Integrating social media analysis and revealed preference methods to value the recreation services of ecologically engineered wetlands

Abstract Social media provide a wealth of behavioral data that can be used to investigate the provision of environmental services. In this study, the preferences revealed by photo-sharing social media users are analyzed through travel cost modeling to infer the monetary value of recreation in 115 man-made wetland ecosystems. Photographs’ metadata and other publicly available information are used to determine the frequency of recreational trips and the home location of visitors. The mean willingness to pay for access to 115 wetlands is found to range between $5.3 and $374 (2015 international $). The comparison of estimated recreational benefits with the capital, operation and maintenance costs of 74 wetlands reveals that such benefits are considerable and should be taken into account in the design and management of these systems. The approach demonstrates the potential for ecosystem service valuation techniques to incorporate the large amounts of behavioral data available from online resources.

[1]  J. Gower.  A General Coefficient of Similarity and Some of Its Properties , 1971 .

[2]  J. Podani.  Extending Gower's general coefficient of similarity to ordinal characters , 1999 .

[3]  Mary Jo Kealy,et al.  Randomly Drawn Opportunity Sets in a Random Utility Model of Lake Recreation , 1992 .

[4]  Dennis Zielstra,et al.  Positional accuracy analysis of Flickr and Panoramio images for selected world regions , 2013 .

[5]  Kay W. Axhausen,et al.  Self-tracing and reporting: state of the art in the capture of revealed behaviour , 2014 .

[6]  M. Goodchild,et al.  Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice , 2012 .

[7]  G. Parsons.  The Travel Cost Model , 2003 .

[8]  Christian Heipke,et al.  Crowdsourcing geospatial data , 2010 .

[9]  A. Ghermandi,et al.  Cultural ecosystem services of multifunctional constructed treatment wetlands and waste stabilization ponds: Time to enter the mainstream? , 2015 .

[10]  Stephen Polasky,et al.  Recreational demand for clean water: evidence from geotagged photographs by visitors to lakes , 2014 .

[11]  Andrea Ghermandi,et al.  Analysis of intensity and spatial patterns of public use in natural treatment systems using geotagged photos from social media. , 2016, Water research.

[12]  R. L. Knight.  Ancillary benefits and potential problems with the use of wetlands for nonpoint source pollution control , 1992 .

[13]  Andrea Ghermandi,et al.  Integrating similarity analysis and ecosystem service value transfer: Results from a tropical coastal wetland in India , 2016 .

[14]  Jeffrey Englin,et al.  Estimating Social Welfare Using Count Data Models: An Application to Long-Run Recreation Demand under Conditions of Endogenous Stratification and Truncation , 1995 .

[15]  Alex Singleton.  Neogeography: Engaging People for Common Good , 2010 .

[16]  T. Ricketts,et al.  Spatial and Temporal Dynamics and Value of Nature-Based Recreation, Estimated via Social Media , 2016, PloS one.

[17]  M. Goodchild.  Citizens as sensors: the world of volunteered geography , 2007 .

[18]  S. Navrud,et al.  Using Meta-Analysis and GIS for Value Transfer and Scaling Up: Valuing Climate Change Induced Losses of European Wetlands , 2012 .

[19]  Carlo Ratti,et al.  Choosing the Right Home Location Definition Method for the Given Dataset , 2015, SocInfo.

[20]  I. Bateman,et al.  Using revealed preferences to estimate the Value of Travel Time to recreation sites , 2014 .

[21]  Spencer A Wood,et al.  Measuring recreational visitation at U.S. National Parks with crowd-sourced photographs. , 2016, Journal of environmental management.

[22]  V. Resh,et al.  Wastewater‐effluent‐dominated streams as ecosystem‐management tools in a drier climate , 2015 .

[23]  David W. Graham,et al.  Designed ecosystem services: application of ecological principles in wastewater treatment engineering , 2004 .

[24]  M. Goodchild,et al.  Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr , 2013 .

[25]  S. Lovell,et al.  Creating multifunctional landscapes: how can the field of ecology inform the design of the landscape? , 2009 .

[26]  Henrikki Tenkanen,et al.  User-Generated Geographic Information for Visitor Monitoring in a National Park: A Comparison of Social Media Data and Visitor Survey , 2017, ISPRS Int. J. Geo Inf..

[27]  R. L. Knight.  Wildlife habitat and public use benefits of treatment wetlands , 1997 .

[28]  A. Cottam,et al.  Using Social Media to Measure the Contribution of Red List Species to the Nature-Based Tourism Potential of African Protected Areas , 2015, PloS one.

[29]  Gary Austin.  Multifunctional Wastewater Treatment Landscapes , 2013, Landscape Journal.

[30]  Oscar Alfranca,et al.  Economic valuation of a created wetland fed with treated wastewater located in a peri-urban park in Catalonia, Spain. , 2011, Water science and technology : a journal of the International Association on Water Pollution Research.

[31]  E. Rutherford,et al.  Using cultural ecosystem services to inform restoration priorities in the Laurentian Great Lakes , 2015 .

[32]  Michael Creel,et al.  Theoretical and Empirical Advantages of Truncated Count Data Estimators for Analysis of Deer Hunting in California , 1990 .

[33]  A. Guerry,et al.  Using social media to quantify nature-based tourism and recreation , 2013, Scientific Reports.