Quantitative evaluation of urban green exposure and its impact on human health: A case study on the 3-30-300 green space rule.

BACKGROUND AND AIMS: Urban green spaces offer various health benefits, yet the impact of comprehensive green exposure criteria on multidimensional health remains unclear. The 3-30-300 green space rule represents the green exposure indicators with specific thresholds. This study aims to quantitatively evaluate urban green exposure in cities and can support investigation of its relationship with human health. METHODS: We conducted a cross-sectional study based on 902 investigated individuals in 261 residential locations aged 11-95 years from Xiamen City, China. 3-30-300 green exposure was calculated using field surveys, GIS, and Baidu Maps Application Programming Interface (API). Physical health data was based on Occupational Stress Indicator (OSI)-2. Mental health was from the 12-item General Health Questionnaire (GHQ-12). Social health was from a self-constructed evaluation questionnaire. Statistical analyses were conducted using Geographically Weighted Regression and Geographically Weighted Logistic Regression for global and local effects on green exposure and multidimensional health. RESULT: Among the investigated individuals, only 3.55 % (32/902) fully meet the 3-30-300 rule in Xiamen. Global results show that individuals achieved at least 30 % vegetation coverage (Yes) is associated with better physical (ß: 0.76, p < 0.01) and social (ß: 0.5, p < 0.01) health. GWLR global results indicate that individuals can "see at least 3 trees from home" meeting one (OR = 0.46, 95%CI: 0.25-0.86, p < 0.05) or two (OR = 0.41, 95%CI: 0.22,0.78, p < 0.01; OR = 0.24, 95%CI: 0.07-0.77, p < 0.05) 3-30-300 rule components are significantly associated with reduced medical visits and hospitalizations refer to not met these criterias. In the GWR local analysis, achieved 30 % vegetation cover is significantly related to improved social health at all locations. Meeting any two indicators also contribute to improved social health (n = 511, ß: 0.46-0.51, P < 0.05). CONCLUSION: Green exposure indicators based on the 3-30-300 rule guiding healthy urban green space development. We observed multidimensional health benefits when 1/3 or 2/3 of the indicators were met.

Quantitative Evaluation of Spatial Accessibility of Various Urban Medical Services Based on Big Data of Outpatient Appointments.

Equity of urban medical services affects human health and well-being in cities and is important in building 'just' cities. We carried out a quantitative analysis of the spatial accessibility of medical services considering the diverse demands of people of different ages, using outpatient appointment big data and refining the two-step floating catchment area (2SFCA) method. We used the traditional 2SFCA method to evaluate the overall spatial accessibility of medical services of 504 communities in Xiamen city, considering the total population and the supply of medical resources. Approximately half the communities had good access to medical services. The communities with high accessibility were mainly on Xiamen Island, and those with low accessibility were further from the central city. The refined 2SFCA method showed a more diverse and complex spatial distribution of accessibility to medical services. Overall, 209 communities had high accessibility to internal medicine services, 133 to surgery services, 50 to gynecology and obstetrics services, and 18 to pediatric services. The traditional method may over-evaluate or under-evaluate the accessibility of different types of medical services for most communities compared with the refined evaluation method. Our study can provide more precise information on urban medical service spatial accessibility to support just city development and design.

Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI.

Global Navigation Satellite System (GNSS) operation can be affected by several environmental factors, of which ionospheric scintillation is one of the most significant. Scintillation is usually characterized by two indices, namely the amplitude scintillation index (S4) and phase scintillation index (σφ). However, these two indices can only be generated by specialized GNSS receivers, which are not widely available all around the world. To popularize the study of scintillation, this article proposes to use more accessible parameters, namely multipath (MP) and rate of change of total electron content index (ROTI), to characterize scintillation. Using GPS data obtained on six days in total from three stations, namely PRU2 and SAO0P located in Sao Paulo, Brazil and SNA0P located in Antarctica, respectively, both the time series plots and 2D maps were generated to investigate the relationship of scintillation indices (S4 and σφ) with MP and ROTI. To prevent the effect of the real multipath error, a 30-degree satellite elevation mask is applied to all the data. As the scintillation indices S4 and σφ have a sampling interval of 1 min, MP and ROTI are calculated with the same sampling interval for a more direct comparison. The results show that the structural similarity (SSIM) and correlation coefficient (CC) between parameters was greater than 0.7 for 70% of outputs. In addition, the variogram and cross-variogram are applied to investigate the spatial structure of the MP, ROTI, S4 and σφ in order to support the results of SSIM and CC. With outputs in three forms, promising spatial and temporal relationships between parameters was observed.

Spatiotemporal patterns and environmental drivers of human echinococcoses over a twenty-year period in Ningxia Hui Autonomous Region, China.

BACKGROUND: Human cystic (CE) and alveolar (AE) echinococcoses are zoonotic parasitic diseases that can be influenced by environmental variability and change through effects on the parasites, animal intermediate and definitive hosts, and human populations. We aimed to assess and quantify the spatiotemporal patterns of human echinococcoses in Ningxia Hui Autonomous Region (NHAR), China between January 1994 and December 2013, and examine associations between these infections and indicators of environmental variability and change, including large-scale landscape regeneration undertaken by the Chinese authorities. METHODS: Data on the number of human echinococcosis cases were obtained from a hospital-based retrospective survey conducted in NHAR for the period 1 January 1994 through 31 December 2013. High-resolution imagery from Landsat 4/5-TM and 8-OLI was used to create single date land cover maps. Meteorological data were also collected for the period January 1980 to December 2013 to derive time series of bioclimatic variables. A Bayesian spatio-temporal conditional autoregressive model was used to quantify the relationship between annual cases of CE and AE and environmental variables. RESULTS: Annual CE incidence demonstrated a negative temporal trend and was positively associated with winter mean temperature at a 10-year lag. There was also a significant, nonlinear effect of annual mean temperature at 13-year lag. The findings also revealed a negative association between AE incidence with temporal moving averages of bareland/artificial surface coverage and annual mean temperature calculated for the period 11-15 years before diagnosis and winter mean temperature for the period 0-4 years. Unlike CE risk, the selected environmental covariates accounted for some of the spatial variation in the risk of AE. CONCLUSIONS: The present study contributes towards efforts to understand the role of environmental factors in determining the spatial heterogeneity of human echinococcoses. The identification of areas with high incidence of CE and AE may assist in the development and refinement of interventions for these diseases, and enhanced environmental change risk assessment.

Land cover change during a period of extensive landscape restoration in Ningxia Hui Autonomous Region, China.

Environmental change has been a topic of great interest over the last century due to its potential impact on ecosystem services that are fundamental for sustainable development and human well-being. Here, we assess and quantify the spatial and temporal variation in land cover in Ningxia Hui Autonomous Region (NHAR), China. With high-resolution (30m) imagery from Landsat 4/5-TM and 8-OLI for the entire region, land cover maps of the region were created to explore local land cover changes in a spatially explicit way. The results suggest that land cover changes observed in NHAR from 1991 to 2015 reflect the main goals of a national policy implemented there to recover degraded landscapes. Forest, herbaceous vegetation and cultivated land increased by approximately 410,200ha, 708,600ha and 164,300ha, respectively. The largest relative land cover change over the entire study period was the increase in forestland. Forest growth resulted mainly from the conversion of herbaceous vegetation (53.8%) and cultivated land (30.8%). Accurate information on the local patterns of land cover in NHAR may contribute to the future establishment of better landscape policies for ecosystem management and protection. Spatially explicit information on land cover change may also help decision makers to understand and respond appropriately to emerging environmental risks for the local population.

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence.

BACKGROUND: Spatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their interpretation, and consequences for the mapped values. Using currently published literature on the spatial epidemiology of helminth infections we identified: (1) the main uncertainty sources, their definition and quantification and (2) how uncertainty is informative for STH programme managers and scientists working in this domain. METHODOLOGY/PRINCIPAL FINDINGS: We performed a systematic literature search using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol. We searched Web of Knowledge and PubMed using a combination of uncertainty, geographic and disease terms. A total of 73 papers fulfilled the inclusion criteria for the systematic review. Only 9% of the studies did not address any element of uncertainty, while 91% of studies quantified uncertainty in the predicted morbidity indicators and 23% of studies mapped it. In addition, 57% of the studies quantified uncertainty in the regression coefficients but only 7% incorporated it in the regression response variable (morbidity indicator). Fifty percent of the studies discussed uncertainty in the covariates but did not quantify it. Uncertainty was mostly defined as precision, and quantified using credible intervals by means of Bayesian approaches. CONCLUSION/SIGNIFICANCE: None of the studies considered adequately all sources of uncertainties. We highlighted the need for uncertainty in the morbidity indicator and predictor variable to be incorporated into the modelling framework. Study design and spatial support require further attention and uncertainty associated with Earth observation data should be quantified. Finally, more attention should be given to mapping and interpreting uncertainty, since they are relevant to inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration.

The landscape epidemiology of echinococcoses.

Echinococcoses are parasitic diseases of major public health importance globally. Human infection results in chronic disease with poor prognosis and serious medical, social and economic consequences for vulnerable populations. According to recent estimates, the geographical distribution of Echinococcus spp. infections is expanding and becoming an emerging and re-emerging problem in several regions of the world. Echinococcosis endemicity is geographically heterogeneous and over time it may be affected by global environmental change. Therefore, landscape epidemiology offers a unique opportunity to quantify and predict the ecological risk of infection at multiple spatial and temporal scales. Here, we review the most relevant environmental sources of spatial variation in human echinococcosis risk, and describe the potential applications of landscape epidemiological studies to characterise the current patterns of parasite transmission across natural and human-altered landscapes. We advocate future work promoting the use of this approach as a support tool for decision-making that facilitates the design, implementation and monitoring of spatially targeted interventions to reduce the burden of human echinococcoses in disease-endemic areas.

NONSEPARABLE DYNAMIC NEAREST NEIGHBOR GAUSSIAN PROCESS MODELS FOR LARGE SPATIO-TEMPORAL DATA WITH AN APPLICATION TO PARTICULATE MATTER ANALYSIS.

Particulate matter (PM) is a class of malicious environmental pollutants known to be detrimental to human health. Regulatory efforts aimed at curbing PM levels in different countries often require high resolution space-time maps that can identify red-flag regions exceeding statutory concentration limits. Continuous spatio-temporal Gaussian Process (GP) models can deliver maps depicting predicted PM levels and quantify predictive uncertainty. However, GP-based approaches are usually thwarted by computational challenges posed by large datasets. We construct a novel class of scalable Dynamic Nearest Neighbor Gaussian Process (DNNGP) models that can provide a sparse approximation to any spatio-temporal GP (e.g., with nonseparable covariance structures). The DNNGP we develop here can be used as a sparsity-inducing prior for spatio-temporal random effects in any Bayesian hierarchical model to deliver full posterior inference. Storage and memory requirements for a DNNGP model are linear in the size of the dataset, thereby delivering massive scalability without sacrificing inferential richness. Extensive numerical studies reveal that the DNNGP provides substantially superior approximations to the underlying process than low-rank approximations. Finally, we use the DNNGP to analyze a massive air quality dataset to substantially improve predictions of PM levels across Europe in conjunction with the LOTOS-EUROS chemistry transport models (CTMs).

Earth Observation, Spatial Data Quality, and Neglected Tropical Diseases.

Earth observation (EO) is the use of remote sensing and in situ observations to gather data on the environment. It finds increasing application in the study of environmentally modulated neglected tropical diseases (NTDs). Obtaining and assuring the quality of the relevant spatially and temporally indexed EO data remain challenges. Our objective was to review the Earth observation products currently used in studies of NTD epidemiology and to discuss fundamental issues relating to spatial data quality (SDQ), which limit the utilization of EO and pose challenges for its more effective use. We searched Web of Science and PubMed for studies related to EO and echinococossis, leptospirosis, schistosomiasis, and soil-transmitted helminth infections. Relevant literature was also identified from the bibliographies of those papers. We found that extensive use is made of EO products in the study of NTD epidemiology; however, the quality of these products is usually given little explicit attention. We review key issues in SDQ concerning spatial and temporal scale, uncertainty, and the documentation and use of quality information. We give examples of how these issues may interact with uncertainty in NTD data to affect the output of an epidemiological analysis. We conclude that researchers should give careful attention to SDQ when designing NTD spatial-epidemiological studies. This should be used to inform uncertainty analysis in the epidemiological study. SDQ should be documented and made available to other researchers.