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    Review on Spatiotemporal Analysis and Modeling of COVID-19 Pandemic
    PEI Tao, WANG Xi, SONG Ci, LIU Yaxi, HUANG Qiang, SHU Hua, CHEN Xiao, GUO Sihui, ZHOU Chenghu
    Journal of Geo-information Science    2021, 23 (2): 188-210.   DOI: 10.12082/dqxxkx.2021.200434
    Abstract863)   HTML46)    PDF (12855KB)(450)      

    The COVID-19 pandemic is the most serious global public health event since the 21 st century, and has become a hot topic concerned by different disciplines. According to the bibliometric analysis, more than 13,000 papers related to the COVID-19 have been published since the beginning of the pandemic. Related researches include not only the pathogenic mechanism of the virus and the development of specific drugs and vaccines from the medical and biological perspectives, but also the non-pharmaceutical prevention and control methods for the pandemic. The latter is the focus of this paper, in which the research progress on the pandemic is discussed from six aspects: detection of transmission relationships, spatiotemporal pattern analysis, prediction models, spread simulation, risk assessment, and impact evaluation. The research on the detection of transmission relationship mainly includes the detection of cluster cases and transmission relations, among which individual trajectory big data have become the key to research. The progress of the analysis of spatiotemporal patterns of the pandemic shows that the spatiotemporal distribution of the pandemic has significant temporal and spatial heterogeneity, and the spatiotemporal transmission presents typical network characteristics. The prediction of the pandemic mainly relies on dynamic models scaling from macro to micro, in which the non-negligible impact of population migration makes the human flow big data become one of the key elements of model prediction accuracy. In the study of epidemic spread simulation, the focus is on evaluating the effects of controlling measures such as traffic restrictions, community prevention and control, and medical resources allocation through simulation methods. Results show that traffic interruption and community control measures are the most effective means among non-pharmaceutical interventions at present, and the guarantee and reasonable deployment of medical resources are the basis for pandemic prevention and control. After the pandemic is controlled under the effective measures, the resumption of work and production must be in an orderly manner. The research on pandemic risk assessment currently focuses on biological factors, natural factors and social factors. As to biological factors, researches show that the underlying disease and the male (due to their high mobility) are related to a higher risk of infection. Among natural factors, temperature, precipitation and climate have limited influence on the spread of the pandemic. As to social factors, human mobility, population density, and differences in medical conditions caused by social inequity have significant influences on the infection rate. Regarding the impact of the COVID-19 pandemic, we mainly focus on three aspects: the public psychology, natural environment and economic development. Specifically, the impact of the pandemic is mainly negative on the public psychology and economy, and positive on the natural environment. In conclusion, big data especially individual trajectories and population big data are indeed pervasive in research of non-pharmaceutical intervention. To prevent and control the major outbreaks, the intersection of multiple disciplines and the collaboration of personnel in different fields are indispensable. Although a great progress has been made on various aspects such as the effect of controlling measures and the influencing factors of the pandemic, the spatial traceability, precise prediction and future impact of the pandemic are still unsolved problems.

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    Analysis and Visualization of Multi-dimensional Characteristics of Network Public Opinion Situation and Sentiment: Taking COVID-19 Epidemic as an Example
    DU Yixian, XU Jiapeng, ZHONG Linying, HOU Yingxu, SHEN Jie
    Journal of Geo-information Science    2021, 23 (2): 318-330.   DOI: 10.12082/dqxxkx.2021.200268
    Abstract317)   HTML8)    PDF (10736KB)(344)      

    At the beginning of 2020, COVID-19 epidemic swept across China, and the development of COVID-19 attracted extensive attention from all sectors of society. Social media platform is an important carrier of online public opinion. In the process of epidemic prevention and control, it is very important to analyze the characteristics of network public opinion comprehensively and accurately. Firstly, from the perspective of spatiotemporal correlation between public opinion ontology and object, we construct a multi-dimensional analysis model of network public opinion during the epidemic period. We obtained the network public opinion data related to the covid-19 epidemic in multiple media platforms from January 17 to March 17, 2020. Secondly, from the perspective of epidemic spread, the spatial and temporal evolution and semantic characteristics of network public opinion in Wuhan, Hubei and the national scale are explored by comparative study and Spearman correlation coefficient. Finally, we use HowNet sentiment dictionary and emotional vocabulary ontology to analyze public opinion sentiment, and use interactive information chart to visualize the above results. The results show that: (1) The characteristics of time changes of public opinions are basically the same in Wuhan, Hubei province and China. There is a positive correlation between the number of daily public opinions and the number of new cases per day. With the rapid spread of the epidemic, the number of daily public opinions continues to increase. As the epidemic is gradually brought under control, the number of daily public opinions has shown a tortuous downward trend. (2) There is a positive correlation between the spatial distribution of public opinion data and the distribution of epidemic situation. The spatial distribution of the number of public opinions is similar to the distribution of the epidemic situation, and the areas with a large number of public opinions are mostly areas with severe epidemics. Changes in public opinions are spatially related to the development of the epidemic. (3) During the epidemic, the neutral sentiment of online public opinions was the most. Compared with forums, WeChat and Weibo, news platforms have a more positive overall sentiment. (4) At different stages of the development of the epidemic, the emotional characteristics of Weibo hot search data are quite different. The mood changed from anxiety in the early stage of the epidemic to excitement in the mid-term. And as the epidemic is gradually brought under control, emotions have also stabilized. Generally speaking, there are more positive emotions than negative emotions. Research shows that the multi-dimensional analysis model proposed in this article can visually show the public opinions situation, public opinions focus, and emotional changes at multiple scales during the epidemic.

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    Geographic Knowledge Graph for Remote Sensing Big Data
    WANG Zhihua, YANG Xiaomei, ZHOU Chenghu
    Journal of Geo-information Science    2021, 23 (1): 16-28.   DOI: 10.12082/dqxxkx.2021.200632
    Abstract853)   HTML51)    PDF (1365KB)(343)      

    Due to the temporal and spatial heterogeneity of the complex earth's surface, the traditional idea of developing new intelligent interpretation algorithms to solve the remote sensing geoscience cognition based on the features of remote sensing images has hit the bottleneck in terms of accuracy and geographic usage when analyzing remote sensing big data. To overcome the bottleneck, we proposed the Geographic Knowledge Graph (GKG) that based on the geographic knowledge to analyze the remote sensing big data, which is inspired by the recently proposed Knowledge Graph from the geographic perspective. It expands the concept of the geographic knowledge and classifies the geographic knowledge into three levels: Data knowledge, conception knowledge, and regularity knowledge. Then, it represents and connects all geographic knowledge in Graph by nodes and edges and realizes the feedback iteration and update between different levels of the geographic knowledge. This representation enables GKG to perform well at knowledge inquiring, reasoning, calibration, and expanding. How to construct multiscale high-dimension geo-entities and how to connect different levels of the geographic knowledge with heterogeneous features are two key technologies. These functions make GKG promising in refining existing geographic knowledge in the era of remote sensing big data, promoting remote sensing interpretation accuracy and geographic usage, and promoting the development of geoscience.

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    Visualization of the Epidemic Situation of COVID-19
    YING Shen, DOU Xiaoying, XU Yajie, SU Junru, LI Lin
    Journal of Geo-information Science    2021, 23 (2): 211-221.   DOI: 10.12082/dqxxkx.2021.200301
    Abstract500)   HTML19)    PDF (19128KB)(305)      

    The COVID-19 epidemic has extremely attracted our attentions and lots of maps and visualization charts were created to represent and disseminate the information about COVID-19 in time, which exactly became a key role for the public to acquire and understand the quantitative information and spatial-temporal information of COVID-19. The paper analyzed the dimension of data for COVID-19 and processing levels about them, then divided the COVID-19 visualization into three types, that is 1-order visualization, 2-order visualization and multi-order visualization for COVID-19, based on direct data or indirect data of COVID-19 with the corresponding visualization methods, characteristics and information transmission Shortcomings and weakness of visualization methods for COVID-19 were analyzed in details, from the aspects of multiple scale unit in spatial data statistics, max value dealing in data classification, also many key design points were described including color connotation in disease visualization, the influences of area / unit size in visualization, symbol overlapping, multiple-scale heat maps and labels in statistical tables. The paper indicated the visualization traps of COVID-19, such as misuse of visual effects and excessive visualization, and reasonable abilities of COVID-19 visualization including map-story narrative methods and visualization pertinence for specific problems should be considered sufficiently to provide the references for cartographers to design the maps and for readers to understand the maps.

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    Development and Prospect of GIS Platform Software Technology System
    SONG Guanfu, CHEN Yong, LUO Qiang, WU Mengyao
    Journal of Geo-information Science    2021, 23 (1): 2-15.   DOI: 10.12082/dqxxkx.2021.210015
    Abstract539)   HTML43)    PDF (6405KB)(276)      

    As an important part of the IT system, every advancement of GIS technology is closely related to the rise of the latest IT technology. With the development and application of cloud computing, big data, artificial intelligence and other technologies, nowadays GIS basic software has formed five major technology systems. The big data GIS technology increases the storage management, analysis, processing and visualization of spatial big data, enriching the connotation of spatial data. Artificial intelligence GIS technology enables GIS to enhance the analysis and prediction capabilities of GIS models by combining AI related algorithms. At the same time, the two empower each other. While enhancing GIS capabilities, AI also has spatial analysis and visualization capabilities and expands Its scope of application. The new 3D GIS technology realizes the integration of 2D and 3D GIS and the integration of multi-source heterogeneous data. It promotes 3D GIS from outdoor to indoor, from the macro to the micro. Distributed GIS technology breaks through the limitations of data types and resource capacity. The performance of GIS software is improved by orders of magnitude. It makes highly available and highly reliable GIS applications possible. Cross-platform GIS technology enables GIS software to run on different types of CPU structures and operating systems, meeting the increasingly diverse needs of multi-terminal applications. The five technologies complement each other, and they further expand the capabilities and application scenarios of GIS basic software. Taking SuperMap GIS as an example, this article introduces the specific content of the five GIS technology systems in detail and explains the difficulties and innovations of each technology. Finally, this article uses the hype cycle to divide the development stages of the five major technology systems and discusses the future development trend of GIS technology.

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    Evolution of the Multiple Accumulated Temperature Across Mainland China in 1961-2018 with the Gridded Meteorological Dataset
    BAI Lei, ZHANG Fan, SHANG Ming, SHI Chunxiang, SUN Shuai, LIU Lijun, WEN Yuanqiao, SU Chuancheng
    Journal of Geo-information Science    2021, 23 (8): 1446-1460.   DOI: 10.12082/dqxxkx.2021.200500
    Abstract147)   HTML3)    PDF (32794KB)(269)      

    Accumulated Temperature (AT) could affect plants' phonological period and crops' yield and spatial distribution. AT is usually obtained by extrapolation of surface observations. However, AT would have greater spatial uncertainties in regions where the surface observations are sparsely distributed with complex terrain. In recent years, there have been some gridded meteorological data with well spatial representation. If studies used these high spatial resolution gridded meteorological data to directly calculate AT, the problem mentioned above would be solved. This study used the gridded dataset (CN05.1) with high spatial resolution and long term time series from 1961-2018 to analyze the spatiotemporal changes of the four Accumulated Temperatures (ATs) in mainland China with the thresholds of ≥0 ℃, ≥5 ℃, ≥10 ℃, and ≥15 ℃, respectively. The gridded dataset was made using more than 2400 surface meteorological stations across mainland China and was well extrapolated by the plate spline method. The main conclusions are summarized as follows: ① In mainland China, the four ATs (≥0 ℃, ≥5 ℃, ≥10 ℃ and ≥15 ℃) have low-value areas in the Qinghai-Tibet Plateau, Tianshan Mountains in Xinjiang, and Northeast China, but high-value areas in South China. Their spatial patterns are similar to those of the 2-m air temperature. ② All four ATs show significant increasing trends, especially in Inner Mongolia and Northeast China. ③ Due to changes in the AT spatial trends, the area of tropical and subtropical regions, identified by a threshold of 10 ℃, have a significant increase. In contrast, the area of mid-temperate and cold-temperate regions have a significant decrease. ④ During 1961-2018, starting time of four ATs had significantly advanced while the ending time had significantly delayed in both regional and point scales. The interval period of temperature transition ranges of 0~5 ℃, 5~10 ℃, and 10~15 ℃’s starting time has more severe changes in the Loess Plateau and Inner Mongolia. For interval period of ending time, Central China Plain changes greatly. These significant changes would impact the farming plan, crop physiology, plant diseases, and insect pests. In the future, the gridded dataset with more high spatial resolution and longer time series could be used to study the changes of accumulated temperature under climate change.

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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
    Abstract522)   HTML21)    PDF (4167KB)(253)      

    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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    A Dense Matching Algorithm for Remote Sensing Images based on Reliable Matched Points Constraint
    ZHANG Xin, WANG Jingxue, LIU Suyan, GAO Song
    Journal of Geo-information Science    2021, 23 (8): 1508-1523.   DOI: 10.12082/dqxxkx.2021.200660
    Abstract82)   HTML2)    PDF (73426KB)(244)      

    To avoid the problem of mismatches caused by initial matched points that may contain false matches during iterative dense matching based on corresponding points, a dense matching algorithm for remote sensing images based on reliable matched point constraint is presented. Firstly, to increase the number of initial matching points and expand the covering range of initial matching points, the initial set of matched points containing the matched Scale-invariant Feature Transform (SIFT) points and virtual corresponding points is constructed, where the virtual corresponding points are generated from the intersections of corresponding lines obtained by the line matching algorithm based on the matched SIFT points constraint. Secondly, the initial set of matched points is checked to remove the false matches using local image information and local geometry constraints in turn. This process first uses the local texture feature constraint constructed based on fingerprint information and gradient information to eliminate the mismatched points with low similarity, and then uses the local geometric constraint constructed by Delaunay triangulation to remove the false matches generated by similar textures, thereby obtaining the optimized set of reliable matched points. Finally, the Delaunay triangulation is constructed using reliable matched points, and the gravity center of the triangles satisfying the areal threshold is used as the matching primitive during the dense matching process. The matching based on the epipolar constraint and affine transformation constraint is performed iteratively to obtain the dense matching results. This paper used four sets of forward and backward viewing data of ZY-3 to perform parameter analysis experiment and comparative analysis experiment to prove the effectiveness of the proposed dense matching algorithm. The results of parameter analysis experiment show that the reliable matched points can be obtained when the weighted index, texture feature similarity threshold, and local geometric similarity threshold are 0.3, 0.95, and 0.85, respectively. The average matching accuracy of the reliable matched points on the four sets of data is improved by 19% compared with the initial matched point. Meanwhile, the results of comparative analysis experiment show that the dense matching algorithm based on the reliable matched point constraint can effectively avoid the error propagation, which has higher matching accuracy compared with the comparison algorithms selected in this paper. The average matching accuracy of the four sets of data is 95%. Therefore, the algorithm can obtain better dense matching results by effectively eliminating mismatched points.

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    Analysis of Public Opinion Evolution in COVID-19 Pandemic from a Perspective of Sentiment Variation
    ZHANG Chen, MA Xiangyuan, ZHOU Yang, GUO Renzhong
    Journal of Geo-information Science    2021, 23 (2): 341-350.   DOI: 10.12082/dqxxkx.2021.200248
    Abstract467)   HTML28)    PDF (13034KB)(242)      

    As a Public Health Emergency of International Concern (PHEIC), the COVID-19 pandemic caused great concern in social media all over the world. The content of Weibo comments is a collection of users' perceptions, attitudes, tendencies, and behaviors of the pandemic, and provides a high-timeliness and high-sequence text corpus for public opinion evolution research based on sentiment analysis. In this paper, we used a corpus obtained from People's Daily on Weibo during COVID-19 pandemic (January 23 - April 8, 2020) as our research data. First, we extracted emotional tendencies to classify text comments into positive and negative sentiments with SnowNLP, a Chinese natural language processing tool. Second, based on the Single-Pass clustering algorithm, we implemented text cluster analysis to explore hot topics about the pandemic situation. Moreover, we realized the information mining about public attention by using the Louvain community analysis algorithm. (1) On temporal dimension, the result of daily emotional trend analysis shows that the public has experienced three emotional phases, which are a period presenting anxiety and fear (January 23 - February 18), a period presenting steadiness and confidence (February 19 - March 15) and a period presenting tension and concern (March 16 - April 8). (2) On a spatial dimension, joint analysis of the number of users, the emotional states, and emotional projections among different provinces shows obvious differences in the public attention and emotional value of the COVID-19 pandemic. Additionally, for those Weibo users in COVID-19 affected areas, the level of their online participation is positively correlated with the pandemic severity and the value of the emotional state and emotional projection is lower. Meanwhile, those in worst-hit areas tend to have a higher impact on the evolution of public opinion. The results show that Weibo users in Guangdong Province and Heilongjiang Province have high levels of attention and low averages of emotional state and emotional projection. It can be judged the two provinces are still facing great pressure for pandemic prevention and control. Although Hubei Province is most affected by the pandemic, with a low emotional state value but a high emotional projection value, it is speculated Weibo users' comments on Hubei Province are more encouraging and praised. In addition, the number of confirmed cases in the northwestern region is relatively small, and the number of comment participation is less than in other regions, but the averages of emotional state and emotional projection are higher. The research applies natural language processing and network community detection algorithms to construct a methodological framework of public opinion analysis for social media comments. The developed framework has promising potentials, as it provides theoretical and practical support for related research on major public events.

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    Evaluation Method of Medical Facilities Service Coverage in Mountainous Cities based on Map Data
    LIAO Xinzhi, WANG Hua, ZHAO Wanmin
    Journal of Geo-information Science    2021, 23 (4): 604-616.   DOI: 10.12082/dqxxkx.2021.200489
    Abstract145)   HTML8)    PDF (18546KB)(231)      

    With the continuous development of China's social economy, residents' demand for medical services is increasing. It is of great significance to analyze and evaluate the service scope of urban medical facilities to solve the contradiction between medical supply and demand and improve the level of urban health. At present, the coverage assessment of medical facilities in China mostly ignores the traffic network and population distribution, resulting in many blind areas of urban medical services. The complex terrain environment of mountainous high-density city affects the travel ability and mode of residents, and increases the difficulty of medical facilities service coverage, so it is difficult to accurately evaluate it by traditional methods. Based on the analysis and comparison of the advantages and disadvantages of the existing medical accessibility research methods, taking the main urban area of Chongqing as the experimental area, this paper attempts to adopt the optimized 2SFCA, and according to the network map data and official statistical data, establishes the medical facility accessibility analysis model based on GIS platform, The coverage of medical facilities and medical accessibility of each street and town were scientifically evaluated from three levels of city, district, and community. The results show that the improved method can deal with massive medical data, accurately simulate the scope of medical services, and output the evaluation results of full level medical facility service coverage, which is more suitable for mountainous areas with complex transportation and multi-level medical facility service coverage evaluation. The comprehensive evaluation shows that the medical facilities in the main urban area of Chongqing have the problems of uneven spatial distribution of large general hospitals and incomplete internal coverage of primary medical facilities, and the streets and towns with better medical coverage only account for 33.1% of the total. Therefore, it is suggested that the high-quality large-scale medical resources gathered in the old urban areas should be shared with the new urban areas. At the same time, according to the geographical location and technical ability, medical districts with complete levels should be established to make up for the shortcomings of primary medical services in the old urban areas, to improve the allocation of medical facilities in the main urban areas of Chongqing.

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    An Extraction Method of Rural Mechanically Cultivated Road Under Dynamic Weight Constraint
    DAI Jiguang, WANG Xiaotong, ZHI Xinyu, MA Rongchen, ZHANG Yilei
    Journal of Geo-information Science    2021, 23 (5): 773-784.   DOI: 10.12082/dqxxkx.2021.200709
    Abstract75)   HTML4)    PDF (15870KB)(226)      

    The agricultural machinery field work has developed rapidly. There is an urgent need for more accurate Mechanically Cultivated Road (MCR) network data in agricultural production scheduling. Thus, it is necessary to obtain accurate and effective rural MCR information. However, compared with other types of roads, the narrow pavement width and the small difference between pavement material and farmland are the typical characteristics of rural MCR, which are the main factors leading to the low degree of automation in existing template matching methods. In order to solve the problems mentioned above and improve the accuracy of the MCR extraction, the solutions are proposed as follows: Firstly, by improving the Multi-Scale Line Segment Orientation Histogram (MLSOH) model, we can not only predict the local road direction of MCR, but also reduce the probability of wrong prediction of road direction due to the interference of ridges. Secondly, the line segments of the image are extracted, which can clearly characterize the linear characteristics of MCR. The length of line segments in the local area is taken as the main factor of the dynamic weight distribution. The dynamic weight distribution is carried out for different road prediction directions, so as to solve the problem of the decrease in matching accuracy due to the narrow width of the MCR. Finally, the similarity analysis model of HSL color space is combined with the dynamic weight factor to form the HSL dynamic matching model to improve the contrast between the MCR and the farmland, so as to increase the accuracy of the MCR extraction. In this paper, in order to verify the effectiveness of the proposed algorithm, three high-resolution remote sensing images of different regions and data types are acquired. Two GF-2 images, with a spatial resolution of 0.8 m, covered areas in Tongliao City, Inner Mongolia, and areas in Enshi City, Hubei Province, respectively. One Geo-Eye image, with a spatial resolution of 0.5 m, covered the town of Hobart, Australia. Through qualitative and quantitative analysis of the proposed and comparison algorithms, the conclusions are as follows: the road extraction integrity, accuracy, and quality of the proposed algorithm are all above 95 %. The proposed algorithm has the advantage of high automation while ensuring the extraction accuracy of MCR. It can also be extended to other rural roads.

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    Multi-scenario Simulation of Land Use Change Along China-Pakistan Economic Corridor through Coupling FLUS Model with SD Model
    ZHANG Xiaorong, LI Ainong, NAN Xi, LEI Guangbin, WANG Changbo
    Journal of Geo-information Science    2020, 22 (12): 2393-2409.   DOI: 10.12082/dqxxkx.2020.190618
    Abstract192)   HTML5)    PDF (20030KB)(223)      

    Planning and construction of China-Pakistan Economic Corridor (CPEC) is inseparable from the scientific cognition of the spatial patterns and changing processes of land resources and eco-environment in this area. Land Use and Land Cover Change (LUCC) simulation can provide reliable prediction data for regional land resources management, eco-environment sustainability, and eco-environment risk assessment. In this paper, based on the coupled System Dynamics Model (SD) and future Land Use Simulation Model (FLUS), combined with the China-Pakistan Economic Corridor construction and regional eco-environment policies, various scenarios were set up to simulate the land use change of the China-Pakistan Economic Corridor, taking full advantages of the two models in macro land demand simulation and micro land allocation. Firstly, the SD-FLUS model was constructed and validated using the historical data of CPEC in 2009-2015. Then the land use changes from 2016 to 2030 under three different scenarios, namely Baseline Development (BD)scenario, Investment Priority Oriented (IPO) scenario, and Harmonious Development (HD) scenario, were simulated. Results show that: (1) The relative error of demand simulation was less than 9%, and the overall accuracy and Kappa coefficient of the simulation were over 90% against the actual land use data in 2015, which indicates the SD-FLUS coupling model effectively reflected the land use change pattern of the China-Pakistan Economic Corridor. The model could be used for further simulation of land use changes in CPEC under different scenarios; (2) There are significant differences in simulated land use under different scenarios until 2030. Construction land expanded under all three scenarios but at different speeds. The expansion speed of HD scenario was in the middle. Under this scenario, the construction land in Kashgar and Pakistan increased by 235.17 km2 and 4942.80 km2, respectively. The expansion speed under the IPO scenario was the fastest, with the construction land in Kashgar increased by 265.23 km2 and construction land in Pakistan increased by 5918.91 km2. Under the BD scenario, the construction land in Kashgar and Pakistan increased by 163.71 km2 and 2861.84 km2, respectively. Under the HD scenario, increment of Pakistan's cultivated land area was less than half of that under BD scenario. Kashgar's cultivated land area increased the most in IPO scenario (about 882.54 km2), which was about three quarters of that in the HD scenario. The forest land was effectively restored only under the HD scenario. Generally, the HD scenario taking both social-economic development and eco-environment protection into account is the most ideal scenario among the three scenarios. Our simulation results can provide useful data support for the construction of China-Pakistan Economic Corridor and the assessment of eco-environment in the future.

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    Analysis of PM 2.5 Population Exposure Doses Characteristics in Beijing in 2019
    LIN Jinhuang, CHEN Wenhui, ZHANG An
    Journal of Geo-information Science    2020, 22 (12): 2348-2357.   DOI: 10.12082/dqxxkx.2020.190624
    Abstract240)   HTML8)    PDF (14002KB)(218)      

    In recent years, PM2.5 has become one of the main pollutants in the haze outbreak. The risk of long-term exposure to PM2.5 of high concentration may greatly increase the risk of disease and endanger the physical and mental health of residents. In this study, Beijing was taken as the research area where air pollution was serious and the population was highly concentrated. Based on the data of PM2.5 concentration, the grid data of population spatial distribution, and the long-term respiratory volume of different populations in Beijing in 2019, an assessment model of PM2.5 population exposure was established. Furthermore, the spatial distribution characteristics of PM2.5 population exposure intensity and the differences of exposure-response among different populations in Beijing in 2019 were analyzed. The results show that: (1) In 2019, the PM2.5 concentration in Beijing is the highest in winter, and the daily average concentration is 48.89 μg/m3, which shows an overall trend of low in the north and high in the south; (2) There are significant spatial differences in PM2.5 population exposure, and the PM2.5 exposure of different populations shows an overall trend of weakening from the center of the city to the surrounding areas, and the high exposure areas were mainly concentrated in urban areas; (3) There are obvious spatial differences in the exposure intensity of PM2.5 population in different gender and age groups, and there were also significant differences in the response of PM2.5 exposure among different populations in the city; (4) The exposure risk of PM2.5 was not entirely determined by the concentration of pollutants, but by the concentration of pollution sources and exposure receptors, the high-risk area of population exposure to PM2.5 in Beijing urban area was the high-risk area, and it was the core area for the government to effectively prevent and control pollution hazards in the future.

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    Study on Agglomeration, Evolution and Autocorrelation Effects of Spatio-temporal of COVID-19 Epidemic in Prefecture-level Cities in China during Government's Strict Control Period
    WU Xibo, LAI Changqiang, GE Zhizhuan
    Journal of Geo-information Science    2021, 23 (2): 246-258.   DOI: 10.12082/dqxxkx.2021.200362
    Abstract136)   HTML3)    PDF (11968KB)(216)      

    The spatio-temporal evolution of major public infectious epidemics during government's strict control period in prefecture-level city can effectively reflect china's comprehensive emergency prevention and control capabilities. Based on statistical data including number of active cases, total confirmed, deaths of COVID-19 in 312 cities in China from January 24 to March 5, 2020, this paper uses methods including exploratory spatial data analysis, optimized hot spot analysis, spatial Markov chain, spatial panel data model to analyze spatio-temporal evolution characteristics of COVID-19 epidemic in China under government's strict control.The study found that: (1) The number of active cases of COVID-19 in China experienced characteristics of "rapid growth and diffusion, basic control, gradual decline, and complete control in some areas" and reached its peak on February 17, with an average daily growth rate of 17.5% during rising period and an average daily decline rate of 5.1% during falling period, and the epidemic change characteristics of most cities are similar to Nationwide's situation;(2) The high population mobility during Spring Festival transportation period is main reason for rapid expansion of epidemic. The Baidu's migration scale index for the 14 days prior to Wuhan closure was significantly correlated with total confirmed cases of COVID-19 in some cities; (3) The method called optimized hot spot analysis has identified that spatial distribution of hot spots of epidemic is stable and mainly distributed in 36 cities with Wuhan as the center and a radius of about 350 kilometers, while no statistically significant cold spot cities were identified; (4) The results of Markov chain transfer probability matrix analysis of active cased of COVID-19 in 312 cities show that various types are more stable and the probability of maintaining original type is greater than 0.85. The average probability of downward transfer is significantly higher than the probability of upward transfer. The probability of each type of transition changes significantly under the influence of different spatial lag types; (5) The estimation results of the spatial panel data model show that the number of active cases of COVID-19 in cites has spatial-temporal autocorrelation. This paper analyzed spatio-temporal evolution characteristics of COVID-19 epidemic during government's strict control period at prefecture-level city level from multiple perspectives, the focus of COVID-19 prevention and control is to reduce its spatio-temporal autocorrelation effects, this study provides a decision-making reference for government's current and future response to major public infectious epidemics.

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    Spatial-temporal Characteristics of COVID-19 in Chongqing and Its Relationship with Human Mobility
    LIU Yaxi, SONG Ci, LIU Qiyong, ZHANG Zhixin, WANG Xi, MA Jia, CHEN Xiao, PEI Tao
    Journal of Geo-information Science    2021, 23 (2): 222-235.   DOI: 10.12082/dqxxkx.2021.200296
    Abstract232)   HTML7)    PDF (32066KB)(210)      

    Based on the epidemiological investigation data of 545 COVID-19 cases and mobile phone trajectory data of 15 million users during the epidemic ( from 21 January, 2020 to 24 February, 2020 ), this paper analyzed the spatial-temporal characteristics of COVID-19 and the human mobility changes in Chongqing. Furthermore, the correlation relationship between them was explored to explain these characteristics and changes. The results show that: (1) The epidemic pattern in Chongqing can be divided into three stages ( i.e. imported cases stage, imported cases plus local cases stage, and local cases stage ) and the real time reproduction number (Rt) was high at early stage, but declined significantly after prevention and control measures were taken; The spatial distribution of cases presented a significant clustering, and the high clustering areas were mainly distributed in the northeastern and the southwestern of Chongqing; (2) After the epidemic, the total amount of human mobility decreased to 53.20% and the decrease was mainly concentrated in the main urban area, while that of in the suburbs and rural areas did not change, or even increased; (3) The relationship between human mobility and case occurrence lies in two aspects: The correlation coefficient between daily human mobility and Rt, daily increased number of cases after an average incubation period (7 d) were 0.98, 0.87, revealing the time correlation between human mobility and case growth; The correlation coefficient between total amount of human mobility and total number of cases, number of local cases in each street (township) were 0.40, 0.35, revealing the correlation between human mobility and spatial distribution of cases. The cases clustering area corresponds to the network community of human mobility, revealing the local clustering transmission is the major transmission model. By aggregating the big data and the epidemic data, we suggests that cutting off the connection between different human mobility network communities and blocking the local transmission inside the high risk communities is an effective measure for the prevention and control of epidemics in cities.

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    Delineating China's Urban Traffic Layout by Integrating Complex Graph Theory and Road Network Data
    KOU Shihao, YAO Yao, ZHENG Hong, ZHOU Jianfeng, ZHANG Jiaqi, REN Shuliang, WANG Ruifan, GUAN Qingfeng
    Journal of Geo-information Science    2021, 23 (5): 812-824.   DOI: 10.12082/dqxxkx.2021.200340
    Abstract215)   HTML6)    PDF (14219KB)(210)      

    The rapid development of urbanization has promoted China's urban road network's continuous expansion and growth. The urban road network is a dynamic, open, and self-organized spatial complex network, which constitutes a city's structural framework. The study on urban road networks' structural characteristics can provide a significant application value for road network planning and urban construction. In the related studies of the structural characteristics of urban road networks, few scholars have paid attention to the whole urban road network structure from the perspective of road alignment in China. Besides, recent studies lack an overall evaluation on the road network of major cities in China. In this paper, 49 cities, including the first- and second-tier and first-tier new cities in China, are selected as study areas and the urban road network data in February 2020 are taken as experimental data. Firstly, we use the graph theory and rose diagrams to visualize the road network's directional characteristics in 49 cities. The complex structure of the urban road network is qualitatively analyzed. Then, we select five road network indicators including the maximum ratio R, the road primacy degree S, the ratio over threshold T, the orientation-order φ, and the road network density δ. Based on the five indicators, cluster analysis is carried out for the road networks of 49 cities in this paper. And the characteristics of the spatial distribution of urban road network in China are explored. The results show that the north-south and east-west roads are the main alignment of urban roads in China. Because of the influence of terrain, some cities plan routes along the direction, which is favorable to traffic and resident's living. Based on the clustering of road network indicators, four types of the urban road network are obtained, including cross orthogonal type, cross to windmill type, windmill to arc type, and mixed complex type. There are significant differences among four types of the urban road network in directional characteristics highlighted in complexity and order. In view of the spatial distribution of road network types, road networks of cross orthogonal type and cross to windmill type are mainly distributed in China's inland areas. In contrast, road networks of the windmill to arc type and mixed complex type are mainly distributed in coastal areas. This paper explores the current traffic layout in major cities in China by analyzing the characteristics of road network's distribution in the first- and second-tier and first-tier new cities in China. This study can provide a reference for road planning and optimization of road network layout in new urban districts.

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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
    Abstract211)   HTML6)    PDF (11992KB)(199)      

    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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    Temporal and Spatial Characteristics of Sea Ice Condition and Its Influencing Factors in Liaodong Bay from 2015 to 2020
    ZHAO Quanhua, WANG Xiao, WANG Xuefeng, LI Yu
    Journal of Geo-information Science    2021, 23 (11): 2025-2041.   DOI: 10.12082/dqxxkx.2021.210225
    Abstract74)   HTML0)    PDF (6293KB)(190)      

    Under the attack of strong cold wave in winter, a large-scale freezing phenomenon appears in Liaodong Bay. In order to analyze the change rules of sea ice condition and the environmental influencing factors in Liaodong Bay from 2015 to 2020, Sentinel-1A/B data are selected to carry out sea ice monitoring in Liaodong Bay. First, the Gray Level Co-occurrence Matrix is used to count the texture features. Then the optimal feature combination is selected based on the Bhattacharyya Distance, and the Maximum Likelihood method is adopted to classify sea ice. Then, according to the classification results of sea ice, the sea ice condition levels of Liaodong Bay in recent 5 years are determined. The change rules of ice condition characters such as the outer edge of sea ice, area, type, and freezing probability are analyzed. Finally, the influence of sea water depth on sea ice condition is discussed, the relationship between sea ice condition and sea temperature, air temperature, and wind speed is studied through correlation analysis. The main conclusions are as follows. Firstly, the Sentinel-1B image on February 1, 2020 is tested using the proposed method and different texture feature combination methods. The results show that the overall classification accuracy and Kappa coefficient of the proposed method are 93.16% and 0.85, respectively, which is the highest classification accuracy among all methods. The overall classification accuracy and Kappa coefficient of all images from 2015 to 2020 are above 85% and 0.80, respectively, which meet the accuracy requirements for sea ice monitoring. Secondly, in last November and December, the sea ice types are mainly primary ice, with gray ice in between. Gray ice is the main ice in January and early February, with primary and white ice in between. Gray and primary ice is dominant in late February and early March. There are differences in the sea ice freezing probability in Liaodong Bay. The sea ice freezing probability in the north coast is higher than that in the south, and the probability in the east is higher than that in the west. Sea depth in Liaodong Bay has different effects on sea ice development in different ranges of [-10, 0], [-20, -10], and [-30, -20]. The Pearson correlation coefficients of sea ice condition with sea temperature, air temperature, and wind speed in Liaodong Bay are -0.55 (P<0.01)、-0.59 (P<0.01)、and -0.22 (P=0.19), indicating that the sea ice condition is negatively correlated with sea temperature and air temperature, and has a low correlation with wind speed.

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    A Method for Dynamic Risk Assessment and Prediction of Public Health Emergencies based on an Improved SEIR Model: Novel Coronavirus COVID-19 in Ten European Countries
    BI Jia, WANG Xianmin, HU Yueyi, LUO Menghan, ZHANG Junhua, HU Fengchang, DING Ziyang
    Journal of Geo-information Science    2021, 23 (2): 259-273.   DOI: 10.12082/dqxxkx.2021.200356
    Abstract184)   HTML4)    PDF (13911KB)(181)      

    Public health emergencies can seriously affect public health and people's lives, and risk assessment and prediction provide a scientific basis for effective prevention and control of public health emergencies. This work proposes a new method for risk dynamic assessment and prediction of public health emergencies based on a revised SEIR model. This work combines transmission rules of public health emergencies with demographic, medical, and economic conditions and establishes rational and comprehensive indices of risk assessment by coupling hazard evaluation and vulnerability estimation. An integrated model of entropy-AHP is employed to implement risk dynamic assessments of public health emergencies. Moreover, this work establishes a modified SEIR model and combines infectious disease transmission dynamics and risk assessment to predict evolutional trends and dynamic risks. The COVID-19 epidemic at the end of December 2019 was an important public health emergency characterized by rapid spread, widespread infection, and great difficulty in prevention and control. The COVID-19 epidemic in 10 European countries is employed as a case study for risk assessment and dynamic prediction. Based on the epidemic data from the beginning to April 16, 2020, the epidemic evolutionary trends and dynamic risks are predicted in these countries from April 17, 2020 to May 10, 2020. According to the prediction results, the epidemic situation in 10 European countries will be severe by May 10, 2020. The goodness of fit R2 is larger than 0.92, and the prediction results are basically consistent with the real epidemic situation. Work resumption will be unfavorable for epidemic prevention and control in this case. The method proposed in this work may offer continuous epidemic risk assessments and predictions for countries and regions with serious outbreaks, support effective decisions for disease prevention and control, and also provide emergency risk evaluations and predictions in new epidemic outbreak periods and for other public security emergencies in the future.

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    Research on Organization and Management of Spatio-temporal Objects in Pan-spatial Digital World based on Spatio-temporal Domain
    HUA Yixin, ZHANG Jiangshui, CAO Yibing
    Journal of Geo-information Science    2021, 23 (1): 76-83.   DOI: 10.12082/dqxxkx.2021.200417
    Abstract189)   HTML8)    PDF (2577KB)(178)      

    The pan-spatial digital world is a comprehensive data body composed of spatiotemporal objects describing various entities and elements in the real/virtual world in the computer system. It is an object-oriented description of the real/virtual world from macro to micro dimensions. Since the traditional management method using blocks and layers in GIS is not suitable for managing the complex and dynamic spatio-temporal objects in the pan-spatial digital world, an organization and management method of pan-spatial spatio-temporal objects based on spatio-temporal domain is proposed in this study. Based on analyzing the concept, composition, and characteristics of pan-spatial digital world, the concept of spatio-temporal domain is put forward. This study defines the data organization mode and essential characteristics in the spatio-temporal domain, and establishes a data organization system and management method including sub spatiotemporal domain, spatio-temporal object class, object relation class, and life cycle sequence of spatio-temporal objects in the spatio-temporal domain. The storage and management experiments of spatio-temporal data are carried out, which proves the practicability and feasibility of the organization and management of spatio-temporal objects based on spatio-temporal domain. This study provides an underlying support for the generation and processing, inspection and verification, visualization and output of spatio-temporal objects for future application scenarios.

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    Sequential Evolution Analysis of International Relations Network in Special Events
    YAO Borui, QIN Kun, LUO Ping, ZHU Zhaoyuan, QI Lin
    Journal of Geo-information Science    2021, 23 (4): 632-645.   DOI: 10.12082/dqxxkx.2021.200366
    Abstract125)   HTML8)    PDF (11836KB)(174)      

    Since the beginning of the 21st century, the complex and fast-changing international relations have brought profound effects to the world economy, politics, security, and diplomacy. Keeping abreast of the changes in international relations is of great significance to China's foreign policy making and overall development planning. With the advent of the era of big data, the application of big data combined with quantitative analysis tools of international relations to timely and effectively mine the change patterns of international relations has become an important issue. Big data of news events with strong timeliness and high information content can timely reflect the information that international events affect global international relations. As an information mining method oriented to big data, network mining is a good choice for data-driven international relations research because of its figurative relational expression and rich structural analysis methods. Taking short-term international events as the background, the network mining of big data of news events and the sequential evolution analysis of international relations network can provide solutions to the changes of international relations in the context of changes of international relations caused by short-term international events. This paper takes the Trade War between China and the United States as an example to study the temporal evolution pattern of the international relations network in special events. Based on the GDELT news event data, the international relations network is constructed, and methods based on complex network theory are used for information mining and analysis of international relations. Firstly, the data are used to construct the international relationship network, then the temporal evolution patterns are detected by dynamic community partition method, and finally the spatial characteristics are analyzed by combining the spatial analysis methods such as analysis of point distribution patterns, nuclear density analysis, and spatial autocorrelation analysis. The results show that: (1) In the process of the occurrence of special events, there is a strong correlation between the evolution of the network community and the type of sub-events; and (2) Nodes in the same community generally show obvious clustering characteristics in spatial distribution. Nodes in a specific region join different communities with high frequency. The spatial distribution of the high values of node network attribute changes with the occurrence of events. The local eigenvalues of the network change dramatically with the occurrence of sub-events. The research in this paper provides a new perspective for the empirical analysis of the dynamic changes of international relations in short-term international events, provides a new idea for the spatial shift of international relations research, complements the data-driven international relations research at the methodological level, and also provides a reference for the network mining of big data.

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    Research Progress on Policing Strategy of Crime Prevention and the Evaluation of Its Effectiveness in Space and Time
    LIU Lin, WU Yuhan, SONG Guangwen, XIAO Luzi
    Journal of Geo-information Science    2021, 23 (1): 29-42.   DOI: 10.12082/dqxxkx.2021.200482
    Abstract210)   HTML14)    PDF (1696KB)(172)      

    Policing strategy of crime prevention is one of the important topics in crime geography.This paper systematically reviews the development and progress of policing strategy from three aspects: the characteristics of different types of crime prevention policing strategies, crime prevention experiments and the evaluation of crime prevention effectiveness. Our research shows that: ① Community policing, problem-oriented policing , hot spots policing and intelligent policing are the four main types of crime prevention policing strategies. Community policing takes the community as a unit and reduces crime through cooperation between the police and the local residents; Problem-oriented policing aims at identifying and solving social problems through a structured approach named SARA (Scanning, Analysis, Response, Assessment). Hot spots policing makes policing plan based on crime hot spots, and intervenes crime hot spots to reduce crime. Intelligent policing uses advanced technologies such as big data and artificial intelligence for more effective policing prevention. ② Crime prevention experiments compare the changes of crime before and after the intervention of police strategy between the experimental group and the control group. Most policing experiments are mainly focused on hot spots policing, which typically reduces crime in the experimental area, but may displace crime or diffuse the benefits of crime reduction to the neighboring areas; ③ Traditional police prevention evaluation mainly considers three aspects: detection rate, public security perception and social and economic benefit. To evaluate the effect of crime displacement or benefits diffusion, scholars put forward Weighted Displacement Quotient (WDQ), Spatio-Temporal Weighted Displacement Quotient (STWDQ), Difference in Differences (DID), Propensity Score Matching (PSM),integration of DID and PSM(PSM-DID)and DID-based quadrant method to measure the space-time benefits of policing strategies. In sum,while there exist abundant international research on policing strategy about crime prevention and evaluation of their space-time benefit such research in China still remain in its infancy. In the end, this paper further discusses the direction of future research on China's policing strategy.

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    Forest Fire Risk Rapid Warning Model based on Meteorological Monitoring Network
    LI Yu, ZHANG Liming, ZHANG Xingguo, WANG Hao, ZHANG Xingang
    Journal of Geo-information Science    2020, 22 (12): 2317-2325.   DOI: 10.12082/dqxxkx.2020.190799
    Abstract201)   HTML4)    PDF (6359KB)(170)      

    Forest fire occurs frequently and suddenly. Therefore, it is essential to carry out the rapid warning of forest fire danger for the reduction of the loss caused by forest fire and the promotion of sustainable development of forest resources. This paper designs an early-warning model based on GIS spatial analysis and visualization technology and the construction of real-time meteorological monitoring network using ground meteorological stations, which can achieve timely and rapid warning of forest fire danger. To build the model, this paper first determines the forest fire danger early-warning factors, which are the input parameters of the model. Secondly, a hierarchy model of the importance of early warning indicators is constructed to determine the weight of the early warning factors via using the AHP method and combining the analysis of early warning factors. Then, the thresholds and grade division criteria of the early-warning factor are determined according to the national, industrial, and local regulations for determining forest fire danger levels, which is suitable for the model. Finally, the Voronoi Diagrams are used to establish a meteorological monitoring network based on weather stations and real-time weather data. The Overlay Analysis technology is used to calculate the early warning result. Based on the model and real-time acquisition and processing of data, a rapid warning system for forest fires was constructed. This paper took Qinghai Province as the experimental area where the feasibility and applicability of the system were verified, which indicates that early warning of forest fire danger can be realized by the model comprehensively, accurately, and rapidly. Results show that: (1) According to the early-warning model, the real-time early-warning indicators which were set before, and real-time meteorological monitoring data, the early-warning signal can be sent in time, which can quickly realize early warning and timely response of forest fire risks at the county and forest farm levels; (2) Via introducing GIS visualization methods, the thematic map of forest fire risk spatial distribution can be generated by the model quickly, which is conducive to observe changes in early-warning levels visually. The rapid warning of forest fire risks has important guiding functions for effective prevention, interruption management, and prevention measures of forest fire, and has great significance for forest fire prevention work, protection of forest resources, and safety of human life and property.

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    Research on Comprehensive Suitability Evaluation Method of Rice Planting Environment
    WANG Xingfeng, LI Daichao, WU Sheng, XIE Xiaowei, LU Jiaqi
    Journal of Geo-information Science    2021, 23 (8): 1484-1496.   DOI: 10.12082/dqxxkx.2021.200644
    Abstract102)   HTML2)    PDF (16655KB)(165)      

    Carrying out the layout of the rice planting industry in a specific area is an important content of scientifically formulating the regional agricultural planting industry plan, and the comprehensive suitability evaluation of rice planting environment is the premise of rice planting industry layout. This paper takes Pucheng County, Fujian Province, a good grain and oil Demonstration County in China as the research area. The Analytic Hierarchy Model was used to construct a rice planting suitability evaluation system with 21 indicators in five categories: soil conditions, site conditions, irrigation and drainage conditions, climate conditions and mechanical farming conditions. The evaluation system uses geological models, regression models and spatial interpolation methods to calculate and simulate the spatial distribution data of evaluation indicators to form a 5 m×5 m resolution evaluation index grid data set. The suitability index model was established by using experience index method to carry out comprehensive suitability evaluation of rice planting environment in fine scale. Analyzing the rice yield of the actual samples and the comprehensive suitability index of the rice planting environment, it was found that the two were significantly positively correlated, which verified the correctness and feasibility of the evaluation work of this study. Finally, the K-means attribute clustering method was used to identify the spatial pattern of multi-dimensional environmental suitability of rice planting in the research area. The results show that: ① The cultivated land area with high, relatively and moderately suitable rice planting in the study area accounted for 84.4% of the cultivated land area of the whole county, and the sub-suitable cultivated land only accounted for 15.6%. The overall suitability of cultivated land was relatively high. ② The comprehensive suitability for rice planting and the suitability of various indicators are higher in the type I cluster area. Type II cluster area have higher comprehensive suitability for rice planting, but the suitability of irrigation and drainage conditions is very low. The comprehensive suitability of rice planting in type III cluster area is relatively high, but the suitability of site conditions and soil conditions are lower. Type IV cluster area have low overall suitability for rice planting, and the lowest suitability for irrigation and drainage conditions. This study can provide a method for the evaluation of the suitability of rice planting, and provide a basis for Pucheng County to carry out agricultural planting planning more rationally and scientifically.

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    Delineation of Urban Growth Boundary based on Improved FLUS Model Considering Dynamic Data
    WANG Zhiyuan, ZHANG Kao, DING Zhipeng, WU Suiyi, HUANG Chunhua
    Journal of Geo-information Science    2020, 22 (12): 2326-2337.   DOI: 10.12082/dqxxkx.2020.200373
    Abstract156)   HTML5)    PDF (16135KB)(140)      

    Measuring the urban growth boundary is important to control the disorderly expansion of urban constructed land. How to define the urban growth boundary scientifically is a hot topic of current researches. This study attempts to introduce Baidu dynamic traffic data and POI data to improve the FLUS model to simulate land use changes. Taking the central of Changsha city as an example, the simulation accuracy of the improved FLUS model is first verified by comparing with the land use data of 2000, 2010, and 2018. Then, the land use of central Changsha in 2030 is simulated based on two scenarios using the improved FLUS model. The urban growth boundary is finally defined based on land suitability evaluation. The results show that: (1) Compared with the original FLUS model, the kappa coefficient of the improved FLUS model with dynamic data increases by 2.90% and 2.74% in 2010 and 2018, respectively, and the overall accuracy increases by 1.79% and 1.83%, respectively, which indicates a higher simulation accuracy of the improved model; (2) Based on the simulated land use of central Changsha in 2030, the area of constructed land is 930.06 km2 and 881.36 km2 respectively in benchmark scenario and ecological protection scenario. The largest proportion of land converted to construction land is cultivated land; (3) The area within the rigid growth boundary of central Changsha is 1479.59 km2, accounting for 37.38% of the total area of the central city. These areas include Furong District, Tianxin District, Yuhua District, Yuelu District, and Kaifu District; (4) The area within the elastic growth boundary of central Changsha is 799.35 km2 and 742.92 km2 under the benchmark scenario and ecological protection scenario, respectively. The expanded construction mainly occurs in Changsha County and Wangcheng District, which is consistent with the development direction of 2010 Changsha urban master plan. The improved FLUS model with dynamic data can simulate the urban growth boundary in multiple scenarios, which provides a scientific basis for future planning decision.

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    An Improved RANSAC Algorithm for Point Cloud Segmentation of Complex Building Roofs
    LIU Yakun, LI Yongqiang, LIU Huiyun, SUN Du, ZHAO Shangbin
    Journal of Geo-information Science    2021, 23 (8): 1497-1507.   DOI: 10.12082/dqxxkx.2021.200742
    Abstract98)   HTML0)    PDF (14248KB)(114)      

    Roof model reconstruction affects the quality of building complete model reconstruction, and the segmentation quality of roof point cloud is of great significance for roof model reconstruction. Aiming at the problems of wrong segmentation and over segmentation in the traditional RANSAC algorithm, this paper proposes an improved RANSAC algorithm to redistribute the point cloud, considering the location information of the point cloud. The algorithm eliminates the non planar points temporarily, and selects three points from the planar points set as the initial samples in the way of R radius neighborhood to fit them. The distance between the remaining points in the neighborhood and the fitting plane is calculated, and the neighborhood meeting the threshold requirements is classified as an effective neighborhood, three points with the minimum standard deviation are selected as the initial model, RANSAC algorithm is used to segment the roof point cloud. Aiming at the misclassification phenomenon in segmentation results, the distance between misclassification points and patches is calculated by k-nearest neighbor algorithm, and then the misclassification points are reclassified, at the same time, the angleθ and the distance d between patches are considered to merge the over segmented patches, the Euclidean distance based clustering segmentation algorithm is used to analyze the connectivity of the merged patches. By using the distance from a point to a plane and the consistency of the normal vectors between the point and the plane, the non planar points are redistributed. In order to verify the effectiveness of the algorithm, three independent roofs of complex buildings in Helsinki area of Finland and six roofs of buildings in a residential area of Shanghai are selected as experimental data. In the first group of experiments data, the average accuracy of the segmentation of roof patch is 92.17%, and the highest accuracy is 93.18%. In the second group of experiments data, the average accuracy of the segmentation of the roof patch is 87.82%, and the highest accuracy is 94.44%. The average standard deviation of the distance between the points on all the segmentation patches and the corresponding best fitting plane is 0.030 m. According to the above two groups of experiments data, 78% of the buildings have no over segmentation, and the average accuracy is 90%. The experimental results show that the algorithm has a high accuracy in extracting the roof plane slice, which can suppress the over segmentation and has a good anti noise ability.

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    Journal of Geo-information Science    2021, 23 (8): 1524-1524.  
    Abstract167)      PDF (230KB)(113)      
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    Overview and Prospect for Spatial-Temporal Prediction of Crime
    GU Haisuo, CHEN Peng, LI Huibo
    Journal of Geo-information Science    2021, 23 (1): 43-57.   DOI: 10.12082/dqxxkx.2021.200247
    Abstract166)   HTML5)    PDF (1817KB)(110)      

    As the core technology of predictive policing, Spatial-Temporal (ST) prediction of crime has developed rapidly from around 2000 to the present. We introduce the basic theory of ST prediction of crime at the beginning. We regard the ST prediction method of crime as a process combining corresponding models to predict the ST distribution of crimes in the future and deconstruct it into relationships between three objects: case, ST backcloth, and individual behavior. Then, based on the input factors of prediction models, we sum up three current main methods, including ① the prediction method based on the information of cases' ST location, ② the prediction method based on the backcloth and the information of cases' ST location, and ③ the prediction method based on individual behavior, the backcloth, and the information of cases' ST location. We further summarize the mechanisms of different methods in detail respectively. In addition, we compare and analyze each method based on their applicable scenarios and predictive capacities. Finally, with the development of big data technology, we present solutions to improve current prediction methods, that are to construct a data-fusion system, refine data granularity, and integrate new types of data. For model optimization, we need to improve the ability of integrating heterogeneous data from multiple sources and balancing the interpretability and predictive ability of models.

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    The Application and Prospect of Spatiotemporal Statistics in Poverty Research
    GE Yong, LIU Mengxiao, HU Shan, REN Zhoupeng
    Journal of Geo-information Science    2021, 23 (1): 58-74.   DOI: 10.12082/dqxxkx.2021.200628
    Abstract233)   HTML12)    PDF (3791KB)(100)      

    Eliminating poverty is a common goal of human society. Poverty has the characteristics of spatial heterogeneity and spatial autocorrelation. Spatiotemporal statistical methods dealing with georeferenced or spatiotemporal data have been widely employed for analyzing spatiotemporal poverty data. This paper reviews the applications of spatiotemporal statistical methods in spatiotemporal poverty analysis and classifies the applications into four categories: (1) exploratory analysis of poverty, mainly to identify and quantitatively analyze the spatiotemporal distribution pattern of poverty; (2) identification of spatial determinants of poverty, to analyze the influencing factors of poverty by constructing a model of the relationship between poverty and various geographical elements; (3) spatial mapping of poverty, to obtain the distribution of poverty in the entire region using sampling data; and (4) spatiotemporal analysis of poverty, to reveal the spatiotemporal changes of poverty and their driving factors. On the basis of explaining the principles of these methods, we give examples of recent applications to illustrate how specific spatiotemporal statistical methods are applied to spatial poverty research. On this basis, the shortcomings of current spatiotemporal poverty research and potential development on future poverty research are also summarized.

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    Research on Public Opinion Analysis Methods in Major Public Health Events: Take COVID-19 Epidemic as an Example
    HAN Keke, XING Ziyao, LIU Zhe, LIU Junming, ZHANG Xiaodong
    Journal of Geo-information Science    2021, 23 (2): 331-340.   DOI: 10.12082/dqxxkx.2021.200226
    Abstract336)   HTML10)    PDF (5681KB)(86)      

    Since December 2019, COVID-19 has rapidly swept the world. As of May 10, 2020, 16:40 PM, Beijing time, the global confirmed COVID-19 cases reached 4,115,662, which has become a major global issue. Social media platforms such as microblog have become the important channel for information transmission and an effective sensor of public sentiment. In-depth mining and analysis of microblog information can not only characterize the public opinion, but also help the government to conduct targeted guidance on public sentiment and properly control public opinion. Therefore, this study collected more than 330,000 Sina Weibo data about COVID-19 from January 18, 2020 to January 28, 2020. Based on the spatial clustering method using Louvain and K-means and an improved BTM subject word extraction algorithm, users' attention information and emotional characteristics are labeled with their locations. Thus, the evaluation method of public opinion is constructed by integrating user's location information, which is able to analyze the characteristics of public opinion and the difference in the topics concerned at different regions. Our results show that the characteristics of public opinion in different regions can be comprehensively evaluated using the spatial clustering method based on Louwain and K-mean. The BTM subject word extraction method based on BERT word vector can effectively make up the disadvantages of traditional subject word extraction methods that need large computation and have data redundancy, and thus has stronger expression ability in user data mining. The hot topics concerned in different regions have certain differences. The public opinion analysis method proposed in this paper can effectively reflect the public opinion characteristics of different regions and provide reference for the public opinion analysis of major public health events.

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