Search

Accessibility to parks could be an important determinant of housing prices. This article applies the gravity model to calculate accessibility based on park classification in Shenzhen, China. Unlike most traditional studies that use the ratio method and nearest distance (including straight-line distance and network distance) to measure accessibility to given facilities, in this study, we use gravity-based accessibility by park type. Then, we explore the relationships between accessibility to parks and housing prices using a hedonic price model. In addition, we apply a geographical detector method to assess the association between housing price and related factors. The results indicate the following conclusions: (1) compared to traditional methods, the gravity model provides a more effective and objective measure of accessibility to parks because it considers distance decay effects, supply, and demand; (2) it is necessary and important to investigate the effects of the accessibility to different park types on housing prices; and (3) geographical detector models can efficiently detect correlations and interactions among housing prices and related factors.

The world’s future development depends on effective human-computer linkages. From local to global, the virtual illustrations of a geographical place have to emphasize in an integrative approach peoples‘ key position in the Geosystem. Human values and social networks are now empowered by the unlimited creativity of smartphone applications. Our Geosystem grounded theory envisions that the sustainable management of natural resources is a lifelong learning environment where the poor communities have access to the new technological advances. This paper will attempt to show the effectiveness of Geomedia techniques in the Geosystems identification, evaluation, and valorization processes for the benefit of local inhabitants. This present research methodology uses smartphone apps, Google Earth environmental datasets, Global Positioning Systems, and WebGIS for a geographical investigation and objective assessment of regions throughout the world. The results demonstrate that self-sustainable Geosystems will always be capable to regulate, control and assess progress towards their dynamic equilibrium state, continuously adapting to environmental and societal changes.

The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters.

To date, the prevailing carto-linguistics studies have taken either macrocosmic or social perspectives. In this paper, the research into the linguistic paradigms is concerned with methodology; it regards the representation of spatial information as an analogy to language and establishes phonetic, semantic and syntactic theories of spatial information. There are at least two reasons for this analogy: one is the wide acceptance of carto-semiotics and carto-linguistics, and the other is that we still need a unified paradigm for the disciplines of digital mapping and GIScience. A more methodologically oriented linguistic paradigm could potentially fill this gap. In this paper, taking the viewpoints of ontology, methodology and a qualitative approach, we construct a conceptual model of the internal linguistic structure of spatial information based on phonetics, semantics and syntax. We believe that this methodological approach to the carto-linguistic paradigm will enhance its implementation rather than weaken its influence on digital cartography and GIScience because it lends these fields a perspective that integrates the ‘morphology, meaning and structure’ aspects of spatial information.

Housing market segmentation is significant at both the conceptual and empirical levels because it reflects the spatial heterogeneity of housing prices, improves the predictive accuracy of h...

To study the Dongting Lake water level variation and its relationship with the upstream Three Gorges Dam (TGD), a deep learning method based on a Long Short-Term Memory (LSTM) network is used to establish a model that predicts the daily water levels of Dongting Lake. Seven factors are used as the input for the LSTM model and eight years of daily data (from 2003 to 2012) are used to train the model. Then, the model is applied to the test dataset (from 2011 to 2013) for forecasting and is evaluated using the root mean squared error (RMSE) and the coefficient of determination (R2). The test shows the LSTM model has better accuracy compared to the support vector machine (SVM) model. Furthermore, the model is adjusted to simulate the situation where the TGD does not exist to explore the dam’s impact. The experiment shows that the water level of Dongting Lake drops conspicuously every year from September to November during the TGD impounding period, and the water level increases mildly during dry seasons due to TGD replenishment. Additionally, the impact of the TGD results in a water level decline in Dongting Lake during flood peaks and a subsequent lagged rise. This research provides a tool for flood forecasting and offers a reference for TGD water regulation.

Given their totipotency, human embryonic stem cells (hESCs) can differentiate into all types of cells, including adipocytes, and provide an excellent research model for studying diseases associated with the metabolism of adipocytes, such as obesity and diabetes mellitus. Epigenetic regulation, including DNA methylation and histone modification, plays an essential role in the development and differentiation of hESCs. Lysine-specific demethylase 1 (LSD1), a well-characterized histone-modifying enzyme, demethylates dimethylated histone H3 lysine 4 (H3K4) through a flavin adenine dinucleotide (FAD)-dependent oxidative reaction. LSD1 affects the growth and differentiation of human and mouse ES cells, and the deletion of this gene in mice leads to embryonic lethality. Here, we investigated the functional role of LSD1 during the adipogenic differentiation of hESCs involving the demethylation of H3K4. We also found that treating hESCs with the LSD1 inhibitor CBB1007 promotes the adipogenic differentiation of hESCs.

Urban vibrancy is the key and the foundation for monitoring the status of urban spatial development, assisting in data-driven urban development planning and realizing sustainable urban development. Based on a dataset of multisource geographical big data, the understanding and analysis of urban vibrancy can be deepened with fine granularity. The working framework in this study focuses on the comprehensive perspective of urban morphology, which is decomposed into two dimensions (formality and functionality) and four elements (road, block, building, point of interest). The geographically and temporally weighted regression model was first applied to determine the spatiotemporal effect of the morphological metrics on vibrancy, and then, the geographical detector was employed from the perspective of spatially stratified heterogeneity to reveal the synergetic impacts. The following findings were revealed. (1) Dense street networks, small and medium-sized blocks, and the diversification and intensification of building and land use are beneficial to urban vibrancy. (2) Under the premise of adapting to local conditions, urban spaces combine multiple morphological metrics for the accomplishment of a full-region and all-time vibrancy. (3) The mixture of urban functions is worthy of attention for vibrancy growth because of its extraordinary synergy, not its capacity. Morphological metrics serve to foster and prolong urban vibrancy, adapt to urban sustainability, and contend against inefficient, disorderly urban sprawl. These findings provide significant implications for urban planners/designers and policymakers to optimize urban morphology, improve the vibrancy in large cities, and implement high-quality city planning.

Landslides are a type of serious geologic disaster causing great damage to the human environment. Landslide susceptibility mapping is an effective means to reduce landslide risk. However, previous studies have not considered spatial heterogeneity. In this study, a hybrid model considering spatial heterogeneity is designed by integrating GeoSOM and Stacking ensemble methods and is applied to map the landslide susceptibility of Zhejiang Province, China. The GeoSOM method was used to cluster the study area into several homogeneous regions to solve the heterogeneity problem, and each region was assigned a cluster attribute as one of the landslide model inputs. The Stacking ensemble technique was utilized to design a high-performance landslide model by combining three traditional machine learning methods (support vector machine (SVM), artificial neural network (ANN), and gradient-boosting decision tree (GBDT)). We collected 1051 landslide samples and fourteen affecting factors after feature selection. For landslide modelling, the landslides were randomly split into two subsets: 70% samples for training and the rest for validation. Landslide models were assessed by the receiver operating characteristic (ROC) curve and statistical measures. The results indicated that the hybrid model was 0.11–0.135 higher than those of traditional machine learning methods in term of the area under the ROC curve (AUC). In general, this hybrid model can generate high-quality landslide susceptibility maps and help to develop policies that reduce the burden of landslides.

Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R2 > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined.

Estimation of hourly and continuous ground-level fine particulate matter (PM2.5) concentrations is essential for PM2.5 pollution sources identifications, targeted policy development and population exposure research. However, current PM2.5 estimation studies rely heavily on satellite-based aerosol optical depth (AOD) data, and the limited transit times of polar-orbiting satellites such as Terra and Aqua, nighttime gaps in data from geostationary satellites such as Himawari-8, and cloud contamination reported for both types of satellites challenge the estimation of spatiotemporally continuous PM2.5 concentrations. In this study, spatiotemporal PM2.5 characteristic was constructed by the spatiotemporal fusion method. Specifically, multi-source data, including spatiotemporal, periodic, meteorological, vegetation, anthropogenic and topological characteristics, were incorporated into an ensemble learning method that combined extreme gradient boosting (XGBoost), k-nearest neighbour (KNN) and back-propagation neural network (BPNN) algorithms in level 1 and used linear regression (LR) for integration in level 2. The optimized stacking strategy that considered PM2.5 spatiotemporal autocorrelation was called the ST-stacking model. The model was trained, validated and tested with data acquired for China in 2017. The ST-stacking model outperformed XGBoost, KNN and BPNN models by 9.27% on average, with an R2 = 0.9191. Using the model, the 24-h and continuous ground-level PM2.5 concentrations in mainland China on 11 May 2017 were mapped, and parts of Beijing and Chengdu were selected for more detailed analysis. The PM2.5 concentrations in Taklimakan Desert, North China Plain, Sichuan Basin and Yangtze Plain were much higher than those in other locations on this day, which was generally consistent with the long-term patterns reported in previous studies.

In urban areas, the spatial configuration of urban elements is vital to the sustainable development of cities. Due to the inherent spatial and networked characteristics of UBNs, research on the spatial configuration of UBNs requires a comprehensive approach involving the integration of spatial and network analysis methods. We propose a methodological framework for geospatial network analysis that combines spatial and network analysis to analyse the spatial configuration of UBNs. This framework is based on the construction of an urban bus spatial network (UBSN) in which the network nodes are spatial division units of urban space with more realistic geographical significance. Then, the framework analyses the spatial configuration of the UBN from the identification of macroscopic statistical characteristics to reveal the explicit roles played by microscopic elements such as individual nodes and to further identify the mesoscopic structural organization. The importance of nodes has spatial heterogeneity and directional imbalance. The community detection results demonstrate the UBSN presents obvious spatial agglomeration features, and the overlapping nodes are mostly distributed in the suburbs. With a novel viewpoint and detailed description of the spatial configuration of UBNs, this study provides meaningful insights for policy makers and planners seeking to optimize traffic infrastructure planning.

Introduction The existing healthcare-services-related literature tends to examine accessibility under a single travel mode, and measurement approaches are remaining limited for several inherent deficiency. This paper proposed a methodological enhancement of the three-step floating catchment area approach. Methods First, we incorporates real-time travel time and trip distance of private car and public transport obtained from open-source route planning API into model, which aims to differentiate the impact of multiple travel costs on spatial accessibility outcomes; next, an arithmetic mean-based Gaussian weight algorithm was introduced for achieve stable accessibility index; then, exploratory factor analysis was further employed to evaluate healthcare capacity, with the total score as the healthcare supply indicator to calculate the provider-to-population ratios; finally, an empirical study was conducted to verify the model’s advantages. We investigate accessibility to three tiers of healthcare facilities (including 22 tertiary hospitals, 88 secondary hospitals, and 55 community healthcare centres), and reveal disparities between supply and demand, via conjoint analysis of the accessibility of facilities and the population density under four associate patterns in the district of Wuhan at community scale (total 830 communities). Results: The results suggest that in terms of travel modes, the travel time and trip distance under the private car mode are shorter than these calculated under the public transport mode. Highly accessible communities are more concentrated in the central urban areas and distributed near a healthcare service centre, and community healthcare center have the greatest accessibility among the three tiers of healthcare. Moreover, statistical analysis highlights that distinct polarized differentiation appears in the number of communities with low and high accessibility, and more than half of the communities have accessibility levels that are inappropriate for their population size. Conclusions These findings may have important policy implications for health planners and decision-makers who must reasonably allocate public health resources.

Income mobility is an important issue that can balance equity and efficiency in economic development. Specifically, income mobility is one of the most effective metrics for addressing the problem of income inequality and also a useful criterion for judging the vitality of economic development. Given its increasing regional income inequality, the study of regional income mobility is particularly significant for China. Considering the scale-dependent nature of income distribution, this article explores regional income mobility at multiple scales, including provincial, economic region and national from 1985 to 2015 under an analytical framework integrating a global indicator of mobility association (GIMA) and a family of local indicators of mobility association (LIMAs). The results can be used to fairly and comprehensively evaluate the levels of multiscale regional income mobility by combining corresponding regional economic policies. Based on the eight economic zones, intraregional concordance and interregional mobility are found to be the main characteristics of income mobility in the past few decades in China. This article concludes that policies aiming to increase income mobility in China should accelerate regional economic integration and develop leading cities to drive the economic development of the province.

Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field.

As the era of big data approaches, big data has attracted increasing amounts of attention from researchers. Various types of studies have been conducted and these studies have focused particularly on the management, organization, and correlation of data and calculations using data. Most studies involving big data address applications in scientific, commercial, and ecological fields. However, the application of big data to government management is also needed. This paper examines the application of multi-source government data to urban construction and supervision in Tianjin, China. The analytic hierarchy process and a new approach called the correlation degree algorithm are introduced to calculate the degree of correlation between different approval items in one construction project and between different construction projects. The results show that more than 75% of the construction projects and their approval items are highly correlated. The results of this study suggest that most of the examined construction projects are well supervised, have relatively high probabilities of satisfying the relevant legal requirements, and observe their initial planning schemes.

In recent years, research on the spatiotemporal distribution and health effects of fine particulate matter (PM2.5) has been conducted in China. However, the limitations of different research scopes and methods have led to low comparability between regions regarding the mortality burden of PM2.5. A kriging model was used to simulate the distribution of PM2.5 in 2015 and 2016. Relative risk (RR) at a specified PM2.5 exposure concentration was estimated with an integrated exposure–response (IER) model for different causes of mortality: lung cancer (LC), ischaemic heart disease (IHD), cerebrovascular disease (stroke) and chronic obstructive pulmonary disease (COPD). The population attributable fraction (PAF) was adopted to estimate deaths attributed to PM2.5. 72.02% of cities experienced decreases in PM2.5 from 2015 to 2016. Due to the overall decrease in the PM2.5 concentration, the total number of deaths decreased by approximately 10,658 per million in 336 cities, including a decrease of 1400, 1836, 6312 and 1110 caused by LC, IHD, stroke and COPD, respectively. Our results suggest that the overall PM2.5 concentration and PM2.5-related deaths exhibited decreasing trends in China, although air quality in local areas has deteriorated. To improve air pollution control strategies, regional PM2.5 concentrations and trends should be fully considered.

Due to the rapid urbanization in China, the government must explore a new sustainable source of fiscal revenue. Therefore, property tax reform has been the topic of active discussion. As an important technical support for property tax reform, mass appraisal is an effective technique for implementing the tax base assessment. To achieve high precision and reduce the cost of valuation, an innovative framework for commercial real estate appraisal is proposed through the introduction of a spatial error model (SEM), fuzzy mathematics and econometrics. Several modifications are made to the conventional SEM so that it can be extended to the solution of commercial property mass appraisals. The fuzzy mathematics approach is adopted to specify the spatial weight matrix (SWM). Additionally, based on econometrics, the impacts of the entity and locatio0n factors on commercial real estate prices are discussed. The proposed method is then implemented for commercial real estate, based on the empirical analysis of Shenzhen. Experiments demonstrate the accuracy of the assessment results and that the consistency among different appraisal objects is improved. Further, through the introduction of fuzzy mathematics, we extend the SWM construction from a single variable to multi-variables. This advantage allows us to increase the usage of our technique and to provide technical support for the undergoing property tax reform in the Chinese real estate market.

Spatial data infrastructures, which are characterized by multi-represented datasets, are prevalent through- out the world. The multi-represented datasets contain different representations for identical real-world entities. Therefore, update propagation is useful and required for maintaining multi-represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real-world entities and the detection of changes in updated datasets. Using polygon fea- tures of settlements as examples, this article addresses these key problems and proposes an approach for multi-represented feature matching based on spatial similarity and a back-propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in con- trast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one-to-one matching but also to one-to-many and many-to-many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to dem- onstrate the weight differences in different data contexts and to measure the performance of the BPNN- based feature matching approach.

In this study, we measured photosynthetically active radiation (PAR) and global solar radiation (G) in Lhasa, located on the Tibetan Plateau, from 2006 to 2012 to examine the PAR and PAR/G (PAR fraction) seasonal characteristics. The maximum and minimum values of both PAR and the PAR fraction occurred in summer and winter, respectively. Moreover, the PAR and PAR fraction annual averages were 38.64 mol m−2 d−1 and 1.84 mol M J−1, respectively. An efficient all-weather model used for estimating PAR under various sky conditions was developed based on the relationships among PAR, the cosine of the solar zenith angle and the clearness index in Lhasa. The model also produced acceptable estimations of PAR with high accuracy at the Donghu and Sanjiang weather stations. A PAR dataset was reconstructed from G using the newly developed model for the period 1961–2012. The modelled annual mean daily PAR was approximately 37.62 mol m−2 d−1. A significant decreasing trend (−0.61 mol m−2 per decade) over the last 50 years was observed on the Tibetan Plateau; this decrease was largest in autumn (−1.024 mol m−2 per decade), and relatively small decreases were observed in summer. The results also revealed that PAR began increasing at 0.164 mol m−2 per year from 1991 to 2012, which was inconsistent with the variations of G. The proposed all-weather PAR model could be useful for ecological modelling and agricultural processes in the Tibetan Plateau region of China.

The geoinformation industry is a high-tech service industry based on modern surveying and mapping technologies, remote sensing technologies and satellite navigation and positioning technologies. In China, the geoinformation industry is an emerging strategic industry that has rapidly developed; it has played an increasingly important role in the economy and society. This essay briefly introduces the present state of the international geoinformation industry. The state of the geoinformation industry enterprises in China from 2011 to 2013 is introduced. Although this industry has greatly progressed in China, problems and limitations still exist. The structure of the industry is not sustainable. The innovation capacity of Chinese enterprises is weak. Various market demands, increasing social investments, rapid progress in surveying, mapping and geoinformation technology and attention from the government are the main driving factors in this industry. Developing trends in the industry are proposed. Overall, the government will remain the main user of geoinformation applications, and enterprises will continue to merge and reorganize. Mobile geoinformation applications have broad prospects, and new business models will continue to emerge.

Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations.

The rapid development of the Chinese economy has led to an increasing fraction of agricultural land being converted to nonagricultural uses. The zoning and protection of farmland with the best agricultural quality (basic farmland) is extremely intensive and complicated work. In this paper we establish a remote sensing, geographic information system, and particle swarm optimisation (PSO) multiobjective decision model (MODM) to calculate the optimum solution for basic farmland protection. Furthermore, a new particle evolution rule combined with a genetic algorithm is introduced to improve the solution performance. The PSO-based zoning model is then utilised in the case study of Yicheng, Hubei Province, China, to demonstrate that our MODM framework excels in providing an optimum solution for balancing the three objectives of basic farmland zoning and protection: maximising farmland spatial compactness, maximising farmland soil fertility, and minimising transportation cost. In particular, the model compares alternative Pareto-optimal scenarios in which several objectives can be achieved without compromising the other objectives to obtain a real and practical blueprint for action. Our model enables urban planners to test and compare the different scenarios under various particle swarm conditions. In addition, the PSO-based zoning model constitutes a true guide for real-world planners, and this model can be extended to specify basic farmland protection optimisations in other regions of China.

Socioeconomic factors are extrinsic factors that drive spatial variability. They play an important role in land resource systems and sometimes are more important than that of the natural setting. The study aims to build a comprehensive framework for assessing unconsolidated cultivated land (UCL) in the south-central and southwestern portions of Hubei Province, China, which have not experienced project management and land consolidation, to identify the roles of natural and especially socioeconomic factors. Moreover, the study attempts to identify the attributes and indicators that describe the characteristics of the extrinsic factors affecting land spatial variability. Assessment supplement 12 proposed land use indicators on the basis of natural factors using the method of gradation of agricultural land quality (GALQ). The overall level of cultivated land quality (CLQ) in the two study areas is moderate, and this quantity is significantly correlated with topography. Excellent and high-quality UCL are mainly distributed in the south-central plain division of Hubei Province (SCPDHP), whereas lower grades are mainly distributed in the area of the southwestern mountainous division of Hubei Province (SWMDHP). These results suggest that the pattern of small-scale agricultural development depends strongly on the labor force and is the key land use-related factor that limits the improvement of regional CLQ. Such assessments and their findings are essential for the protection of cultivated land and the adjustment of agricultural structure to promote the sustainable use of UCL.

Excess weight and obesity have become a serious problem in adult men of reproductive age throughout the world. The purpose of this retrospective study was to assess the relationships between body mass index and sperm quality in subfertile couples in a Chinese Han population. Sperm analyses were performed and demographic data collected from 2384 male partners in subfertile couples who visited a reproductive medical center for treatment and preconception counseling. The subjects were classified into four groups according to their body mass index: underweight, normal, overweight, and obese. Of these subjects, 918 (38.3%) had a body mass index of >25.0 kg m-0 2 . No significant differences were found between the four groups with respect to age, occupation, level of education, smoking status, alcohol use, duration of sexual abstinence, or the collection time of year for sperm. The results clearly indicated lower sperm quality (total sperm count, sperm concentration, motile sperm, relative amounts of type A motility, and progressive motility sperm [A + B]) in overweight and obese participants than in those with normal body mass index. Normal sperm morphology and sperm volume showed no clear difference between the four groups. This study indicates that body mass index has a negative effect on sperm quality in men of subfertile couples in a Northern Chinese population. Further study should be performed to investigate the relationship between body mass index and sperm quality in a larger population.

This paper describes a study of urban flooding in downtown Kunming, China, simulating a major flood event that occurred in July 2008 using an improved two-dimensional (2D) hydraulic model enhanced with courtyard-level sewer data (CLSD). Although municipal authorities are not responsible for ‘private’ courtyard sewers, available records were specifically added to this model, enhancing its accuracy and usefulness. Geographic information system (GIS) flood maps, a mapping overlay approach and statistical method compared both predicted results and the recorded flood area. A statistical method also provided a measure of the correlation between the extent of the predicted flood areas and recorded flood areas (parameter ‘F’). Results of the improved 2D/CLSD model showed a correlation value for F of 51, 32.6% higher than the basic one-dimensional municipal-level sewer data (1D/MLSD) model; 26.2% higher than an interim version of the model that included a 2D ground surface (2D/MLSD). The 2D/CLSD model predicted flooding in 10 of the 12 courtyards with observed flooding. This was a major improvement over the 1D/MLSD model (three out of 12) and the 2D/MLSD model (five out of 12). Thus a CLSD-enhanced 2D hydraulic model potentially improves accuracy in predicting, mapping and understanding flood risk in urban areas.

Measurements of ultraviolet radiation (UV) and global solar radiation (G) in Lhasa, Tibetan Plateau, from 2006 to 2012 were used to investigate their seasonal characteristics. The maximum UV irradiation and UV fraction (percentage of UV compared to G, FUV) occurred at noon in summer (July) and the minimum values occurred in winter (December); the annual means of UV and FUV were approximately 0.91 MJ m−2 day−1 and 4.3%, respectively. By analysing the dependence of UV irradiances on clearness index (Kt) and cosine of the solar zenith angle, an efficient all-sky model was developed for estimating UV radiation in Tibetan Plateau. The model was tested at Sanjiang and Donghu stations and produced satisfactory estimates. UV dataset was then reconstructed from G using the newly developed model for the period 1961–2012. It was discovered that annual mean daily UV irradiation was approximately 0.89 MJ m−2 day−1 and there was a significant decreasing trend (at the rate of 0.013 MJ m−2 decade−1) over the last 50 years in the Tibetan Plateau. The largest decrease occurred in autumn (−0.023 MJ m−2 decade−1) and a small decrease observed in summer. Meanwhile, results also revealed that UV radiation increased at 0.05 MJ m−2 year−1 from 1989 to 2012, which was inconsistent with the variations of G in the study area. The proposed all-weather UV model is very important for ecological modelling, atmospheric environment, agricultural processes and solar energy applications in the Tibetan Plateau, China.

Urban competitiveness aids local development by encouraging the exploitation of opportunities for economic development and by enhancing overall performance. Previous studies have evaluated urban competitiveness primarily from an economic perspective and few studies have considered locational conditions as factors that might influence local industrialization and urbanization. In response to the publishing of a national plan for the development of major function-oriented zones (MFOZs) in 2010, the present essay employs MFOZs as constraints to enable a balanced and comprehensive study of urban competitiveness that includes four dimensions of competitiveness: Economic, social-cultural, environmental, and locational (accessibility and hypsography). A four-level hierarchical indicator system and an entropy weighting method were used to assess the urban competitiveness of 31 Chinese provincial capitals based on a spatial analysis of data acquired in 2010 using Geographic Information System technology. The results reveal the overall ranking of provincial capitals in terms of urban competitiveness and their performances with respect to the four dimensions of competitiveness. Unlike previous studies, this analysis was performed by overlaying the strategy of the national MFOZ with the urban competitiveness rankings. The development orientation of each provincial city is discussed according to its characteristics of urban competitiveness under the conditions of a MFOZ.

Background: Hepatoma associated with hepatitis B infection is a major public health problem in Shenzhen (China) and worldwide. China has the largest number of people infected with the hepatitis B virus (HBV), and many studies have demonstrated that HBV infection is associated with hepatoma development. Shenzhen officials have been attempting to monitor and control these diseases for many years. The methodology and the results of this study may be useful in developing a system to monitor, prevent and control these diseases. Methods: The aim of the study was to analyze HBV infection and hepatoma distribution characteristics and patterns in Shenzhen by combining geographic information system (GIS) technology and spatial analysis. The study used data from patients at the district level from the 2010–2012 population censuses. Results: Only one-third of the patients were female, and 70.7% of all cases were 20–50 years of age. There was no global spatial correlation of the distribution of hepatitis B infections and hepatomas, however, there was a local spatial correlation, and certain sub-districts of the Nanshan district had significant agglomeration effects. Based on incidence density and rate maps, we can conclude that the Shenzhen special zone had a higher incidence density and rate of hepatitis B infections and hepatomas compared with the area outside of the Shenzhen special zone during 2010–2012. Conclusions: This study demonstrated substantial geographic variation in the incidence of hepatitis B infection and hepatoma in Shenzhen. The prediction and control of hepatitis B infections and hepatoma development and interventions for these diseases should focus on disadvantaged areas to reduce disparities. GIS and spatial analysis play an important role in public health risk-reduction programs and may become integral components in the epidemiologic description, analysis and risk assessment of hepatitis B and hepatoma.

In China, awareness about hypertension, the treatment rate and the control rate are low compared to developed countries, even though China’s aging population has grown, especially in those areas with a high degree of urbanization. However, limited epidemiological studies have attempted to describe the spatial variation of the geo-referenced data on hypertension disease over an urban area of China. In this study, we applied hierarchical Bayesian models to explore the spatial heterogeneity of the relative risk for hypertension admissions throughout Shenzhen in 2011. The final model specification includes an intercept and spatial components (structured and unstructured). Although the road density could be used as a covariate in modeling, it is an indirect factor on the relative risk. In addition, spatial scan statistics and spatial analysis were utilized to identify the spatial pattern and to map the clusters. The results showed that the relative risk for hospital admission for hypertension has high-value clusters in the south and southeastern Shenzhen. This study aimed to identify some specific regions with high relative risk, and this information is useful for the health administrators. Further research should address more-detailed data collection and an explanation of the spatial patterns.

The causation between bioavailability of heavy metals and environmental factors are generally obtained from field experiments at local scales at present, and lack sufficient evidence from large scales. However, inferring causation between bioavailability of heavy metals and environmental factors across large-scale regions is challenging. Because the conventional correlation-based approaches used for causation assessments across large-scale regions, at the expense of actual causation, can result in spurious insights. In this study, a general approach framework, Intervention calculus when the directed acyclic graph (DAG) is absent (IDA) combined with the backdoor criterion (BC), was introduced to identify causation between the bioavailability of heavy metals and the potential environmental factors across large-scale regions. We take the Pearl River Delta (PRD) in China as a case study. The causal structures and effects were identified based on the concentrations of heavy metals (Zn, As, Cu, Hg, Pb, Cr, Ni and Cd) in soil (0-20 cm depth) and vegetable (lettuce) and 40 environmental factors (soil properties, extractable heavy metals and weathering indices) in 94 samples across the PRD. Results show that the bioavailability of heavy metals (Cd, Zn, Cr, Ni and As) was causally influenced by soil properties and soil weathering factors, whereas no causal factor impacted the bioavailability of Cu, Hg and Pb. No latent factor was found between the bioavailability of heavy metals and environmental factors. The causation between the bioavailability of heavy metals and environmental factors at field experiments is consistent with that on a large scale. The IDA combined with the BC provides a powerful tool to identify causation between the bioavailability of heavy metals and environmental factors across large-scale regions. Causal inference in a large system with the dynamic changes has great implications for system-based risk management.

Trauma is considered a “modern civilized sickness”, and its occurrence substantially affects all of society, as well as individuals. The implementation of trauma emergency systems in cities with young, prosperous, and highly mobile populations is necessary and significant. A complete trauma emergency system includes both low-level trauma centers that offer basic emergency services and high-level trauma centers that offer comprehensive services. GIS and operational research methods were used to solve the location problem associated with these centers. This study analyzed the spatial distribution characteristics of trauma demands and the candidate locations of trauma centers based on a spatial analysis and presented a hierarchical location-allocation model for low- and high-level trauma centers in Shenzhen. The response, coverage, treatment and cost capacities of the trauma center locations were considered, and an ant colony optimization was used to calculate the optimal solution. The objectives of this study were to optimize trauma center locations, improve the allocation of medical trauma resources and reduce the rate of deaths and disabilities due to trauma.

Complex systems are very often organized under the form of networks, where nodes and edges are embedded in the geographic space in which human beings live. A geospatial network is a complex network that includes road networks, flight connections, physical internet infrastructures, and power grids, etc. The properties of geographic spaces and network topologies are key to the geospatial network function and evolution. It is crucial to characterize and understand the structure and evolution of these geospatial networks for different domains, ranging from social sciences to geographic sciences. Community structures, whose nodes often cluster into tightly knit groups (with a high density of within-group edges and a low density of between-group edges), have been given much attention because of their significance in analyzing complex geospatial networks. Community structures, which account for the functionality of a complex system in geographic space, represent a partition (disjoint community) or cover (overlapping community) of a geospatial network that is expected to have an optimal solution. Community structure found in geospatial network, such as those present in networks capturing the movements of people or animals, are subject to environmental factors, physical constraints, and other spatial influences. Therefore, communities in spatial networks often have geo-graphic meanings such as neighborhood boundaries, urban structures, and habitat territories. Moreover, the influence of geospatial indicators on the detection of community structure must not be overlooked.Compared with disjoint community structures, overlapping community structures are more ubiquitous community structures, which represent the fundamental characteristic of any complex network form in the physical world. In a geospatial network, community structures represent the different functions or structural units of a geographic space. Furthermore, overlapping communities are the result of the network evolution process, which represent the share channel of a neighbouring community. In geographical space, overlapping communities are always in the middle of two different communities. Spatially and functionally, overlapping communities are the bridge in communication between neighbouring communities. Therefore, the detection of overlapping communities in geospatial networks is of great significance. Although there are many methods for the detection of overlapping community structures, existing algorithms usually focus on network topology characteristics and ignore the effects of geospatial attributes on overlapping community structures.Hence, in this paper, we proposed a new methodology for uncovering communities in geospatial networks. The focus of our methodology is the spatially constrained speaker-listener label propagation algorithm (SLPA), which adds spatial resistance and spatial adjacency relations to the community detection algorithm. Furthermore, we make a case study using urban bus network in Hangzhou, China. The results show that the algorithm is efficient and reasonable to discovering overlapping community structures in geospatial networks. Overall, this study will help to understand and monitor the spatial interaction development of geographic phenomena and explore the functional characteristics of the urban complex system that are useful and meaningful for policy makers and planners.

Research on human location prediction is beneficial personalized location recommendations, location-based advertising, and ride-sharing services. The prediction of future locations is generally based on historical trajectory data. With the development and prevalence of mobile sensors, large volumes of data that can capture human trajectories are generated and provide researchers with more adequate and accurate data to study human trajectories and location predictions. In this paper, we propose a model containing long short-term memory (LSTM) to understand historical trajectories and predict future locations. LSTM is a special recurrent neural network (RNN) architecture that contains three gates and one internal state to control the storage of information. Additionally, we leverage the LINE (Large-scale Information Network Embedding), an embedding method to transform vertices into large scale networks, to transform locations into spatial vectors according to their distance information, which we name it “Location2Vec”. By feeding the summation of the dynamic location embeddings and pre-trained Location2Vec embeddings into LSTM model, it can effectively predict next locations of a trajectory and improve the predicting accuracy. Finally, we verify the reliability of the model, and the results show that our model outperforms other traditional trajectory models.

The ability to extract useful information from data is a topic of considerable interest, especially with regard to organizing point-of-interest (POI) information containing many attributes as well ...

A semantics-based method for density-based clustering with constraints imposed by geographical background knowledge is proposed. In this paper, we apply an ontological approach to the DBSCAN (Density-Based Geospatial Clustering of Applications with Noise) algorithm in the form of knowledge representation for constraint clustering. When used in the process of clustering geographic information, semantic reasoning based on a defined ontology and its relationships is primarily intended to overcome the lack of knowledge of the relevant geospatial data. Better constraints on the geographical knowledge yield more reasonable clustering results. This article uses an ontology to describe the four types of semantic constraints for geographical backgrounds: “No Constraints”, “Constraints”, “Cannot-Link Constraints”, and “Must-Link Constraints”. This paper also reports the implementation of a prototype clustering program. Based on the proposed approach, DBSCAN can be applied with both obstacle and non-obstacle constraints as a semi-supervised clustering algorithm and the clustering results are displayed on a digital map.

With the coming era of big data and the rapid development and widespread applications of Geographical Information Systems (GISs), geocoding technology is playing an increasingly important role in bridging the gap between non-spatial data resources and spatial data in various fields. However, Chinese geocoding faces great challenges because of the complexity of the address string format in Chinese, which contains no delimiters between Chinese words, and the poor address management resulting from the existence of multiple address authorities spread among different governmental agencies. This paper presents a geocoding service based on an optimized Chinese address matching method, including address modeling, address standardization and address matching. The address model focuses on the spatial semantics of each address element, and the address standardization process is based on an address tree model. A geocoding service application is implemented in practice using a large quantity of data from Shenzhen Municipality. More than 1,460,000 data records were used to test the geocoding service, and good matching rates were achieved with good adaptability and intelligence.

Incorporating the information of hypertension, this paper applies Bayesian multi-disease analysis to model the spatial patterns of Ischemic Heart Disease (IHD) risks. Patterns of harmful alcohol intake (HAI) and overweight/obesity are also modelled as they are common risk factors contributing to both IHD and hypertension. The hospitalization data of IHD and hypertension in 2012 were analyzed with three Bayesian multi-disease models at the sub-district level of Shenzhen. Results revealed that the IHD high-risk cluster shifted slightly north-eastward compared with the IHD Standardized Hospitalization Ratio (SHR). Spatial variations of overweight/obesity and HAI were found to contribute most to the IHD patterns. Identified patterns of IHD risk would benefit IHD integrated prevention. Spatial patterns of overweight/obesity and HAI could supplement the current disease surveillance system by providing information about small-area level risk factors, and thus benefit integrated prevention of related chronic diseases. Middle southern Shenzhen, where high risk of IHD, overweight/obesity, and HAI are present, should be prioritized for interventions, including alcohol control, innovative healthy diet toolkit distribution, insurance system revision, and community-based chronic disease intervention. Related health resource planning is also suggested to focus on these areas first.

Urban-rural equalized development (URED) is recognized as strongly contributing to the narrowing of societal, economic, life, and environmental gaps between urban and rural areas and is also an effective way to solve the “three rural issues” of rapid industrialization and urbanization in China. This paper explores the spatio-temporal patterns of URED in the state-designated experimental zone of Chengdu at a county level by using quantitative survey data from 2004 to 2013. The major findings are as follows: (1) the regions that are closer to the central city of Chengdu had a more optimistic urban-rural equalized development outlook (i.e., the three-tier geographical distribution phenomenon); (2) this distribution characteristic was gradually broken up in the process of urban and rural integration, and the differences between the three tiers has been narrowing; and (3) the gap between urban and rural areas has been significantly improved and exhibited a higher dynamic degree in the second and third tiers than in the first tier, which suggests a new development mode that exhibits better quality and higher sustainability. Given these results, the development orientation and strategy of each tier are discussed according to the characteristics of urban and rural equalized development.

For the automatic generation of geographical information service chain, this article defines the quality of service (QoS) metrics based on service response time, reliability, and matching degree, among others, and the error propagation model. Based on the semantic matching and trustworthiness assessment of the geographical information services, this article proposes a trustworthy adaptive composition framework and implementation algorithm for geographical information services, ensuring the composition of service chains to better meet various QoS constraints. The effectiveness of this approach is proven in the simulation experiments.

Shenzhen is a city that is highly representative of China’s rapid urbanization process. As the city rapidly expands, there are enormous challenges to the sustainable use of land resources. This paper introduces the evolution of urban land expansion and the sustainable land use policy of the Shenzhen Government since 2005. The policy covers the reduction in rural-to-urban land conversion, the delineation of urban growth boundaries, arable land reclamation and the establishment of farmland protection areas, urban redevelopment, and the investigation and prosecution of illegal construction. This paper considers the aspects of urbanization and land management systems that are unique to China. The current top-down indicative and mandatory mode of control, which relies on the central government, has very limited effects. Good results were achieved in Shenzhen for the following elements: governmental self-restraint, governmental identity change, and policy innovation. Shenzhen’s sustainable land use practices can provide a reference for other cities in China.