Published in last 1 year | In last 2 years| In last 3 years| All| Most Downloaded in Recent Month | Most Downloaded in Recent Year| In last 2 years
 Select Spatio-temporal Analysis Methods for Multi-modal Geographic Big Data DENG Min, CAI Jiannan, YANG Wentao, TANG Jianbo, YANG Xuexi, LIU Qiliang, SHI Yan Journal of Geo-information Science    2020, 22 (1): 41-56.   DOI: 10.12082/dqxxkx.2020.190491 Abstract （1400）   HTML （51）    PDF （11720KB）（620）       Multi-modal spatio-temporal analysis is aimed at discovering valuable knowledge about the spatio-temporal distributions, associations and revolutions underlying the multi-modal geographic big data. It is a core task of the pan-spatial information system, and is expected to facilitate the study of relationship between human and space. With emerging opportunities and challenges in an era of geographic big data, we systematically summarized four main methods for spatial-temporal analysis based on previous study, including spatio-temporal cluster analysis, spatio-temporal outlier detection, spatio-temporal association mining and spatio-temporal prediction. We discussed the challenges when applying the four methods in multi-scale modeling, multi-view fusion, multi-characteristic cognition, and multi-characteristic expression for spatial-temporal analysis. First, two types of scales (including data scale and analysis scale) are of great importance in the spatio-temporal clustering task. Given the data scale, the best analysis scale for detecting spatio-temporal clusters can be determined using a permutation test method by evaluating the significance of clusters. Second, in the spatio-temporal outlier detection method, the cross-outliers in the context of two types of points are known as the abnormal associations between different types of points and the validity of cross-outliers is assessed through significance tests under the null hypothesis of complete spatial randomness. Third, in the spatio-temporal association mining method, the multi-modal distribution characteristics of each feature quantitatively described in the observed dataset are employed to construct the null hypothesis that the spatio-temporal distributions of different features are independent of each other, and then the evaluation of spatio-temporal associations is modeled as a significance test problem under the null hypothesis of independence. Finally, in the spatio-temporal prediction model, the effects of multiple characteristics of spatio-temporal data (e.g., spatio-temporal auto-correlation and heterogeneity) on the prediction results are fully considered using a space-time support vector regression model. These methods can reveal the geographic knowledge in a more comprehensive, objective, and accurate way, and play a key role in supporting the smart city applications, such as meteorological and environmental monitoring, public safety management, and urban facility planning. For example, the spatio-temporal clustering method can be used to identify the meteorological division, the spatio-temporal outliers can contribute to the detection of the abnormal distribution of urban facilities, the spatio-temporal association mining method can help discover and understand the relationship among different types of crimes, and the spatio-temporal prediction method can be employed to predict the concentration of air pollutants.
 Select Multi-level Spatial Distribution Estimation Model of the Inter-regional Migrant Population Using Multi-source Spatio-temporal Big Data: A Case Study of Migrants from Wuhan during the Spread of COVID-19 LIU Zhang, QIAN Jiale, DU Yunyan, WANG Nan, YI Jiawei, SUN Yeran, MA Ting, PEI Tao, ZHOU Chenghu Journal of Geo-information Science    2020, 22 (2): 147-160.   DOI: 10.12082/dqxxkx.2020.200045 Abstract （1792）   HTML （115）    PDF （12593KB）（618）       Previous researches have paid little attention to the multi-level spatial distribution dynamic estimation of the inter-regional migrant population. Preventing the spread of COVID-19 is the most urgent need for society now. Before the closure of Wuhan on Jan 23, 2020, more than 5 million people had left Wuhan to other regions. A better understanding of the destinations of those people will assist in the decision making and prevention of the coronavirus spread. However, few studies have focused on the dynamic estimation of multi-level spatial distribution of inter-regional migrant populations. In this study, by using multi-source spatiotemporal big data, including Tencent location request data, Baidu migration data, and land cover data, we proposed a dynamic estimation model of multi-level spatial distribution of inter-regional migrant population, and further characterized the spatial distribution of the population migrating from Wuhan to other regions of Hubei Province. The results showed that: (1) During the Spring Festival, the average ratio between the number of population increase in the rural areas and the total population change was 124.7% in the prefecture-level cities in Hubei Province. At least 51.3% of the population moving from Wuhan to prefecture-level cities has flowed into rural areas; (2) the spatial distribution of migrants among cities and counties in Hubei Province exhibits a 3-ring structure. The 1st ring is core area of disease, ncludes Wuhan and its surrounding areas, which are mainly characterized by population outflows. The 2nd ring is primary focus area, includes Huanggang, Huangshi, Xiantao, Tianmen, Qianjiang, Suizhou, Xiangyang and parts of Xiaogan, Jingzhou, Jingmen, Xianning, where the total population and the population in rural areas increased significantly during the Spring Festival. The 3rd ring is the secondary focus area, includes Yichang, Enshi, Shennongjia, and parts of Jingzhou and Jingmen, which are located in the western part of Hubei Province and are mainly characterized by a small inflow of population. We suggest higher attention to those rural areas of the counties located in the 2nd ring to better control and prevent the coronavirus spread. The research was completed in 2-3 days, showing that big data can quickly respond to major public safety events and provide support for decision-making. Cited: CSCD(2)
 Select Geographic Similarity: Third Law of Geography? ZHU Axing, LV Guonian, ZHOU Chenghu, QIN Chengzhi Journal of Geo-information Science    2020, 22 (4): 673-679.   DOI: 10.12082/dqxxkx.2020.200069 Abstract （2063）   HTML （79）    PDF （1329KB）（545）       Laws, in expressing the relationships that existed in the world, are powerful ways for people to understand and communicate human understandings. In this paper through the comparison of laws in geography and those well accepted laws in physics (namely Newton's Laws), we concluded that the laws in geography also fit the definition of "law" albeit the laws in geography are different from the laws in physics in how they are generated and how they are expressed. We further compared the geographic similarity principle or the Third Law of Geography as suggested by Zhu et al (Annals of GIS, 2018,24(4):225-240) with the existing laws of geography from the perspectives of broadness, independence and applicability and found that the geographic similarity principle has the similar broad implications in geography as the other two laws but it is fundamentally different from the other two. It solves problems in geographic analysis that the other two were found to be insufficient. We thus believe that geographic similarity principle would serve a great candidate of the Third Law of Geography.
 Select The Intelligent Processing and Service of Spatiotemporal Big Data LI Deren Journal of Geo-information Science    2019, 21 (12): 1825-1831.   DOI: 10.12082/dqxxkx.2019.190694 Abstract （1001）   HTML （70）    PDF （11258KB）（539）       The intelligent processing and service of spatiotemporal big data is an important application and development opportunity of Geo Spatial Information Science, which is centered on surveying and mapping, remote sensing and geographic information technology. The development, main characteristics and mining methods of spatiotemporal big data are comprehensively discussed in this paper; Then automatic matching, change detection and intelligent decision-making of intelligent processing technologies based on spatiotemporal big data are introduced; On this basis, the "3S" socialized applications from earth observation to human observation are discussed; Finally, the current situation, development goal, key technologies, and application prospects of PNTRC based on spatiotemporal big data are introduced. Many practices have proved that in the age of big data and artificial intelligence,facing on the massive multi-source and heterogeneous spatiotemporal big data, focusing on the construction of automation, real-timized, intelligence, popularization and socialization, the innovation and development of Geo Spatial Information Science will have a bright future! Cited: CSCD(2)
 Select 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 Abstract （863）   HTML （46）    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.
 Select Mapping Impervious Surface Dynamics of Guangzhou Downtown based on Google Earth Engine LI Peilin, LIU Xiaoping, HUANG Yinghuai, ZHANG Honghui Journal of Geo-information Science    2020, 22 (3): 638-648.   DOI: 10.12082/dqxxkx.2020.190047 Abstract （1028）   HTML （39）    PDF （24363KB）（415）       For assessing urbanization level and urban environment, the mapping of impervious surface has become a research hotspot. Compared with single-phase imagery, time series mapping can depict temporal trends, which is of great significance for monitoring urban expansion. Based on the Google Earth Engine platform, this paper calculated BCI and NDVI using Landsat TOA data from 2000 to 2017, and determined their thresholds by an adaptive iteration method to extract the initial impervious surface. Then, Temporal Consistency Check (TCC) was performed to make the time series of impervious surface more reasonable. Results show that: (1) Adding NDVI to both BCI and TCC improved the quality of impervious surface mapping. (2) The average accuracy of impervious surface mapping in this paper was 90.4%, and the average Kappa coefficient was 0.812. (3) The impervious surface area of Guangzhou downtown nearly doubled from 2000 to 2017 with a decreasing growth rate. (4)The newly developed impervious surface mainly concentrated on the relatively backward outskirts of Guangzhou downtown. (5) Elevation, road density, and shopping mart density were the main factors influencing the expansion of impervious surface.
 Select A Tentative Study on System of Software Technology for Artificial Intelligence GIS SONG Guanfu, LU Hao, WANG Chenliang, HU Chenpu, HUANG Kejia Journal of Geo-information Science    2020, 22 (1): 76-87.   DOI: 10.12082/dqxxkx.2020.190701 Abstract （893）   HTML （23）    PDF （12875KB）（381）       As the representative technology of Artificial Intelligence, deep learning has been the most exciting breakthrough technologies in big data analysis and other domains researches due to its novel data-driven feature representations learning, instead of handcrafting features based on domain-specific knowledge in traditional modeling. Driven by these technological developments. Artificial Intelligence plays a key role in the researches and applications of next-generation geographical information system software technology. Nevertheless, most researches about AI GIS are still in the stage of immature and preliminary exploration. As a method and technology for the novel architecture of GIS fundamental software, AI GIS is widely used in many earth science applications including remote sensing data analysis, water resources research, spatial epidemiology and environmental health. All these technologies are significantly improving capabilities of data processing of traditional GIS, and being able to extract geospatial information and characteristics from unstructured datasets such as street view or remote sensing imagery, texts. These applications are showing great value and developing potential of AI GIS. However, the existing research on the system of software technology of AI GIS is not comprehensive enough. A variety of AI GIS algorithms or models and their scenario-specific applications are commonly considered to be the most important topic. Few researchers have addressed the issues or theory of Artificial Intelligence GIS technologies system and software architecture. This paper presents and analyzes several levels of Geo-intelligence and discuss its relationships to AI GIS technology system , reviewed the research status in AI and GIS technologies from the domestic and abroad perspectives. Then, the system of software technology of AI GIS is proposed according to the relationships between Artificial Intelligence and GIS. This paper define the architecture of AI GIS into three parts including Geospatial Artificial Intelligence(GeoAI), AI for GIS, and GIS for AI. And concepts and examples for each parts of Artificial Intelligence GIS are also analyzed for illustration. Furthermore, in order to deeply explain and investigate the AI GIS software technologies architecture, this paper provide the example of the design and implementation of SuperMap AI GIS software architectures and production. Finally, this paper discusses the problems that need to be solved in the future development of GIS. The tentative study of AI GIS in this paper may provide a theory for establishing the fundamental GIS software technology architecture of Geo-intelligence, which would helps to promote the deep integration and development of AI and GIS technology, and make suggestions for further research about Geo-intelligence. Cited: CSCD(1)
 Select Cognitive Transformation from Geographic Information System to Virtual Geographic Environments LIN Hui, HU Mingyuan, CHEN Min, ZHANG Fan, YOU Lan, CHEN Yuting Journal of Geo-information Science    2020, 22 (4): 662-672.   DOI: 10.12082/dqxxkx.2020.200048 Abstract （534）   HTML （23）    PDF （10496KB）（372）       Since the beginning of 1960s, Geographic Information System (GIS) has been advanced in the analysis of geographic information and the services generated from it. Yet the rate of demands from geographers and large engineering projects continues to accelerate in the multi-dimensional geographic process simulation and the assessment of simulation results before those projects carried out. The set of increasing demands gives the Chinese scholars a sense of direction to explore the emerging concept Virtual Geographic Environments (VGEs) over the subsequent decades. In a broad sense, the VGEs is a collective term for all geographic environments except the real geographic environment while in the narrow sense, the virtual geographic environment can be considered as a computer-generated digital geographic environment in which the complex geographic systems are perceived and cognized by means of multi-channel human-computer interaction, distributed geographic modeling and simulation, and cyberspace geographic collaboration. From the very beginning, this paper elaborates on the transformation from the understanding of GIS to VGEs. In the second place, the evolution process of VGEs is analyzed including its current developing stage and a series of challenges it faced with. Aimed at facilitating the research on geoscience in the context of advanced technologies and accumulated geospatial information, this paper describes the new perspectives of VGEs research as followed: geographic space based on VGEs cognitive research, VGEs and experimental geography, virtual geographic cognitive experimental methods, and VGEs and geographic knowledge engineering in the context of big data. It can be foreseen that the study of VGEs is gradually moving towards an open, group-participated, collaborative scientific research paradigm. This is also a true reflection of the development trend of scientific research in the field of geography.
 Select Revealing the Behavioral Patterns of Different Socioeconomic Groups in Cities with Mobile Phone Data and House Price Data GUAN Qingfeng, REN Shuliang, YAO Yao, LIANG Xun, ZHOU Jianfeng, YUAN Zehao, DAI Liangyang Journal of Geo-information Science    2020, 22 (1): 100-112.   DOI: 10.12082/dqxxkx.2020.190406 Abstract （707）   HTML （27）    PDF （27769KB）（350）       The spatial distribution characteristics and activity patterns of urban populations play essential roles in studies of spatial isolation, optimizing urban resource allocation, and so on. Because of the sensitivity of population activity data and socioeconomic data, previous studies focus mostly on the macro level. They have difficulties in dividing the socioeconomic status and quantitatively analyzing human mobility regulation. In recent years, geospatial big data, such as the mobile app data, provide us with a rare opportunity to analyze the human activity of urban internal problems. In this study, we constructed a fine-grained activity portrait of mobile phone users based on the mobile phone signaling data of Shenzhen residents, and coupled the high-resolution Shenzhen house price distribution data to achieve accurate division of people by their economic levels. Then, we extracted six activity indicators, which include the number of active locations, activity entropy, moment of inertia, travel time, travel distance, and travel speed, to quantify the spatial distribution and analyze the activity patterns of people at different economic levels. The results reveal the correlation between mobility and socioeconomic status. The distribution of people's activities at different economic levels in Shenzhen was related to the economic development of each administrative region. The results also demonstrated that three activity indicators (moment of inertia, travel distance, travel speed) were positively related to the economic level. Residents across different socioeconomic classes exhibited different travel patterns. Likely because the rich people live in the southwest of Shenzhen, but their work locations have more self-selectivity. This leads to the distribution of home and work locations in different administrative districts and the home-work distance of high-economic people are larger than others. For the other three activity indicators (number of active locations, activity entropy, travel time) that reflect the similar pattern of activity between different socioeconomic status, we found that people were mainly concentrated in living and working locations on weekdays. These locations share activities on weekdays for people at different socioeconomic levels. The socioeconomic status does not affect the number of daily activities nor the scheduling of activities. This study provides necessary data and policy guidance for government and urban planners.
 Select 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 Abstract （317）   HTML （8）    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.
 Select 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 Abstract （853）   HTML （51）    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.
 Select The Concept and Classification of Spatial Patterns of Geographical Flow PEI Tao, SHU Hua, GUO Sihui, SONG Ci, CHEN Jie, LIU Yaxi, WANG Xi Journal of Geo-information Science    2020, 22 (1): 30-40.   DOI: 10.12082/dqxxkx.2020.190736 Abstract （1232）   HTML （40）    PDF （8267KB）（322）       Geographical flow can be defined as the movements of geographical objects between different locations, which are usually displayed as the movement of matter, information, energy and funds, e.g. the jobs-housing flow in a city, communications between different mobile phone holders and the fund transferred between different business entities. Due to the existence of the various flows, the link strength between different locations may not depend on distance only, say one may strongly related to a store faraway through express delivery rather than a store nearby. The traditional knowledge of distance-decay law may be changed. As a result, research on the geographical flow may help to understand geographical patterns and their mechanism from a new point of view. Two conceptual models are introduced for the expression of geographical flows in this paper. In the first model, a flow is abstracted as a coordinate quaternion composed of the origin point and the destination point (called the orthonormal flow model). Thus, the flow space can be defined as a 4-D space which is formed by the Cartesian product of two 2-D spaces. In the second model, a flow is composed of the origin point coordinates, the flow length and the flow angle (called the polar coordinate model). Based on the expression models, four distances are defined, specifically, maximum distance, additive distance, average distance and weighted distance. In addition, this paper defines some other flow measurements, including flow direction, the volume of a flow's $r$ -neighborhood and the flow density. According to the combination of different statistical features (i.e. heterogeneity, homogeneity and randomness) between variables in the polar coordinate model, the spatial patterns of geographical flows are divided into six single patterns including random, clustering, convergent and divergent, community, parallel (angle-clustered) and equilong (length-clustered). The methods for identifying different flow patterns are also analyzed and summarized. Besides the single patterns, the combination of different single patterns will generate mixed patterns, and if more than one type of flows coexists, multi-flow patterns can be produced. Regarding research directions of geographical flow in the future, three aspects should be given more attentions: the basic statistical theory of flow, the mining method of flow pattern and its application in practical problems. Cited: CSCD(1)
 Select Construction of Technical System for National Urban Ecological Environment Comprehensive Monitoring WANG Qiao, ZHAO Shaohua, FENG Hong'e, WANG Yu, BAI Zhijie, MENG Bin, CHEN Hui Journal of Geo-information Science    2020, 22 (10): 1922-1934.   DOI: 10.12082/dqxxkx.2020.190488 Abstract （566）   HTML （18）    PDF （12176KB）（316）       More and more people have paid attention to the severe problems of urban ecological environment in recent years, such as air pollution in key urban agglomerations, water pollution, urban black and odorous water, risk of drinking water source, urban heat island, soil pollution, municipal solid waste, and so on. As a vital part of environment protection, with the rapid urbanization, the monitoring of urban ecological environment is becoming more and more important and the demand is getting higher and higher. Many studies have documented the monitoring of urban ecological environment at home and abroad, however, these works are discrete and unsystematic. There is a lack of general technical system in China, including key technology system, index system, and technical standards. The integrated space and ground monitoring is very urgent and necessary, and it is badly need to establish its technical system to guide and normalize the development of comprehensive monitoring of urban ecological environment. Given the national demand, this work (1) designs and constructs the technical system framework, index system framework, and standard system framework of urban ecological environment comprehensive monitoring from three aspects: urban polluted gas, water quality, and ecological resource; (2) puts forward the series concerned key technologies, gives the current monitoring status and accuracy of main indies of urban ecological environment; (3) on the untangling basis of key science problems, in combination with the characteristics of remote sensing data and the needs of national ecological environment monitoring, the study subsequently designs the operational application scheme of ecological environment comprehensive monitoring, gives the main monitoring emphasis of urban polluted gas, water quality, and ecological resource, plots the application scheme which includes the region demonstration, application products and services based on the constructed information service platform of urban ecological environment comprehensive monitoring, and provides the application examples of theme maps of PM2.5, urban black and odorous water, and urban island effect. The work will provide important support for the state and local government monitoring and management in urban ecological environment.
 Select Methods of Intelligent Computation and Pattern Mining based on Geo-parcels LUO Jiancheng, WU Tianjun, WU Zhifeng, ZHOU Ya'nan, GAO Lijing, SUN Yingwei, WU Wei, YANG Yingpin, HU Xiaodong, ZHANG Xin, SHEN Zhanfeng Journal of Geo-information Science    2020, 22 (1): 57-75.   DOI: 10.12082/dqxxkx.2020.190462 Abstract （696）   HTML （13）    PDF （30177KB）（308）       In the era of big data, high-resolution Earth observation technologies have been able to provide the most authentic, quantitative, comprehensive-coverage, and fast-updating data about the geographic phenomena and processes on the Earth's surface. Such data provide precise spatiotemporal benchmarks of information aggregation and computation of data mining for new developments of geospatial cognitive research. Geo-parcels are abstract expressions for mapping geographical entities from image-space to geographic-space. Geo-parcels are the smallest units of pattern mining with the construction of geographic scenes and loading various geospatial information. In this paper, a synergistic calculation mechanism with the machine learning methods of visual simulation and symbol inference were analyzed based on the basic unit of geo-parcels. From the dimensions of space, time, and attribute, we constructed an intelligent computation model based on geo-parcels by integrating three sub-models: zoning-stratified perception, spatiotemporal synergistically inversion, and multi-granular decision-making. Furthermore, this paper explored the pattern mining methods of geo-parcels for their distribution, growth, and function via two case studies: the agricultural planting structure mapping in Xifeng County, Guizhou province and the planning decision in Jiangzhou District of Guangxi Zhuang Autonomous Region. Cited: CSCD(3)
 Select 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 Abstract （500）   HTML （19）    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.
 Select A Review of Historical GIS and Its Trend ZHAO Yaolong, CHAO Zihao Journal of Geo-information Science    2020, 22 (5): 929-944.   DOI: 10.12082/dqxxkx.2020.190732 Abstract （757）   HTML （29）    PDF （882KB）（289）       Historical Geographical Information System and Science (HGIS) is a new interdiscipline subject between Geographical Information System (GIS) and History. The geographical process in historical period has been studied quantitatively and the geographical process model is built accordingly through HGIS to provide research basis on which to make future-oriented scientific predictions, by combining the technical methods of GIS, the space perspective of geographers, and the time perspective of historians together organically. HGIS emerged in the mid-1990s and has been bringing opportunities and dynamism to the development of GIS, History, and Historical Geography. HGIS maintains a sound momentum of growth, meanwhile there are some unresolved issues with it. In recent years, HGIS is overcoming systematic technical obstacles and developing in the direction of science, offering increasingly rich HGIS service. This article reviews the generation progress and the early practice of HGIS, andthen summarizes the current research status of HGIS from five aspects: digitization, data model, database and its system, spatial analysis, and visualization, by combing and analyzing the related literatures at home and abroad. In the last part of this article, the trends of HGIS are analyzed and summarized from several aspects: the spatialization and digitization of historical data, historical geographical spatio-temporal big data, the construction of historical geo-spatial framework and its service, the research on historical geographical spatio-temporal process and the construction of its model, and the formation of the discipline system of historical geographical information science and technology. These trends provide new research ideas for the future of HGIS.
 Select Spatial Simulation of Population in China's Coastal Zone based on Multi-source Data DU Peipei, HOU Xiyong Journal of Geo-information Science    2020, 22 (2): 207-217.   DOI: 10.12082/dqxxkx.2020.190192 Abstract （397）   HTML （21）    PDF （10140KB）（287）       Coastal zone is not only the hotspot of population aggregation and rapid economic growth, but also eco-environmentally sensitive, vulnerable to natural disasters. Detailed spatial distribution information of population is of great significance for solving resource allocation and disaster risk management in the coastal zone. This paper took the coastal cities in China as the study area. We combined the NPP - VIIRS data and NDVI data to construct Human Settlements Index (HSI), and selected the proportion of residential area per unit grid as a parameter to enhance the inter-demographic difference. Then, we used the dynamic partitioning samples and model to obtain the 1000 m grid population distribution data in the coastal zone of China (POP). To show the advantages of our proposed modeling approach, the published Chinese 1000 m grid population data (TPOP) and world population data with 100 m resolution (WorldPOP) were used to compare with our simulated POP data. For the comparison, three indicators were chosen: the macroscopic distribution characteristics, difference between urban and rural area, and population distribution within city. Results show that all the three data can reflect the macroscopic distribution characteristics of population in China's coastal zone, while POP has the best performance of depicting urban and rural differences in population distribution and the most detailed features of population distribution within city. According to the census and POP data, due to the influence of the coastal terrain and regional to national economic development strategies, the population distribution in China's coastal zone has obvious regional characteristics: (1) Topographically, the population density of montane and tidal flats areas is generally low (below 5 person/hm 2), while that of plain and estuary delta areas is generally high (over 10 person/hm 2). (2) At macroscopic scale, areas with high population density (over 25 person/hm 2) are mainly concentrated in coastal plain areas such as the Circum Bohai Sea region, the Yangtze River delta and the Pearl River delta; the population distribution in the north of the Yangtze River has the pattern of large dispersion and small concentration, especially in the provinces of Shandong and Jiangsu, the population distribution in the south of the Yangtze River is relatively concentrated, mainly in the coastal lowlands and plains in Zhejiang, Fujian, and Guangdong. (3) Regarding the urban and rural differences, the higher the urban level is, the more significant the gradient characteristics of population distribution will be; meanwhile, there is a huge difference in population density among urban, suburbs and exurban areas.
 Select State of the Art and Perspective of Agricultural Land Use Remote Sensing Information Extraction DONG Jinwei, WU Wenbin, HUANG Jianxi, YOU Nanshan, HE Yingli, YAN Huimin Journal of Geo-information Science    2020, 22 (4): 772-783.   DOI: 10.12082/dqxxkx.2020.200192 Abstract （629）   HTML （27）    PDF （798KB）（284）       Agricultural lands account for nearly half of the global land area, and changes in agricultural land use directly affect food security, water security, ecological security, and climate change. Remote sensing is the main means for acquiring agricultural land use information. In recent years, the free opening of medium-resolution remote sensing data such as Landsat, Sentinel, and China's GaoFen satellites has opened unprecedented opportunities for extraction of agricultural land use information. A series of promising research progress has been made. This review paper analyzes the state of the art of agricultural land use information extraction from four aspects:cropland, crop type, agricultural planting system, and agricultural land management. We found that: (1) The products of cropland mapping have been improved from the past coarse resolution (500~1000 m) to a higher spatial resolution of 10~30 m in the past decade. The global and regional cropland layers have been well established; but there is a need to track historical cropland changes, especially to identify the key turning points, by making full use of the existing remote sensing data (data fusion and satellite constellation approaches). (2) Existing crop type mapping efforts have been mostly carried out by combining ground survey data with satellite remote sensing (mainly Landsat and Sentinel-2). It has been operationalized in North America and Europe, but the ability to monitor crop planting areas needs to be strengthened in other countries including China. Also, the early season monitoring capacity of crop type mapping needs to be improved; (3) Existing studies on tracking agricultural planting systems are mainly concentrated in Eastern Europe (e.g., the abandonment after the breakup of the Soviet Union). In China, cropland abandonment, rotation, and fallow are also common in the recent decade, due to economic and policy factors; however, existing studies are lacking on this issue. (4) in terms of the agricultural land management, encouraging progress has been made on the regional products of irrigation, but the reliability and accuracy of the products need to be improved. New technologies, including the emerging multiple sources of remote sensing data so-called remote sensing big data, deep learning algorithms, and cloud computing platforms (e.g., Google Earth Engineand Amazon Web Services) provide unprecedented opportunities for future agricultural land use information extraction, which will rely on (1) the fusion of multi-source data to form remote sensing big data with higher spatial, spectral, and temporal resolutions, (2) coupling of intelligent methods such as machine learning and deep learning algorithms with expert knowledge-based methods considering geographical and phenological information, and (3) the application of cutting-edge technologies such as remote sensing cloud computing platforms. Cited: CSCD(1)
 Select A Perceptual Hash Algorithm for DEM Data Authentication and Tamper Localization ZHANG Xingang, YAN Haowen, ZHANG Liming Journal of Geo-information Science    2020, 22 (3): 379-388.   DOI: 10.12082/dqxxkx.2020.190336 Abstract （428）   HTML （4）    PDF （9236KB）（281）       As a type of fundamental and important geographic data, the integrity of DEM data cannot be ignored. The commonly used technology for data integrity authentication is mainly based on traditional cryptography and digital watermarking technology. The former is very sensitive to the change of every bit of data, suitable for accurate authentication of text data; while latter is mostly based on data carrier for authentication, seldom considers if DEM data content changes or not, and needs additional secure channels and communication media. In this paper, based on the requirement of authenticity and integrity of DEM data and the shortcomings of related authentication algorithms, a DEM data authentication algorithm was designed based on the Perceptual Hashing technology, which can achieve tamper localization. Perceptual hashing is a kind of method that maps multimedia data unidirectionally into perceptual summary sets (i.e. hash sequences). It inherits the characteristics of traditional Hash functions such as unidirectionality, anti-collision, and summarization, and is robust to the operation of content preservation, so it can better meet the requirements of DEM data authentication. The main ideas of this algorithm are as follows: Based on the characteristics of a large amount of DEM data and abundant details, the DEM data is divided into regular and non-overlapping grids. Feature extraction is the key of Perceptual Hashing algorithm. In this paper, the discrete cosine transform was used to extract features and generate the eigenvector matrix. Then the eigenvector matrix was digested. Next, the simplified eigenvector matrix was scrambled by using a Logistic chaotic system to meet the security requirements of authentication. Followingly, the scrambled matrix was quantized and coded to generate perceptual hash sequence. In the data authentication stage, the relative error of elevation between the original data and the data to be validated was calculated firstly. Subsequently, the perceptual hash sequence of the original data and the data to be validated was normalized to measure the Hamming distance. Combined with the determination threshold, the DEM data was authenticated. The scope of tampering would be located on the "grid unit" mentioned above. The algorithm has strong robustness against DEM data format conversion, watermarking embedding and other attacks. It is sensitive to various operations of changing contents, and can recognize and locate minor tampering of DEM data. Compared with the traditional DEM authentication algorithm, this algorithm innovatively regards "content" as the sole criterion of identity determination, which effectively compensates for the traditional digital watermarking method's excessive dependence on information carriers.
 Select Identifying Metro Trip Purpose using Multi-source Geographic Big Data and Machine Learning Approach ZHAO Pengjun, CAO Yushu Journal of Geo-information Science    2020, 22 (9): 1753-1765.   DOI: 10.12082/dqxxkx.2020.200134 Abstract （776）   HTML （69）    PDF （4652KB）（277）       Identifying metro trip purpose using Smart Card Data (SCD) is important to expand the application of SCD in transport research and transport planning. This paper integrates different types of big data and combines the theories on the interaction between transport and land use. By taking Beijing as a case, we firstly analyze the metro trip purposes of individual passengers using travel survey data from 5565 respondents. Secondly, we investigate the land use features of trip origin and destination using Point of Interest(POI) data . Thirdly, a metro trip dataset is developed which includes the information of trip purpose, trip duration, and spatial distribution of trip origin and destination. Fourthly, a Random Forest (RF) algorithm is used to establish a RF classifier using the metro trip dataset as training data. Finally, this trained classifier is used to classify each metro trip recorded by the SCD to identify the metro trip purpose and the spatial distribution of metro trips for different purposes. The results of analysis show that the random forest classifier trained in this study can effectively identify metro trip purposes from SCD. For trips with "go to work" and "go home" purposes, the accuracy of identification can reach over 90%. One reason for the high identification accuracy is that land use information is included in the RF classifier. Our results confirm the theory of spatial-temporal interactions between transport and land use. There is an increasing availability of multi-source geographic big data and traffic survey data of residents in large cities, which means that the method developed in this study would have a high value in metro trip predicting and monitoring, transport planning, and land use policy-making around the metro stations. Also, our results enhance our knowledge of metro travel behavior in megacities.
 Select The Construction of Knowledge Graph Towards Multi-Source Geospatial Data LIU Junnan, LIU Haiyan, CHEN Xiaohui, GUO Xuan, GUO Wenyue, ZHU Xinming, ZHAO Qingbo Journal of Geo-information Science    2020, 22 (7): 1476-1486.   DOI: 10.12082/dqxxkx.2020.190565 Abstract （948）   HTML （28）    PDF （3286KB）（276）       Knowledge graph is widely applied in the field of artificial intelligence. Fusing multisource geospatial data is a hot topic for the transformation of “data-knowledge”. However, the general knowledge graph has low spatial knowledge and some of them is incorrect. Moreover, geographic knowledge graph from Wikipedia has some problems such as missing spatial relation, Chinese attribute, and exact coordinates information. In this paper, we analyze the characteristics of geospatial data and baidubaike.In addition, we propose a knowledge graph construction method based on geographic entities which are extracted from geospatial data and supplemented by attribute information from baidubaike.At the end, the scale of knowledge graph is analyzed in terms of entities and relations. The experiment proves that the conceptual description information of geographic entities is expanded, and there is a higher success rate of linking web page with geographic entities than ever. In addition, the coverage of geographic coordinates is increased to 100%. The knowledge graph constructed in this paper will have an important significance to extend geospatialdata to knowledge.
 Select 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 Abstract （539）   HTML （43）    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.
 Select Patterns of Phytoplankton Phenology and Its Response to Temperature of Water Surface in Lake Taihu based on MODIS Data HONG Tianlin, LI Yunmei, LV Heng, MENG Bin, BI Sun, ZHOU Ling Journal of Geo-information Science    2020, 22 (10): 1935-1945.   DOI: 10.12082/dqxxkx.2020.200206 Abstract （396）   HTML （17）    PDF （26018KB）（274）       Due to the influence of water temperature and nutrient concentration, phytoplankton phenology can reflect the growth of phytoplankton and the changes of lake ecosystem. Because of the serious eutrophication in Lake Taihu, the effect of water temperature on phytoplankton growth is more and more significant. Thus, it is of great significance to study the relationship between phenology and water temperature for understanding, controlling and improving the ecosystem of Lake Taihu. This study firstly calculated the phytoplankton phenology metrics and the Temperature of Water Surface (LSWT) by MODIS data from 2003 to 2018, and then explored the phenological characteristics of different regions by analyzing the temporal-spatial distribution variation of phytoplankton phenology. At last, the response of phytoplankton phenology to LSWT change was revealed by combining the LSTW and the phenological characteristics. The results showed that: ① Different phytoplankton phenological indexes had different spatial distribution characteristics. The number of blooms, the peak value of Chlorophyll a (Chla) concentration and the total duration of algal blooms showed a decreasing trend from the western coast to the center of lake; the dates when the phytoplankton began to grow and the Chla peak appeared were complex in the lake. However, the date was relatively early in the coastal area; ② Lake Taihu could be divided into four types of areas with different phenological characteristics. The Type I area was mainly located in Gonghu Bay, eastern coast and the central part of Lake Taihu, where the fluctuation of Chla concentration (50~60 μg/L) was gentle, the number of blooms was the lowest, the start date was the latest, and the duration was the shortest. Type II area was mainly distributed along the western coast, with the violently fluctuating Chla concentration (50~90 μg/L), the most frequent blooms, the earliest onset, and the longest duration. Types III and IV were the transition areas. The former was mainly distributed in Meiliang Bay, Zhushan Bay and their exits, while the latter was mainly located in the southern coast and central lake. ③ The response of phytoplankton phenology to LSWT changes was affected by the level of nutrients. When the nutrient level was high, the promotion effect of LSWT on phytoplankton growth was more significant. The increasing trend of inter-annual LSWT had obvious influence on the advance of phytoplankton phenology and the increase of biomass. On the contrary, the effect of LSWT on the growth of phytoplankton was weakened.
 Select 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 Abstract （147）   HTML （3）    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.
 Select Soil Moisture Retrieval Study based on GF-3 and Landsat8 Remote Sensing Data LEI Zhibin, MENG Qingyan, TIAN Shufang, ZHANG Linlin, MA Jianwei Journal of Geo-information Science    2019, 21 (12): 1965-1976.   DOI: 10.12082/dqxxkx.2019.190115 Abstract （598）   HTML （16）    PDF （15292KB）（262）       As an important component of soil, soil moisture plays an important role in crop growth. The GaoFen-3(GF-3) satellite, as the first C-band full-polarization Synthetic-Aperture Radar (SAR) satellite of China, provides a valuable data source for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed over densely-vegetated areas based on GF-3 and Landsat8 data. To improve the accuracy of the soil moisture retrieval, this paper firstly analyzed the correlation between eight vegetation indices and Vegetation Canopy Water Content (VCWC) based on the PROSAIL model, measured vegetation parameters and the Landsat8 optical data. The Normalized Difference Water Index (NDWI5), which was identified as the optimal index from these indexes, was used to obtain the VCWC. The inversion model of Vegetation Water Content (VWC) was established by analyzing the relationship between measured VWC and the VCWC. Secondly, the model was integrated with simplified Michigan Microwave Canopy Scattering (MIMICS) model to correct the effects of vegetation on the radar backscattering coefficient. Finally, the backscattering coefficient simulation dataset of bare soil was established based on the Advanced Integrated Equation Model (AIEM) for developing the soil moisture retrieval model over densely-vegetated areas by combining active microwave and optical remote sensing data. The soil moisture retrieval algorithm was validated in a region of corn in Yucheng city, Shandong province, with soil moisture retrievals obtained at HH, VV and HH+VV combination, respectively. Results show: ① NDWI5 had the best fit with measured VCWC values among the eight vegetation indices, with the coefficient of determination (R 2) reaching 0.7433, and the Root Mean Square Error (RMSE) being 0.5146 kg/m 2. Thus, it was adopted to correct the effects of vegetation. ② The proposed algorithm based on GF-3 and Landsat8 satellite data performed well in soil moisture retrieval that resulted in improved accuracy in soil moisture monitoring. ③ Compared with the HH and VV polarization, the HH+VV dual-channel mode exhibited the highest accuracy, with a R 2 of 0.4037 and a RMSE of 0.0667 m 3m -3, followed by the HH polarization (R 2=0.2894, RMSE=0.0692 m 3m -3) and the VV polarization (R 2=0.3577, RMSE=0.0675 m 3m -3). Our findings suggest that the proposed algorithm has good potential for operationally estimating soil moisture from the new GF-3 satellite data with high accuracy. Cited: CSCD(1)
 Select Estimating Ground-Level PM 2.5 Concentrations Across China Using Geographically Neural Network Weighted Regression DU Zhenhong, WU Sensen, WANG Zhongyi, WANG Yuanyuan, ZHANG Feng, LIU Renyi Journal of Geo-information Science    2020, 22 (1): 122-135.   DOI: 10.12082/dqxxkx.2020.190533 Abstract （867）   HTML （33）    PDF （15972KB）（256）       China is becoming one of the most air-polluted countries and is experiencing severe PM2.5 pollution. To acquire spatially continuous PM2.5 estimates, numerous statistical methods have been developed through the integration of ground-level measurements and satellite-based observations. The estimation of PM2.5 concentrations in China is characterized by significant spatial nonstationarity and complex nonlinearity due to the complicated terrain variability and wide geographical scope. Mapping the PM2.5 distributions across China with high accuracy and reasonable details is still challenging. Superior satellite-based PM2.5 estimation models need to be developed. Taking advantage of a newly proposed Geographically Neural Network Weighted Regression (GNNWR) model that simultaneously accounts for spatial nonstationarity and complex nonlinearity, we developed a satellite-based GNNWR model to obtain spatially continuous PM2.5 estimates in China. To comprehensively assess the predictive power of the GNNWR model, the widely used Ordinary Linear Regression (OLR) and Geographically Weighted Regression (GWR) models were also carried out for performance comparison. Experimental results demonstrated that the GNNWR model performed considerably better than the OLR and GWR models in terms of multiple statistical indicators, including coefficient of determination (R 2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Most notably, the fitting accuracy of GNNWR was slightly better than GWR, but its prediction ability was much superior to GWR since the predictive R 2of GWR was significantly improved from 0.683 to 0.831 and the RMSE value was considerably reduced from 9.359 to 6.837. Moreover, the mapped PM2.5 distributions derived from the GNNWR model presented more reasonable and finer details at a higher accuracy than the other models. Although the spatial trends estimated by GWR and GNNWR models were quite consistent, the estimates of the GNNWR model were more accurate and reasonable since its values were much closer to the ground monitoring observations than those of the GWR model, especially for areas with high PM2.5 concentrations, such as Hebei Province and southern Shaanxi Province. In addition, thanks to the excellent learning ability of the neural network, the spatial variations in GNNWR estimates were more sophisticated and displayed a richer hierarchical structure of local changes than that of GWR estimates, which better described the varying details of the PM2.5 across China. In summary, the GNNWR model is a reliable method to effectively estimate PM2.5 concentrations and can also be used to model various air pollution parameters.
 Select 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 Abstract （522）   HTML （21）    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.