Living in urban areas is known to increase the risk of psychosocial disorders, including stress, depression, and anxiety. Existing studies suggest that experiential places, including places of interest or favourite places, can mitigate these negative effects on psychological and physical health often associated with urban living. This study aims to model the spatial patterns of the benefits derived from favourite locations in two cities in Denmark: an urban metropolitan area (the capital city) and a provincial commuter town. Additionally, it examines the influence of individual and household socioeconomic factors on the benefits derived from these favourite places. Employing an online Public Participatory Geographic Information System (PPGIS) approach, data on favourite locations, derived benefits, and socioeconomic characteristics of 1400 respondents were collected. Bayesian modelling with Stochastic Partial Differential Equations under the Integrated Nested Laplace Approximation framework (INLA-SPDE) was utilized to predict the spatial patterns of four types of benefits – restorative, physical activity, socializing, and cultural – associated with enjoying favourite places in the two municipalities. This geostatistical approach allows for the identification of specific locations within the cities with perceived benefits and areas lacking such benefits. The findings provide insights into potential inequalities in the spatial distribution of perceived benefits of favourite places in Copenhagen and Roskilde, thereby informing urban planning policies and programs aimed at addressing these disparities.
AngelACohenADalyotS, et al. (2022) Impact of COVID-19 policies on pedestrian traffic and walking patterns. Environment and Planning B: Urban Analytics and City Science50(5): 1178–1193. DOI: 10.1177/23998083221113332.
2.
BakkaHRueHFuglstadGA, et al. (2018) Spatial modeling with R-INLA: a review. WIREs Computational Statistics10(6): 1–24. DOI: 10.1002/wics.1443.
3.
BezoldCPBanayRFCoullBA, et al. (2018) The association between natural environments and depressive symptoms in adolescents living in the United States. Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine62(4): 488–495. DOI: 10.1016/j.jadohealth.2017.10.008.
4.
BiggerPNeimarkBD (2017) Weaponizing nature: the geopolitical ecology of the US Navy’s biofuel program. Political Geography60: 13–22. DOI: 10.1016/j.polgeo.2017.03.007.
5.
BivandRRowlingsonBDiggleP, et al. (2023) Splancs: spatial and space-time point pattern analysis. 2.01-44. R package, Available at: https://cran.r-project.org/package=splancs.
6.
BlangiardoMCamelettiM (2015) Spatial and Spatio-Temporal Bayesian Models with R-INLA. Hoboken, NJ: John Wiley & Sons.
7.
BrownGWeberD (2012) Measuring change in place values using public participation GIS (PPGIS). Applied Geography34: 316–324. DOI: 10.1016/j.apgeog.2011.12.007.
8.
BrownGRaymondCMCorcoranJ (2015) Mapping and measuring place attachment. Applied Geography57: 42–53. DOI: 10.1016/j.apgeog.2014.12.011.
9.
CerinELeslieE (2008) How socio-economic status contributes to participation in leisure-time physical activity. Social Science & Medicine66(12): 2596–2609. DOI: 10.1016/j.socscimed.2008.02.012.
10.
EvansBEHuizinkACGreaves-LordK, et al. (2020) Urbanicity, biological stress system functioning and mental health in adolescents. PLoS One15(3): e0228659. DOI: 10.1371/journal.pone.0228659.
11.
FoleyRKistemannT (2015) Blue space geographies: enabling health in place. Health & Place35: 157–165. DOI: 10.1016/j.healthplace.2015.07.003.
12.
Giles-CortiBDonovanRJ (2002) Socioeconomic status differences in recreational physical activity levels and real and perceived access to a supportive physical environment. Preventive Medicine35(6): 601–611. DOI: 10.1006/pmed.2002.1115.
13.
HelbichMKleinNRobertsH, et al. (2018) More green space is related to less antidepressant prescription rates in The Netherlands: a Bayesian geoadditive quantile regression approach. Environmental Research166: 290–297. DOI: 10.1016/j.envres.2018.06.010.
14.
KnezIEliassonI (2017) Relationships between personal and collective place identity and well-being in mountain communities. Frontiers in Psychology8(JAN): 79–712. DOI: 10.3389/fpsyg.2017.00079.
15.
KorpelaKHartigT (1996) Restorative qualities of favorite places. Journal of Environmental Psychology16(3): 221–233. DOI: 10.1006/jevp.1996.0018.
16.
KorpelaKMYlénM (2007) Perceived health is associated with visiting natural favourite places in the vicinity. Health & Place13(1): 138–151. DOI: 10.1016/j.healthplace.2005.11.002.
17.
KorpelaKMHartigTKaiserFG, et al. (2001) Restorative experience and self-regulation in favorite places. Environment and Behavior33(4): 572–589. DOI: 10.1177/00139160121973133.
18.
KorpelaKMYlénMTyrväinenL, et al. (2008) Determinants of restorative experiences in everyday favorite places. Health & Place14(4): 636–652. DOI: 10.1016/j.healthplace.2007.10.008.
19.
KorpelaKKorhonenMNummiT, et al. (2020) Environmental self-regulation in favourite places of Finnish and Hungarian adults. Journal of Environmental Psychology67: 101384. DOI: 10.1016/j.jenvp.2019.101384.
20.
KrainskiETGómez-RubioVBakkaH, et al. (2019) Advanced Spatial Differential Equations Stochastic Partial Modeling with Using R and INLA. New York, NY: Chapman and Hall/CRC. DOI: 10.1201/9780429031892.
21.
KyttäMBrobergAHaybatollahiM, et al. (2016) Urban happiness: context-sensitive study of the social sustainability of urban settings. Environment and Planning B: Planning and Design43(1): 34–57. DOI: 10.1177/0265813515600121.
22.
Lezama-OchoaNPenninoMGHallMA, et al. (2020) Using a bayesian modelling approach (INLA-SPDE) to predict the occurrence of the spinetail devil ray (mobular mobular). Scientific Reports10(1): 18822. DOI: 10.1038/s41598-020-73879-3.
23.
LinCCLockwoodM (2014) Forms and sources of place attachment: evidence from two protected areas. Geoforum53: 74–81. DOI: 10.1016/j.geoforum.2014.02.008.
24.
LindgrenFRueH (2015) Bayesian spatial modelling with R-INLA. Journal of Statistical Software63(19): 1–25. DOI: 10.18637/jss.v063.i19.
25.
LindgrenFRueHLindströmJ (2011a) An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society - Series B: Statistical Methodology73(4): 423–498. DOI: 10.1111/j.1467-9868.2011.00777.x.
26.
LindgrenFRueHLindströmJ (2011b) An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society - Series B: Statistical Methodology73(4): 423–498.
27.
LorenzenMAndersenKVLaursenS (2012) Creating: the creative-class based knowledge city models of Denmark. In: YigitcanlarTMetaxiotisKCarrilloFJ (eds) Building Prosperous Knowledge Cities - Policies, Plans and Metrics. Cheltenham, UK & Northampton, MA, USA: Edward Elgar Publishing Limited, 24–39.
28.
Lozano-GraciaN (2020) COVID and cities – a new demon that points at old problems: how can research help?Environment and Planning B: Urban Analytics and City Science47(7): 1128–1132. DOI: 10.1177/2399808320949298.
29.
MansteadASR (2018) The psychology of social class: how socioeconomic status impacts thought, feelings, and behaviour. British Journal of Social Psychology57(2): 267–291. DOI: 10.1111/bjso.12251.
30.
MartinsTGSimpsonDLindgrenF, et al. (2013) Bayesian computing with INLA: new features. Computational Statistics & Data Analysis67: 68–83. DOI: 10.1016/j.csda.2013.04.014.
31.
MavoaSLucassenMDennyS, et al. (2019) Natural neighbourhood environments and the emotional health of urban New Zealand adolescents. Landscape and Urban Planning191: 103638. DOI: 10.1016/j.landurbplan.2019.103638.
32.
MielkeGIMaltaDCNunesBP, et al. (2022) All are equal, but some are more equal than others: social determinants of leisure time physical activity through the lens of intersectionality. BMC Public Health22(1): 36. DOI: 10.1186/s12889-021-12428-7.
33.
MuñozFPenninoMGConesaD, et al. (2013) Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models. Stochastic Environmental Research and Risk Assessment27(5): 1171–1180. DOI: 10.1007/s00477-012-0652-3.
34.
OjalaAKorpelaKTyrväinenL, et al. (2019) Restorative effects of urban green environments and the role of urban-nature orientedness and noise sensitivity: a field experiment. Health & Place55: 59–70. DOI: 10.1016/j.healthplace.2018.11.004.
35.
PearceJRRichardsonEAMitchellRJ, et al. (2011) Environmental justice and health: a study of multiple environmental deprivation and geographical inequalities in health in New Zealand. Social Science & Medicine73(3): 410–420. DOI: 10.1016/j.socscimed.2011.05.039.
36.
PetersenCBBekker-JeppesenMAadahlM, et al. (2021) Participation in recreational activities varies with socioeconomic position and is associated with self-rated health and well-being. Preventive Medicine Reports24: 101610. DOI: 10.1016/j.pmedr.2021.101610.
37.
PurenKRoosVCoetzeeH (2018) Sense of place: using people’s experiences in relation to a rural landscape to inform spatial planning guidelines. International Planning Studies23(1): 16–36. DOI: 10.1080/13563475.2017.1329087.
38.
RatcliffeEKorpelaKM (2016) Memory and place attachment as predictors of imagined restorative perceptions of favourite places. Journal of Environmental Psychology48: 120–130. DOI: 10.1016/j.jenvp.2016.09.005.
39.
RatcliffeEKorpelaKM (2018) Time- and self-related memories predict restorative perceptions of favorite places via place identity. Environment and Behavior50(6): 690–720. DOI: 10.1177/0013916517712002.
40.
RueHMartinoSChopinN (2009) Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society - Series B: Statistical Methodology71(2): 319–392. DOI: 10.1111/j.1467-9868.2008.00700.x.
41.
RueHRieblerASørbyeSH, et al. (2017) Bayesian computing with INLA: a review. Annual Review of Statistics and Its Application4: 395–421. DOI: 10.1146/annurev-statistics-060116-054045.
42.
SabelCEAmegborPMZhangZ, et al. (2021) Urban health and well-being challenges. In: ShiWGoodchildMFBattyM, et al. (eds) Urban Informatics. The Urban Book Series. Singapore: Springer Singapore, 259–280. DOI: 10.1007/978-981-15-8983-6_17.
43.
SamuelssonKGiustiMPetersonGD, et al. (2018) Impact of environment on people’s everyday experiences in Stockholm. Landscape and Urban Planning171(July 2017): 7–17. DOI: 10.1016/j.landurbplan.2017.11.009.
44.
SamuelssonKColdingJBarthelS (2019) Urban resilience at eye level: spatial analysis of empirically defined experiential landscapes. Landscape and Urban Planning187(March): 70–80. DOI: 10.1016/j.landurbplan.2019.03.015.
45.
ShorttNKRichardsonEAPearceJ, et al. (2012) Mortality inequalities by environment type in New Zealand. Health & Place18(5): 1132–1136. DOI: 10.1016/j.healthplace.2012.04.008.
46.
Sonntag-ÖströmENordinMLundellY, et al. (2014) Restorative effects of visits to urban and forest environments in patients with exhaustion disorder. Urban Forestry and Urban Greening13(2): 344–354. DOI: 10.1016/j.ufug.2013.12.007.
StigsdotterUKCorazonSSSideniusU, et al. (2017) It is not all bad for the grey city – a crossover study on physiological and psychological restoration in a forest and an urban environment. Health & Place46: 145–154. DOI: 10.1016/j.healthplace.2017.05.007.
49.
ThomasF (2015) The role of natural environments within women’s everyday health and wellbeing in Copenhagen, Denmark. Health & Place35: 187–195. DOI: 10.1016/j.healthplace.2014.11.005.
50.
TuanY-F (1979) Space and place: humanistic perspective. In: GaleSOlssonG (eds) Philosophy in Geography. Theory and. Dordrecht: Springer, 379–427. DOI: 10.1007/978-94-009-9394-5_19.
51.
van BuurenSGroothuis-OudshoornK (2011) mice: Multivariate imputation by chained equations in R. Journal of Statistical Software45(3): 1–67. DOI: 10.18637/jss.v045.i03.
52.
WheelerBWLovellRHigginsSL, et al. (2015) Beyond greenspace: an ecological study of population general health and indicators of natural environment type and quality. International Journal of Health Geographics14(1): 17.
53.
WhiteMPElliottLRGrellierJ, et al. (2021) Associations between green/blue spaces and mental health across 18 countries. Scientific Reports11: 8903–8912. DOI: 10.1038/s41598-021-87675-0.
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