Understanding spatial-temporal changes in ecosystem services using mobile phone network data

Abstract

Climate change is predicted to have substantial impacts on ecosystem services [1]. In particular, seasonal outdoor recreation at coastal and mountain areas can be degraded and threatened. So far, many valuation studies have assessed recreational services over the world; however, the spatial and temporal limits of data prevented estimation of the reliable values of seasonal recreation services. Recently, to resolve these challenges in the context of ecosystem services, there is a growing body of literature on applications of social media data and the platforms, such as Twitter, Instagram, and Flickr [2, 3]; although few studies have evaluated the services in monetary terms. The travel cost model, which is one of the traditional valuation methods, using mobile phone network data was applied to understand recreational ecosystem services at coastal and mountain areas in Japan. The data, which called Mobile Spatial Statistics (MSS), is derived from operational data of a mobile phone network on an hourly basis and non-self reported. Thus, to protect the mobile phone users' privacy, mesh aggregation has been implemented on the data by the company. Our findings from the travel cost model with the unique mobile phone data demonstrated the ecosystem service values at coastal and mountain areas. We found the spatial-temporal differences in the estimated values; especially, the change on a daily basis in each recreational site implied the seasonal changes of ecosystem services associated with changes in temporal environmental conditions and qualities. Although our unique data enabled us to estimate the value by 500m grid cell, still it was difficult to identify what kinds of recreational activities were performed and whether or not the activities have negative impacts on biodiversity. Further studies are recommended to integrate other social media data with photos and texts into our approach to remedy these issues. Development of applications of new technologies and data sources into conservation science will enhance understanding ecosystem services and nature-human relationships and help decision making under climate change.

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