| Literature DB >> 33204057 |
Dehui Christina Geng1, John Innes1, Wanli Wu1, Guangyu Wang1.
Abstract
The COVID-19 pandemic has resulted in over 33 million confirmed cases and over 1 million deaths globally, as of 1 October 2020. During the lockdown and restrictions placed on public activities and gatherings, green spaces have become one of the only sources of resilience amidst the coronavirus pandemic, in part because of their positive effects on psychological, physical and social cohesion and spiritual wellness. This study analyzes the impacts of COVID-19 and government response policies to the pandemic on park visitation at global, regional and national levels and assesses the importance of parks during this global pandemic. The data we collected primarily from Google's Community Mobility Reports and the Oxford Coronavirus Government Response Tracker. The results for most countries included in the analysis show that park visitation has increased since February 16th, 2020 compared to visitor numbers prior to the COVID-19 pandemic. Restrictions on social gathering, movement, and the closure of workplace and indoor recreational places, are correlated with more visits to parks. Stay-at-home restrictions and government stringency index are negatively associated with park visits at a global scale. Demand from residents for parks and outdoor green spaces has increased since the outbreak began, and highlights the important role and benefits provided by parks, especially urban and community parks, under the COVID-19 pandemic. We provide recommendations for park managers and other decision-makers in terms of park management and planning during health crises, as well as for park design and development. In particular, parks could be utilized during pandemics to increase the physical and mental health and social well-being of individuals.Entities:
Keywords: COVID-19; COVID-19 response policies; Parks visitation; Stepwise regression analysis; Urban parks
Year: 2020 PMID: 33204057 PMCID: PMC7660132 DOI: 10.1007/s11676-020-01249-w
Source DB: PubMed Journal: J For Res (Harbin) ISSN: 1007-662X Impact factor: 2.149
Fig. 1Framework for COVID-19 impacts on park visitation analysis
Fig. 2The geographical scope of the study
Correlation and significance tests between park visitor numbers, COVID-19 and government responses at a global scale
| Daily case increase | Restrictions on stay at home | Public event cancellation | Social gathering restriction | Public information campaign | Internal movement restrictions | Workplace closure | Government stringency index | ||
|---|---|---|---|---|---|---|---|---|---|
| Park visitor number | Pearson correlation | ||||||||
| Sig. (2-tailed) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| N | 4848 | 4848 | 4848 | 4848 | 4848 | 4848 | 4848 | 4848 |
* indicates P ≤ 0.05; ** indicates P ≤ 0.01, bold font indicates statistical significance
Variables entered in the stepwise regression model for the global scale
| Model | Variable entered | Adjusted | Std. Error of the Estimate | R Square Chang | Change Statistics | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| F Change | df1 | df2 | Sig. F Change | |||||||
| 1 | Stay at home restriction | 0.511 | 0.261 | 0.261 | 36.46459 | 0.261 | 1712.843 | 1 | 4846 | 0 |
| 2 | Daily COVID-19 increase case | 0.515 | 0.266 | 0.265 | 36.35891 | 0.004 | 29.211 | 1 | 4845 | 0 |
| 3 | Government stringency index | 0.518 | 0.269 | 0.268 | 36.282 | 0.003 | 21.564 | 1 | 4844 | 0 |
| 4 | Social gathering restriction | 0.524 | 0.275 | 0.274 | 36.13209 | 0.006 | 41.277 | 1 | 4843 | 0 |
| 5 | Public information campaign | 0.531 | 0.282 | 0.282 | 35.95051 | 0.007 | 50.047 | 1 | 4842 | 0 |
| 6 | Public event cancelation | 0.534 | 0.285 | 0.284 | 35.89664 | 0.002 | 15.542 | 1 | 4841 | 0 |
| 7 | Workplace closure | 0.535 | 0.286 | 0.285 | 35.866 | 0.001 | 9.276 | 1 | 4840 | 0.002 |
| 8 | Internal Movement restrictions | 0.535 | 0.287 | 0.286 | 35.85393 | 0.001 | 4.259 | 1 | 4839 | 0.039 |
Country and region classification based on the correlation between park visitor numbers and COVID-19 daily increase in cases
| Significant negative correlation | Significant positive correlation | Pearson correlation | No significant correlation | Pearson correlation | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Very strong (0.8 − 1) | Pearson correlation | Strong (0.60– 0.79) | Pearson correlation | Moderate (0.40– 0.59) | Pearson correlation | Weak (0.20–0.39) | Pearson correlation | ||||
| Italy | − 0.815** | Austria | − 0.655** | Argentina | − 0.521** | Australia | − 0.251* | Denmark | 0.397** | Canada | − 0.004 |
| Singapore | − 0.880** | France | − 0.614** | Belgium | − 0.464** | Bolivia | − 0.373** | Finland | 0.365** | Ecuador | − 0.187 |
| Mexico | − 0.613** | Colombia | − 0.419** | Brazil | − 0.362** | Sweden | 0.630** | Germany | − 0.037 | ||
| Panama | − 0.739** | India | − 0.598** | Chile | − 0.355** | Japan | − 0.039 | ||||
| Philippines | − 0.619** | Indonesia | − 0.585** | Egypt | − 0.361** | Mongolia | 0.119 | ||||
| Portugal | − 0.683** | Kenya | − 0.480** | Hong Kong | − 0.359** | Netherland | − 0.003 | ||||
| Romania | − 0.700** | Malaysia | − 0.504** | Hungary | − 0.288** | Norway | − 0.041 | ||||
| Saudi Arabia | − 0.607** | New Zealand | − 0.476** | Ireland | − 0.246* | Poland | − 0.144 | ||||
| Spain | − 0.723** | Nigeria | − 0.517** | Thailand | − 0.371** | Taiwan | 0.041 | ||||
| Peru | − 0.434** | UK | − 0.322** | Vietnam | − 0.139 | ||||||
| South Africa | − 0.416** | ||||||||||
| South Korea | − 0.418** | ||||||||||
| United States | − 0.405** | ||||||||||
* indicates P ≤ 0.05; ** indicates P ≤ 0.01
Impacts of Covid-19 on park visits in selected countries and regions
| Countries | Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
|---|---|---|---|---|---|---|---|
| Italy | Daily increase cases | − 0.815** | − 0.483** | − 0.375** | − 0.465** | − 0.370** | − 0.277** |
| Government stringency index | − 0.469** | − 0.864** | − 1.010** | -1.254** | − 1.470** | ||
| Internal movement restriction | 0.426** | 0.374** | 0.382** | 0.221** | |||
| stay at home restriction | 0.289** | 0.287** | 0.149 | ||||
| Public event cancellation | 0.322** | ||||||
| Workplace closure | 0.373** | ||||||
| Spain | Daily increase cases | − 0.828** | − 0.622** | ||||
| Government stringency index | − 0.398** | ||||||
| South Korea | Workplace closure | 0.493** | 0.384** | ||||
| Daily increase cases | − 0.251** | ||||||
| United Kingdom | Internal movement restriction | − 0.387** | − 1.618** | − 0.063 | |||
| Social gathering restriction | 1.331** | 1.483** | 1.484** | ||||
| Stay at home restriction | − 1.706** | − 1.770** | |||||
| Denmark | Workplace closure | 0.611** | |||||
| Sweden | Daily increase cases | 0.630** | |||||
| Japan | Stay at home restriction | − 0.204** |
* indicates P ≤ 0.05; ** indicates P ≤ 0.01
Fig. 3New COVID-19 cases and park visits in selected countries
Correlation between park visitation change and the 17 indices of economic, social and cultural factors
| Variable | Pearson correlation | Sig. (2-tailed) |
|---|---|---|
| Human density | 0 | |
| GDP/capita | 0 | |
| Life satisfaction index | 0 | |
| Environmental performance index | 0 | |
| Power distance index | 0 | |
| Individualism index | 0 | |
| Masculinity index | 0 | |
| Uncertainty avoidance index | 0 | |
| Personal free index | 0 | |
| Economic free index | 0 | |
| Human free index | 0 | |
| Environmental health index | 0 | |
| Ecosystem vitality index | 0 | |
| Poverty index | 0 | |
| Life expectancy | 0 | |
| Unemployment rate | 0 | |
| Satisfaction with transportation | 0 |
* indicates P ≤ 0.05; ** indicates P ≤ 0.01, bold font indicates statistical significance