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Urban national parks (UNPs) provide tourism services in cities worldwide. However, the factors affecting tourist distributions in UNP activity and path spaces remain uncertain. Using Web crawler technology, location big data were tracked and sampled in Donghu National Park in Wuhan, China, and 12 predictor variables were analyzed using a machine-learning method (i.e., random forest). The consistency of the big data compared to the population census and tourist observations was determined at 79.5% and 77.8%, respectively. The tourist number (p) and tourist density (p/ha) per day in the park space in Donghu National Park were 0–2,531 p and 0–198.0 p/ha, respectively. Peak tourist periods showed pressure flows of 0.3–34.5‰ between scenic areas in the park. An analytical framework was formulated for UNPs to link the urban environment, park attributes, and configurational attributes, which here explained 66.4%–72.5% of the tourist distribution in the path and activity spaces. Random forest models performed better than geographically weighted regression (GWR) or ordinary least squares (OLS) models, indicating a complex nonlinear relationship between the independent variables and tourist distribution in UNP spaces, rather than the linear relationship that has previously been found in urban parks. First, both activity and path spaces near developed urban environments or park entrances bore higher tourism pressure. Second, winding routes attracted tourists to path spaces, while water landscapes attracted tourists to both path and activity spaces. Third, tourism pressure in path spaces was determined by configurational attributes. These results are important reference points for the planning and management of UNPs. [ABSTRACT FROM AUTHOR]

The exploration of park use and its affecting factors is necessary for urban park planning and management. Using big data, 70 urban parks in Wuhan were selected as the study objects in the present study. Two groups of linear regression models were developed to examine the effects of various factors on park use. The total numbers of visits and public demands in urban parks were recorded as 1,291,894 and 4,022, respectively. Social media data reflect visits, while public demand data reflect demands. First, a large area and sufficient facilities greatly help improve park use; moreover, we should pay attention to old urban parks in disrepair. More squares need to be considered in urban parks; attention should be paid to the water pollution and safety concerns that may arise in urban parks. Second, the influence of accessibility on park use is stable. It is beneficial for park use to construct greenways connecting residential areas and urban parks. The layout of pocket parks in the city center is also a feasible way to improve park use. Third, to improve park use, we should arrange commercial entertainment facilities and services in urban parks in point of interest (POI)-rich areas, as well as facilities and services for residents' daily activities in urban parks in population-rich areas. This study provides a way to combine multisource data that reflect the number of park visits and park demands to explore affecting factors of park use. [ABSTRACT FROM AUTHOR]

High-quality urban blue-green spaces are essential to enhance the well-being of residents. Public preferences in urban landscapes are closely related to visual behavior and can meet human needs. By combining objective eye-movement data with subjective evaluation data, public preferences for blue-green typical spatial waterfront parks were analyzed based on visual behavior. Taking Wuhan, a megacity in China, as an example, four types of landscapes were identified: waterscape, plantscape, hardscape, and integrated landscape. Using visual clustering to identify eye movement indicators, we analyzed the influence of element types, proportions and combinations on public preferences based on visual behavior differences related to landscape elements. The results indicated that: (1) visual behaviors differ among elements and preferences, arbor commands overall attention, and pavilion is the element attended to first. (2) The positive correlation between water and fatigue recovery increased by 0.163 after combination with reflection, while the correlation between arbor and fatigue recovery changed from 0.353 (p < 0.01) to − 0.251 (p < 0.01) after combination with shrub and grass. (3) Plantscape and waterscape were rated as more natural, intimate, and restorative, but the most element-rich integrated landscape had the highest beauty ratings. Hardscape had significantly lower ratings but were generally considered to be fully utilized spaces. (4) The most significant visual behavior factor affecting evaluations was fixation duration, and the most critical landscape elements were arbor and pavilion. This study reveals differences in people's visual perceptions and preferences for waterfront parks through eye-movement experiments, and could lead to more judicious landscape element selections and park space compositions. • Selecting and clustering of indicators based on a full visual search process. • Artificial elements have more frequent visual searches but shorter sightline. • Waterscape elements increase in relevance after being combined. • Fixation duration of arbor and pavilion had the greatest effect on preferences. [ABSTRACT FROM AUTHOR]

Studies on associations of fine particulate matter (PM 2.5) with immunity in people living with HIV/AIDS (PLWHA) were absent. We aimed to explore whether changes of immune biomarkers were associated with short-term exposure to PM 2.5 in PLWHA. Based on a panel study in Wuhan, we selected 163 PLWHA as participants with up to 4 repeated visits from March 2020 to January 2021. Immune biomarkers, including CD4+T cell count, CD8+T cell count, HIV viral load (VL) and CD4+T/CD8+T ratio were tested for all participants at each visit. Residential exposures of PM 2.5 and PM 2.5 constituents for each participant were assessed using spatial-temporal models. Linear mixed-effect models and general linear mixed models were applied to evaluate the associations between PM 2.5 and immune biomarkers. To estimate the combined effect of PM 2.5 constituents, weighted quantile sum regression and Bayesian kernel machine regression were employed. Each 10 μg/m3 increase of 7-day average PM 2.5 concentrations was associated with an 8.75 cells/mm3 (95%CI: −15.55, −1.98) decrease in CD4+T cell count and a 92% (OR: 1.92, 95%CI: 1.43, 2.58) increased odds ratio of detectable HIV VL. However, the odds ratio of inverted CD4+T/CD8+T was only positively associated with PM 2.5 concentrations at lag2 day (OR:1.27, 95%CI:1.02, 1.57). CD4+T may be a potential mediator between PM 2.5 and detectable HIV VL with 3.83% mediated proportion. Besides, the combined effect of PM 2.5 chemical constituents indicated that NO 3 − and SO 4 2− were the main constituents in reducing CD4+T cell count and increasing odds ratio of detectable HIV VL. Our finding revealed that short-term exposure to PM 2.5 was negatively associated with CD4+T cell count but positively related to the odds ratio of detectable HIV VL in PLWHA. This research may provide new evidence in associations between PM 2.5 and immune biomarkers as well as improving prognosis of PLWHA. [Display omitted] • A panel study was conducted in 163 people living with HIV/AIDS. • Residential PM 2.5 concentrations was negatively associated with CD4+T cell count. • CD4+T cells might be a mediator between PM 2.5 and detectable HIV viral load. • Nitrate and sulfate in PM 2.5 showed strongest association with immune biomarkers. [ABSTRACT FROM AUTHOR]

• Landscape features of cropland, woodland, water bodies, and urban land have an obvious correlation with infection risk. • CONTAG, DIVISION, and SHEI are the most relevant landscape-level metrics linked to infection risk. • The construction intensity, landscape features, urban forest and urban water are principal components, reaching 84.562% of the total variance. • Class- and landscape-level indices explain infection risk of 70.9% - 79.9% and 32.6-39.5%, respectively. • Natural ecological patches should be preserved in new urban subdistricts, and the protection of water bodies and urban forest should be increased in the city center. COVID-19 case numbers in 161 sub-districts of Wuhan were investigated based on landscape epidemiology, and their landscape metrics were calculated based on land use/land cover (LULC). Initially, a mediation model verified a partially mediated population role in the relationship between landscape pattern and infection number. Adjusted incidence rate (AIR) and community safety index (CSI), two indicators for infection risk in sub-districts, were 25.82∼63.56 ‱ and 3.00∼15.87 respectively, and central urban sub-districts were at higher infection risk. Geographically weighted regression (GWR) performed better than OLS regression with AICc differences of 7.951∼181.261. The adjusted R2 in GWR models of class-level index and infection risk were 0.697 to 0.817, while for the landscape-level index they were 0.668 to 0.835. Secondly, 16 key landscape metrics were identified based on GWR, and then a prediction model for infection risk in sub-districts and communities was developed. Using principal component analysis (PCA), development intensity, landscape level, and urban blue-green space were considered to be principal components affecting disease infection risk, explaining 73.1 % of the total variance. Cropland (PLAND and LSI), urban land (NP, LPI, and LSI) and unused land (NP) represent development intensity, greatly affecting infection risk in urban areas. Landscape level CONTAG, DIVISION, SHDI, and SHEI represent mobility and connectivity, having a profound impact on infection risk in both urban and suburban areas. Water (PLAND, NP, LPI, and LSI) and woodland (NP, and LSI) represent urban blue-green spaces, and were particularly important for infection risk in suburban areas. Based on urban landscape pattern, we proposed a framework to understand and evaluate infection risk. These findings provide a basis for risk evaluation and policy-making of urban infectious disease, which is significant for community management and urban planning for infectious disease worldwide. [ABSTRACT FROM AUTHOR]

As important and typical urban blue-green spaces, urban wetland parks (UWPs) have a variety of environmental and social benefits. However, existing studies usually focus on a particular UWP benefit and factors affecting it, so there is no comprehensive evaluation method and framework for evaluating UWP benefits and guiding their development. Through field survey, geographic information system (GIS), text analysis and an AHP-entropy method, environmental and social benefits of 38 UWPs were determined in Wuhan, China. Eight geographically weighted regression (GWR) models and three coupling coordination models were constructed to explore park attributes and built environment factors that affect UWP benefits, and to evaluate UWP coupling coordination. GWR models explained UWP environmental (Adj. R2 = 0.493∼0.777) and social (Adj. R2 = 0.419∼0.709) benefits well. Environmental benefits were more affected by park attributes, while social benefits were more affected by the built environment. Most UWP coupling coordination was medium (with CCD from 0.55 to 0.75, and an average of 0.630), which is caused by insufficient environmental benefits or uncoordinated environmental and social benefits. We built a framework for diagnosis, analysis, and improvement that can quickly evaluate UWP benefits, put forward development suggestions to promote UWP coordinated development so that UWPs will bring more well-being to residents. In the future, UWP benefit evaluation frameworks can be further enriched by more potential benefits and and promoted to improve more extensive urban blue-green spaces. [ABSTRACT FROM AUTHOR]