Innovation is the primary driving force behind development. As an important part of the regional innovation system and the core supporting carrier for the accumulation of innovative resources, innovation platforms are the main way to promote innovation-driven development. This study took the urban agglomerations of the middle reaches of the Yangtze River Basin as the research area and used the nearest neighbor index, kernel density estimation, Ripley's K function, and geo-detector to explore the spatial distribution characteristics and influencing factors of the innovation platforms above provincial level in the urban agglomerations in 2017. The results show that: 1) The overall spatial distribution of the innovation platforms in the urban agglomerations is clustered, showing a triangular distribution pattern with Wuhan, Changsha, and Nanchang as the cores. 2) Innovation platforms of various scales and functional types exhibit significant agglomeration characteristics, but there exist certain differences in agglomeration intensity and state among them. 3) The overall innovation platforms and various types of innovation platforms show scale effect, which first strengthens and then weakens with the change of geographical distance. 4) The spatial differentiation of the innovation platforms is the result of multiple factors. Strength of financial resources, degree of informationalization, level of foreign investment, and conditions of human capital are most significant, followed by traffic accessibility, urbanization level, economic base, and innovation atmosphere.
As the main spatial carrier of regional development, urban agglomerations play an increasingly important role in regional development competition. This study took five urban agglomerations in the Yangtze River Economic Belt as the research object and used the Internet big data and other means to analyze their differences of factors aggregating ability and spatial patterns of factors aggregation. The main conclusions are as follows: 1) The difference in the factors aggregating ability between urban agglomerations in the Yangtze River Economic Belt is very significant. This difference is consistent with the regional differences in the level of social and economic developments within the Yangtze River Economic Belt. The agglomeration pattern of factors is characterized by a gradient from east to west. 2) The difference in factors aggregating ability between cities shows the characteristics of polarization, and the core-periphery spatial structure of factors agglomeration of the urban agglomerations is distinct. 3) In the Yangtze River Economic Belt the spatial agglomeration patterns of factors such as technological innovation, opening up, and finance show clear differentiation and centralized distribution features. 4) The rank-size structure distribution of factors aggregating ability of the five urban agglomerations in the Yangtze River Economic Belt is in line with the Zipf's law, and can be divided into two types: balanced and discrete. The relationship between the primacy ratio of urban agglomeration and factors aggregating ability of urban agglomerations presents a relationship of "high at both ends and low in the middle".
Global cities are the management and control centers of the world's investment networks. Thus, building a powerful outward investment network will help Shanghai to implement its developmental strategy for becoming an outstanding global city by 2035. We created the A-share listed companies branch network database of Shanghai based on their annual reports in 2005, 2010, and 2017, then used rank-size analysis, social network analysis, and negative binomial regression analysis methods to explore the spatial network structure and its determinants. The results of this empirical research are as follow: 1) Shanghai listed companies' outward investment network agglomerates in Western Europe, North America, South East Asia and East Asia. It is worth noting that cities along the Belt and Road Initiative attracted 155 subsidiaries of Shanghai listed companies from 2005 to 2017. 2) Shanghai listed companies' outward investment agglomerates in overseas central cities initially, then diffuses to nearby cities surrounding the associated central cities. 3) Manufacturing, finance, transportation, warehousing and postal services are the main sectors of the outward investment activities. The outward investment network of manufacturing industry covers the largest number of oversea cities. From 2005 to 2017, the number of oversea cities involved in the outward investment network of Shanghai manufacturing listed companies increased by 102. 4) Social proximity, position in the hierarchy of world city network, lower tax of offshore financial centers, convenient flag countries and sea ports have a positive impact on the evolution of Shanghai listed companies' outward investment network.
Quantitative assessment of regional human activity intensity can reflect the relationship between people and environment, which can provide a scientific basis for landuse-related decision making and eco-environment construction. Based on the land use/land cover data with 1 km resolution, we quantitatively evaluated the spatial-temporal features of human activity intensity in Hainan Island from 1980 to 2018. On this basis, we analyzed the overall change and spatial correlation of human activity intensity and explored its driving mechanism. The results showed that: 1) The main types of land use were woodland and farmland in the study area in 2018, accounting for 62.85% and 25.27%, respectively. Only bulit-up land increased sharply, and areas of all other land use types reduced from 1980 to 2018. The areal reductions of farmland and grassland were the largest and the decreases were 397.81 km2 and 303.02 km2, respectively. 2) Human activity intensity gradually increased from 10.54% (1980) to 12.86% (2018), risen by 22.01%. Before 2000, it was relatively stable and after that, it grew fast. 3) The spatial distribution of human activity intensity was high in the coastal areas and low in the central part of the island. Human activity intensity showed significant spatial agglomeration effect and the hotspots were concentrated mainly in areas of rapid urban development, such as Haikou City and Sanya City. 4) The human activity intensity in Hainan Island was mainly driven by government policy, rapid population growth, economic development, and so on.
Rapid land use changes in the arid area of western China make the region a hotspot of global land use change research. In this study, land use inventories in the Hetian Tarim Basin in 1990, 2000, 2010, and 2016 were completed by using stratified and object-oriented image processing method based on Landsat images with a consistent spatial resolution of 30 m to reveal the characteristics of land-use change, patterns and trend, and spatial heterogeneity. The results show that from 1990 to 2016: 1) Cropland continuously expanded with an increase rate of 2.9%/a, with gradual expansion and sudden reclamation. 2) The area of construction land increased significantly at a rate of 12.1%/a, which can be mainly attributed to the increase in area of urbanization, rural residential land, and transportation land. 3) The increase in cropland and construction land resulted in the decrease of forests, shrubs, and grasslands. 4) Spatial heterogeneity in land use developments among counties of Hetian Prefecture are significant: Hetian City had the highest percentage of construction lands; Hetian County and Moyu County experienced the greatest expansion in total area and rates of change in both cropland and construction land, followed by Luopu County, Yutian County, Pishan County, Cele County, and Minfeng County with relatively slow development in cropland and construction land. The increase rates of cropland and construction land in Hetian Tarim Basin are above average in Xinjiang Uighur Autonomous Region. Attention should be focused on the impact of the rapid land-use changes on eco-environmental issues in the near future.
Based on daily maximum temperature and monthly precipitation data of 49 meteorological stations on the Loess Plateau during 1960-2016 and using the 90th percentile of the temperature as extreme temperature thresholds and standardized precipitation index (SPI), this study examined the spatiotemporal variation characteristics of heatwaves and droughts and the dynamics of concurrent droughts and heatwaves across the Loess Plateau. The results show that: 1) The occurrence frequency of heatwaves presented an increasing trend on the Loess Plateau during the recent 57 years with a liner tendency of 0.29 times/a, of which a significant increasing trend was detected after 1995. The significantly increasing areas were mainly distributed in the northeast of Shanxi Province, eastern Qinghai Province, and southern and central Gansu Province. 2) Standardized precipitation index (SPI) showed a downward trend in the recent 57 years, indicating a trend from water-logging to drought, and the early 1990s was a turning point. Particularly, areas with the declining trend of SPI12 accounted for 62% of the whole study area, and regions with significant drought were mainly distributed in the south of the gully areas of the Loess Plateau, the southern part of Northern Shaanxi, the south of Shanxi Province, and the east of Gansu Province. 3) The simultaneous occurrence frequency of droughts and heatwaves showed an overall increasing trend, with a growth rate of 0.66 times/10 a. From 1960 to 1979 it showed a declining trend of -0.26 times/a, then an increasing trend of 0.52 times/a from 1980 to 2002, while a steady increasing trend was detected after 2003. Spatially, the high incidence of droughts and heatwaves was observed in the eastern of Shanxi, the south of Northern Shaanxi, and the southwest of Gansu; areas with increasing trend were mainly concentrated in the northeast of Shanxi, central and eastern Gansu, and northern Ningxia.
The spatial travel behavior of criminals plays an important role in describing and understanding the journey to crime process of criminals. Current studies focus on the statistics of travel distance of criminals, lacking studies on refined individualization of spatial travel behavior under different individual characteristics of criminals. Using theft cases of electric bicycles in Beijing region, an empirical analysis was conducted in this study to explore the spatial distribution of the criminals' travel distance under different individual characteristics. The results show that individual differences that affect travel distance distribution primarily concern gender, age, origin, and frequency of crimes attributes. Among them, the crime travel distance of male criminals is higher than that of female criminals, but their spatial buffer radius is closer than that of female criminals. Middle-aged criminals have larger crime travel distance and spatial buffer radius than juvenile and elderly criminals. The crime travel distance and spatial buffer zone of alien criminals are farther than that of local criminals. The crime offenders who commit multiple crimes have higher travel distance and spatial buffer distance than those who commit fewer crimes. Finally, this study constructed a decision model of spatial travel of criminals from travel cost, risk perception of neighborhood guardianship, and profit on the basis of the least effort principle and rational choice theory, and explained the difference of spatial travel behavior of criminals under the influence of different individual characteristics. The conclusions can provide effective support for crime prediction and policing practice.
To explore whether the Latent Dirichlet Allocation (LDA) topic model is suitable for analyzing the short text of tourism microblogs and whether the result can be consistent with the research results based on interviews and other data, this study established a destination image perception framework including four first-level dimensions and 10 second-level dimensions. Then the meaning of the topics is defined based on the dominant dimensions of each topic, which can reduce the subjectivity of researchers and help them to use the LDA model to extract the destination image perception quantitatively and objectively. The case study of the Old Town of Lijiang shows that in the first-level dimensions, the basic framework of image perception can be fully outlined through the five groups of core spatial and landscape elements, including the human settlements, music culture, character, leisure space and Naxi cuisine, and the special perception of the deep tourists, urban residents, young people and girls, and the characteristics of interaction of human and environmental elements. In the second-level dimensions, more detailed perception of destination image can be vividly presented from three aspects: the slow living in the Old Town of Lijiang, the culture of nightlife and romance, and tourists' perception of the fusion of local culture and modernity. This study proves the feasibility and advantage of this method—it shows that LDA is suitable for short text analysis of social media such as Weibo. Topic analysis based on dominant semantic dimensions successfully portrays the image perception of the Old Town of Lijiang and further analyzes the mechanism of image formation, and provides a new perspective for destination image perception, which has three values. It helps to accurately establish the basic framework of destination image perception; quantitatively extract the core dimensions of image perception; and deeply interpret the local meaning of destination image and clarify the relationship between cognitive, affective, and behavioral images.
Flood similarity identification has important practical significance for floodwater utilization, flood control of reservoirs, and river ecological restoration. In this study, the observations of 125 flood events at the 16 hydrological stations in the middle and upper reaches of the Huai River Basin from 2006 to 2015 were collected, and the metrics of flood magnitude, timing, and rate of changes and patterns were adopted to characterize the entire flood events. Multivariate statistical analysis—principal component analysis and hierarchical clustering method—were adopted to identify the representative flood event types. Finally the temporal and spatial distributions of each flood event type were identified. The results show that: 1) there are five types of representative flood events in the upper and middle reaches of the Huai River Basin, including long duration and extreme variability type, multiple peaks and long duration type, thin and short duration type, fat and short duration type, and conventional type. 2) From the perspective of temporal distribution, the number of flood event types showed a decreasing trend during 2006-2015, and the proportion of conventional floods gradually increased. More flood event types were found in the high flow years (
Based on the hydrological data from 2003 to 2014, Long-Short Term Memory (LSTM) was used to construct a daily runoff prediction model for the Ankang discharge station in the upper reaches of the Hanjiang River. The accuracy of daily runoff prediction was evaluated under different input conditions. The result shows that when the foreseeing period is one day, the efficiency coefficient of the LSTM in the calibration period and the validation period can reach 0.68 and 0.74 respectively under the condition that only the previous daily runoff of the Ankang Station is used as input. When the previous areal rainfall of the catchment and the previous daily runoff of the upstream Shiquan Station were added to the LSTM model as input variables, the daily runoff prediction precision was improved. The efficiency coefficient of the training period and the validation period could reach 0.83 and 0.84, respectively. The root mean square error was also significantly reduced. The accuracy of the main flood peak flow forecasting also increased. The LSTM can effectively avoid the problem of over-fitting, and has better generalization performance. However, when the foreseeable period is extended from one day to two or three days, the performance of LSTM is significantly reduced.
The mid- and long-term runoff prediction with satisfactory accuracy plays an important role as basic information in water resources planning & management and optimal operation of water conservancy projects. Combination and bias reduction are two common post-processing approaches in runoff forecast. Applying them in turn, considering the complicated non-stationary and nonlinear characteristics of runoff, a new mid- and long-term runoff prediction method by connecting time-varying weight combination and Bayesian correction is proposed. This method was used to study the annual and monthly inflow prediction of the Longjiang Reservoir in Yunnan Province. The results show that time-varying weight combination balances the performance difference of the established random forest (RF) and support vector machine (SVM) models in the modeling period and the test period. As a consequence of Bayesian correction, the prediction metrics are close to or better than the best of the predictions of the two individual stages. The proportion of correctly classified hydrological year type reaches 77.2% by employing the forecasted annual runoff, and the Nash-Sutcliffe efficiency coefficient of predicted monthly runoff series is over 0.90. Overall, the method put forward in this study has achieved positive effects in improving the forecast performance.
Manning roughness coefficient is the key parameter of flow velocity calculation. Overland flow is significantly different from open channel flow. In this study, we focused on the application of Manning formula in calculating the velocity of overland flow. Compared with open channel flow, the depth of overland flow is very shallow, sometimes only a few millimeters. Thus, vegetation, soil, surface roughness, and other factors have more obvious impact on overland flow. Therefore, the existing open channel flow Manning roughness coefficient cannot be directly used in overland flow. In order to determine the Manning roughness coefficient of overland flow, in this study we developed an indoor experimental system with variable roughness on slope, which includes a water supply system, an experimental tank, and an observation and data recording system. In this system, we used uniform river sand on the flat plate to simulate different roughness of the underlying surface, and placed it in a water tank. The stability and accuracy of the water supply system were verified by 87 pre-experiments. The results show that when the water supply was stable, the discharge was consistent with the data displayed by the electronic flow meter. The 87 groups of weighing data are relatively stable and consistent with normal distribution, and the data are within the 95% confidence interval. Then we designed 166 experiment scenes through a combination of different slopes, surface roughness, and water supply flow to explore the relationship between experimental conditions and Manning roughness coefficient. Among the 166 experiment scenes, a total of six kinds of roughness were designed. The water supply flow ranged from 1 to 25 m 3/h. The slope was between 4°-25°. We used the volume method to calculate the average diameter of the river sand and the chain method to calculate the surface roughness. The experiment data were processed for Support Vector Machine (SVM) training and forecasting, which used root mean square error (RMSE) and coefficient of determination (R 2) as the evaluation indices, considered slope, measured flow, measured depth, average diameter of the river sand, and surface roughness as the independent variables, and Manning roughness coefficient as the dependent variable. The results show that no matter how many kinds of factors were considered, it was difficult to predict the Manning roughness coefficient of laminar flow and transitional flow by the training results of turbulent flow, which indicates a different influence mechanism in different flow patterns. In order to predict the Manning roughness coefficient accurately, we need three factors at least, and measured water depth must be included. When considering four or more factors at the same time, the Manning roughness coefficient could be accurately predicted in turbulent flow.
Rural living space is the microcosm of rural development, which reflects the human-environment relationship in rural areas. With the implementation of the Urban-Rural Integration Strategy and the Rural Revitalization Strategy, coupled with the development of the new-type urbanization and globalization, rural space, including rural living space, has been restructured as a whole, and the countryside has entered a new period of development. In this process, how rural living space map and influence rural development is a topic worthy of discussion from multiple perspectives. Based on the review of literature, this article holds that the rural living space is the space of the daily life behavior of rural residents, a multi-level territorial complex of rural residents' living, leisure, socializing, consumption, and public service activities, and an important part of rural social space. The study of rural living space in Chinese academia started in the 1920s. According to the characteristics of different stages of economic and social development of China, this study divided the research of rural living space into four periods: the exploration period before the founding of the People's Republic of China; the stagnation period before the Reform and Opening-up; the revival period after the Reform and Opening-up; and the rapid development period since the 21st century. However, compared with international rural living space research and Chinese urban living space research, the study of rural life in China tends to be empirical, lacking comprehensive deep examination and theoretical model construction. In the new era of development, the study of rural living space in China should construct a complete research framework and carry out multi-level and all dimensional exploration on basic theories, research subjects, and diverse research perspectives.
River is one of the most significant factors in driving the formation and evolution of landforms as well as one of the most important freshwater resources on the Earth. River characteristics, including water extent, water level/water depth, river discharge, water quality, and ice coverage, are vital to the dynamic monitoring of water resources and protection of eco-hydrological systems. Traditional methods of acquiring river characteristics are to use in situ data that were collected on hydrological gauges, which costs large amount of financial and material resources. It is urgent to develop a way of supplementing in situ data of rivers quickly and accurately for the increasing demand of river information. With the ability of omnidirectional and multi-temporal Earth observation, remote sensing has greatly improved the efficiency of acquiring river characteristics. It has been applied broadly in multi-scale river monitoring and hydrological simulation in ungauged basins. Therefore, it is necessary to summarize recent progresses in the field of remote sensing based river characteristics inversion, in order to further promote the application of remote sensing data and methods in this field. This article, therefore, focusing on remote sensing of river characteristics, summarizes recent progresses systematically on the extraction of water extent, inversion of water level/water depth, estimation of river discharge, and monitoring of water quality and ice coverage. Advantages and disadvantages of applying optical and microwave remote sensors for obtaining water extent and water level/depth are discussed in detail. The advanced data, specific methods, and related emerging technologies in this field are discussed and the following conclusions are made: 1) Newly available remotely sensed data have been making creative breakthroughs in spatial resolution, temporal resolution, and spectral resolution, which dramatically enrich data sources for river studies. 2) Most optical images still face the challenge of mixed pixels, while the application of SAR images is suffering from difficulties in developing complex processing algorithms. Meanwhile, accurate extractions of narrow and non-open waters need further research in the future. 3) The development of big data and cloud computing technologies provide excellent means for monitoring river characteristics at large spatial scales and long temporal scales, with both high spatial and high temporal resolutions.
With global climate change and rapid urbanization, extreme weather events have become much more frequent. As one of the most typical extreme weather events caused by climate change, heat wave has been enhancing the heat-related health risks, and continuously attracting widespread attention from meteorologists, medical professionals and geographers. As a bridging actor integrating meteorological and medical fields, geography provides spatial indicators for heat wave vulnerability assessment, ideas and methods for mapping, spatial solutions for practical work such as planning and early warning. This article systematically reviews the progress of spatial assessment of heat wave vulnerability based on the perspective of geography. The contents include the definition of heat wave, spatial indicators and mapping methods. The literature reviews indicate that heat wave vulnerability assessment has gradually deepened from single perspective to the interdisciplinary study of meteorology, medicine, geography and other disciplines. In the future, more detailed exploration will be carried out, such as the construction of spatial indicators to integrate the advantages and research demands of various disciplines; the exploration about small-scale community to regional large-scale links and cross-regional research expression by mapping. Spatial approaches require differentiated research among different groups, which provide a scientific basis for climate change adaptation of cities.
Geopolitical risk is one of the five categories of risk identified in the World Economic Forum's Global Risks Report, with significant effects on the global and regional peace, stability, and development. How to identify, measure, forecast, and manage the impact that geopolitical risks on human well-being are taken into consideration by researchers worldwide. Global scientific researchers have not yet reached a consensus on the definition of geopolitical risks. Geopolitical risk research has made notable progress in the fields of 1) analyzing the causal factors of geopolitical risks; 2) identifying the impact of geopolitical risks; and 3) measuring and mapping geopolitical risks. Considering the fact that challenges come from the spatiotemporal differences and variability of geopolitical risk factors, the complexity of the various risk causing factors on interaction and response mechanisms, and the unpredictability and uncertainty of geopolitical risks, geopolitical risk research has made further progress on the following frontier issues: 1) identification of geopolitical risk causing factors; 2) occurrence mechanism of geopolitical risks; and 3) monitoring and simulation of geopolitical risks. Future directions of geopolitical risk research in China include: 1) research on national geo-security with spatial differentiation, especially forthesurrounding areas of China; 2) quantitative reconstruction and numerical simulation of the geopolitical risks across multiple scales and multiple geo-factors; 3) new computation ways supporting the interaction of other scientific disciplines and the use of big data, and new platform integrating early warning of geopolitical risks; 4) focusing on scientific issues and promoting the ability of theoretical and practical research output in dealing with geopolitical issues.
