Abstract
Context
The roles of landscape variables regarding the recreational services provided by nature parks have been widely studied. However, the potential scale effects of the relationships between landscape variables and categorized nature experiences have not been adequately studied from an experimental perspective.
Objectives
This article demonstrates multiscale geographically weighted regression (MGWR) as a new method to quantify the relationship between experiences and landscape variables and aims to answer the following questions: (1) Which dimensions of landscape experiences can be interpreted from geocoded social media data, and how are these experiences associated with specific landscape variables? (2) At what spatial scale and relative magnitude can landscape variables mediate landscape experiences?
Methods
Social media data (Flickr photos) from Amager Nature Park were categorized into different dimensions of landscape experience. Estimated parameter surfaces resulted from the MGWR were generated to show the patterns of the relationship between the landscape variables and the categorized experiences.
Results
All considered landscape variables were identified as relating to certain landscape experiences (nature, animals, scenery, engagement, and culture). Scale effects were observed in all relationships. This highlights the realities of context- and place-specific relationships as well as the limited applicability of simple approaches that are incapable of accounting for spatial heterogeneity and scale.
Conclusions
The spatial effect of landscape variables on landscape experiences was clarified and demonstrated to be important for understanding the spatial patterns of landscape experiences. The demonstrated modelling method may be used to further the study of the value of natural landscapes to human wellbeing.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Code availability
The Python scripts used to produce dataset in the study is available from the corresponding author on reasonable request.
References
Allen T, Starn T (1982) Hierarchy. Perspectives for ecological complexity. University of Chicago, Chicago
Antoniou V, Morley J, Haklay M (2010) Web 2.0 geotagged photos: assessing the spatial dimension of the phenomenon. Geomatica 64(1):99–110
Arkema KK, Verutes GM, Wood SA et al (2015) Embedding ecosystem services in coastal planning leads to better outcomes for people and nature. Proc Natl Acad Sci 112(24):7390–7395
Arnberger A, Aikoh T, Eder R, Shoji Y, Mieno T (2010) How many people should be in the urban forest? A comparison of trail preferences of Vienna and Sapporo forest visitor segments. Urban for Urban Green 9(3):215–225
Assessment ME (2005) Ecosystems and human well-being. Island Press, Washington DC
Atauri JA, Bravo MA, Ruiz A (2000) Visitors’ landscape preferences as a tool for management of recreational use in natural areas: a case study in Sierra de Guadarrama (Madrid, Spain). Landsc Res 25(1):49–62
Bertram C, Rehdanz K (2015) Preferences for cultural urban ecosystem services: comparing attitudes, perception, and use. Ecosyst Serv 12:187–199
Boyd D, Crawford K (2011) Six provocations for big data. In: A decade in internet time: symposium on the dynamics of the internet and society
Brown G, Montag JM, Lyon K (2012) Public participation GIS: a method for identifying ecosystem services. Soc Nat Resour 25(7):633–651
Brown G, Reed P, Raymond CM (2020) Mapping place values: 10 lessons from two decades of public participation GIS empirical research. Appl Geogr 116:102156
Calcagni F, Maia ATA, Connolly JJT, Langemeyer J (2019) Digital co-construction of relational values: understanding the role of social media for sustainability. Sustain Sci 14(5):1309–1321
Callau AÀ, Albert MYP, Rota JJ, Giné DS (2019) Landscape characterization using photographs from crowdsourced platforms: content analysis of social media photographs. Open Geosci 11(1):558–571
Chan KMA, Satterfield T, Goldstein J (2012) Rethinking ecosystem services to better address and navigate cultural values. Ecol Econ 74:8–18
Chien Y-MC, Carver S, Comber A (2020) Using geographically weighted models to explore how crowdsourced landscape perceptions relate to landscape physical characteristics. Landsc Urban Plan 203:103904
Chiesura A (2004) The role of urban parks for the sustainable city. Landsc Urban Plan 68(1):129–138
Cushman SA, McGarigal K (2002) Hierarchical, multi-scale decomposition of species-environment relationships. Landsc Ecol 17(7):637–646
De Groot RS, Alkemade R, Braat L, Hein L, Willemen L (2010) Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol Complex 7(3):260–272
Dramstad WE, Tveit MS, Fjellstad W, Fry GL (2006) Relationships between visual landscape preferences and map-based indicators of landscape structure. Landsc Urban Plan 78(4):465–474
Figueroa-Alfaro RW, Tang Z (2017) Evaluating the aesthetic value of cultural ecosystem services by mapping geo-tagged photographs from social media data on Panoramio and Flickr. J Environ Plan Manag 60(2):266–281
Foltête J-C, Ingensand J, Blanc N (2020) Coupling crowd-sourced imagery and visibility modelling to identify landscape preferences at the panorama level. Landsc Urban Plan 197:103756
Fotheringham AS, Oshan TM (2016) Geographically weighted regression and multicollinearity: dispelling the myth. J Geogr Syst 18(4):303–329
Fotheringham AS, Yang W, Kang W (2017) Multiscale geographically weighted regression (MGWR). Ann Am Assoc Geogr 107(6):1247–1265
Garcia-Martin M, Fagerholm N, Bieling C et al (2017) Participatory mapping of landscape values in a Pan-European perspective. Landsc Ecol 32(11):2133–2150
Garrod B (2007) A snapshot into the past: the utility of volunteer-employed photography in planning and managing heritage tourism. J Herit Tour 2(1):14–35
Gobster PH, Nassauer JI, Daniel TC, Fry G (2007) The shared landscape: what does aesthetics have to do with ecology? Landsc Ecol 22(7):959–972
Gosal AS, Giannichi ML, Beckmann M et al (2021) Do drivers of nature visitation vary spatially? The importance of context for understanding visitation of nature areas in Europe and North America. Sci Total Environ 776:145190
Graf RF, Bollmann K, Suter W, Bugmann H (2005) The importance of spatial scale in habitat models: capercaillie in the Swiss Alps. Landsc Ecol 20(6):703–717
Guerrero P, Møller MS, Olafsson AS, Snizek B (2016) Revealing cultural ecosystem services through Instagram images: the potential of social media volunteered geographic information for urban green infrastructure planning and governance. Urban Plan 1(2):1–17
Hale RL, Cook EM, Beltrán BJ (2019) Cultural ecosystem services provided by rivers across diverse social-ecological landscapes: a social media analysis. Ecol Indic 107:105580
Hammitt WE, Patterson ME, Noe FP (1994) Identifying and predicting visual preference of southern Appalachian forest recreation vistas. Landsc Urban Plan 29(2–3):171–183
Hamstead ZA, Fisher D, Ilieva RT, Wood SA, McPhearson T, Kremer P (2018) Geolocated social media as a rapid indicator of park visitation and equitable park access. Comput Environ Urban Syst 72:38–50
Harris P, Fotheringham AS, Juggins S (2010) Robust geographically weighted regression: a technique for quantifying spatial relationships between freshwater acidification critical loads and catchment attributes. Ann Assoc Am Geogr 100(2):286–306
Hausmann A, Toivonen T, Heikinheimo V, Tenkanen H, Slotow R, Di Minin E (2017) Social media reveal that charismatic species are not the main attractor of ecotourists to sub-Saharan protected areas. Sci Rep 7(1):1–9
Hegetschweiler KT, de Vries S, Arnberger A et al (2017) Linking demand and supply factors in identifying cultural ecosystem services of urban green infrastructures: a review of European studies. Urban for Urban Green 21:48–59
Heikinheimo V, Minin ED, Tenkanen H, Hausmann A, Erkkonen J, Toivonen T (2017) User-generated geographic information for visitor monitoring in a national park: a comparison of social media data and visitor survey. ISPRS Int J Geo Inf 6(3):85
Heikinheimo V, Tenkanen H, Bergroth C, Järv O, Hiippala T, Toivonen T (2020) Understanding the use of urban green spaces from user-generated geographic information. Landsc Urban Plan 201:103845
Helden AJ, Stamp GC, Leather SR (2012) Urban biodiversity: comparison of insect assemblages on native and non-native trees. Urban Ecosyst 15(3):611–624
Huais PY (2018) multifit: an R function for multi-scale analysis in landscape ecology. Landsc Ecol 33(7):1023–1028
Ilieva RT, McPhearson T (2018) Social-media data for urban sustainability. Nat Sustain 1(10):553–565
Ittelson WH (1973) Environment and cognition. Seminar Press, New York
Jaimes NBP, Sendra JB, Delgado MG, Plata RF (2010) Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression. Appl Geogr 30(4):576–591
Johnson ML, Campbell LK, Svendsen ES, McMillen HL (2019) Mapping urban park cultural ecosystem services: a comparison of twitter and semi-structured interview methods. Sustainability 11(21):6137
Kaae BC, Holm J, Caspersen OH, Gulsrud NM (2019) Nature Park Amager–examining the transition from urban wasteland to a rewilded ecotourism destination. J Ecotour 18(4):348–367
Kolasa J, Pickett ST (1991) Ecological heterogeneity. Citeseer
Komossa F, Wartmann FM, Kienast F, Verburg PH (2020) Comparing outdoor recreation preferences in peri-urban landscapes using different data gathering methods. Landsc Urban Plan 199:103796
Lin L, Homma R, Iki K (2018) Preferences for a lake landscape: effects of building height and lake width. Environ Impact Assess Rev 70:22–33
McCay-Peet L, Quan-Haase A (2017) What is social media and what questions can social media research help us answer. In: The SAGE handbook of social media research methods, pp 13–26
McGarigal K, Wan HY, Zeller KA, Timm BC, Cushman SA (2016) Multi-scale habitat selection modeling: a review and outlook. Landsc Ecol 31(6):1161–1175
Meentemeyer V, Box EO (1987) Scale effects in landscape studies. In: Landscape heterogeneity and disturbance. Springer, pp. 15–34
Naturstyrelsen, Københavns Kommune, Tårnby Kommune, Dragør Kommune og By & Havn (2019) naturparkplan 2020–2025 - Naturpark Amager. https://naturparkamager.dk/media/274699/naturparkplan-20-25_endelig_version2_lav-oploesning.pdf
O’Brien L, De Vreese R, Kern M, Sievänen T, Stojanova B, Atmiş E (2017) Cultural ecosystem benefits of urban and peri-urban green infrastructure across different European countries. Urban for Urban Green 24:236–248
Ode Å, Tveit MS, Fry G (2008) Capturing landscape visual character using indicators: touching base with landscape aesthetic theory. Landsc Res 33(1):89–117
Ode Å, Fry G, Tveit MS, Messager P, Miller D (2009) Indicators of perceived naturalness as drivers of landscape preference. J Environ Manag 90(1):375–383
Ogneva-Himmelberger Y, Pearsall H, Rakshit R (2009) Concrete evidence & geographically weighted regression: a regional analysis of wealth and the land cover in Massachusetts. Appl Geogr 29(4):478–487
Oshan TM, Li Z, Kang W, Wolf LJ, Fotheringham AS (2019) mgwr: a Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS Int J Geo Inf 8(6):269
Oshan TM, Smith JP, Fotheringham AS (2020) Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. Int J Health Geogr 19(1):1–17
Oteros-Rozas E, Martín-López B, Fagerholm N, Bieling C, Plieninger T (2018) Using social media photos to explore the relation between cultural ecosystem services and landscape features across five European sites. Ecol Ind 94:74–86
Paracchini ML, Zulian G, Kopperoinen L et al (2014) Mapping cultural ecosystem services: a framework to assess the potential for outdoor recreation across the EU. Ecol Ind 45:371–385
Pastur GM, Peri PL, Lencinas MV, García-Llorente M, Martín-López B (2016) Spatial patterns of cultural ecosystem services provision in Southern Patagonia. Landsc Ecol 31(2):383–399
Pickering C, Walden-Schreiner C, Barros A, Rossi SD (2020) Using social media images and text to examine how tourists view and value the highest mountain in Australia. J Outdoor Recreat Tour 29:100252
Retka J, Jepson P, Ladle RJ et al (2019) Assessing cultural ecosystem services of a large marine protected area through social media photographs. Ocean Coast Manag 176:40–48
Richards R (2001) A new aesthetic for environmental awareness: Chaos theory, the beauty of nature, and our broader humanistic identity. J Humanist Psychol 41(2):59–95
Richards DR, Friess DA (2015) A rapid indicator of cultural ecosystem service usage at a fine spatial scale: content analysis of social media photographs. Ecol Ind 53:187–195
Richards DR, Tunçer B (2018) Using image recognition to automate assessment of cultural ecosystem services from social media photographs. Ecosyst Serv 31:318–325
Russell R, Guerry AD, Balvanera P et al (2013) Humans and nature: how knowing and experiencing nature affect well-being. Annu Rev Environ Resour 38(1):473–502
Ruths D, Pfeffer J (2014) Social media for large studies of behavior. Science 346(6213):1063–1064
Šímová P, Gdulová K (2012) Landscape indices behavior: a review of scale effects. Appl Geogr 34:385–394
SNC-Lavalin Atkins (2020) Natura 2000-væsentlighedsvurdering af ’Naturpark Amager –Hovedindgange og blå støttepunkter’ - Vurdering af rekreative faci- liteter beliggende i henholdsvis Københavns Kommune og Tårnby Kommune. https://tbst.dk/da/-/media/TBST-DA/Miljoevurdering/Lister/VVM-dokumenter/Havne/2020/Naturpark-Amager/N2000VV---Naturpark-Amager.pdf
Tenerelli P, Demšar U, Luque S (2016) Crowdsourcing indicators for cultural ecosystem services: a geographically weighted approach for mountain landscapes. Ecol Ind 64:237–248
Tieskens KF, Van Zanten BT, Schulp CJ, Verburg PH (2018) Aesthetic appreciation of the cultural landscape through social media: an analysis of revealed preference in the Dutch river landscape. Landsc Urban Plan 177:128–137
Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46(sup1):234–240
Toivonen T, Heikinheimo V, Fink C et al (2019) Social media data for conservation science: a methodological overview. Biol Conserv 233:298–315
Tveit MS (2009) Indicators of visual scale as predictors of landscape preference; a comparison between groups. J Environ Manag 90(9):2882–2888
Van Berkel DB, Tabrizian P, Dorning MA et al (2018) Quantifying the visual-sensory landscape qualities that contribute to cultural ecosystem services using social media and LiDAR. Ecosyst Serv 31:326–335
Wang Z, Jin Y, Liu Y, Li D, Zhang B (2018) Comparing social media data and survey data in assessing the attractiveness of Beijing Olympic Forest Park. Sustainability 10(2):382
Wartmann FM, Acheson E, Purves RS (2018) Describing and comparing landscapes using tags, texts, and free lists: an interdisciplinary approach. Int J Geogr Inf Sci 32(8):1572–1592
Wartmann FM, Tieskens KF, van Zanten BT, Verburg PH (2019) Exploring tranquillity experienced in landscapes based on social media. Appl Geogr 113:102112
Wilson MC, Hu G, Jiang L, Liu J, Liu J, Jin Y, Yu M, Wu J (2020) Assessing habitat fragmentation’s hierarchical effects on species diversity at multiple scales: the case of Thousand Island Lake, China. Landscape Ecology 35(2):501–512
Wu J (1999) Hierarchy and scaling: extrapolating information along a scaling ladder. Can J Remote Sens 25(4):367–380
Wu J (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landsc Ecol 19(2):125–138
Wu J (2007) Scale and scaling: a cross-disciplinary perspective. In: Key topics in landscape ecology. Cambridge University Press, Cambridge, pp 115–142
Wu J, Li H (2006) Concepts of scale and scaling. In: Scaling and uncertainty analysis in ecology. Springer, pp 3–15
Wu J, Shen W, Sun W, Tueller PT (2002) Empirical patterns of the effects of changing scale on landscape metrics. Landsc Ecol 17(8):761–782
Yu H, Fotheringham AS, Li Z, Oshan T, Kang W, Wolf LJ (2020) Inference in multiscale geographically weighted regression. Geogr Anal 52(1):87–106
Zhang H, Chen B, Sun Z, Bao Z (2013) Landscape perception and recreation needs in urban green space in Fuyang, Hangzhou, China. Urban for Urban Green 12(1):44–52
Zhang H, Huang R, Zhang Y, Buhalis D (2020) Cultural ecosystem services evaluation using geolocated social media data: a review. Tour Geogr. https://doi.org/10.1080/14616688.2020.1801828
Zube EH, Sell JL, Taylor JG (1982) Landscape perception: research, application and theory. Landsc Plan 9(1):1–33
Acknowledgements
We are grateful to the anonymous reviewers for their valuable comments on the manuscript of this paper. PC gratefully acknowledges the funding from China Scholarship Council.
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PC is funded by China Scholarship Council.
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Both authors contributed to the conceptualization and methodology of the study and manuscript writing and reviewing. PC performed formal analysis, investigation, visualization, and software. ASO provided resources and supervision.
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Chang, P., Olafsson, A.S. The scale effects of landscape variables on landscape experiences: a multi-scale spatial analysis of social media data in an urban nature park context. Landsc Ecol 37, 1271–1291 (2022). https://doi.org/10.1007/s10980-022-01402-2
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DOI: https://doi.org/10.1007/s10980-022-01402-2