Differences in characteristics of population agglomeration areas significantly affect migration. Research on the effect of location-specific characteristics of population agglomeration areas on migration would enrich the study of population geography and provide references for territorial function zoning and the provision of public service facilities. This article explores the effect of pull factors of population agglomeration areas on migration by investigating immigration in Dongguan, Guangdong Province in the period between 1995 and 2000, when the number of immigrants increased most rapidly. Based on the classic push and pull theory, an indicator system was built to calculate the pulling effect of population agglomeration areas. The relationship between different pull factors and population aggregation was analyzed using principle component analysis and multiple regression analysis. The results show that population aggregation varied greatly between different towns and its distribution was spatially uneven in Dongguan. There was a very large number of immigrants aggregated during 1995-2000 and the population aggregation was affected by employment opportunities, per capita income, and the migration path dependence in different towns. The large number of labor-intensive manufacturing industries characterized the industrial development in Dongguan, which provided great employment opportunities for migrant workers from areas lacking job opportunities. The large number of specialized industrial towns gained economies of agglomeration, which led to rapid economy growth and high salary that attracted lots of immigrants to the city. Migration path dependence also affected the immigration. The migrants often followed their predecessors' destination choices. This imitative behavior comes from the consideration of reducing migration risk and cost. These migrants are often countrymen or relatives. The early immigrants often help the late comers for example by introducing the latter to the companies they work and sometime providing accommodations for the new comers. Areas absorbed immigration in earlier period would attract more immigrants in the next period because of the migration path dependence. The regressive equation of the comprehensive pulling force and population aggregation was a cubic curve. The relative importance of the three pull factors to population aggregation, from most significant to least significant, is migration path dependence, employment opportunities, and per capita income. As a typical population aggregation area, Dongguan attracted a large number of immigrants. At the same time, some push factors such as the household registration system, high level of consumption and high housing price also expel the immigrants. This paper does not discuss the push factors because these factors are difficult to quantify, and the expelling force was less than the pulling force in areas of population aggregation. In addition, this paper only focuses on the immigration in Dongguan between 1995 and 2000. Surplus labor during this time period in China was abundant. Therefore, migration path dependence was significant. These three factors may affect migration differently in a different time period and a new relationship would require more empirical studies to test.

Currently, the studies on domestic population aging mostly focus on the demographic effects and social effects, where the present state, the development process, the causes and the effects of population aging have been widely discussed from a non-spatial perspective. However, a growing number of studies shows that population aging also has significant spatial-temporal evolution characteristics that are as important as the economic and social development aspects. This article attempts to describe regional disparity of population aging process in a different perspective, i.e., regional spatial heterogeneity. With this objective, we collected the census data of Fujian in 1990, 2000 and 2010 and various relevant statistical data of corresponding years from the Fujian Statistical Yearbook. A population aging geographical database was established with these data and the sub-county level administrative map of Fujian Province using ArcGIS9.3. Exploratory spatial data analysis (ESDA) methods as well as Moran's I, Getis-Ord G_i^*, function of variogram, and GWR (geographical weighted regression) were applied to examine population aging disparity in Fujian since 1990, aiming to explore the spatial agglomeration pattern, the process of the evolution and the underlying dynamic mechanisms of the spatial-temporal variation of the county level population aging distribution. The result provides further insight into the complexity and uncertainty of the regional disparity of population aging. The conclusions are as follows: (1) The county level population aging distribution of Fujian Province shows a strong spatial correlation with a downward temporal trend. (2) The spatial structures of hotspot and cold spot distributions also have clear differences. The hotspots moved from the coastal area to inland during the study period, while the cold spots shifted in the opposite direction, presenting reversed gradients. (3) In the overall variation, the influence of random factors has increased and structural difference has decreased. At the same time, regional spatial differences have become more significant, and the difference in the northwest-southeast direction was the primary contributor to the widening population aging regional disparity. (4) All factors that were considered affected population aging, but in different areas the direction and degree of influence varied considerably. In general, mortality rate and the number of hospital beds per thousand people played a positive role in prompting population aging, while birth rate, schooling years per capita, and immigration rate have an inhibitory influence on population aging. Per capita GDP affected population aging both positively and negatively. All in all, the dynamic mechanism of spatial-temporal variation of the county level population aging in Fujian Province has been extensively and quantitatively examined in this study from a spatial variability perspective. Admittedly, what have been discussed in this paper are far from complete, especially the population aging evaluation indexes—different evaluation indexes and variables may lead to some differences in parameter estimation and evaluation result. Further research should examine the appropriateness of indexes for evaluating population aging.

In recent years, residential care resources in Beijing have increased rapidly. The supply of residential care resources, however, is still in shortage due to the increase of demand. To reach the "9064" policy goal proposed by the Beijing municipal government, which means that 4 percent of elderly population in Beijing will live in residential care facilities in 2020, about 90 thousand beds of residential care facilities are still needed. An efficient method for measuring accessibility is of crucial importance for the spatial planning of residential care resources. Among a range of methods to measure the spatial accessibility of facilities, the two- step floating catchment area method (2SFCA) is most widely used. But the 2SFCA does not take distance decay among catchment areas into account, and catchment size is constant for facilities with different sizes, which may reduce the accuracy of the assessment. This study uses an improved two-step floating catchment area method, including the addition of a distance-decay function and variable catchment sizes depending on the size of residential care facility, to assess the spatial accessibility to residential care resources in Beijing. Two scenarios are set for comparison. The first scenario was analyzed using one hour catchment area and the second scenario was analyzed using three different catchment areas based on the sizes of residential care facilities. In both scenarios the distance-decay function was taken into account. The results show that the measurement of three catchment areas is a more effective method than the one catchment area for measuring the spatial accessibility to residential care facilities in Beijing. A map of spatial accessibility was developed to show the distribution of shortage areas of residential care resources in Beijing. This map indicates that the geographic distribution of spatial accessibility to residential care facilities in Beijing is fairly uneven. The spatial accessibility is higher in the western part, the northern part and the northeastern part as compared to the southern part of the city. The spatial accessibility in the central part is still poor in spite of a considerable number of residential care facilities located in the central city. This is due to the great size of elderly population in the central area of Beijing. Because land for constructing new residential care facilities is in great shortage in the core area of the city, such facilities should be located outside of the area or in the suburbs to provide services for elderly population in the central city. The spatial accessibility in the southern part is generally poor and very uneven, so new residential care facilities should be located across the area and more focus should be given to areas with relatively poorer spatial accessibility. These recommendations are consistent with the "Special Planning of Residential Care Facility for the Elderly in Beijing" by the Beijing municipal government.

Schistosomiasis japonica is a parasitic disease that debilitates human bodies and greatly impedes socioeconomic progress in endemic areas. It was widespread in southern China several decades ago and the disease prevention effort of the Chinese government and researchers achieved remarkable results in reducing infections. However, in recent year, the epidemic situation has worsened due to a series of changes in the natural environment and socioeconomic conditions. As the only intermediate host of Schistosome, Oncomelania hupensis plays an important role in the spread of this disease and its control is critical for the prevention and control of Schistosome. Therefore, identifying the environmental factors that determine the distribution of the snail could help predict the distribution and extent of snail breeding sites, obtain a macroscopic view on snail spreading trend, and take effective measures to eliminate the snails. In this paper, we aim to determine key indictors that could be used in remote sensing monitoring of Oncomelania hupensis breeding extent and density. Hubei Province is one of the serious epidemic areas in China. Oncomelania hupensis here can be classified into three subtypes: the subtype inside embankments, subtype outside embankments, and subtype in hilly areas, according to the geographical environment of snail habitats. We take into account several environmental factors including elevation, nearest distance to river (water), land use, soil and vegetation to analyze their influence on snail distribution. Geographical Detector Model used in this research is based on spatial variation analysis of the geographical strata to assess the health risks in different environment. It contains four geographical detectors: factor detector identifies which factors are responsible for the risk; ecological detector compares the relative importance of risk factors; risk detector discloses where the high risk areas are; and interaction detector reveals whether the risk factors interact or lead to disease independently. The main procedures of our analysis are as follows: first, both snail statistics and environmental data are collected and preprocessed with ArcGIS Desktop software; then the environmental indicators that are strongly related to snail distribution are identified by the factor detector and ecological detector; finally, favorable (suitable) type or range of each indicator as well as the reference factors that indirectly influence the snails can be computed from the risk detector and interaction detector. It is found that for the subtype inside embankments, vegetation coverage of epidemic season (March to October), especially July to September, determines the extent of distribution, while high density areas are characterized by moderate silt content in soil texture, yellowish red soil and submerged paddy soil, high vegetation coverage in the first quarter of the year. The subtype outsider embankments distributed mainly at lake beaches with high vegetation coverage, while high vegetation coverage in the first quarter, reed and amur silver grass vegetation contributes to its abundance. In hilly areas, there is no clear indicator for the extent of distribution of the subtype due to the relatively complex environment, yet woodland and farmland close to river, waterlogged paddy soil as well as submerged paddy soil are strongly related to high dense of the snails. This result is consistent with previous studies. The result and method of this research could provide scientific reference for policy makers and researchers to take efficient measures to control snail prevalence.

With the development of geography and people's overall health concerns, medical geography as an emerging discipline has also experienced rapid developments. Given that most of the existing medical reference values take into little consideration the influence of geographical factors, a more comprehensive and scientific method should be developed to take these into account. This article takes healthy adult women's lung peak expiratory flow rate reference value as an example, using 3809 cases of healthy adult women's peak expiratory flow reference value collected throughout China to analyze the impact of geographic factors, calculate the differences of different regions' reference values, and explore the mechanism of geographical factor's influence on medical reference value in an effort to improve the methods of medical reference value analysis through analyzing the relationship between geographical factors and medical reference values. As a first step, correlation analysis was used to analyze the relationship between peak expiratory flow value of adult women and the selected 25 indicators of geographical factors. Based on the result, seven geographic indicators (latitude, altitude, average temperature, annual average relative humidity, annual rainfall, topsoil gravel percentage, and topsoil reference capacity) that have significant correlation with peak expiratory flow reference value were extracted for further analysis. Second, Moran's I (spatial autocorrelation module), one of the ArcGIS software's analytical tools, was used to determine if this group of data is impacted by spatial and geographical factors. Third, using the data for the seven selected indicators, ridge regression analysis and SVR (support vector regression) were used to create two regression models and interpolate values. Then the results of these two prediction models were given different weights to establish the optimal combination forecasting model of spatial differences. Student's T test was used to compare the accuracy of ridge regression analysis, SVR and the combination forecasting model. Meanwhile, differences between the true values and results of the above three models were also considered for evaluating the performance of the models. Finally, a spatial difference prediction map was made. Based on this map and the results of correlation analyses, this article discusses why and how these geographical factors influence human tissues/organs and medical reference values. The output of this study indicates that the relationship between geographical factors and healthy adult Chinese women's lung peak expiratory flow rate should not be overlooked. The selected geographical factors (classify into terrain, climatic and soil factors) affect the lung tissue, especially the structure and function of the bronchi, because different living environments impact human tissues and organs differently, and humans living in different regions develop some differences in tissues and organs. The result of this research also shows that the combination forecasting model, which combined ridge regression and SVR, performed better than the individual prediction methods. Combination forecasting model not only can be used in traditional prediction exercises using temporal data: it is also possible to use this method to predict differences in geographic distribution or spatial data. It has the potential to be further expanded and utilized.