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    The Progress and Prospect of Remote Sensing Monitoring of Rocky Desert Dynamic Changes in the Ice and Snow Melting Area of the Qinghai-Tibet Plateau
    JIA Wei, WANG Jing'ai, SHI Peijun, MA Weidong
    Journal of Geo-information Science    2021, 23 (10): 1715-1727.   DOI: 10.12082/dqxxkx.2021.210149
    Abstract632)   HTML9)    PDF (9566KB)(38)      

    The Qinghai-Tibet Plateau is sensitive to climate change. At present, relevant researches mostly focus on the dynamic changes of ice and snow in the Qinghai-Tibet Plateau, and seldom pay attention to the dynamic changes of the rocky desert left by the melting ice and snow. Through the earth-atmosphere interaction, rocky desert may change the regional heterogeneity of climate at a large scale. This paper sorted out the extraction methods of remote sensing monitoring of ice and snow melting and rocky desert dynamic changes in the Qinghai-Tibet Plateau, and analyzed the advantages, disadvantages and applicability of various remote sensing data and extraction methods. We also summarized the data and research methods of the dynamic monitoring of ice and snow and the dynamic changes of the rocky desert in the Qinghai-Tibet Plateau. At present, the remote sensing monitoring data of the snow and ice dynamic changes in the Qinghai-Tibet Plateau are diverse and the research methods are mature. However, the remote sensing monitoring of the rocky desert dynamic changes left by the melting ice and snow has not yet formed a systematic study. Besides, under the condition of insignificant human disturbance, the dynamic changes of the rocky desert in the ice and snow melting area can also be used as a supplement to remote sensing monitoring of ice and snow dynamic changes.

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    Big-data Oriented Commuting Distribution Model and Application in Large Cities
    LIU Yunshu, ZHAO Pengjun, LV Di
    Journal of Geo-information Science    2021, 23 (7): 1185-1195.   DOI: 10.12082/dqxxkx.2021.200334
    Abstract630)   HTML28)    PDF (4167KB)(313)      

    In recent years, big data has been widely applied in traffic analysis. However, they are mostly used for data visualization and phenomenon description. There is a lack of big-data oriented transport modeling, which leads to limited application of big-data in transportation planning. In this study, we propose a Location-Space Dependent Indicator (LSDI) based on the time-space interaction between transportation and land use. Based on this indicator, the urban commuting distribution model is developed, which improves the traditional gravity model. Taking Beijing as a study case, the developed model is applied and verified using mobile phone signaling big data derived from the communication service of an operator in September 2017. Travel generation and distribution models are constructed and verified respectively. Our results show that: (1) For the travel generation model simulations, commuter population and resident population show a good linear relationship. This model generates a significant prediction with a goodness of fit of 0.84; (2) For the travel distribution model simulations, a comparison analysis is conducted between gravity model, radiation model, and modified model with LSDI. The gravity model corrected by real commuting data performs best in regression analysis with a goodness of fit of 0.94. But large errors occur in the probability density distribution. The radiation model performs normal in regression analysis with a goodness of fit of 0.37. It has a better accuracy in the probability density distribution. The modified gravity model with LSDI has the best overall performance. The underestimation phenomenon is optimized in the commuter population distribution with a highest goodness of fit (0.85). Our findings provide new insights in developing big-data oriented transport prediction models and contribute to promote the application of big data in transport planning.

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    Research Progress of the Application of Geo-information Science and Technology in Territorial Spatial Planning
    XIE Hualin, WEN Jiaming, CHEN Qianru, HE Yafen
    Journal of Geo-information Science    2022, 24 (2): 202-219.   DOI: 10.12082/dqxxkx.2022.210317
    Abstract590)   HTML10)    PDF (1876KB)(123)      

    Territorial spatial planning is the spatial blueprint of high-quality social and economic development. With the rapid development of geo-information science and technology, geo-information science and tech- nology has changed the way of territorial spatial planning. Its powerful capability in data acquisition, analysis, prediction, and management provides support in data, method, and platform for territorial spatial planning, thus enabling territorial spatial planning to be more scientific, operable, and forward-looking. Based on literature review, summary, and comparative analysis, this study analyzes the technical requirements of territorial spatial planning compilation, implementation, supervision, public participation, and intelligent transformation, and systematically expounded the application of geo-information science and technology in territorial spatial planning. This study expounds the contributions of geo-information science on China's territorial spatial planning from the following three aspects: (1) Geospatial data, remote sensing data, and socio-economic big data provide data basis for territorial spatial planning; (2) Geographic Information System (GIS) analysis method, geographic simulation method, and artificial intelligence method provide method support for territorial spatial planning; (3) The application of GIS platform, cloud computing, and urban intelligent platform promotes the intelligent transformation of territorial spatial planning. This study also points out shortages of different technologies. However, there are still some problems that need to be further explored: (1) The generation of socio-economic big data and its application scenarios in territorial spatial planning are concentrated in urban space; (2) Both traditional and modern technologies in territorial spatial planning have advantages and disadvantages. These technologies need to be effectively integrated to prepare more scientific territorial spatial planning; (3) The construction of territorial spatial planning platform has not been organically combined with the construction of City Information Modeling (CIM) and other intelligent society platforms, there is a huge space for mining in the future. According to the maturity of its application in territorial spatial planning, these technologies can be divided into mature technology and promising technology. With the promulgation of territorial spatial planning at all levels and types and the initial establishment of Chinese territorial spatial planning system in 2021, attention should be paid to the application of intelligent planning methods in agricultural space and ecological space, technical system construction of intelligent territorial spatial planning, and the improvement of territorial spatial planning intellectualization.

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    Accessibility Analysis of Medical Facilities based on Multiple Transportation Modes of Network Map
    GUO Chenchen, LIANG Juanzhu
    Journal of Geo-information Science    2022, 24 (3): 483-494.   DOI: 10.12082/dqxxkx.2022.210260
    Abstract517)   HTML0)    PDF (6509KB)(0)      

    Owing to the rapid advent of urbanization and the increasing demand for medical services by residents, the pressure on medical services in densely populated areas is surging. The analysis of the accessibility of medical service facilities is of primordial importance. In this study, the medical data was garnered from the Fuzhou Municipal Health Commission, and the crawler technology was used to yield the number of residential households to estimate the population. By use of the Baidu map to obtain the real time road condition information of the peak and non-peak time periods, the access time under the optimal route from the community residential districts to the hospital based on the real-time road condition was calculated, and the time zones of medical services were drawn. The accessibility of general hospitals in the main urban area of Fuzhou was analyzed using the two-step mobile (Ga-2SFCA) search method boosted by the Gaussian distance attenuation function, considering factors such as the travel mode, searching time threshold, and travel peak hours. The results yielded show that: (1) By integrating Baidu Map API into Ga-2SFCA model, multivariate and fine-grained analysis of accessibility was implemented, leading to the accurate measurement of urban medical service supply and demand; (2) The time cost of public transportation at different periods was less affected by traffic congestion, and reaching tertiary hospitals was faster. Under the premise of advocating green transportation, this mode of public transportation was recommended for medical treatment; (3) Under different conditions, the accessibility of medical facilities depended on the space of residential differentiation characteristics significantly, on the whole presenting a "single center" and "diminishing layer coil" distribution. High accessibility of residential areas was mainly distributed in urban core areas, and the lower level of accessibility settlement distribution was in the peripheral urban areas. However, other factors can also influence accessibility, such as the time threshold. The accessibility level of medical services markedly differed with the transportation mode, and the accessibility of medical services was significantly higher along the subway. The choice of off-peak travel time can effectively improve the level of medical service; (4) Due to the layout of urban expressways, the spatial distribution of medical accessibility in driving mode was consistent with that of roads, presenting a "loop level" pattern. However, the spatial distribution of accessibility under the public transport mode was affected by the urban bus microcirculation system, displaying the trait of "axial expansion." The method used in this paper provides a new scientific method for refined measurement and analysis of the accessibility of medical service facilities.

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    Application of Geo-information Science and Technology in Poverty Alleviation in China
    HU Shan, GE Yong, LIU Mengxiao
    Journal of Geo-information Science    2021, 23 (8): 1339-1350.   DOI: 10.12082/dqxxkx.2021.200631
    Abstract509)   HTML20)    PDF (3790KB)(113)      

    Through various exploration and practice of poverty alleviation, China has embarked on a path of poverty alleviation with Chinese characteristics, which has greatly reduced the number of rural poor people and significantly improved the living standard in poverty-stricken areas. For a long time, the monitoring of socioeconomic and environmental conditions in poverty-stricken areas is based on all kinds of statistical data, reports, paper files, etc., based on administrative units, lacking effective and accurate spatial location information. With the rapid development of geo-information science such as Remote Sensing (RS) and Geographic Information System (GIS), the real-time and efficient capture and calculation ability of spatial information greatly improves the efficiency and decision support level of poverty alleviation. This paper expounds the contributions of geo-information science on China's poverty alleviation from the following aspects:① monitoring and evaluation of natural resources and environment in poverty-stricken areas based on multi-source geospatial data; ② monitoring, early warning, and management of natural disasters in poverty-stricken areas; ③ analysis of poverty causing factors and poverty prediction; ④ decision support system for targeted poverty alleviation based on the mechanism of targeted poverty alleviation. China aims to eradicate absolute poverty in 2020, so the application of geo-information science in poverty alleviation will mainly focus on the establishment of monitoring and assistance mechanism to prevent poverty returning and alleviate the relative poverty. Moreover, under the background of rural revitalization, using geo-information science and technology to promote rural infrastructure information construction will be the focus of the next step.

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    Classification and Description of Geographic Information from the Perspective of Geography
    YU Zhaoyuan, YUAN Linwang, WU Mingguang, ZHOU Liangchen, LUO Wen, ZHANG Xueying, LV Guonian
    Journal of Geo-information Science    2022, 24 (1): 17-24.   DOI: 10.12082/dqxxkx.2022.210817
    Abstract472)   HTML6)    PDF (1932KB)(182)      

    Geography is a comprehensive discipline that studies the spatial-temporal pattern, evolution process, and interaction mechanism of various geographic elements. With the evolution of the real world from binary space to the ternary world, it is urgent to deepen and expand the understanding, expression, and mining of geographic information connotation. The existing geographic information expression model of "location + geometry + attributes" is difficult to support the expression of various geographic elements and their laws. From the perspective of geography, based on the concept of the ternary world, we sort out the information elements and the process of their transformation into geographical information and form an information expression system with the "seven elements" of time, place, character, object, event, phenomenon, and scene, and from the geography "seven dimensions" perspective of semantic, spatial location, geometric structure, attribute, interrelationship, evolution process, mechanism of effect to interpret. It realizes the all-around classification and description of the connotation of geographic information from the perspective of geography and provides theoretical support for the multidimensional description and computational analysis of geographic information for comprehensive and integrated research in geography.

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    The Theory Prospect of Crowd Dynamics-oriented Observation
    FANG Zhixiang
    Journal of Geo-information Science    2021, 23 (9): 1527-1536.   DOI: 10.12082/dqxxkx.2021.200787
    Abstract433)   HTML22)    PDF (2675KB)(96)      

    During the development of COVID-19 virus's global epidemic, the fundamental research and various applications of crowd dynamics-oriented observation theories have attracted much attention from many researchers and people all over the world within some related disciplines, such as public health, clinical medicine, geography, public management, etc. Researchers conducted many interdisciplinary explorations in theories and methods of monitoring epidemic dynamics scientifically, preventing and controlling spatial transmission precisely, predicting accurately, and responding effectively. However, no crowd dynamics-oriented observation theories have been proposed in literature so far. This paper revisits the concept and introduces a theory framework of crowd dynamics-oriented observation, which tries to include the core theories of observation from geospatial big data and to support diverse potential developments. Firstly, this article introduces the research background of crowd dynamics-oriented observation, and then summarizes its three core questions (how to observe its change, how to analyze its change, and how to control its change). From the inter-discipline view of geographic information science, surveying and mapping science, this paper explains the research significance and disciplinary value of crowd dynamics-oriented observation theories. Secondly, this paper introduces a framework of crowd dynamics-oriented observation and its spatiotemporal application, and then elaborates on the bottleneck problems of the key observation theories of crowd dynamics, such as fundamental space-time framework theory, space-time quantification and comprehensive observation theory, spatiotemporal process optimization theory, etc. Thirdly, this paper preliminarily introduces some changes of crowd dynamics-oriented observation theories, for example, refined observation driven by the application needs of digital society governance and public safety/health emergency, personal privacy protection and personalized observations by balancing the public interest and personal privacies, the development of integrated observation theories for human-oriented observation and earth-oriented observation, and the theory of crowd dynamics-oriented observation for high-level management and service. Finally, this article points out the potential directions of crowd dynamics-oriented observation theory and methods, such as, the development of big data-driven crowd perception, multi-space refined crowd dynamics observation, and human-land systematical interaction modeling, so as to realize some differentiated, integrated, and hierarchical crowd dynamics-oriented observations. All potential theories are helpful to the scientific decision-making of public management and public service. The crowd dynamics-oriented observation theory should focus on the fundamental research questions related to studying, analyzing, and servicing human beings, which has become a research frontier in geospatial information science, and could play very important roles in supporting national development strategies, such as "New urbanization", "beautiful China", "artificial intelligence", and "new infrastructure", so as to contribute to a green, efficient, smart, and sustainable regional and urban development.

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    Sky View Factor Calculation based on Baidu Street View Images and Its Application in Urban Heat Island Study
    FENG Yehan, CHEN Liang, HE Xiaodong
    Journal of Geo-information Science    2021, 23 (11): 1998-2012.   DOI: 10.12082/dqxxkx.2021.200747
    Abstract425)   HTML9)    PDF (9719KB)(133)      

    The Sky View Factor (SVF) is one of the most important indicators to characterize urban radiation fluxes and urban thermal environment. Therefore, it is a key morphological parameter to study the Urban Heat Island (UHI) effect. Studies have shown that SVF has a strong relationship with UHI intensity. Nevertheless, the relationships found can be contradictory. This is primarily due to the fact that the cases studied are often in different regions with different climatic conditions. In addition, the influences of trees are sometimes ignored due to the lack of vegetation data or the limitation of calculating methods. How to calculate SVF quickly and accurately is important to urban climate research. SVF is typically calculated by four types of methods: fisheye photo methods, 3D GIS methods, GPS methods, and street view image methods. Compared with the other types of methods, calculating SVF using street view images has many advantages, such as widely available data, low cost, high efficiency, and the ability to consider the influences of trees and other obstacles. On the one hand, street view images provide the possibility for fast and accurate calculation of SVF in large-scale areas. On the other hand, the street view image method is still at its developing stage and more work needs to be done to verify its application in various urban environments. In this study, we proposed an automatic SVF calculation method using street view images and deep learning algorithms, and then applied the method to the UHI study in the city center of Shanghai. Baidu static panoramas and Deeplabv3+ were used to detect sky range while MATLAB code was written to calculate SVF. A Landsat-8 OLI / TIRS image was also used to retrieve land surface temperature at street level in the study area. Based on the Local Climate Zones (LCZ) scheme, we combined large-scale SVF value with the land use and building morphology to examine the relationship between SVF and UHI intensity. The results showed that Deeplabv3+ can detect the sky and non-sky range effectively in different scenarios (MIOU=91.64%). The SVF calculated using the proposed method was in good agreement with that calculated using fish-eye photos (R2=0.8869). The LCZ scheme provides new insights for the relationship between SVF and UHI. For LCZ5 and LCZ1, the highest correlation coefficients were 0.68 and -0.79, respectively. The proposed method was shown to be applicable in high-density and complex urban environments. In addition, the calculation of large-scale continuous SVF provides the possibility for zonal understandings of the UHI effect based on the LCZ scheme.

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    Integrating Human Mobility into the Epidemiological Models of COVID-19: Progress and Challenges
    YIN Ling, LIU Kang, ZHANG Hao, XI Guikai, LI Xuan, LI Ziyin, XUE Jianzhang
    Journal of Geo-information Science    2021, 23 (11): 1894-1909.   DOI: 10.12082/dqxxkx.2021.210091
    Abstract383)   HTML10)    PDF (2651KB)(116)      

    The spread of infectious diseases is usually a highly nonlinear space-time diffusion process. Epidemiological models can not only be used to predict the epidemic trend, but also be used to systematically and scientifically study the transmission mechanism of the complex processes under different hypothetical intervention scenarios, which provide crucial analytical and planning tools for public health studies and policy-making. Since host behavior is one of the critical driven factors for the dynamics of infectious diseases, it is important to effectively integrate human spatiotemporal behavior into the epidemiological models for human-hosted infectious diseases. Due to the rapid development of human mobility research and applications aided by big trajectory data, many of the epidemiological models for Coronavirus Disease 2019 (COVID-19) have already coupled human mobility. By incorporating real trajectory data such as mobile phone location data at an individual or aggregated level, researchers are working towards the direction of accurately depicting the real world, so as to improve the effectiveness of the model in guiding actual epidemic prevention and control. The epidemic trend prediction, Non-pharmaceutical Interventions (NPIs) evaluation, vaccination strategy design, and transmission driven factors have been studied by the epidemiological models coupled with human mobility, which provides scientific decision-making aid for controlling epidemic in different countries and regions. In order to systematically understand this important progress of epidemiological models, this study collected and summarized relevant literatures. First, the interactions between the COVID-19 epidemic and human mobility were analyzed, which demonstrated the necessity of integrating the complex spatiotemporal behavior, such as population-based or individual-based mobility, activity, and contact interaction, into the epidemiological models. Then, according to the modeling purpose and mechanism, the models integrated with human mobility were discussed by two types: short-term epidemic prediction models and process simulation models. Among them, based on the coupling methods of human mobility, short-term epidemic prediction models can further be divided into models coupled with first-order and second-order human mobility, while process simulation models can be divided into models coupled with population-based mobility and individual-based mobility. Finally, we concluded that epidemiological models integrating human mobility should be developed towards more complex human spatiotemporal behaviors with a fine spatial granularity. Besides, it is in urgent need to improve the model capability to better understand the disease spread processes over space and time, break through the bottleneck of the huge computational cost of fine-grained models, cooperate cutting-edge artificial intelligence approaches, and develop more universal and accessible modeling data sets and tools for general users.

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    Research on the Characteristics of Urban Network Structure in China based on Baidu Migration Data
    ZHANG Xiaodong, HAN Haoying, TANG Yongjun, LUO Guona
    Journal of Geo-information Science    2021, 23 (10): 1798-1808.   DOI: 10.12082/dqxxkx.2021.210223
    Abstract375)   HTML10)    PDF (11538KB)(134)      

    As a new product of the Internet era, migration flowed is the basic carrier of information flow, capital flow, traffic flow and other flow space. It can objectively reflect the geographical behavior relationship between cities, and it is of great significance to depict the urban network structure. Based on the big data of Baidu migration in cities above prefecture level, this paper attempts to explore and study the characteristics of urban network structure in China from the perspective of full time and net migration, and extracts the hierarchy, association and influencing factors of urban network. The results show that: the national urban network presents a stable and hierarchical pyramid and four vertex "diamond" structure, which is consistent with the spatial distribution of economic scale of major urban agglomerations; the regional network shows the core periphery radial structure of agglomeration to high-level administrative centers. The typical small world characteristics with provincial capital cities as the core are relatively prominent, and the accessibility and connectivity of small world network are high. As far as cities are concerned, Zhoukou, Fuyang, Ganzhou, Shangrao and Chongqing are the main export areas of population resources, while Shenzhen, Dongguan, Guangzhou, Beijing and Shanghai have become the main gathering places of migrant population, and the corresponding population transportation network has been formed. Administrative status, economic scale, transportation hub construction, population resources and other factors all play a decisive role in the control and influence of cities in the urban network. Finally, combined with the characteristics of China's urban network structure and its main influencing factors, the paper puts forward relevant policy suggestions, in order to provide reference for the balanced development and construction of China's urban network structure.

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    Improved Dense Crowd Counting Method based on Residual Neural Network
    SHI Jinlin, ZHOU Liangchen, LV Guonian, LIN Bingxian
    Journal of Geo-information Science    2021, 23 (9): 1537-1547.   DOI: 10.12082/dqxxkx.2021.200604
    Abstract371)   HTML11)    PDF (7153KB)(122)      

    In order to avoid crowd trampling, it is very important to accurately obtain information on the number of crowds from surveillance images. Early crowd counting studies used a feature engineering approach, in which human-designed feature extraction algorithms were used to obtain features that represented the number of people to be counted. However, the counting accuracy of such methods is not sufficient to meet the practical requirements when facing heavily occluded counting scenes with large changes in scene scale. In recent years, with the development of neural network, breakthroughs have been made in image classifications, object detections, and other fields. Neural network methods have also advanced the accuracy and robustness of dense crowd counting. In view of the difficulty of counting dense crowds, small crowd targets, and large changes in scene scale, this paper proposes a new neural network structure named: VGG-ResNeXt. The features extracted by VGG-16 are used as general-purpose visual description features. ResNet has more hidden layers, more activation functions and has more powerful feature extraction capabilities to extract more features from crowd images. Improved residual structure ResNeXt expands on the base of ResNet to further enhance feature extraction capabilities while maintaining the same computing power requirements and number of parameters. Therefore, in this paper, the first 10 layers of VGG-16 are used as the coarse-grained feature extractor, and the improved residual neural network ResNeXt is used as the fine-grained feature extractor. With the improved residual neural network feature of "multi-channel, co-activation", the single-column crowd counting neural network obtains the advantages of the multicolumn crowd counting network (i.e., extracting more features from dense crowd images with small targets and multiple scales), while avoiding the disadvantages of the multicolumn crowd counting network, such as the difficulty of training and structural redundancy. The experimental results show that our model achieves the highest accuracy in the UCF-CC-50 dataset with a very large number of people per image, the ShangHaiTech PartB dataset with a sparse crowd, and the UCF-QNRF dataset with the largest number of images currently included. Our model outperforms other models in the same period by 7.5%, 18.8%, and 2.4%, respectively, in MAE in the above three datasets, demonstrating the effectiveness of the model in improving counting accuracy in dense crowds. The results of this research can effectively help city management, relieve the pressure on public security, and protect people's lives and property.

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    Research Method of Temporal and Spatial Distribution Pattern of Night-time Economy based on Multi-source Data
    ZENG Leixin, LIU Tao, DU Ping
    Journal of Geo-information Science    2022, 24 (1): 38-49.   DOI: 10.12082/dqxxkx.2022.210212
    Abstract355)   HTML37)    PDF (7350KB)(310)      

    Night-time economy refers to the related economic activities mainly in the services taking place in urban space and at night, which is an important representation of a city's economic development and consumption level. Currently, researchers at home and abroad mostly rest on the theoretical level, or small-scale refined research based on market research and questionnaire survey, lacking in-depth mining using models and mathematical statistics methods, and rarely intuitively show the specific temporal and spatial distribution of large-scale night-time economy. With the development of information technology, night lighting data and perception big data provide new data sources for quantitative research of night-time economy. This paper provides a new perspective for night-time economy by fusing multi-source data. Compared with the traditional survey data, it is more rapid, efficient, and extensive, which is suitable for large-scale research of night-time economy. Based on taxi OD flow, this paper uses spatial clustering algorithms such as DBSCAN and K-Means ++ to identify hot areas of night-time activities in Xiamen City from the perspective of consumers. Based on the night-time lighting image and POI, this paper analyzes the supply and demand relationship by the method of profit and loss and identifies the distribution area of night service facilities from the perspective of merchants. Then we analyze the temporal and spatial distribution pattern of night-time economy in Xiamen City. The results show that: ① The spatial distribution of night activities in Xiamen City is multi ring and decreases to the surrounding areas. The distribution of hot spots of night activities varies from place to place; ② The existing service facilities in some areas of Xiamen City fail to serve the night economy well, and the existing lighting infrastructure, such as lighting and nightscape, is insufficient; ③ There is a moderate positive correlation between residential population density and night activity density, and the results are valid. The profit and loss value and quantity of night service facilities, night lighting, and night activity density are moderately and weakly correlated, and catering facilities are more dependent on night lighting. Finally, we put forward some suggestions for Xiamen's future night-time economic construction, such as providing different night-time services according to different consumer groups and psychology, strengthening the construction of night light infrastructure and market support. The research conclusions are of positive significance to promote social employment, and enhance the utilization rate of infrastructure. At the same time, they can also provide reference for urban economic development and policy formulation.

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    Research On the Dissemination Characteristics and Influencing Factors of Network Public Opinion of Sudden Natural Disaster Events
    ZHAO Fei, LIAO Yongfeng
    Journal of Geo-information Science    2021, 23 (6): 992-1001.   DOI: 10.12082/dqxxkx.2021.200526
    Abstract354)   HTML20)    PDF (5552KB)(67)      

    With the development of network technology, the analysis of internet public opinion plays an increasingly important role in dealing with the emergency. After the occurrence of natural disasters, it is helpful for the emergency management department to take effective emergency rescue measures in time to accurately grasp the characteristics of public opinion information and analyze its influencing factors. Based on the network public opinion data related to Typhoon Lekima, including micro-blog, WeChat, forums, websites, and other online public opinion data collected by the "Public opinion on Sina" system, this article analyzes the spatiotemporal characteristics of disaster public sentiment in the process of disaster. The influencing factors of the disaster public opinion information are also analyzed. The results show that the temporal distribution of public opinion information is consistent with the lifecycle of Typhoon Lekima. Compared with the grey EGM (1,1) model, ARIMA model has a higher applicability for short-term prediction of public opinion. The spatial distribution of public opinion is positively related to the severity of the disaster and also related to the economic condition and the network popularity in the affected area. The correlation between the severity of the disaster and the original public opinion information is stronger than that between the severity of the disaster and the transmitted public opinion information. The original public opinion information can better reflect the actual situation of affected areas. The study provides guidance for emergency departments to grasp the trend of public opinion and adjust emergency measures timely.

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    Analysis of Life Expectancy and the Spatial Differences of Its Influencing Factors of Chinese Residents
    ZHANG Ziwei, HUANG Qiuhao, LU Yu, LI Manchun, CHEN Zhenjie, LI Feixue
    Journal of Geo-information Science    2021, 23 (9): 1575-1585.   DOI: 10.12082/dqxxkx.2021.200607
    Abstract330)   HTML14)    PDF (4269KB)(51)      

    Good Health and Human Well-being is one of The Sustainable Development Goals proposed by the United Nations, and increasing the life expectancy is a significant step towards this goal. Due to differences in the natural environment and social development of Chinese cities, understanding the factors that affect life expectancy in different regions is the key to formulate urban public health policy. Based on the data of 286 cities in China in 2015, this paper used exploratory regression, ordinary least squares, and geographically weighted regression to screen out the most relevant influencing factors to life expectancy and explore their spatial differences. Then, the two-step cluster analysis was used to make targeted policy recommendations for each type of cities. The results show that: (1) Economic development, educational conditions, and medical facilities had a significant positive impact on life expectancy, while average altitude and environmental pollution had a negative impact; (2) Compared with other regions, economic development in the southeast region had a greater impact on local life expectancy; medical facilities in the northeast and southwest regions had a higher degree of promotion of life expectancy for its residents; education conditions in the northern region had a higher impact on the life expectancy of local residents; average altitude had the greatest impact on the life expectancy of residents in the West region; The life expectancy of residents in the northwest region was more susceptible to the negative impact of environmental pollution than in other regions; (3) Cities were divided into three categories based on spatial differences, and the key factors affecting the life expectancy are economic development and environmental pollution, educational conditions, and medical facilities in order. City managers in each category of cities should pay attention to different factors to increase their life expectancy.

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    Application Technology Framework and Disciplinary Frontier Progress of Natural Resources Big Data
    SHEN Lei, ZHENG Xinqi, TAO Jiange
    Journal of Geo-information Science    2021, 23 (8): 1351-1361.   DOI: 10.12082/dqxxkx.2021.200671
    Abstract329)   HTML9)    PDF (3645KB)(51)      

    The application of natural resources big data and its processing technology can provide basic support for the research and management of natural resources, especially for revealing the elements, structure, and correlation of natural resources system, and provide new ideas, new methods, and new technologies for the development of resources science. This paper attempts to clarify the concept, main characteristics, and development trend of natural resources big data, and analyzes the practical significance of natural resources big data for national economic and social development. The construction of natural resources big data is not only an important part of natural resources informatization, but also a new way to improve the efficiency of natural resources industry and the whole social economy, and the governance structure of natural resources and the modernization of natural resources governance capacity. In this paper, the knowledge framework of natural resources big data application research is constructed under the earth system science system, based on the structure of "one map, one network, and one platform", this paper proposes to establish a large database of natural resources integrating space, aviation and ground observations and an application framework in terms of production, residential and ecological spaces, and discusses the establishment of a structural system based on data collection, processing, and application of natural resources. The frontier progress and development trend of natural resources big data application research are also analyzed under this technical framework.

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    A Raster Tile Calculation Model Combined with Map Service
    HU Yirong, WANG Chao, DU Zhenhong, ZHANG Feng, LIU Renyi
    Journal of Geo-information Science    2021, 23 (10): 1756-1766.   DOI: 10.12082/dqxxkx.2021.210029
    Abstract323)   HTML10)    PDF (9420KB)(33)      

    With the rapid growth of remote sensing data, greater challenges arise in raster data efficient processing and value mining. Traditional map services focus on content sharing and visualization, but lacking real-time image analysis and processing functions. In this study, the real-time analysis and processing capabilities of raster tile data are realized in the form of map service. The cloud optimized GeoTIFF (Cloud Optimized GeoTIFF, COG) is used as the data organization method. The distributed collaborative prefetching strategy is designed to realize the raster tile loading in a cold or hot way, which optimizes the efficiency of reading image data from the cloud. Based on the efficient raster tile data loading, an expression-based raster tile processing model is proposed. By converting the expression into a calculation workflow, the raster tile is processed in the request of the map service in real time. The massive remote sensing data stored in the cloud is quickly analyzed to realize the direct visual conversion from raw data to products. For scenarios where full data are involved, use appropriate resampling data to simplify calculations to meet the real-time performance of map services. Three types of different complexity models, NDVI, ground object classification, and fractional vegetation cover, are used to perform real-time calculation and analysis on Landsat 8 images in the map service. Experimental results show that the processing model can effectively analyze raster tiles, and can be extended in a distributed manner. It can provide stable map service capabilities in high-concurrency scenarios, adapt to calculations at various levels and scales, and contribute a new idea to the future development of map service.

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    Research on the Rationality of Public Toilets Spatial Layout based on the POI Data from the Perspective of Urban Functional Area
    MA Qiang, WANG Liangxu, GONG Xin, LI Ke
    Journal of Geo-information Science    2022, 24 (1): 50-62.   DOI: 10.12082/dqxxkx.2022.210331
    Abstract319)   HTML24)    PDF (6947KB)(185)      

    As the most typical public facility, public toilets reflect the civilized level and management service level of the city and are an important window for building a civilized image of the city. Current research focuses on the accessibility and coverage of public toilets, treats public toilets as spatial points without discrimination, and ignores the heterogeneity of public toilets in different urban functional areas. How to establish a comprehensive and accurate public toilet space evaluation system and analyze the comprehensive service capabilities of public toilets in different regions is obviously insufficient in the current research, which is not conducive to the deployment of public toilets and the advancement of the equalization of basic public services. The emergence of multi-source data provides a new perspective for the research of urban public facilities. Therefore, this paper proposes a rationality evaluation method of public toilet spatial layout based on POI big data from the perspective of urban functional area. We use Term Frequency-inverse Document Frequency (TF-IDF) information weighting technology combined with Point of Interest (POI) frequency density to identify urban functional areas, and integrate OpenStreetMap (OSM) road network density and WorldPop population data to construct a population travel vitality index and evaluate public toilet services in urban functional areas. Finally, the population and spatial coverage rate and the spatial imbalance index are calculated to determine the difference between streets and towns and the rationality of the layout of public toilets in streets and towns. Based on multi-source data, this method quantitatively analyzes the rationality of the allocation of public toilets in different functional areas and explores the differentiating factors of the space allocation of public toilets. This article takes Shanghai, one of the most urbanized cities in China, as an example for calculation. The study finds that: ① The number of toilets in different urban functional areas is different. The number of planned commercial service functional areas is the largest, but the public toilets in the commercial service functional area have the highest qualifications. "Industry-Commercial service" and "Green Space-Commercial service" and other commercial service-related joint functional areas are also at a high level of qualification. This is because a lot of commercial service organizations provide public toilet services to the outside, which has improved the service capacity of public toilets in the area; ② The public functional area has the lowest qualification rate, only 10.27%, which is related to the openness of this type of attached public toilet facilities; ③ The eligibility of public toilets in Shanghai’s streets and towns is generally reasonable, with an average space coverage rate of 67.31% and an average population coverage rate of 70.72%. The imbalance index between public toilet service and travel vitality distribution in streets and towns ranges significantly from 0 to 0.76, of which 147 have an imbalance index less than 0.4, accounting for 69.34%, indicating the comparison of the space allocation of public toilets between these streets and towns is reasonable. The rationality of the allocation of public toilets decreases significantly from the downtown area of Shanghai to the southwest, southeast, and Chongming Island, while there is no obvious attenuation to the northwest, showing a good contiguous service capacity. The results show that this method fully takes into account the issue of the heterogeneity of the functional areas of public toilets and the travel vitality of the population, and the spatial analysis is more accurate and has more practical value.

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    Division and Feature Analysis of Nanchang Urban Center Isochrone Maps based on Traffic Big Data
    LIU Linlin, ZHENG Bohong, LUO Chen
    Journal of Geo-information Science    2022, 24 (2): 220-234.   DOI: 10.12082/dqxxkx.2022.210372
    Abstract316)   HTML7)    PDF (16424KB)(179)      

    For the current territory development planning in China, the Ministry of Natural Resources has put forward a method to evaluate the accessibility of urban centers based on isochrone maps. The use of dynamic traffic data in isochrone maps studies is becoming more and more recurrent, but comparative analyses between dynamic and static data are still rare. In this paper, Nanchang city is taken as a case study to generate the urban center isochrone maps using static and dynamic traffic data. The city is divided into 500 m×500 m grids, with each grid center point representing a given destination while Bayi Square and Greenland Central Square are set as origins. Using the above origins and destinations, the dynamic data were obtained daily from the Baidu open map platform at 15:00 and at 18:00 over nine days-time (Saturday-next Sunday). Subsequently, the confusion matrix and Kappa coefficient are used to test the consistency between the isochrone maps generated by the two datasets. The results suggest that most of Nanchang urban central areas are within a 60 min-circle and most of Nanchang's urban areas are within a 120 min-circle, when taking Bayi Square or Greenland Central Square as the origin. The isochrone maps generated by the static data has just a fair consistency with those generated by the dynamic data at evening peak time on workdays. Within the urban central areas, the isochrone maps generated by the static data have reached a substantial consistency with those generated by the dynamic data at off-peak time on workdays, indicating that the static data is more suitable for evaluating the urban center accessibility at off-peak time on workdays. Besides, the dynamic data can display the temporal characteristics of the isochrone maps. The isochrone maps of the dynamic data at 4 time-points show that the urban center accessibility at 15:00 on workdays is significantly better than others. But the proportions of isochrone surfaces to the total urban areas are found to increase with the drivetime, and their growth curves are in accordance with the trend of the Logistic curve. The key time nodes of each growth curve can provide more targeted division thresholds for isochrone maps. This research highlights the accuracy of the isochrone maps generated by the dynamic data and explores the applicability of the static data. The research also shows that using the key time nodes of the Logistic curve contributes to a more reasonable subdivision of the isochrone map.

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    Area Change and Cause Analysis of Bosten Lake based on Multi-source Remote Sensing Data and GEE Platform
    PENG Yanfei, LI Zhongqin, YAO Xiaojun, MOU Jianxin, HAN Weixiao, WANG Panpan
    Journal of Geo-information Science    2021, 23 (6): 1131-1153.   DOI: 10.12082/dqxxkx.2021.200361
    Abstract301)   HTML13)    PDF (43615KB)(112)      

    Bosten Lake is a typical inland lake in the arid zone. The change in the lake area is strongly related to local natural and cultural environmental changes. Based on the GIS and RS technologies, this paper combines Landsat imagery and MODIS data, including a total of 2289 scenes, with JRC GSW water mask products to characterize the interannual and intraannual changes of the area of Bosten Lake from 2000 to 2019 through the Google Earth Engine (GEE) platform using index methods. We use the 2019 Sentinel-2 images to compare and analyze the results. To quantify the the causes of the changes, we analyzed the human activities and daily meteorological data of Yanqi, Korla and Bayanbuluk meteorological stations during 2000-2018. Results show that: (1) the GEE is efficient for integrating multi-temporal high-resolution remote sensing data to analyze the temporal change of lake area, especially the intraannual change. Compared with Landsat-5/7/8 and MOD09GQ data, the lake shoreline extracted based on Sentinel-2 images shows more details due to their high temporal and spatial resolution; (2) during 2000-2013, the total lake area decreases by 181.66 km2 with a decreasing rate of 13.98km2/a; while during 2013-2019, the lake area increases by 133.13 km2 with a increasing rate of 22.19 km2/a; (3) Intraannually, the lake area shows an upward trend from Mar. to Jun., keeps peak until September, and decreases from Oct. to Dec. and (4) the interannual change of Bosten Lake area has no significant correlations with the changes of evaporation, precipitation, and accumulated temperature within the watershed. While the intraannual change of Bosten Lake area shows strong correlations with those meteorological varabiles.

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    Spatial and Temporal Transmission Differences between SARS and COVID-19 and Analysis of Influence Factors
    CUI Mingjie, YAO Xia, FANG Haoran, ZHANG Yangchengsi, YANG Degang, PEI Tao
    Journal of Geo-information Science    2021, 23 (11): 1910-1923.   DOI: 10.12082/dqxxkx.2021.210133
    Abstract299)   HTML8)    PDF (4657KB)(42)      

    The outbreaks of SARS and COVID-19 have had a serious impact on public health, social economy and so on in China, in order to reveal the common law and difference characteristics of space-time transmission of respiratory infectious diseases and the reasons behind them, using space-time statistical methods, systematically analyzed and compared the difference characteristics of space-time transmission between SARS and COVID-19, and combined with the transmission characteristics of the virus itself and temperature, traffic and other factors to analyze the causes. The study shows that, ① SARS experiences two stages, the rising period-flat phase, and the COVID-19 experiences three stages, the rising period-sharp rise-slow up period. ② In the mode of spatial transmission, the transmission intensity and range of COVID-19 is greater than that of SARS, and the overall connectivity of COVID-19 is greater and the provinces are more closely related to the outbreak of the virus. Both SARS and COVID-19 transmission have obvious spatial aggregation characteristics. They are based on proximity propagation and long-range leaps, and SARS has a secondary communication center, and COVID-19 diffusion center has not been relocated. ③ In the direction of space communication, SARS is centered in Beijing, Hong Kong and Guangdong, the direction of spatial communication is stronger, and COVID-19 is only spread outwards with Hubei as the center. ④ In terms of spatial transmission speed, the spread time of the first case in each province of SARS is relatively large, and the spread time of the first case in each province of COVID-19 is roughly divided by Hu Huanyong Line, showing a phenomenon of "fast in the east and slow in the west", and the spread time span is relatively short. ⑤ R0 is the main reason for the difference between the spatial transmission range of SARS and COVID-19 and the speed of spatial transmission. The temperature suitability of SARS and COVID-19 viruses is different, but spatial aggregation transmission and adjacent area transmission are occurring in areas with similar temperatures. Besides the virus transmission capacity and temperature impact, traffic is the main reason affecting SARS and COVID-19 space long-range leap transmission, and the spatial transmission speed of both is negatively related to the density of the road network.

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    Automatic Deep Learning Land Cover Classification Methods of High-resolution Remotely Sensed Images
    LI Guoqing, BAI Yongqing, YANG Xuan, CHEN Zhengchao, YU Haikun
    Journal of Geo-information Science    2021, 23 (9): 1690-1704.   DOI: 10.12082/dqxxkx.2021.200795
    Abstract289)   HTML17)    PDF (10580KB)(136)      

    Land cover change refers to the areal change and type transformation between vegetation cover and non-vegetation cover caused by climate change and human activities, which is closely related to human survival and development, ecological environment evolution, and material energy cycle. Accurate classification is the basis of land cover change while land cover change is the core of global change research. The rapid development of high-resolution remote sensing technology poses a dual challenge to the speed and accuracy of land surface classification. In recent years, the development of new artificial intelligence technology has realized the automatic segmentation of natural scene images. Intelligent image processing technology has become an important force to promote the improvement of remote sensing information service level in the era of big data. The deep learning method represented by the convolution neural network also has significant advantages in the field of remote sensing image classification. To compare the impacts of deep learning model design on the classification results of high-resolution remote sensing images, this paper takes Gaofen-1 images of Zhengzhou City in Henan Province in 2019 as an example. This paper compares and studies the differences of four diverse deep learning network models, improved based on UNet model, in the application of automatic land cover classification of high-resolution images. Furthermore, this paper discusses the influence mechanism of encoder and decoder architecture settings, such as residual network, multi-scale loss function, skip-layer connection, and attention mechanism module, on classification accuracy. The results show that the MS-EfficientUNet model with multi-scale loss function, skip-layer connection, and attention mechanism module is the best for Zhengzhou City land cover classification, with an overall classification accuracy, based on pixel evaluation, of 0.7981. By introducing multi-scale loss function into the decoder, the classification accuracy of the forest, water, and other categories can be effectively improved. Moreover, by improving the encoder, adding skip-layer connection and attention mechanism, the classification accuracy of grassland, water, and other categories can be further improved. The results show that the powerful generalization ability of deep learning technology can effectively break through the spectral and time-phased differences between images, and realize the feature adaptive enhancement and intelligent decision-making, which has great potential in the field of high-resolution remote sensing image segmentation. However, further improvement of classification accuracy and multi-level and large-scale fine classification method are still the focus of the next step. At the same time, the unification of image sequence and the expansion of training samples are also the key factors to further improve the classification accuracy.

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    Conceptual Model of Terrain Texture in Loess Plateau based on DEM
    JIANG Sheng, TANG Guoan, YANG Xing, XIONG Liyang, QIAN Chengyang
    Journal of Geo-information Science    2021, 23 (6): 959-968.   DOI: 10.12082/dqxxkx.2021.200411
    Abstract288)   HTML22)    PDF (13076KB)(194)      

    The geomorphic characteristics of "thousands of gullies" in the Loess Plateau show significant self similarity in multi-scale space, and have obvious textural characteristics of local-irregular and macro-regular. Previous studies have shown that there have been specific research results on the selection of texture features, the uncertainty of scale effect, and the combination of texture features with other features in identification and classification of specific landforms. However, the current texture analysis methods are limited to the application of macro terrain classification. For the concept, classification, basic characteristics, and analysis methods of terrain texture, there is a lack of theoretical framework for application support. On the basis of the existing research results, this paper defines the Loess Plateau as the research scope, and puts forward the concept model of the Loess Plateau terrain texture, namely definition, characteristics, classification, and expression. In terms of the definition of terrain texture, this paper expands the scope of the definition. In addition to the existing macro morphological topographic texture, the terrain texture formed by the combination of the characteristics of typical loess geomorphic units (loess yuan, liang, mao, etc.) and the terrain texture formed by the slope characteristics of loess slope are proposed. This paper points out that the data expression based on Digital Elevation Model (DEM) will be more conducive to the quantification of terrain texture, especially the terrain factor derived from DEM can expand the feature space of terrain texture and enrich the data source of terrain texture analysis. In terms of the basic features of terrain texture, this paper puts forward three basic characteristics: regional difference, genetic complexity, and scale dependence. Among them, regional differences can be qualitatively distinguished by visualization or quantified by existing statistical methods, so as to effectively distinguish differences in texture between regions. In the classification system of terrain texture, this paper classifies the terrain texture based on its element saliency, texture origin, and visual form. Taking loess liang in the loess hilly and gully region as an example, a single loess liang can be regarded as a texture element. Through a certain arrangement and combination of several loess liang, the terrain textural characteristics of the loess liang hilly and gully region are formed. However, a single loess liang cannot express the texture features. This paper aims to build a conceptual model of terrain texture oriented to the Loess Plateau, and promotes the application and development of texture analysis method in Loess Plateau.

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    An Epidemic Spread Risk Prediction Model Coupled with LSTM Algorithm and Cloud Model
    LI Zhao, GAO Huiying, DAI Xiaoyi, SUN Hai
    Journal of Geo-information Science    2021, 23 (11): 1924-1925.   DOI: 10.12082/dqxxkx.2021.210576
    Abstract286)   HTML11)    PDF (4468KB)(52)      

    The COVID-19 epidemic poses a great threat to public health and people's lives, which has initiated new challenges to the prevention and control system of the epidemic in China. In all efforts for epidemic control and prevention, predicting the risk of epidemic spread is of great practical importance for scientific prevention and control, and precise strategies. To predict the risk of an epidemic rapidly and quantitatively, this paper fused multi-source spatiotemporal data and established a risk prediction model for epidemic transmission by coupling LSTM algorithm and cloud model. Firstly, a simulation model of the spatiotemporal spread of infectious diseases was built based on GIS and LSTM algorithm, which simulated the infectious disease's spatiotemporal transmission process by learning rules in historical epidemic data. At the same time, to improve the simulation accuracy, this paper took 1 km × 1 km for the spatial scale, and days for the temporal scale as the study scale. Secondly, this paper applied the simulated data of infectious cases and the spatiotemporal influence factors on the spread of the epidemic to construct risk evaluation indicators. Finally, the cloud model and adaptive strategies were applied to construct an epidemic risk assessment model. In this way, the epidemic risk assessment at multiple spatial scales was achieved. In the empirical study phase, based on the Beijing COVID-19 epidemic data from 11 June 2020 to 25 June 2020, this paper simulated the process of the spatial evolution of the epidemic from 26 June 2020 to 1 July 2020. To test the advantage of the LSTM model applied to simulate spatiotemporal spread of infectious diseases, four machine learning models were introduced for comparison, including GA-BP Neural Network, Decision Regression Tree, Random Forest, and Support Vector Machine. The results were as follows: ① Compared with other conventional machine learning models, the LSTM model with time-series relationship had higher simulation accuracy (MAE=0.002 61) and better fitting degree (R-Square=0.9455). This showed that the LSTM model considering the temporal relationship between epidemic data was more suitable for epidemic spatial evolution simulation. ② The application results showed that the coupled model can not only fully consider the influence of infection source factors, weather factors, epidemic spread factors and epidemic prevention factors on the spread of transmission risk and reflect the trend of risk evolution, but also quickly quantify regional risk levels. Therefore, the coupled model based on LSTM algorithm and cloud model can effectively predict the transmission risk of epidemic, and also provide a method reference for establishing spatial-temporal transmission models and assessing epidemic risk.

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    Evaluation and Comparison of Downward Solar Radiation from New Generation Atmospheric Reanalysis ERA5 across Mainland China
    ZHANG Junbing, SHEN Runping, SHI Chunxiang, BAI Lei, LIU Junjian, SUN Shuai
    Journal of Geo-information Science    2021, 23 (12): 2261-2274.   DOI: 10.12082/dqxxkx.2021.180357
    Abstract282)   HTML4)    PDF (20821KB)(21)      

    The European Centre for Medium-Range Weather Forecasts (ECMWF) has developed ERA5, a global atmospheric reanalysis product with high spatiotemporal resolution. The Shortwave Downward Radiation (SWDN) of ERA5 is an important atmospheric forcing dataset which has important applications in regional climate assessment, agriculture, and solar energy resource utilization. In this study, the observed SWDN dataset after quality control was collected from 91 official radiation monitoring stations across mainland China in 2011-2018 and was applied to evaluate the SWDN in ERA5 on different spatial and temporal scales, together with other three reference SWDN datasets from global atmospheric reanalysis products (i.e., ERA-Interim, CFSR, and MERRA2) and the CERES satellite inversion product (SYN1deg). Results show that: ① On the monthly mean scale, the ERA5 product had the highest correlation coefficient (Corr) with the station observation data (0.939) and the lowest Root Mean Square Error (RMSE) (28.309 W/m2), compared with other reanalysis products. The average bias of ERA5 (15.4 W/m2) was slightly higher than that of the ERA-Interim product (13.2W/m2). The Corr between CERES satellite inversion product and observation data was 0.955, the RMSE was 20.042W/m2, and the Bias was 5.3W/m2; ② The radiation values of all these five SWDN products were overestimated against the observation data. In general, the overall accuracy of the ERA5 product in mainland China was higher than the other reanalysis products, but was lower than the CERES satellite inversion product. The comparison of daily mean values between products also showed similar results; ③ Regional evaluation results show that the SWDN in ERA5 had a good consistency with observation data in four regions across mainland China. All five SWDN products performed poorly in the southern region. Compared to the northeastern and northern regions, the RSME and the bias of the ERA5 product and the CERES satellite inversion product relative to observations were larger in the western region.

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    Mapping the Spatial Distribution of Tea Plantations with 10 m Resolution in Fujian Province Using Google Earth Engine
    XIONG Haoli, ZHOU Xiaocheng, WANG Xiaoqin, CUI Yajun
    Journal of Geo-information Science    2021, 23 (7): 1325-1337.   DOI: 10.12082/dqxxkx.2021.200583
    Abstract282)   HTML12)    PDF (5450KB)(70)      

    As a major tea-producing province in China, Fujian has a long history of tea culture. According to the National Bureau of Statistics in recent 10 years, the total planting area of tea in Fujian ranked the fifth among all the provinces in China. Rapid and accurate acquisition of tea plantation spatial distribution has important decision-making significance for agricultural economic development and ecological environment protection in Fujian province. However, it is difficult to obtain the spatial distribution of tea plantation in large areas accurately by traditional methods. Based on the GEE cloud platform, we firstly obtained Sentinel-1、Sentinel-2, and terrain data covering the whole province, and then extracted a total of 98 features including spectral features, texture features, and terrain features. Secondly, the Support Vector Machine-recursive Feature Elimination (SVM_RFE) was used to select features. Four groups of experiments were constructed according to different features and optimized feature subsets. Finally, the Support Vector Machine classifier (SVM) was used to extract tea plantation and obtain the spatial distribution map of tea plantation with a resolution of 10 m in Fujian province in 2019. The results show that: (1) Spectral features play an important role in tea plantation information extraction, followed by texture and terrain features. (2) It can improve the extraction accuracy by using SVM_RFE to select some features, that are useful to tea plantation extraction, from a large number of spectral, textural and topographic features. The overall accuracy is 94.65% while the kappa coefficient is 0.93. The producer accuracy and user accuracy of the tea plantation are 91.64% and 92.91%, respectively. (3) In 2019, the tea plantation area in Fujian province was 1913 km2. Tea plantations were mainly distributed in Anxi County, Fuding City, Fuan City, Wuyishan City, and Shouning County, with a total area of 910 km2, accounting for ~48% of the entire tea plantation area in Fujian province. The cloud computing technology based on GEE platform can overcome the problem of lacking computing power for large-scale tea plantation monitoring. This research can extract tea plantation distribution in Fujian province accurately, which has reference value for tea plantation and other crop extraction in hilly and mountainous areas of South China, and provides support for the government and related departments to manage tea plantation.

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    Global Location Information Superposition Protocol and Location-based Service Network Technology: Progress and Prospects
    GONG Jianya, HUANG Wenzhe, CHEN Zeqiang, LIU Yuting, LI Lin, TANG Weiming, ZHANG Qianli, CHEN Jing, CHEN Bo, YUE Peng, LIU Jun, XIAO Jihua
    Journal of Geo-information Science    2022, 24 (1): 2-16.   DOI: 10.12082/dqxxkx.2022.210762
    Abstract280)   HTML12)    PDF (7264KB)(38)      

    With the rapid development of information technology, the world has entered an era of explosive growth of information, the Internet, the Internet of Things, and sensor networks have flooded with massive amounts of human society related information, providing us with a new way to solve urban governance and social management issues. The biggest challenge to further improve the ability of urban smart management is that information cannot be integrated and shared quickly and effectively. Therefore, the global location information superposition protocol and location-based service network technology have been proposed, based on the location-based fact of most social information, which become a key technology to break the barriers between systems in various fields, and to make the automatic collection, integration of computing, and intelligent services of massive heterogeneous information across networks, platforms, systems, and languages come true. This paper summarizes the current domestic and foreign research progress on the key technologies of the global location information superposition protocol and location-based service network, and then introduces the demonstration application of the global location-based service network. Finally, the technology and application of the global location-based service network on the future research directions are discussed, which can be the reference for the development of the global location-based service network in the future.

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    Landslide Susceptibility Analysis based on Deep Learning
    WANG Yi, FANG Zhice, NIU Ruiqing, PENG Ling
    Journal of Geo-information Science    2021, 23 (12): 2244-2260.   DOI: 10.12082/dqxxkx.2021.210057
    Abstract277)   HTML11)    PDF (43266KB)(222)      

    The formation mechanism of landslide disasters is complicated and there are many influencing factors. It is imperative to explore a low-cost and highly applicable method to manage and prevent landslide disasters. As a hot spot in the current artificial intelligence field, deep learning can better simulate the formation of landslide disasters and accurately predict potential slopes. Thus, to explore the application potential of deep learning, this paper constructs one-dimensional, two-dimensional, and three-dimensional forms of landslide data, and then introduces three Convolutional Neural Networks (CNN)-based landslide susceptibility analysis frameworks, including CNN-based classifiers, integrated models, and ensemble models. The proposed deep learning methods were applied to Yanshan County, Jiangxi Province for experiments. 16 landslide influencing factors were first selected for modelling based on the geomorphological, hydrological, and geological environment conditions of the study area. These factors include altitude, aspect, distance to faults, land use, lithology, normalized difference vegetation index, plan curvature, profile curvature, rainfall, distance to rivers, distance to roads, slope, soil, stream power index, sediment transport index, and topographic wetness index. Then, the multi-collinearity analysis and relief-F algorithm were used to analyze and screen the influencing factors. All CNN-based methods were constructed and validated based on several statistical measures of accuracy, root mean square error, mean absolute error, sensitivity, specificity, and the receiver operation characteristic curve. Finally, the susceptibility value of each pixel in the study area was predicted based on the CNN-based methods, and the entire study areas were reclassified into five susceptibility categories: very low, low, moderate, high, and very high. The factor analysis results show that the plan curvature, profile curvature, stream power index, and sediment transport index are redundant factors and should be removed from further modelling process. The model evaluation results demonstrate that all CNN-based models can obtain accurate and reliable landslide susceptibility mapping results. The two-dimensional CNN model achieved the highest prediction accuracy of 78.95% among single CNN models. Moreover, the performance of logistic regression was effectively improved by combining the two-dimensional CNN for feature extraction, with an accuracy improvement of 7.9%. Besides, the heterogeneous ensemble strategy can greatly improve landslide prediction accuracy when using CNN classifiers, with an accuracy improvement between 4.35% and 8.78%. Generally, the CNN has been proven to have huge application potential in landslide susceptibility analysis and can be implemented in other landslide-prone areas with similar geo-environmental conditions.

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    Spatial Distribution Characteristics of People with Small Activity Space in Urban based on Mobile Phone Signaling Data
    ZHANG Xuexia, WU Sheng, ZHAO Zhiyuan, WANG Pengzhou, CHEN Zuoqi, FANG Zhixiang
    Journal of Geo-information Science    2021, 23 (8): 1433-1445.   DOI: 10.12082/dqxxkx.2021.200686
    Abstract277)   HTML12)    PDF (11992KB)(237)      

    The People with Small Activity Space (PwSAS) refers to the residents with a small range of daily activity locations. Their demand for urban public resources is mainly concentrated in the area around their home. Analyzing the spatial and temporal characteristics of their activities can help to better realize the equalization and precise allocation of urban public resources. However, little attention has been paid to this kind of people in current researches. This study proposed a research method to identify the spatial distribution of PwSAS based on mobile phone signaling data. Firstly, we identified each user's home location and stay location. An indicator of HmaxD, the maximum distance from the home location, was proposed to measure the activity space range centered on the home location. This indicator was also used to filter the PwSAS. Secondly, we transformed the traditional trajectory into a new form in a "time-distance" coordinate based on the distance between the location of each record and the home location. An area-based approach was constructed to measure the similarity between different trajectories. Then an optimized hierarchical clustering algorithm was applied to identify typical activity patterns of PwSAS based on the similarity approach. Finally, the spatial distribution patterns were analyzed based on the home locations of the users belonging to each pattern. A signaling dataset, a typical type of mobile phone location data of Shanghai, was used to test the effectiveness of the method. We found that: (1) the area-based trajectory similarity method constructed based on "time-distance" framework can reflect the spatiotemporal characteristics of users' activities based on home location, and the hierarchical clustering algorithm merged level by level can significantly improve the efficiency of mining typical activity patterns. This means that the proposed method can effectively support the mining of the mobility patterns of urban residents; and (2) in the suburbs, the commercial centers and places with many factories or universities tended to have more PwSAS; While, the transition area in the suburban had less PwSAS. Therefore, the method proposed in this paper can be used to analyze the temporal and spatial distribution characteristics of people in a small activity area in a city and can provide support for the current large cities' decision to build community life circles.

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    Theory, Method and Technological Application of Territorial Spatial Development Suitability Evaluation
    LIU Xiaobo, WANG Yukuan, LI Ming
    Journal of Geo-information Science    2021, 23 (12): 2097-2110.   DOI: 10.12082/dqxxkx.2021.210037
    Abstract272)   HTML8)    PDF (3245KB)(102)      

    Suitability evaluation of territorial space is the premise and basis for scientific territory space planning, transition of territory space governance mode and creation of high-quality territory spaces. It is of important significance to optimize the development and protection pattern of territory space and perfect subjective functional orientation of regions. Based on literature review, summary, inductive and comparative analysis, this study reviewed conceptual connotation, development course, evaluation units, evaluation index system, evaluation method, modern technologies and application framework of suitability evaluation of territory space development. It pointed out shortages of existing studies and it suggested to deepening research fields and directions. At present, the theory and practice of suitability evaluation of territorial space have made great breakthrough, and the evaluation index system has formed a more comprehensive system, and the evaluation methods show a trend of diversification. Although many studies have discussed suitability evaluation of territory space development, there are inadequate attentions to multi-objective collaborative evaluation of territory space development. Specifically, it fails to achieve satisfying results in refining of evaluation index system, multi-scale comprehensive study and intelligence level of evaluation methods. The firstly, the construction of the index system is lack of uniformity and standardization. Different studies are based on different goals and perspectives, and the index systems constructed are obviously different. Different scholars have great differences in the evaluation of the same area, which affects the objectivity of the evaluation results. The basic information data can not meet the evaluation requirements of the whole area, the whole element and the whole temporal phase. The ability of Geo-information science and technology to support "smart evaluation" is insufficient. Secondly, in the existing studies, the micro scale evaluation is the majority, the macro scale evaluation is less, and the multi-scale comprehensive evaluation research is very rare. Finally, there is a lack of integration and application of existing evaluation methods for the suitability of territorial space development with intelligent frontier technologies such as spatiotemporal big data, cloud computing, unmanned aerial vehicles, Internet of Things, and 5G network, and there is a huge space for future mining. In future, we need to do well in the following aspects. The firstly, it is suggested to carry out more multi-scale suitability evaluations, promote the integration, transformation and transmission of multi-scale evaluation. Secondly, improve standardization and refining of evaluation index system. Attach importance to the positioning of regional development, pay attention to the special needs and industrial advantages of regional development. In addition, follow the pace of "smart society" construction closely, strengthen the integration and application of earth information technology and Internet, database, cloud computing and other emerging technologies, promote the formulation of land and space multi-source big data and the effective integration of suitability evaluation system; couple GIS, remote sensing and big data to carry out information mining, and provide strong technical support for "smart evaluation".

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    Progress and Prospect on Mapping Cropping Systems Using Time Series Images
    QIU Bingwen, YAN Chao, HUANG Wenqing
    Journal of Geo-information Science    2022, 24 (1): 176-188.   DOI: 10.12082/dqxxkx.2022.210604
    Abstract262)   HTML9)    PDF (2127KB)(42)      

    Updated spatiotemporal explicit data on cropping system is vital for ensuring the implementation of the national food security strategy and reasonable cropping structures. Time series analysis techniques are playing a more important role in agricultural remote sensing along with the continuously improved quality of remote sensing time series images. This paper analyzes main progresses and challenges in the field of cropping systems mapping using time series images from three aspects: mapping framework, remote sensing feature parameters, and data products. We find that: (1) The current cropping system mapping framework which mainly includes cropping intensity and agricultural planting structures, needs to cope with the problems of pre-requirements of cropland distribution data with high-quality. However, the existing land use/cover data could not fully fulfil this requirement due to the complex spectral characteristics of cropland introduced by multiple cropping systems over large regions. It is difficult to accurately derive information on cropping intensity using traditional time series vegetation indices datasets. Specifically, cropland fallow/abandonment in humid regions might be misclassified as single crop due to its corresponding high values of vegetation indices. Cropland abandonment and fallow are not negligible in recent decades and need further investigations, especially in China; (2) Novel multi-dimensional spectral indices based on red-edge and short-wave near-infrared bands are efficient in revealing the crop growth processes. Great progresses have been made in crop mapping in recent years. However, crop mapping at large scale is challenged by the minor differences among different crops as well as distinct heterogeneity within the same crop across different regions and multiple years; (3) There are increasing available remote sensing products of cropping intensity from national to global scale, however, the timeliness and spatiotemporal continuity need to be further improved; (4) Except for a few countries in North America and Europe, crop distribution maps at national scale are not fully available or limited to several staple crops with coarse resolution. There is a deficiency of finer datasets on cropping systems at large scale, especially in the complex multi-cropped regions. Fortunately, new technologies (i.e., cloud computing platform and deep learning algorithms) and emerging multi-sources remote sensing data with higher spatial, spectral, and temporal resolution provide great opportunities for spatiotemporally continuously detecting changes in cropping system at large scale. Future research should be focused on the following directions. First, we could improve the research strategy by developing an integrated mapping framework for directly deriving information on cropland and cropping patterns without relying on existing cropland distribution data. Second, we need to enrich the phenological features through exploring multiple-dimensional and less exploited spectral indices, such as the pigment indices, soil indices, nitrogen indices, and dry matter indices. Finally, we can develop spatiotemporal continuous change detection techniques for automatically tracking changes in cropping systems at multiple years and large scale.

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