Analysis Of Geographical Differences Research Articles

Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data

Accurate high-spatial-resolution precipitation is significantly important in hydrological and meteorological modelling, especially in rain-gauge-sparse areas. Some methods and strategies have been applied for satellite-based precipitation downscaling, residual correction and precipitation calibration. However, which downscaling scheme can provide reliable high-resolution precipitation efficiently remains unanswered. To address this issue, this study aimed to present a framework combining the machine learning downscaling algorithm and post-process procedures. Firstly, four ML-based models, namely support vector regression, random forest, spatial random forest (SRF) and eXtreme gradient boosting (XGBoost), were tested for downscaling and compared with conventional downscaling methods. Then, the effectiveness of the residual correction process using ordinary Kriging and the calibration process using the geographical difference analysis (GDA) method was investigated. The results showed that the ML-based methods had better performance than the conventional regression and interpolation approaches. The SRF and XGBoost outperformed others in generating accurate precipitation estimation with a high resolution. The GDA calibration process significantly improved the downscaled results. However, the residual correction process decreased the downscaling performance of the ML-based models. Combining the SRF or XGBoost downscaling algorithm with the GDA calibration method could be a promising downscaling scheme for precipitation data. The scheme could be used to generate high-resolution precipitation, especially in areas urgently requiring data, which would benefit regional water resource management and hydrological disaster prevention.

Framework for Spatio-Temporal Distribution of Disasters and Influencing Factors: Exploratory Study of Tianjin, China

Facing the increased frequency of disasters and resulting massive damages, many countries have developed their frameworks for disaster risk management (DRM). However, these frameworks are highly regional and few of them suggest corresponding methods for specific analysis content, which makes them less practical and operational. In this paper, we investigate an exploratory framework for spatio-temporal distribution of disasters and analysis of influencing factors, including suggested methods for all the content. Time series analysis, geographical difference analysis, direction distribution analysis, and spatial autocorrelation analysis are selected as the content of spatio-temporal distribution of disasters part. Mann–Kendall test method, Lorentz curve, standard deviation ellipse method, and exploratory spatial data analysis method are introduced to analyze them. The impact analysis part was carried out on individual disaster and multiple disasters, which are suggested as the two aspects of influencing factors analysis, by means of a geographic detector and a novel comprehensive impact evaluation model that was developed. Then, the framework is applied to analyze disasters including flood, storm surge, and earthquake in Tianjin of China. The research results help to understand the spatial and temporal distribution of disasters and the disaster mechanism in Tianjin. Further, the superiority and limitations of the framework are discussed. Finally, the framework developed is expected to be widely applied in other districts for better DRM in the future.

Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy

Abstract. Precipitation data with high resolution and high accuracy are significantly important in numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on a spatial random forest (SRF-DC) is proposed in this study, where the spatial autocorrelation of precipitation measurements between neighboring locations is considered. SRF-DC consists of two main stages. First, the satellite-based precipitation is downscaled by the SRF with the incorporation of high-resolution variables including latitude, longitude, normalized difference vegetation index (NDVI), digital elevation model (DEM), terrain slope, aspect, relief and land surface temperatures. Then, the downscaled precipitation is calibrated by the SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) over Sichuan Province, China, from 2015 to 2019 was processed using SRF-DC, and its results were compared with those of classical methods including geographically weighted regression (GWR), artificial neural network (ANN), random forest (RF), kriging interpolation only on gauge measurements, bilinear interpolation-based downscaling and then SRF-based calibration (Bi-SRF), and SRF-based downscaling and then geographical difference analysis (GDA)-based calibration (SRF-GDA). Comparative analyses with respect to root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC) demonstrate that (1) SRF-DC outperforms the classical methods as well as the original IMERG; (2) the monthly based SRF estimation is slightly more accurate than the annually based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration perform better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging is more accurate than GWR and ANN, whereas its precipitation map loses detailed spatial precipitation patterns; and (5) based on the variable-importance rank of the RF, the precipitation interpolated by kriging on the rain gauge measurements is the most important variable, indicating the significance of incorporating spatial autocorrelation for precipitation estimation.

Reconstructing high-resolution gridded precipitation data using an improved downscaling approach over the high altitude mountain regions of Upper Indus Basin (UIB)

Understanding the basin-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision, as well as high-resolution precipitation data. We have made an attempt to develop an Integrated Downscaling and Calibration (IDAC) framework to generate high-resolution (1 km × 1 km) gridded precipitation data. Traditionally, GWR (Geographical weighted regression) model has widely been applied to generate high-resolution precipitation data for regional scales. The GWR model generally assumes a spatially varied relationships between precipitation and its associated environmental variables, however, the relationships need to remain constant (fixed) for some variables over space. In this study, a Mixed Geographically Weighted Regression (MGWR) model, capable of dealing with the fixed and spatially varied environmental variables, is proposed to downscale the Original-TRMM precipitation data from a coarse resolution (0.25o × 0.25o) to a high-resolution (1 km × 1 km) for the period of 2000–2018 over the Upper Indus Basin (UIB). Additionally, accuracy of the downscaled precipitation data was further improved by merging it with the recorded data from rain gauge stations (RGS) using two calibration approaches such as Geographical Ratio Analysis (GRA) and Geographical Difference Analysis (GDA). We found MGWR to perform better given its higher R2 and lower RMSE and bias values (R2 = 0.96; RMSE = 56.01 mm, bias = 0.014) in comparison to the GWR model (R2 = 0.95; RMSE = 60.76 mm, bias = 0.094). It was observed that the GDA and GRA calibrated-downscaled precipitation datasets were superior to the Original-TRMM, yet GRA outperformed GDA. Annual precipitation from downscaled and calibrated-downscaled datasets was further temporally downscaled to obtain high-resolution monthly and daily precipitations. The results revealed that the monthly-downscaled precipitation (R2 = 0.82, bias = −0.02 and RMSE = 11.93 mm/month) and the calibrated-downscaled (R2 = 0.89, bias = −0.006 and RMSE = 9.19 mm/month) series outperformed the Original-TRMM (R2 = 0.72, bias = 0.14 and RMSE = 19.8 mm/month) as compared to the RGS observations. The results of daily calibrated-downscaled precipitation (R2 = 0.79, bias = 0.001 and RMSE = 1.7 mm/day) were better than the Original-TRMM (R2 = 0.64, bias = − 0.12 and RMSE = 6.82 mm/day). In general, the proposed IDAC approach is suitable for retrieving high spatial resolution gridded data for annual, monthly, and daily time scales over the UIB with varying climate and complex topography.

Comparison and assessment of spatial downscaling methods for enhancing the accuracy of satellite-based precipitation over Lake Urmia Basin

Estimating precipitation at high spatial-temporal resolution is vital in manifold hydrological, meteorological and water management applications, especially over areas with un-gauged networks and regions where water resources are on the wane. This study aims to evaluate five downscaling methods to determine the accuracy and efficiency of which on generating high-resolution precipitation data at annual and monthly scales. To establish precipitation-Land surface characteristics relationship, environmental factors, including Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Digital Elevation Model (DEM), were considered as proxies in the spatial downscaling procedure. The downscaling algorithms, namely support vector machine (SVM), random forest (RF), geographically weighted regression (GWR), multiple linear regression (MLR) and exponential regression (ER), were implemented to downscale the version 7 of TRMM (Tropical Rainfall Measuring Mission) precipitation (3B43 V7 product) over Lake Urmia Basin (LUB) from 0.25° to 1 km spatial resolution. The downscaled precipitation data was validated against observations from meteorological stations. Monthly fractions derived from TRMM 3B43 were used to disaggregate 1 km annual precipitation to 1 km monthly precipitation. Furthermore, the best method was selected for calibration based on Geographical Difference Analysis (GDA) to assess the effectiveness of the calibration as a viable option. The results indicate that SVM not only outperforms the other methods, but also has good agreements with in-situ measurements compared to the original TRMM. The results confirm that inclusion of LST and geographic information along with NDVI can improve the downscaling performance. Downscaling and GDA calibration significantly improve the accuracy of TRMM 3B43 product at both spatial and temporal resolution and should be considered as an essential step in calibration of TRMM precipitation. Calibration at monthly scale yields slightly better results than calibration at annual scale and then disaggregating into monthly maps in terms of accuracy assessment.

Evaluating the hydrological utility of satellite-based rainfall products using neural network models over the Ghare Ghieh River basin, Iran

Abstract In recent years, gridded precipitation data derived from satellite rainfall products have become critical data sources for hydrological applications, especially in ungauged basins where rain gauges are sparse or nonexistent. Also, in streamflow simulations, since the existing rainfall–runoff modelling methods require exogenous input with some assumptions, neural networks can be an efficient solution. In this paper, to simulate daily streamflow on the Ghare Ghieh River basin in northwestern Iran, the Levenberg–Marquardt Neural Network (LMNN) and the Particle Swarm Optimization Neural Network (PSONN) models are proposed. These models are trained and tested with different input patterns from ground-based data for water years of 1988–2008. Then, three satellite-based precipitation datasets, including TRMM-3B42V7, TRMM-3B42RT, and PERSIANN with 0.25° × 0.25° resolutions from 2003 to 2008, are used as inputs for the best-trained models which were selected in the testing step. These products are evaluated before and after calibration in streamflow simulation, and the Geographical Difference Analysis method is used to calibrate them. The results showed that the PSONN model performed better than the LMNN model. Also, in both models, before calibration of satellite precipitation products, TRMM-3B42 showed better performance in streamflow simulation, and after calibration, TRMM-3B42RT performed much better.

Blending long-term satellite-based precipitation data with gauge observations for drought monitoring: Considering effects of different gauge densities

Blending satellite-based precipitation estimation (SPE) data and in-situ gauge observation data can generate effective spatially-continuous-precipitation estimates with improved accuracy. This study assessed the improvement of the long-term SPE when blending with in-situ gauge observations for drought monitoring, using a simple but effective blending method named the geographical difference analysis (GDA) method and with the Precipitation Estimation from Remote Sensed Information by using Artificial Neural Networks-Climate Data Records (PERSIANN-CDR) as case study. In-situ precipitation observations from three meteorological station sets with different densities—the sparse (50), medium (200), dense (727) station set—were adopted to evaluate the effect of gauge density on the performance of SPE-gauge data blending. Two widely-used indices—standardized precipitation index (SPI) and self-calibrating Palmer drought severity index (SC_PDSI)—were used as case studies. Except the case of sparse 50-station subset, the SPE-gauge blending shows apparent improvement to the raw PERSIANN-CDR data, for both the accuracy of precipitation input and many aspects of drought monitoring, e.g. reproducing drought magnitude and revealing spatial pattern of drought, in which SC_PDSI shows more significant improvement than SPI. The dense 727-station set shows the largest improvement in the blending data, but the corresponding station-only interpolations also exhibit comparable performance to the blending data, indicating lower utilization value of the SPE data for these cases. Only the blending results of the medium-density 200-station set shows satisfactory drought monitoring performance as well as significant improvements relative to the station-only interpolations. According to the quantitative analyses, the medium density (about 50–75 gauges per 106km2 in our cases) might be the most economic gauge density for SPE-gauge blending, as it has satisfactory improvement in blending results, can make fullest use of the advantages of SPE data and requires relatively fewer gauges. Our results can help to understand how the SPE-gauge blending could improve the SPE-based drought monitoring and serves as a reference for applying drought monitoring under the data-limited conditions. Subsequent studies or applications should also carefully consider the effect of gauge density.

A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data

Deriving high quality precipitation estimates at high spatial resolution is of prime importance for many hydrological, meteorological, and environmental investigations. Rain gauge observations and satellite-derived precipitation data are two main sources of precipitation estimates. Gauge observations are accurate and reliable, but are heavily point-based and sparse in areas of rugged or complex terrains. Satellite-derived precipitation products can cover large areas, but they are generally characterized by inherent bias. To optimize the use of both datasets, we propose in this paper, a downscaling-integration framework to generate high quality monthly precipitation datasets at 1 km spatial resolution by merging rain gauge observations and TRMM 3B43 products. Firstly, an area-to-point kriging (ATPK) approach is used to downscale the original TRMM product to 1 km, so as to ensure a fair comparison with rain gauge data. Then, the downscaled TRMM precipitation datasets are integrated with the gauge observations using geographically weighted regression kriging (GWRK). The geographical factors (i.e. longitude, latitude and elevation) are also used as auxiliary variables in the GWRK model. Applying this approach to an experiment conducted at the middle and lower reaches of the Yangtze River in China from 2001 to 2014 shows that: (1) the downscaled monthly TRMM precipitation data by ATPK are more accurate than the original TRMM estimates; (2) the GWRK model employing the downscaled TRMM precipitation data and geographical factors provides better monthly precipitation estimates than the conventional ordinary kriging (OK) interpolation and the commonly used merging methods (i.e. geographical difference analysis, GDA and kriging with external drift, KED); (3) the GWRK method reduces the influence of the inaccuracy (bias) of satellite-derived precipitation data on the precipitation estimates compared to GDA. The approach presented in this study has provided an efficient alternative for solving the scale mismatch problem between point-based gauge data and low resolution satellite data, and producing improved precipitation data at high spatial resolution.

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Accurate precipitation data at a high spatial resolution are essential for hydrological, meteorological, and ecological research at regional scales. This study presented a geostatistical downscaling-calibration procedure to derive the high spatial resolution maps of precipitation over a mountainous watershed affected by a monsoon climate. Based on the relationships between precipitation and other environmental variables, such as the Normalized Difference Vegetation Index (NDVI) and digital elevation model (DEM), a regression model with a residual correction method was applied to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 product from coarse resolution (25 km) to fine resolution (1 km). Two methods, geographical difference analysis (GDA) and geographical ratio analysis (GRA), were used to calibrate the downscaled TRMM precipitation data. Monthly 1 km precipitation data were obtained by disaggregating 1 km annual downscaled and calibrated precipitation data using monthly fractions derived from original TRMM data. The downscaled precipitation datasets were validated against ground observations measured by rain gauges. According to the comparison of different regression models and residual interpolation methods, a geographically-weighted regression kriging (GWRK) method was accepted to conduct the downscaling of TRMM data. The downscaled TRMM precipitation data obtained using GWRK described the spatial patterns of precipitation reasonably well at a spatial resolution of 1 km with more detailed information when compared with the original TRMM precipitation. The results of validation indicated that the GRA method provided results with higher accuracy than that of the GDA method. The final annual and monthly downscaled precipitation not only had significant improvement in spatial resolution, but also agreed well with data from the validation rain gauge stations (i.e., R2 = 0.72, RMSE = 161.0 mm, MAE = 127.5 mm, and Bias = 0.050 for annual downscaled precipitation during 2001 to 2015; and R2 = 0.91, RMSE = 22.2 mm, MAE = 13.5 mm, and Bias = 0.048 for monthly downscaled precipitation during 2001 to 2015). In general, the downscaling-calibration procedure is useful for complex mountainous areas with insufficient ground gauges.

Updating Edwin Chadwick's seminal work on geographical inequalities by occupation

To honour the 175th anniversary of Edwin Chadwick's seminal ‘Report on the Sanitary Conditions of the Labouring Poor’, we update Chadwick's famous analysis of geographical differences in occupational based inequalities. Much of the field of Health Geography owes both its direction of development and its initial impetus to his 1842 report. The report presented evidence for the importance of local context to health, with individuals of the lowest occupations in Rutland living longer than individuals of the highest occupations in Liverpool. Here we update the 1842 analysis using data from the Office of National Statistics on individual mortality records by occupation (2010-12) and population data from the 2011 Census. Sex-specific directly standardised premature (16-74) mortality rates were calculated for hierarchical occupational categories similar to Chadwick's categories, for the nearest equivalent areas to those used in Chadwick's report. Although there is no longer consistent evidence on individuals in the lowest occupational group having lower mortality rates than those in the highest group, there were clear social gradients in mortality within each area and the extent of these inequalities varied between areas. Individuals who live in Rutland had lower premature mortality rates across each occupational group compared to the other areas. Our results demonstrate that while life expectancy has nearly doubled since Chadwick's report, social and spatial inequalities in health have persisted. We suggest that Chadwick's legacy on the importance of locality continues.

Spatial downscaling of TRMM‐based precipitation data using vegetative response in Xinjiang, China

ABSTRACTStation‐based observed precipitation data are available in a limited way and only at low spatial resolution. High‐resolution satellite rainfall products can help to monitor precipitation changes over large areas. The Tropical Rainfall Measuring Mission 3B43 (TRMM 3B43) precipitation data with coarse spatial resolution and low data accuracy are capable of depicting the spatial variability of precipitation, but fail to estimate the accurate magnitude. These data available in Xinjiang, China, need to be evaluated, especially because Xinjiang has a complex terrain and application of such data becomes a challenging task. Based on the relation between precipitation and normalized difference vegetation index (NDVI), a proxy for vegetation, a new statistical downscaling algorithm is proposed in this study. The calibration was based on geographical difference analysis (GDA) and the monthly fractions derived from the un‐calibrated TRMM data can be used to disaggregate high‐resolution annual precipitation to high‐resolution monthly precipitation. The accuracy of downscaled TRMM precipitation was evaluated based on station‐based observed precipitation for a period of 1998–2010. Results indicated that: (1) optimal relations shown by R2 between TRMM precipitation and NDVI at different temporal and spatial scales were different: the largest R2 value was 0.69 for the period of 1998–2010 at a spatial resolution of 1.00° × 1.00°, was 0.70 for the dry year (2001 in this study) at a spatial resolution of 0.75° × 0.75°, and was 0.67 for the wet year (2010 in this study) at a spatial resolution of 1.25° × 1.25°; (2) the downscaled TRMM precipitation data obtained using NDVI described spatial patterns of precipitation reasonably well at a spatial resolution of 8 km × 8 km with more detailed information when compared to the raw TRMM precipitation at a spatial resolution of 0.25° × 0.25°; (3) the downscaled TRMM precipitation with GDA calibration, PDSGDA, can exclude the regions with irrigation‐ and/or ground water‐induced vegetation coverage. Therefore, PDSGDA obtained in this study can be regarded as estimated alternative precipitation data across Xinjiang for management of water resources and agricultural irrigation activities.

Comparative observational study of mortality amenable by health policy and care between rural and urban Finland: no excess segregation of mortality in the capital despite its increasing residential differentiation.

BackgroundLarge cities are often claimed to display more distinct geographical and socioeconomic health inequalities than other areas due to increasing residential differentiation. Our aim was to assess whether geographical inequalities in mortality within the capital (City of Helsinki) both exceeded that in other types of geographical areas in Finland, and whether those differences were dependent on socioeconomic inequalities.MethodsWe analysed the inequality of distribution separately for overall, ischemic heart disease and alcohol-related mortality, and mortality amenable (AM) to health care interventions in 1992–2008 in three types of geographical areas in Finland: City of Helsinki, other large cities, and small towns and rural areas. Mortality data were acquired as secondary data from the Causes of Death statistics from Statistics Finland. The assessment of changing geographical differences over time, that is geographical inequalities, was performed using Gini coefficients. As some of these differences might arise from socioeconomic factors, we assessed socioeconomic differences with concentration indices in parallel to an analysis of geographical differences. To conclude the analysis, we compared the changes over time of these inequalities between the three geographical areas.ResultsWhile mortality rates mainly decreased, alcohol-related mortality in the lowest income quintile increased. Statistically significant differences over time were found in all mortality groups, varying between geographical areas. Socioeconomic differences existed in all mortality groups and geographical areas. In the study period, geographical differences in mortality remained relatively stable but income differences increased substantially. For instance, the values of concentration indices for AM changed by 54 % in men (p < 0.027) and by 62 % in women (p < 0.016). Only slight differences existed in the time trends of Gini or in the concentration indices between the geographical areas.ConclusionsNo geographical or income-related differences in the distribution of mortality existed between Helsinki and other urban or rural areas of Finland. This suggests that the effect of increasing residential differentiation in the capital may have been mitigated by the policies of positive discrimination and social mixing. One of the main reasons for the increase in health inequalities was growth of alcohol-related mortality, especially among those with the lowest incomes.

상세화 기법을 통한 한반도 공간 강우장 분석

강우는 수문 순환에서 중요한 요소 중에 하나로 시 공간적 변동성이 크므로 정확한 공간 강우장의 파악이 요구된다. 열대강우 관측 위성(Tropical Rainfall Monitoring Mission, TRMM)에서 제공하는 3B43 월 누적 강우량 자료는 25 km의 공간 해상도를 갖고 있어 공간 강우장의 정확성을 높이기 위해 상세화 기법을 적용하여 1 km의 공간 해상도로 생성하였다. Terra 위성에 탑재된 MODIS (Moderate Resolution Imaging Spectroradiometers) 센서가 제공하는 정규식생지수(Normalized Difference Vegetation Index, NDVI) (공간 해상도 1 km)와 강우 자료의 관계성을 회귀식으로 나타냈고 상세화 기법에 적용하였다. 이에 따른 결과를 지점과 위성 강우 자료와의 차이를 통해 보정하는 방법인 GDA (Geographical Difference Analysis)와 지점과 위성 강우 자료와의 비율로 편차를 보정하는 GRA (Geographical Ratio Analysis) 상세화 기법을 사용하여 공간 강우장을 나타내었다. 우리나라의 공간 강우장 결과를 지점 자료를 기준으로 비교 검증을 실시하였다. 그 결과 GDA 상세화 기법의 경우가 2009년(Bias=4.26 mm, RMSE=172.16 mm, MAE=141.95 mm, IOA=0.64), 2011년(Bias=17.21 mm, RMSE=253.43 mm, MAE=310.56 mm, IOA=0.62)으로 가장 잘 맞는 것으로 나타났다. 이를 바탕으로 우리나라의 공간 강우장을 1 km의 공간 해상도로 파악할 수 있었으며, 더 나아가 지점의 수를 늘려 보정을 정밀하게 하거나, 강우 레이더 자료를 가지고 상세화 기법을 적용한다면 더욱 정확한 공간 강우장을 파악할 수 있을 것이다. Precipitation is one of the important factors in the hydrological cycle. It needs to understand accurate of spatial precipitation field because it has large spatio-temporal variability. Precipitation data obtained through the Tropical Rainfall Monitoring Mission (TRMM) 3B43 product is inaccurate because it has 25 km space scale. Downscaling of TRMM 3B43 product can increase the accuracy of spatial precipitation field from 25 km to 1 km scale. The relationship between precipitation and the normalized difference vegetation index(NDVI) (1 km space scale) which is obtained from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor loaded in Terra satellite is variable at different scales. Therefore regression equations were established and these equations apply to downscaling. Two renormalization strategies, Geographical Difference Analysis (GDA) and Geographical Ratio Analysis (GRA) are implemented for correcting the differences between remote sensing-derived and rain gauge data. As for considering the GDA method results, biases, the root mean-squared error (RMSE), MAE and Index of agreement (IOA) is equal to 4.26 mm, 172.16 mm, 141.95 mm, 0.64 in 2009 and 17.21 mm, 253.43 mm, 310.56 mm, 0.62 in 2011. In this study, we can see the 1km spatial precipitation field map over Korea. It will be possible to get more accurate spatial analysis of the precipitation field through using the additional rain gauges or radar data.

Women live longer than men

In most parts of the world women live longer. This gender gap in life expectancy (LE) is remarkable and it has challenged scientific research for decades. Closer analysis of geographic differences related to such gender gap and attempts to recognize its cause may provide a powerful tool to understand the basic process of aging and to device preventive health care strategies. In countries with established socio-economic order women live on the average 4-7 years longer. The main contributing factor is higher cardiovascular disease (CVD) morbidity and mortality in men. In the European established democracies male mortality is on decline and the gender gap in LE is becoming smaller. Current trends in Iceland and in other Scandinavian countries indicate that by the year 2050 men will live as long as women. Unfortunately, the gender gap continues to grow in the countries of the former Soviet influence where it represents up to 13 years. Dependence of the gender gap on the socio-economic order of a particular society points out the importance of environmental factors. Other mechanisms influencing the gender difference are the different hormonal and genetic function (Fig. 6, Ref. 23).

An assessment of the geographical approach to health inequality

New interest is being shown in the geographical approach to health inequality at both the research and the service provider level. The scientific and methodological basis of this approach does not take into consideration the social structure and the history of the locations/communities under investigation. The analysis of geographical differences must be verified and consideration given to possible variations in internal health inequalities between entities compared. Our approach to health inequalities is based on the theory that social health inequalities are essentially the final product of living conditions and lifestyle taking account of individual and collective history.

A Robust Deterministic Model Describing the Bomb Radiocarbon Signal for Use in Fish Age Validation

AbstractThe bomb radiocarbon chronometer has become a standard tool for assessing the accuracy of otolith‐based fish age estimates. Use of the chronometer depends upon the increase in 14C that occurred due to the atmospheric testing of nuclear weapons in the middle of the 20th century. In previous analyses, a variety of ad hoc methods have been used to compare test chronologies with reference chronologies. This paper proposes a deterministic coupled‐functions model that describes bomb radiocarbon chronologies and standardizes and facilitates comparisons within and across species. In this model, a Gaussian pulse of radiocarbon over time is assumed, and dispersion and dilution are described by continuous exponential decay. The model simplifies to the product of a cumulative normal function and an exponential function; despite its simplicity, the model approximates the observed changes in oceanic radiocarbon quite well. It also allows for statistical testing of the timing of increase between different time series and thus permits quantitative aging validation as well as analysis of geographical differences in the timing of the bomb radiocarbon signal. Estimated model parameters describe the timing of the onset and temporal midpoint of the bomb‐related increase in 14C and the rate of 14C dispersion or dilution, which corresponds to the observed rate of decline during the postbomb period. Nine published chronologies are used to demonstrate the model and compare it with other published methods of summarizing bomb radiocarbon data for age validation purposes.

Self-perceived health status of schizophrenic patients in Spain: analysis of geographic differences

This report explores the use of regression models for estimating health status of schizophrenic patients from a Bayesian perspective. The aims are: to obtain a set of values of health states of the EQ-5D based on self-assessed health from a sample of schizophrenic patients; and to analyze the differences in the health status and in patients’ perceptions of their health status between four mental health districts in Spain. The authors develop two linear models with dummy variables. The first model seeks to obtain an index of the health status of the patients using a visual analog scale as a dependent variable and the different dimensions of EQ-5D as regressors. The second model enables analysis of the differences between the self-assessed health status in the different geographic areas and also the differences between the patients’ self-assessed health states, irrespective of their actual health state, in the different geographic areas. The analysis is done using a Bayesian approach with Gibbs sampling (computer program WinBUGS 1.4). Data concerning self-assessed EQ-5D with visual analog scale from four geographic areas of schizophrenic patients were obtained for the purposes of this analysis. The health status index for this sample was obtained and the differences for this index between the four geographic areas were analyzed. The study reveals variables that explain the differences in patients’ health status and health state assessment. Four possible scenarios are considered.

Analysis of geographical differences in the seasonal variation of mean birth weight in Japan (1974-1983)

Geographical differences in the seasonal variation in mean birth weight (MBW) were examined by a traditional time series analysis based on the birth data of 47 different prefectures in Japan for the period from January 1974 to December 1983. The general pattern on the seasonality in MBW has two peaks in spring and autumn, and two troughs in summer and winter. There is a general trend in the seasonality with the autumn-peak-dominant pattern observed in the northern part of Japan (Hokkaido, Tohoku, Kita-Kanto) giving way to the spring-peak-dominant pattern in the southwest. Another interesting finding is that there is high statistical significance as a general tendency, in the seasonality among immensely populated prefectures such as Tokyo, Kanagawa, Aichi, Osaka, and Fukuoka, while in the case of scarcely populated rural prefectures, the time series fluctuates almost randomly. Some socio-cultural and biometerological hypotheses were evaluated to explain these results.

PROBLEMS AND USE OF STRUCTURE IN THE CLASSIFICATION OF VEGETATION

It is a well-known fact that vegetation can be classified on the basis of quite different criteria; e.g. by physiognomy, structure, dynamic processes, floristical composition, and even – a scientifically less satisfying way – by habitat. It is not the aim of this study to give a critical review of these starting-points. The author’s purpose is to consider the Braun-Blanquet system of vegetation classification, which is a system claiming to be based on floristical composition, and to analyse how far, in reality, structural criteria are involved in it. This article does not consider whether Braun-Blanquet syntaxonomy is the only valuable or even the best one. In fact it is, however, the most widely used and uniform system of vegetation classification, enabling us to compare plant communities over an area as large as (e.g.) Europe, and, therefore, also presenting a basis for such items as geographical comparison of habitats, vegetation mapping of large areas, or the analysis of geographical differences in the autecological behaviour of taxa. It may thus be useful to see whether the higher units of this inductive system correspond to the units of the more or less deductive formation systems of the world, based on physiognomy and structure.