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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
    Abstract784)   HTML46)    PDF (1365KB)(287)      

    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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    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
    Abstract774)   HTML41)    PDF (12855KB)(416)      

    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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    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
    Abstract491)   HTML38)    PDF (6405KB)(255)      

    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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    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
    Abstract481)   HTML18)    PDF (4167KB)(221)      

    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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    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
    Abstract410)   HTML8)    PDF (19128KB)(270)      

    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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    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
    Abstract396)   HTML21)    PDF (13034KB)(210)      

    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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    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
    Abstract390)   HTML0)    PDF (3790KB)(0)      

    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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    The City Agent Model of COVID-19 based on GIS and Application: A Case Study of Guangzhou
    CAO Zhonghao, ZHANG Jianqin, YANG Mu, JIA Lipeng, DENG Shaocun
    Journal of Geo-information Science    2021, 23 (2): 297-306.   DOI: 10.12082/dqxxkx.2021.200449
    Abstract359)   HTML8)    PDF (6096KB)(54)      

    Since December 2019, a new type of coronavirus pneumonia has occurred in Wuhan, Hubei. The strong spread ability of the new coronavirus has led to the rapidly emergence of new coronaviruses throughout the country and even all over the world. In order to portray the spread line of the new coronavirus within the city and then provide reasonable suggestions for the prevention and control of the urban epidemic, this article constructs a new coronavirus intelligent simulation model by combining complex network theory and GIS technology based on the behavior and social relationships of individuals in the city. Considering to the facts that it is necessary to strictly prevent the import of overseas cases to prevent the local epidemic from rebounding in cities with complex composition of population. This agent model takes the first entry point for overseas entry, Guangzhou city, as the research object to review the development of the epidemic. The attributes and rules of the model was determined by collecting statistical data from the literatures. Then the parameters were fitted by the Markov chain Monte Carlo method to achieve an accurate review of the epidemic situation in Guangzhou. The model is of high accuracy whose MAPE value have achieved 0.17. Meanwhile, this model also has good applicability which can simulate the impact of imported cases from abroad on the development of urban epidemics. Since the agent model marks the individual's time and space location and social relationship, this paper proposes a method for epidemiological investigation through the agent model, which is more convenient and more efficient than traditional epidemiological investigations.This article also visually displays the results of the infection chain, which is convenient for analyzing the activity trajectory of virus carriers and close contacts. This model provides valuable decision-making information for urban epidemic prevention and control. Moreover, the simulation results show that if there is another epidemic outbreak in the city, the epidemic will be controlled within 14-20 days so the citizens don't need to be panic. However, it is still necessary to improve self-protection awareness and protect individuals finely, especially the children and the elderly. When the epidemic comes again, it is recommended that schools and enterprises should establish a joint health monitoring mechanism to strengthen the health monitoring of children and employees, respectively. Relevant governmental departments have to strengthened the spread of epidemic prevention knowledge and persuaded retired people to reduce gatherings and wear masks reasonably.

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    Spatio-temporal Analysis of Population Dynamics based on Multi-source Data Integration for Beijing Municipal City
    CUI Xiaolin, ZHANG Jiabei, WU Feng, ZHANG Qian, WU Yaohui
    Journal of Geo-information Science    2020, 22 (11): 2199-2211.   DOI: 10.12082/dqxxkx.2020.190769
    Abstract334)   HTML19)    PDF (6523KB)(221)      

    High-precision spatially-explicit population data performs a quantitative reference for evaluating urban resources and environment pressure and promoting a rational population distribution. This study first classified and ranked street blocks of Beijing based on land use categories and VANUI index. Based on this, a hierarchical population spatialization model was built to generate the spatial distribution of population at 100 m resolution. In addition, Beijing permanent resident demographic information of 2012 and 2017, NPP/VIIRS nighttime lights data, land use, road networks, and other auxiliary data were also used as model inputs. In our study, the model simulation error against the verified data was less than 10%. Compared with other published results, the population distribution result generated in this study had a higher overall accuracy and local accuracy. We further analyzed the spatio-temporal pattern of population in Beijing and its impact factors. Results show that the population of Beijing in each 100 m grid varied from -2564 to 1904, with -500~500 being the main change level. The spatial patterns of population in 2012 and 2017 both demonstrated that central Beijing was densely populated while Beijing suburb was sparsely populated. Between these two years, population of Beijing declined by approximately 210,000, which mainly happened in six main districts. The core functional area of Beijing had a remarkable reduction in population, accounting for 62% of the total population decline within the six districts of the city. In addition, population between the second and third ring of Beijing decreased the most, with nearly 110 000 people moved out, accounting for 52% of the population decline within the six districts. On the contrary, the population increased in the surrounding street blocks at the border of the six districts, which might form new population centers in the future. The spatial and temporal dynamics of Beijing's population were closely related to factors, such as the functional orientation of the capital, industrial upgrading and transformation, and the implementation of population redistribution policies. This study provides a scientific reference for the rational layout of Beijing's population space and formulation of Beijing's population redistribution policies in the future.

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    Spatio-Temporal Distribution Analysis of Climate Comfort Level in China
    LIU Yanxia, FENG Li, TIAN Huihui, YANG Shaoqi
    Journal of Geo-information Science    2020, 22 (12): 2338-2347.   DOI: 10.12082/dqxxkx.2020.190513
    Abstract284)   HTML4)    PDF (16447KB)(48)      

    Climate comfort level has great significance to human activities and regional suitability assessment, and temperature humidity index is important for climate comfort evaluation. Traditional temperature-humidity index is obtained based on the observed data from some sations, which cannot reflect the spatio-temporal characteristics of climate comfort in large-scale areas. In this paper, the modified temperature humidity index model is proposed using Land Surface Temperature (LST) and Precipitable Water Vapor (PWV) from 2005 to 2018 retrieved from MODIS. Using this new index, the spatio-temporal characteristics of climate comfort level in China are calculated and analyzed. The results are shown as follows: (1) The GWR method is used to fit the surface temperature and air temperature. The fitting accuracy (Adjusted R2 = 0.90~0.98, RMSE = 0.14~1.89 ℃) is ideal, which indicates that LST, NDVI, and DEM are used as the independent variables for geographical weight Regression analysis can more accurately fit the air temperature; (2) The statistical results of the annual average temperature and humidity index from 2005 to 2018 show that the cumulative number of comfortable months in Yunnan Province is the most, up to 167 months, and the central provinces are relatively comfortable compared to the southeast coastal provinces, and the difference between the highest comfort months can reach 41 months. The spatial distribution of China's average annual comfort level is basically the same. Except for parts of Xinjiang, Tibet, and the northeast, the comfort level in China is from south to north, and the comfort level changes from comfortable to cold. Judging from the changes in the area of each comfort level, the national comfort level showed a trend from cold to comfortable from 2005 to 2018; (3) The month with largest comfortable area in 2018 is May, followed by October. Uncomfortable months are concentrated in January and July when the country is extremely cold or hot. The spatial distribution characteristics of spring and autumn are similar, showing a gradual decreasing trend from southeast to northwest; except for the Qinghai-Tibet Plateau, summer and winter show a decreasing trend from south to north. The comfort zone is mainly concentrated in low-latitude and middle-altitude areas.

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    The Analysis Method of Changes in "Global-China" International Relations during the COVID-19 Event based on News Data
    XUE Haonan, ZHANG Xueying, WU Mingguang, CAO Tianyang
    Journal of Geo-information Science    2021, 23 (2): 351-363.   DOI: 10.12082/dqxxkx.2021.200294
    Abstract281)   HTML7)    PDF (21370KB)(37)      

    The outbreak of the COVID-19 event has been a major international concern since the first case was discovered in December 2019. After mid-to-late February 2020, the daily number of newly diagnosed cases abroad has increased rapidly, showing the characteristics of a pandemic disease. Under the deep impact of the COVID-19 event, the international relations are intricate and ever-changing. The instability and uncertainty of international relations have increased dramatically and have brought profound changes to the economy, security, and diplomacy. A comprehensive and timely analysis of "Global-China" international relations and its changing characteristics has important reference value for China's diplomatic development planning. Complex international relations can be split up into a series of event units. News data contains key information such as time, location, people, things, etc. It is the most direct and comprehensive source of information for constructing events. The GDELT ( Global Database of Events, Language, and Tone ) is a free and open news database which monitors news from print, broadcast, and online media in the world then analyzes the texts and extracts the key information such as people, location, organization, and event. From the perspective of "Global-China", this paper takes GDELT as the data source and uses global news data about the COVID-19 event from January to May 2020 to analyze the changes in international relations. First of all, the characteristics of international relations, such as intensity, similarity and polarity, are consistent with emotions. According to Plutchik's wheel of emotions, this paper provides a representation and calculation model of international relations to solve the problem of ambiguity in representation and the difficulty in calculation, using key variables including the number of events, the intensity of events, and the number of mentioned events. Then, the features of the changes in international relations are obviously displayed from the perspective of spatio-temporal visualization. Finally, this paper analyzes the causes of changes in international relations by important international events during the COVID-19 event. The results show that the analysis method can accurately reveal the development degree of the "Global-China" international relations during the COVID-19 event and find out the rules and causes of changes and has important application value. This paper can provide a new perspective for the exploration of international relations and a reference for the analysis of news data in the era of big data. And it shows the great potential and broad prospect of the research on international relations of big data.

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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
    Abstract266)   HTML8)    PDF (5681KB)(52)      

    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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    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
    Abstract258)   HTML5)    PDF (10736KB)(319)      

    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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    Error Spatial Distribution Characteristics of TanDEM-X 90 m DEM over China
    LI Wenliang, WANG Chisheng, ZHU Wu
    Journal of Geo-information Science    2020, 22 (12): 2277-2288.   DOI: 10.12082/dqxxkx.2020.190739
    Abstract257)   HTML7)    PDF (14330KB)(52)      

    Some topographic factors such as slope, aspect, and land cover may cause errors on TanDEM-X 90 m Digital Elevation Model (DEM) product when collecting and processing of these data. In order to better understand the error distribution and serve the research in this field, the comparison between TanDEM-X 90 m DEM and ICESat/GLA14 DEM was conducted over the entire China. The findings are summarized: ① The average absolute error, Root Mean Square Error (RMSE) and Standard Deviation (STD) of TanDEM-X 90 m DEM over the entire China are about 3.89, 9.03, and 8.85 m, respectively. ② The error increases when the slope increases. The mean absolute error is about 1.29 m and the STD is about 2.84 m when the slope is below 3°. In comparison, the mean absolute error is above 20 m and the STD is about 30 m when the slope is above 25°. ③ For the aspect, the mean value of absolute error in the north-south direction is obviously smaller than that in the east-west direction, indicating the influence of aspect on TanDEM-X 90 m DEM product. ④ For the land cover, the uncultivated land shows the smallest error with the mean absolute error of 1.85 m, while the region covered with snow and glacier show the largest error with the mean absolute error of 12.68 m. Comparisons of the contour map and profile between TanDEM-X 90 m DEM and UAV-derived DEM suggest that the TanDEM-X 90 m DEM can reflect the real topography. However, due to the influence of resolution in some areas, it can not be expressed for some detailed terrains, especially for valley and ridge. The absolute error distribution of TanDEM-X 90 m DEM over the entire China is produced and evaluated based the weights of different influencing factors, which are considered to be reliable. Through the analysis of error distribution map, it is found that the accuracy of TanDEM-X 90 m DEM shows a trend of high in the north and low in the South over the entire mainland of China. In the North China region, the overall accuracy is higher, while the error in the northwest region is smaller, but the overall accuracy in the Central South region is poor. By referring to the relevant data, when using the data of TanDEM to generate DEM, its accuracy has a great relationship with the vegetation coverage rate of the area. High forest coverage rate will seriously affect the coherence of SAR data, and then affect the accuracy of generated DEM.

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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
    Abstract237)   HTML0)    PDF (3645KB)(0)      

    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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    Analysis of Time Series Features of COVID-19 in Various Countries based on Pedigree Clustering
    XIE Conghui, WU Shixin, ZHANG Chen, SUN Wentao, HE Haifang, PEI Tao, LUO Geping
    Journal of Geo-information Science    2021, 23 (2): 236-245.   DOI: 10.12082/dqxxkx.2021.200470
    Abstract229)   HTML9)    PDF (5415KB)(52)      

    Since the outbreak of COVID-19, countries around the world have shown different time-series characteristics. Studying the characteristics of the development patterns of different countries and revealing the dominant factors behind them can provide references for future prevention and control strategies. In order to reveal the similarities and differences between the epidemic time series in different countries, this article extracts the standard deviation, Hurst index, cure rate, growth time, average growth rate, and prevention and control efficiency of the daily time series of new cases in the main epidemic countries for pedigree clustering. We also analyzes the causes of clustering results from the aspects of economics, medical treatment, and humanistic conflicts. The results show that the global epidemic development model can be divided into three categories: C-type, S-type, and I-type. The time series of C-type countries are characterized by continuous fluctuations and rising, and the cure rate is low. The reason is that humanistic conflicts are not conducive to epidemic prevention and control. Economic and medical resources have become scarce after a long period of large consumption. It is recommended to strengthen publicity and guidance in prevention and control, change concepts, and coordinate the allocation of economic and medical resources. The time series of S-type countries is characterized by a rapid rise and then an immediate decline, and eventually maintains a stable trend. The overall cure rate is relatively high. The reason is that these countries have domestic stability, high economic and medical standards, and timely prevention and control measures. It is recommended to strengthen international cooperation and scientific research, and prepare for the possible second epidemic. The time series of I-shaped countries is characterized by a slow rise, the overall development trend is unstable, and the cure rate is low. The reason is that its outbreak is relatively late and less severe. Most of the economic and medical levels and humanistic conflicts are not conducive to epidemic prevention and control. It is recommended to learn better prevention and control experience, implement strict isolation measures, try to meet the material needs during the epidemic, and optimize treatment methods.

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    Vehicle Trajectory-map Matching based on Particle Filter
    ZHENG Shichen, SHENG Yehua, LV Haiyang
    Journal of Geo-information Science    2020, 22 (11): 2109-2117.   DOI: 10.12082/dqxxkx.2020.190738
    Abstract226)   HTML10)    PDF (6069KB)(27)      

    Vehicle trajectory is a time series geospatial location sampling data. The traditional vehicle trajectory-map matching methods are mainly computed by ways of global or local incremental optimization, which limited the relative independence in matching process of the trajectory data in spatial temporal situation. To address this problem, this paper proposes the method of computing matching relationships between vehicle trajectory and road map based on the Particle Filter (PF) method. First, construct the road network from the road dataset, and search the neighboring nodes from the road network based on the vehicle sampling locations along the moving direction that are detected from the vehicle trajectory. Then, construct the motion model based on the vehicle trajectories, randomly generate particles on the road arcs that are related to the searched nodes, and move the particles along the sampled road segments according to the trajectory motion model. Second, compute the motion states of the particles according to the motion model in each time state, get the distance errors between the particles and the vehicle position sampling locations, obtain the particle weights based on the Gaussian probability density function, resample particles based on the random resampling method, and then update the motion states of particles iteratively. Finally, compute the accumulated weights of the particles in each of the topologically related road arcs, which are searched by the neighboring nodes, and calculate the matching relations between the vehicle trajectories and the map based on the accumulated weights of the particles. With this method, the experiments were conducted based on the vehicles' trajectories, which were two long sequenced trajectories with the total length > 102 km. The results showed that 85.51% and 93.01% correctness rates of vehicle trajectory-map matching experiments had been achieved for each of the vehicle trajectories. Besides, the motion of the vehicle sampling locations could be reflected by the spatial-temporal movements of the particles, where particles started to follow the motion of the vehicle sampling locations after a few time states. The results showed that it could achieve the accurate matching relations between the vehicle trajectories and the road map.

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    Spatialization and Autocorrelation Analysis of Urban Population Kernel Density Supported by Nighttime Light Remote Sensing
    SUN Xiaofang
    Journal of Geo-information Science    2020, 22 (11): 2256-2266.   DOI: 10.12082/dqxxkx.2020.200289
    Abstract218)   HTML7)    PDF (4345KB)(67)      

    Based on the demographic data, nighttime light remote sensing images and Landsat8 images of streets and communities in Gulou district, Fuzhou city, Fujian province, combined with the kernel density and regression equation are integrated to draw a 30 m spatial resolution population density map and conduct spatial autocorrelation analysis. Firstly, the population density distribution map of 69 communities are calculated by kernel density method. Based on a quantile-quantile plot between the population density and nighttime light remote sensing of 786 residential community points, we find that the population density has a large error in wufeng street and hongshan town. Secondly, the binary quadratic regression equation is established to correct the population density error in these two regions. This equation expresses the relationship between population density, and the impervious surface image of Landsat 8 using linear unmixing and nighttime light remote sensing. Thirdly, Getis-Ord General G, Getis-Ord Gi*, and Anselin local Moran I are used to obtain the high clustering attributes of population in Gulou district to show the largest business circle area, the largest population density residential area in the city, and the local spatial pattern of population clustering. In this study, the population spatialization technique integrates two spatialization methods: kernel density and regression equation. The population density map with 30 m spatial resolution is generated finally. The mean population density of Gulou district is divided into three types: 11 000 people/km2, 25 000 people/km2, and 50000 people/km2. The population density approximately obeys a normal distribution. When the mean population density of Gulou district is greater than 33 000 people/km2, the correlation between the impervious surface gray value and population density is stronger. Otherwise, the correlation between nighttime light remote sensing image and population density is stronger.

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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
    Abstract218)   HTML8)    PDF (14002KB)(214)      

    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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    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
    Abstract216)   HTML12)    PDF (3791KB)(81)      

    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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    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
    Abstract214)   HTML0)    PDF (7153KB)(0)      

    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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    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
    Abstract208)   HTML2)    PDF (32066KB)(199)      

    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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    The Spatio-temporal Characteristics of Tropical Cyclones Hazard in the Maritime Silk Road
    XU Xinliang, SHEN Zhicheng, LI Jiahao, WANG Shikuan
    Journal of Geo-information Science    2020, 22 (12): 2383-2392.   DOI: 10.12082/dqxxkx.2020.190369
    Abstract206)   HTML2)    PDF (16711KB)(16)      

    As a powerful meteorological disaster, tropical cyclones have a great impact on the maritime shipping of the Maritime Silk Road. Base on the best track data of tropical cyclones over the North Indian Ocean and the Northwest Pacific Ocean from 1990 to 2017, this paper analyzes the spatio-temporal characteristics of the tropicalcycloneshazard. First, the tropical cyclones are classified into 4 grades. Next, the Modified Rankine Vortex (MRV) model is used to simulate the wind field of each tropical cyclone. Then, the frequency of each grade of tropical cyclone that occurred in the research area can be obtained. Finally, a tropical cyclone hazard assessment model is used to assess the tropical cyclone hazard level in the main sea areas of the Maritime Silk Road.The main conclusions are as follows: (1) The sea areas of the Maritime Silk Road are seriously affected by tropical cycloneswith high occurrence frequency of tropical cyclones. The Northwest Pacific Ocean is more severely affected by tropical cyclones than the North Indian Ocean. (2)The sea areas between 15~30° North and 120~145° East are at the highest hazard level. (3) The seasonal change of tropical cyclone hazard is obvious. The tropical cyclone hazard levels of the sea areas of the North Indian Ocean and the Northwest Pacific Oceanare higher in autumn and summer than winter and spring. Moreover,in the summer and autumn, the sea areas in July, August, September and October have the relatively higher hazard levels. (4) Among the main sea areas, Eastern China Seais at the highest hazard level, followed by the South China Sea, the Sea of Japan, the Bay of Bengal, and the Arabian Sea, while the Red Sea and the Persian Gulf are not affected by tropical cyclones. Among the main channels, the Luzon Strait is at the highest hazard level, followed by the Taiwan Strait, the Tsushima Strait,the Soya Strait, the Tartar Strait, the Paco Strait, and the Strait of Hormuz, however the Strait of Malacca and the Strait of Mande are not affected by tropical cyclone.

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    Impact of Regional Economic Development Represented by Nighttime Light on the Prevalence Rate of Elderly Hypertension and Type 2 Diabetes
    LIAO Shubing, CAI Hong, YUAN Yanqiong, ZHANG Beibei, LI Yiping
    Journal of Geo-information Science    2020, 22 (11): 2177-2187.   DOI: 10.12082/dqxxkx.2020.190743
    Abstract201)   HTML5)    PDF (2315KB)(56)      

    The prevalence of elderly hypertension and type 2 diabetes diseases have a strong positive correlation with regional socioeconomic development. As nighttime light images can reflect the regional socio-economic development directly, the application of nighttime light data to study of diseases in the elderly become very significant. Selecting Changning City as the study area, this paper analyzed the difference in spatial distributions of the prevalence of elderly hypertension and type 2 diabetes among 26 townships based on the Luojia1-01 nighttime light data and the prevalence rate data of these two diseases in the study area. The spatial distribution of the prevalence of these two diseases in the study area was simulated by linear regression models. Results show that: (1) The correlation between mean nighttime light values and prevalence of hypertension or type 2 diabetes was stronger than that between total nighttime light values and prevalence of hypertension or type 2 diabetes. The relationship between mean or total nighttime light values and prevalence of hypertension was weaker than that between mean or total nighttime light values and prevalence of type 2 diabetes in the elderly; (2) The impacts of mean nighttime light on the distribution of the both diseases were larger than that of total nighttime value. And both the mean and total nighttime light had larger impacts on the spatial distribution of type 2 diabetes; (3) The risk of the elderly living in areas with high nighttime light was 6.493 times higher than those living in areas with low nighttime light, with the OR value for type 2 diabetes was 8.556; and (4) The linear regression model between the prevalence of either elderly hypertension or type 2 diabetes and mean nighttime light showed a high accuracy, which could accurately predict the spatial distribution of the prevalence of hypertension or type 2 diabetes of the elderly in the study area. Our research results can provide reference for the application of nighttime light data in disease researches and the analysis of the causes of regional hypertension and type 2 diabetes diseases in the elderly, as well as the investigation and prediction of similar diseases.

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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
    Abstract197)   HTML0)    PDF (2675KB)(0)      

    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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    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
    Abstract195)   HTML13)    PDF (13076KB)(20)      

    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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    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
    Abstract192)   HTML5)    PDF (14219KB)(197)      

    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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    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
    Abstract191)   HTML0)    PDF (9566KB)(0)      

    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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    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
    Abstract190)   HTML12)    PDF (1696KB)(151)      

    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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    Saturation Correction Method of DMSP/OLS Nighttime Lights Image based on Compound Exponential Model
    XU Wenxin, LIANG Juanzhu
    Journal of Geo-information Science    2020, 22 (11): 2227-2237.   DOI: 10.12082/dqxxkx.2020.190559
    Abstract186)   HTML2)    PDF (6831KB)(18)      

    The lack of gain recording and cross-calibration during the OLS sensor navigation makes DMSP nighttime lights image oversaturated in the city center, which affects the accuracy of using night light data to evaluate human activity intensity. In order to suppress the occurrence of saturation, the radiometric calibration night light data developed by Elvedge have been widely used. The radiometric calibration data products have a high accuracy and strong reliability. However, the calibration process is complex, and the required data is usually difficult to obtain. At present, only a few results have applied the calibration data to the continuity analysis. In recent years, many scholars found that NDVI can desaturate DMSP/OLS night light images and enhance the heterogeneity of urban center. Based on this, non-radiation calibration method has been applied to correct the saturation effect and shown a good correction result. On the basis of summarizing the idea by VANUI that the difference between night light intensity and vegetation coverage shows a decreasing trend from the city center to the suburb, this paper considers that the population density increases exponentially with the increase of rural-urban distance. We proposed a correction of nighttime light index based on compound exponential model (CEANI). Results show that (1) compared with VANUI, CEANI showed better details and spatial heterogeneity when characterizing the saturated regions of the city. In addition, CEANI not only identified areas where human activity was concentrated, such as stations, airports, and business areas with high traffic and people flow, but also clearly identified the areas with high vegetation coverage and low DN values such as forests and parks with sparse road network; (2) in the correlation analysis using 25 random samples, CEANI showed a higher correlation (R2mean = 0.79) with radiometric calibration products than VANUI (R2mean = 0.68); (3) CEANI had a stronger correlation with the number of permanent residents and significantly estimated population indicators better than VANUI, which suggests the better calculation index for describing the intensity of human activity. In summary, the CEANI can be used to correct the saturation problem in DMSP/OLS luminous data products. It better shows the internal details of the city and its spatial heterogeneity, and thus can derive more accurate results for the evaluation of human activity intensity.

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