trails Association, Washington Department of Transportation, Environmental Systems Research Institute map service and Natural Earth, facilitated by the basemaps and rnaturalearth packages for R (ESRI, 2009; Massicotte & South, 2023; Schwalb-Willmann, 2022; USDA Forest Service, 2019; WSDOT, 2023; WTA, 2023c). 1. Outdoor recreation is an essential way many people engage with nature. The provision of public spaces for recreation intersects with conservation practices motivated by intertwined social and ecological values, such as strict practices associated with the concept of ‘wilderness’. Debates persist about how such concepts and management practices influence people’s recreation experiences. 2. Many US public land management agencies facilitate opportunities for outdoor recreation, relying on management frameworks and tools intended to foster specific experiential qualities. But these frameworks and tools assume simplistic relationships between settings and people’s experiences, and managers rarely assess these relationships. 3. This study uses a data set of nearly 70,000 crowdsourced trip reports from a hiking website to understand the qualities of visitors’ experiences on trails. We study the geographic distribution of experiential qualities commonly associated with US wilderness areas: aesthetics, awe, challenge, pristineness, quietness, solitude and timelessness. Using analytical methods that rely on machine learning and natural language processing, we identify these experiential qualities in trip reports from hundreds of routes, and use generalized linear models to analyse relationships between the frequency of each experiential quality and the route’s administrative, built, biophysical, geographic and social settings. 4. We find that four of the seven experiential qualities (aesthetics, awe, challenge and solitude) are commonly described in trip reports, each appearing in 15%–55% of manually coded reports. The extent to which setting characteristics explained variability in experiences differed, ranging from 34% of the variability in the proportion of trip reports describing aesthetics to 55% for awe. The setting characteristics associated with each experiential quality also differed, with characteristics such as trail mileage and summit destinations having stronger influences on experiential qualities than characteristics such as wilderness designation. 5. Synthesis and applications. Our findings suggest the need to consider more diverse variables in experience–setting relationships, develop more robust models to characterize those relationships and create new data sources to represent understudied variables. These advances would help empirically inform and improve frameworks and tools used for recreation and wilderness planning and monitoring, and potentially promote more responsive management to evolving social– ecological values.
Read moreThe Relationship Between Natural Environments and Subjective Well-Being as Measured by Sentiment Expressed on Twitter
Fig. 1. (A) The spatial distribution of tweets in the sample. (B) Land cover in Seattle. (C) Tree canopy coverage in Seattle. (D) Parks in Seattle. Land-cover and tree-canopy data were taken from the 2016 National Land Cover Database (NLCD) (Dewitz, 2019). Polygons representing urban parks within Seattle were downloaded from Seattle GeoData portal (Seattle Parks, 2012). There is growing evidence that time spent in nature can affect well-being. Nonetheless, assessing this relationship can be difficult. We used 1,971,045 geolocated tweets sent by 81,140 users from Seattle, Washington, USA to advance our understanding of the relationship between subjective well-being and natural environments. Specifically, we quantified the relationships between sentiment (negative/neutral/positive) expressed in geolocated tweets and their surrounding environments, focusing on three environmental indicators: land-cover type, tree-canopy density, and urban parks. We allowed the relationships to vary according to the broader type of environment (i.e., land-use zoning). We estimated three random-intercept partial proportional odds models corresponding to the three environmental indicators while controlling for multiple covariates. Our results suggested that for a given land-use type, tweets sent from some natural land-cover types were less likely to be negative compared to tweets sent from the urban built land-cover type. For tweets sent in industrial zones, an increase in tree-canopy cover was associated with a lower probability of having negative sentiments and with a higher probability of having positive sentiments; but for tweets sent in commercial/mixed zones, the association was reversed. Also, urban parks were generally associated with a lower probability of having negative sentiments, but tweets sent from large natural parks in residential zones were less likely to be positive. Our results suggest that the relationship between subjective well-being and natural environments depends on where people are situated in the built environment and may be more complex than previously thought. The more nuanced understanding provided by analyzing geolocated social media has potential to inform urban planning and land management.
Read moreEffects of the COVID-19 Pandemic on Park Visitation Measured by Social Media
Figure 1. Time series of weekly PUD from 2019–2020, in four US cities. Major COVID-related policies are denoted as vertical lines. The COVID-19 pandemic and the resulting economic recession have negatively affected many people’s physical, social, and psychological health. Parks and green-spaces may have ameliorated the negative effects of the pandemic by creating opportunities for outdoor recreation and nature exposure, while other public activities and gatherings were restricted due to risk of disease transmissions. Using park visitation estimates derived from 140K Instagram images shared in four US metropolitan areas, this study investigates trends in park use over the span of the COVID-19 pandemic. We find that while COVID-related stay-at-home orders are associated with shifts in park visitation, the cities that we analysed do not follow uniform trends. Our analyses suggest that future research may be able to explain variability in park visitation based on local factors such as park location and the socio-demographics of visitors. However, the research community does not currently have access to the volume and resolution of data that is necessary to study the issue. There is an urgent need for the CSCW community and social computing researchers to address this data gap if we are to understand the impacts of the pandemic, plan for urban resiliency, and ensure equitable access to parks and other shared resources.
Read moreNo Walk in the Park: the Viability and Fairness of Social Media Analysis for Parks and Recreational Policy Making
Figure 1. Social Vulnerability Index classes by census tract in Seattle (WA). The yellow regions depict the selected parks. Recent years have seen an increase in the use of social media for various decision-making purposes in the context of urban computing and smart cities, including management of public parks. However, as such decision-making tasks are becoming more autonomous, a critical concern that arises is the extent to which such analysis are fair and inclusive. In this article, we examine the biases that exist in social media analysis pipelines that focus on researching recreational visits to urban parks. More precisely, we demonstrate the potential biases that exist in different data sources for estimating the number and demographics of visitors through a comparison of image content shared on Instagram and Flickr from 10 urban parks in Seattle, Washington. We draw a comparison against a traditional intercept survey of park visitors and a multi-modal city-wide survey of residents. We evaluate the viability of using more complex AI facial recognition algorithms and its capabilities for removing some of the presented biases. We evaluate the AI algorithm through the lens of algorithmic fairness and its impact on sensitive demographic groups. We show that despite the promising results, there are new sets of concerns regarding equity that arise when we use AI algorithms.
Read moreNext-Generation Visitation Models using Social Media to Estimate Recreation on Public Lands
Figure 1. Locations of geotagged social media posts made by visitors to public lands in WWA and NNM. Points represent the latitude and longitude where a Flickr photograph (purple) or tweet (green) was created. For Instagram, points represent places to which images were assigned by users (blue). Larger points represent a greater number of Instagram images from the location. Study sites are contained within the management units (shaded grey). Figure created using R48 version 3.5.3. Outdoor and nature-based recreation provides countless social benefits, yet public land managers often lack information on the spatial and temporal extent of recreation activities. Social media is a promising source of data to fill information gaps because the amount of recreational use is positively correlated with social media activity. However, despite the implication that these correlations could be employed to accurately estimate visitation, there are no known transferable models parameterized for use with multiple social media data sources. This study tackles these issues by examining the relative value of multiple sources of social media in models that estimate visitation at unmonitored sites and times across multiple destinations. Using a novel dataset of over 30,000 social media posts and 286,000 observed visits from two regions in the United States, we compare multiple competing statistical models for estimating visitation. We find social media data substantially improve visitor estimates at unmonitored sites, even when a model is parameterized with data from another region. Visitation estimates are further improved when models are parameterized with on-site counts. These findings indicate that while social media do not fully substitute for on-site data, they are a powerful component of recreation research and visitor management.
Read moreUses and Limitations of Social Media to Inform Visitor Use Management in Parks and Protected Areas: A Systematic Review
Figure 1. Papers published by year (n = 58). These are papers published through April 2020, so the number of papers in 2020 only represents 4 months Social media are being increasingly used to inform visitor use management in parks and protected areas. We review the state of the scientific literature to understand the ways social media has been, and can be, used to measure visitation, spatial patterns of use, and visitors’ experiences in parks and protected areas. Geotagged social media are a good proxy for actual visitation; however, the correlations observed by previous studies between social media and other sources of visitation data vary substantially. Most studies using social media to measure visitation aggregate data across many years, with very few testing the use of social media as a visitation proxy at smaller temporal scales. No studies have tested the use of social media to estimate visitation in near real-time. Studies have used geotags and GPS tracks to understand spatial patterns of where visitors travel within parks, and how that may relate to other variables (e.g., infrastructure), or differ by visitor type. Researchers have also found the text content, photograph content, and geotags from social media posts useful to understand aspects of visitors’ experiences, such as behaviors, preferences, and sentiment. The most cited concern with using social media is that this data may not be representative of all park users. Collectively, this body of research demonstrates a broad range of applications for social media. We synthesize our findings by identifying gaps and opportunities for future research and presenting a set of best practices for using social media in parks and protected areas.
Read moreGeolocated Social Media as a Rapid Indicator of Park Visitation and Equitable Park Access
Figure 1. Alignment of Census block group, park and park buffer boundaries. The block group boundary in which Central Park is contained includes residential areas bordering the park. When population density within the 400m buffer is averaged over Census block groups, density of the residential areas bordering the park are underestimated due to the inclusion of the park’s area. Understanding why some parks are used more regularly or intensely than others can inform ways in which urban parkland is developed and managed to meet the needs of a rapidly expanding urban population. Although geolocated social media (GSM) indicators have been used to examine park visitation rates, studies applying this approach are generally limited to flagship parks, national parks, or a small subset of urban parks. Here, we use geolocated Flickr and Twitter data to explore variation in use across New York City’s 2143 diverse parks and model visitation based on spatially-explicit park characteristics and facilities, neighborhood-level accessibility features and neighborhood-level demographics. Findings indicate that social media activity in parks is positively correlated with proximity to public transportation and bike routes, as well as particular park characteristics such as water bodies, athletic facilities, and impervious surfaces, but negatively associated with green space and increased proportion of minority ethnicity and minority race in neighborhoods in which parks are located. Contrary to previous studies which describe park visitation as a form of nature-based recreation, our findings indicate that the kinds of green spaces present in many parks may not motivate visitation. From a social equity perspective, our findings may imply that parks in high-minority neighborhoods are not as accessible, do not accommodate as many visitors, and/or are of lower quality than those in low-minority neighborhoods. These implications are consistent with previous studies showing that minority populations disproportionately experience barriers to park access. In applying GSM data to questions of park access, we demonstrate a rapid, big data approach for providing information crucial for park management in a way that is less resource-intensive than field surveys.
Read moreRecreational Use in Dispersed Public Lands Measured Using Social Media Data and On-Site Counts
Figure 1. MBSNF (boundary in gray) in western WA. Left: The 15 trail areas observed in this study are dispersed across 4 ranger districts (MTB: Mt. Baker, DAR: Darrington, SKY: Skykomish, SNO: Snoqualmie). Right: Geotagged Flickr photos (purple points) taken in and around the MBSNF. Sources: USFS and Flickr. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) Outdoor recreation is one of many important benefits provided by public lands. Data on recreational use are critical for informing management of recreation resources, however, managers often lack actionable information on visitor use for large protected areas that lack controlled access points. The purpose of this study is to explore the potential for social media data (e.g., geotagged images shared on Flickr and trip reports shared on a hiking forum) to provide land managers with useful measures of recreational use to dispersed areas, and to provide lessons learned from comparing several more traditional counting methods. First, we measure daily and monthly visitation rates to individual trails within the Mount Baker-Snoqualmie National Forest (MBSNF) in western Washington. At 15 trailheads, we compare counts of hikers from infrared sensors, timelapse cameras, and manual on-site counts, to counts based on the number of shared geotagged images and trip reports from those locations. Second, we measure visitation rates to each National Forest System (NFS) unit across the US and compare annual measurements derived from the number of geotagged images to estimates from the US Forest Service National Visitor Use Monitoring Program. At both the NFS unit and the individual-trail scales, we found strong correlations between traditional measures of recreational use and measures based on user-generated content shared on the internet. For national forests in every region of the country, correlations between official Forest Service statistics and geotagged images ranged between 55% and 95%. For individual trails within the MBSNF, monthly visitor counts from on-site measurements were strongly correlated with counts from geotagged images (79%) and trip reports (91%). The convenient, cost-efficient and timely nature of collecting and analyzing user-generated data could allow land managers to monitor use over different seasons of the year and at sites and scales never previously monitored, contributing to a more comprehensive understanding of recreational use patterns and values. Related News Univ. of Washington program Nature and Health studies link between environment and well-being (December 30, 2021)
Read moreUsing Social Media to Understand Drivers of Urban Park Visitation in the Twin Cities, MN
Figure 1. Distribution of urban park and green space polygons in the Twin Cities Metropolitan Area, MN, USA. Green space and parks in urban environments provide a range of ecosystem services and public benefits. However, planners and park managers can lack tools and resources to gather local information on how parks are used and what makes them desirable places for recreation and a wide variety of uses. Traditional survey methods to monitor park use and user preferences can be costly, time consuming, and challenging to apply at scale. Here, we overcome this limitation by using geotagged social media data to assess patterns of visitation to urban and peri-urban green space across park systems in the metropolitan area of the Twin Cities, Minnesota, USA. We find that parks with nearby water features, more amenities, greater accessibility from the presence of trails, and that are located within neighborhoods with higher population density, are associated with higher rates of visitation. As cities grow and shifts in demographics occur, more responsive management of public green space will become increasingly important to ensure urban parks provide ecosystem services and meet users’ needs. Using social media data to rapidly assess park use at a lower cost than traditional surveys has the potential to inform public green space management with targeted information on user behavior and values of urban residents.
Read morePhotos, Tweets, and Trails: Are Social Media Proxies for Urban Trail Use?
Figure 1: Maps of annual average daily trail traffic (AADTT), annual average photo-user days (AAPUD), and annual averageTwitter-user days (AATUD) Decision makers need information on the use of, and demand for, public recreation and transportation facilities. Innovations in monitoring technologies and diffusion of social media enable new approaches to estimation of demand. We assess the feasibility of using geo-tagged photographs uploaded to the image-sharing website Flickr and tweets from Twitter as proxy measures for urban trail use. We summarize geo-tagged Flickr uploads and tweets along 80 one-mile segments of the multiuse trail network in Minneapolis, Minnesota, and correlate results with previously published estimates of annual average daily trail traffic derived from infrared trail monitors. Although heat maps of Flickr images and tweets show some similarities with maps of variation in trail traffic, the correlation between photographs and trail traffic is moderately weak (0.43), and there is no meaningful statistical correlation between tweets and trail traffic. Use of a simple log-log bivariate regression to estimate trail traffic from photographs results in relatively high error. The predictor variables included in published demand models for the same trails explain roughly the same amount of variation in photo-derived use, but some of the neighborhood socio-demographic and built-environment independent variables have different effects. Taken together, these findings show that both Flickr images and tweets have limitations as proxies for demand for urban trails, and that neither can be used to develop valid, reliable estimates of trail use. These results differ from previously published results that indicate social media may be useful in assessing relative demand for recreational destinations. This difference may be because urban trails are used for multiple purposes, including routine commuting and shopping, and that trail users are less inclined to use social media on trips for these purposes.
Read moreMeasuring Recreational Visitation at US National Parks with Crowd-Sourced Photographs
Figure 1. Average monthly visitation in each park, from 2007 to 2012, expressed as the percent of total visits measured by NPS and Flickr PUD. Land managers rely on visitation data to inform policy and management decisions. However, visitation data is often costly and burdensome to obtain, and provides a limited depth of information. In this paper, we assess the validity of using crowd-sourced, online photographs to infer information about the habits and preferences of recreational visitors by comparing empirical data from the National Park Service to photograph data from the online platform Flickr for 38 National Parks in the western United States. Using multiple regression analysis, we find that the number of photos posted monthly in a park can reliably indicate the number of visitors to a park in a given month. Through additional statistical testing we also find that the home locations of photo-takers, provided voluntarily on an online profile, accurately show the home origins of park visitors. Together, these findings validate a new method for measuring recreational visitation, opening an opportunity for land managers worldwide to track and understand visitation by augmenting current data collection methods with crowd-sourced, online data that is easy and inexpensive to obtain. In addition, it enables future research on how visitation rates change with changes in access, management or infrastructure, weather events, or ecosystem health, and facilitates valuation research, such as travel cost studies.
Read moreSpatial and Temporal Dynamics and Value of Nature-Based Recreation, Estimated via Social Media
Figure 1. Conserved lands in Vermont. Conserved lands provide multiple ecosystem services, including opportunities for nature-based recreation. Managing this service requires understanding the landscape attributes underpinning its provision, and how changes in land management affect its contribution to human wellbeing over time. However, evidence from both spatially explicit and temporally dynamic analyses is scarce, often due to data limitations. In this study, we investigated nature-based recreation within conserved lands in Vermont, USA. We used geotagged photographs uploaded to the photo-sharing website Flickr to quantify visits by in-state and out-of-state visitors, and we multiplied visits by mean trip expenditures to show that conserved lands contributed US $1.8 billion (US $0.18–20.2 at 95% confidence) to Vermont’s tourism industry between 2007 and 2014. We found eight landscape attributes explained the pattern of visits to conserved lands; visits were higher in larger conserved lands, with less forest cover, greater trail density and more opportunities for snow sports. Some of these attributes differed from those found in other locations, but all aligned with our understanding of recreation in Vermont. We also found that using temporally static models to inform conservation decisions may have perverse outcomes for nature-based recreation. For example, static models suggest conserved land with less forest cover receive more visits, but temporally dynamic models suggest clearing forests decreases, rather than increases, visits to these sites. Our results illustrate the importance of understanding both the spatial and temporal dynamics of ecosystem services for conservation decision-making.
Read moreApplying Novel Visitation Models using Diverse Social Media to Understand Recreation Change after Wildfire and Site Closure
Figure 1. Location of study sites in the Columbia River Gorge, USA. The area burned in the Eagle Creek Fire is shown in red. Sites are numbered from west to east. Names and closure and reopening dates are in Table S1. The purple star on the reference map shows the location of the study area in the USA. The Columbia River forms the border between Washington (to the north) and Oregon (to the south) in this region. Natural disturbances such as wildfires are increasing in severity and frequency. Although the ecological impacts of disturbance are well documented, we have limited understanding of how disturbances and associated management responses influence recreation use patterns. This reflects, in part, difficulty in quantifying recreation use across different land ownerships with inconsistent, or non-existent, recreation monitoring practices. In this study, we use visitation models based on social media to examine how recreation use changed after a wildfire and site closures in a large, mixed-ownership landscape. We find that wildfire and associated closures resulted in visitation loss to the recreation system as a whole and little site-to-site displacement within the system in the two years following the wildfire. Our study highlights the importance, when considering how wildfire and management may alter recreation use patterns, of considering the many factors that influence substitution behavior, including the relative locations of visitor origins, disturbances, and substitute sites.
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