Ecotourism, defined as tourism to areas of ecological interest, is a multi-trillion dollar global industry which by its very nature tend to be concentrated in areas where ecosystems are at risk (Buckley, 2011). Because of this, tourism is often degrading or destructive to the environment, especially for nature parks and conservation areas within alpine and sub-alpine ecosystems. For example, Pickering and Growcock (2009), studied the effects of tourists in the grasslands of the Australian Alps, finding that only moderate levels of trampling (approx. 280 passes) required over a year to reach 75% recovery in vegetation height and cover. In a study done in China, tourism disturbance had a significant (p-value <0.001) negative influence on species richness and heterogeneity in sub-alpine meadows, altering the composition of the ecosystem to become more homogeneous and unbalanced (Zhang et al., 2011). The negative impacts of tourist disturbance (via trampling or other disturbances) pose a significant problem, because changes in the composition of these sub-alpine ecosystems “...may alter important ecosystem functions such as productivity, nutrient retention and resistance to plant invasions” (Pickering & Barros, 2015, p.134). While changes in the composition of sub-alpine parks have been associated with climate change as well as tourism, it is outside of the scope of this project to address climate change (Langdon & Lawler, 2015). Furthermore, parks and conservation areas of all kinds, including sub-alpine and alpine ones, are important for meeting our global biodiversity targets and to provide humans with the ability to connect with nature. Thus there is an incentive to protect these parks from tourist impacts. (Tenkanen et al., 2017).
To maintain their esthetic beauty and ecological functionality, it is required that park management monitor both the movements of visitors within their conservancy and identify where ecologically sensitive areas located (Hadwen et al., 2007 ; Hale, 2017; Tenkanen et al., 2017). However, current methods of visitor monitoring, such as surveys and visitor logs used in European and North American parks, are insufficient. These methods often fail to track the true spatial extent of visitors within the parks, which is important information to know if there are ecosystems that are sensitive to the presence of tourists. A significant number of parks and conservation areas often do not have the funding, the staff, nor the expertise to properly monitor visitors and ecologically sensitive areas (Hadwen et al., 2007 ; Hale, 2017; Tenkanen et al., 2017). Even if there are measures in place to manage tourist activity, such as enforcing restrictions, it is difficult to properly assess the success of these management practices (Zhang et al., 2011). Additionally, there is not much research done in Canadian parks on monitoring visitor spatial patterns within ecologically sensitive sites, as most of this research is performed in Europe or the United States of America.
To solve this problem, publically available geotagged data from social media platforms can be leveraged to assess the spatial distribution of visitors in parks and conservation areas through the use of their publically available application programming interface (API). This data can then be combined with ecological sensitivity indices (ESI) using GIS software to determine how prevalent tourist are within ecologically sensitive areas. In a study by Hale (2017), conducted in the Westfjords region of Iceland, this workflow was used to determine how sensitive frequently visited sites are to ecological degradation using data from Flickr and an ESI that was developed by Olafsdottir and Runnström (2009). Social media platforms, like Instagram, Flickr and Twitter, create geo-location data (longitude and latitude) from every post that has geo-tags enabled (Hale, 2017; Heikinheimo et al., 2017; Tenkanen et al., 2017). This accessible data has been utilized in previous studies on spatial distributions of tourism, often used in collaboration with geographic information systems (GIS) to map-out popular restaurants and tourist activity within urban areas (Zhai et al., 2015). Recently, researchers have started to integrate social media API into their studies on tourist activity in protected areas and national parks, as it serves as a cost-effective and time saving alternative to traditional means of visitor monitoring. For example, Heikinheimo et al (2017) used social media data to gather statistical data on visitors to the Pallas-Yllästunturi National Park in Finland. Using GIS, the researchers were able to map out where visitors most often congregated in parks and when they most visited (Heikinheimo et al., 2017). A study on the use of Instagram, Flickr, and Twitter within 56 national parks in Finland and South Africa showed that over 60% of national parks have social media activity. Additionally, the study showed a Spearman’s rank correlation of 0.75 between social media data and official visitor data records (Tenkanen et al., 2017).
For the purpose of monitoring visitor impacts within parks and conservation areas, this publically available data can be combined with ecological sensitivity indices (ESI) created from data provided from resources like government databases. This process is similar to how Hale (2017) used Flickr data with an ESI developed by Olafsdottir and Runnström (2009). This ESI was created in ArcGIS software (ESRI) by taking a variety of input layers (soil type, vegetation cover, and a DEM) and combining them in a single raster overlay that displays the accumulated sensitivity values. The sensitivity value is an indication of a site’s susceptibility to tourism pressure; a high sensitivity value indicates a need for full protection from tourist trampling (Olafsdottir and Runnström, 2009). Hale (2017) then used the ESI and overlaid the Flickr data, creating a density map (heatmap function in QGIS) of where visitors most often congregated. Finally, to determine the overall impact of tourist activity, “the average values of the ESI and the heat score were calculated for a zone of 50 m surrounding each point” (Hale, 2017, pg. 5).
Since research into tourist ecological impacts is lacking in Canada, it is important that a similar study to Hale (2017) is done within a Canadian setting. Additionally, because sub-alpine and alpine ecosystems are quite sensitive to tourist activities, a sub-alpine nature park was chosen for this project. Due to it's proximity to the City of Vancouver, and its sub-alpine ecosystem, Mount Seymour Provincial Park was chosen as the study area for this project. Through the use of Flickr data extracted from Flickr API, information on the spatial distribution of tourists in Mount Seymour Provincial Park was retrieved and combined with a self-constructed ESI created based on the methods of Olafsdottir and Runnström (2009).
Purpose of Research
The purpose of this project was to create a GIS-based model of ecological sensitivity and tourist distribution for Mount Seymour Provincial Park derived from Geotagged Social Media data and an Ecological Sensitivity Index.
1: To identify the factors that contribute to the ecological sensitivity of Mount Seymour Provincial Park.
2: To develop and apply a GIS-based Ecological Sensitivity Index (ESI).
3: To retrieve geotagged data from Flickr or Twitter.
4: To combine the geotagged data with the ESI to determine ecotourism sustainability.
5: To evaluate the assumptions and limitations of this model.