25 January 2020, Volume 22 Issue 1
 From Geographic Information System to Intelligent Geographic System SU Fenzhen, WU Wenzhou, ZHANG Yu, KANG Lu, LI Xiaoen 2020, 22 (1):  2-10.  doi: 10.12082/dqxxkx.2020.190802 Abstract ( 55 )   HTML ( 1 )   PDF (9762KB) ( 2 )   Geographical system, composed of natural and human elements, distributed in the earth's surface space, is provided with the functions of generating, maintaining or transforming material forms and energy forms, or driving the flow of material and energy. It is characterized by natural and human elements and their interactions on spatial distribution, structure, pattern, evolution and function factors. Geographical system is an objective reality system which generally consisted in earth with multi-levels. The geographic system was represented by the geographic information system by a information world with digital form. Then make the plans to transform geographical system by investigating and analyzing the received information. Therefore, two geographic systems, namely real geographic system and information geographic system, are co-existed for a long time and evolved independently. In recent years, with the integration of real-time observation and Internet of Things Technology (IoT), the real-time representation from the real geographic system represented into the information geographic system is coming true. Therefore, two systems evolve no longer independently, and the information geographic system is becoming a mirror of the real geographic system. Once the real geographic system changes, the information geographic system changes simultaneously. At present, the development with an unprecedented speed of human science and technology, especially the cloud services, big data and artificial intelligence, makes the information geographic system no longer be satisfied with being a mirror of the real geographic system. The artificial intelligence and unmanned automation control technology are adopted to achieve the integration of the real world and information world. The real geographic system can be changed through the operation of information geographic system. In the future, the boundary between the real geographic system and the information geographic system will become blurred and the two systems will be fully integrated with no distinguish at last. This article takes the intelligent geographic system to indicate this system, which integrates two worlds, and uses an unattended intelligent system designed for the South Island Reef as an example to show the integration and interoperation of the real geographic system and the information geographic system. The system realizes unmanned and intelligent monitoring, protection, response and operation for the island reef geographic environment and artificial facilities.
 Technology and Application of the Fusion Service of Geospatial Sensor Web CHEN Nengcheng, XIAO Changjiang, YANG Chao, WANG Wei 2020, 22 (1):  11-20.  doi: 10.12082/dqxxkx.2020.190479 Abstract ( 26 )   HTML ( 1 )   PDF (17187KB) ( 2 )   The space-air-ground-sea stereo and integrated geospatial sensor web have gradually formed along with the development of remote sensing data network, sensor web, internet of things (IoT), and artificial intelligence. The sensing resources of the geospatial sensor web are of multiple sources, heterogeneity, and dispersion. These characteristics result in the grand technical challenges of sharing and managing heterogeneous resources, real-time access of geospatial information with multiple protocols, spatiotemporally seamless and autonomous sensing of geospatial information, and accurate prediction of key parameters, especially when facing the personalized, instant, and smart application needs of multi-level users. It is hard for static geographical information service to meet the demands of geo-events for integrated monitoring, early warning and decision support, and focusing application. Therefore, there are urgent needs to develop fusion service technologies of the geospatial sensor web, as well as real-time dynamic geographical information service platforms. To solve these problems, this paper proposed online access, integrated management, space-ground fusion, spatiotemporal prediction, and focusing service models and methods. With the online access, dynamic management methods for sensing spectrum resources, and transparent access methods based on heterogeneous sensor protocols pool were proposed; a cyber-physical spatiotemporal information service environment was established, which realized the efficient access of spatiotemporal information with heterogeneous protocols. With the integrated management, sharable and interoperable information models including sensor observation process information description model, observation data description model, observation event description model, and dynamic observation capability index model were proposed, which tackled the coupling problem of sensor web and GIS, and realized large-scale integrated management and sharing of space-air-ground-sea platforms and sensors for the integrated monitoring of fairway, hydrology, soil, meteorology, and ocean. With space-ground fusion, a point-surface-collaboration and seamless reconstruction model, and evaluation-collaboration-reconstruction, cross-scale, seamless and continuous sensing methods were proposed, which improved the sensing quality by 14 times with respect to using satellite only and meanwhile keeping sensing frequency the same as the station networks, providing new ways for continuous monitoring of resources, environments, and disasters. For spatiotemporal predictions, ensemble models of multiple machine learning models, ensemble models of statistical models and dynamic models, and a spatiotemporal deep learning model were proposed, which realized high-resolution and high-precision predictions of meteorological parameters at regional scales. For the focusing service, a geo-control method based on instant sensing feedback, a time-continuous maximal covering location model, and an automatic aggregation sensing method were proposed, which improved the spatiotemporal coverage of sensing by 18%, and realized active and on-demand sensing of spatiotemporal information. Based on the sensor web observation information models and using the architecture of satellite-ground-collaboration spatiotemporal information sensing as a service, a geospatial sensor web spatiotemporal information sensing and service system named GeoSensor was developed, which has the functions of sensing, access, cognition, and control. The GeoSensor has been successfully applied to the sensing management and service of spatiotemporal information in the Yangtze River, the ocean and the smart city. In the future, the theory of smart sensing and cognition of people, water, and city will be further developed, the technology of crowd-sourced sensing, spatial intelligence, and cognition service of space-air-ground-sea-people will be developed, and large-scale applications in the Yangtze River Economic Zone will be conducted as well.
 Geometric Algebraic Modeling and Movement Behavior Analysis of the PIR Sensor Network YUAN Linwang, YU Zhaoyuan, LUO Wen, YUAN Shuai, ZHOU Chunye 2020, 22 (1):  21-29.  doi: 10.12082/dqxxkx.2020.190552 Abstract ( 17 )   HTML ( 1 )   PDF (13167KB) ( 2 )   Existing research on human behavior based on the PIR (Passive InfraRed) sensor data is limited by the spatial-temporal distribution of motion, clustering, and so on. The reconstruction of behavior trajectory and analysis of semantic features are relatively few, so it is urgent to develop new modeling and behavior analysis methods for the PIR data. This paper attempts to reconstruct the spatial and temporal trajectories by using the PIR (Passive InfraRed) sensor monitoring data. PIR sensors have the characteristics of low price and privacy protection. However, because only Boolean logical response sequence can be obtained by PIR sensors, it is difficult to accurately obtain movement trajectories. Its application has been relatively limited, and it is difficult to conduct movement behavior feature analysis. Traditional PIR sensor network analysis methods are mostly based on the signal extraction idea, which cannot integrate geometric features and semantic information at the same time. By introducing the geometric algebra theory, the sensor scene network can be constructed to realize the path expression and calculation of dynamic network in the geometric algebra space. This paper analyzed the characteristics of human movement features and semantic features, established semantic units, and realized the transformation from spatial data to semantic features. The spatial and topological characteristics of individual and crowd movements were analyzed. We proposed a generation and transformation-based methods of algebraic structures in the geometric algebra system, which provides a new idea and mathematical basis for solving non-deterministic problems such as the PIR sensor network data based analysis, and can provide reference for the construction of internet of things GIS.
 The Concept and Classification of Spatial Patterns of Geographical Flow PEI Tao, SHU Hua, GUO Sihui, SONG Ci, CHEN Jie, LIU Yaxi, WANG Xi 2020, 22 (1):  30-40.  doi: 10.12082/dqxxkx.2020.190736 Abstract ( 28 )   HTML ( 1 )   PDF (8267KB) ( 2 )   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.
 Spatio-temporal Analysis Methods for Multi-modal Geographic Big Data DENG Min, CAI Jiannan, YANG Wentao, TANG Jianbo, YANG Xuexi, LIU Qiliang, SHI Yan 2020, 22 (1):  41-56.  doi: 10.12082/dqxxkx.2020.190491 Abstract ( 28 )   HTML ( 1 )   PDF (11720KB) ( 6 )   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.
 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 2020, 22 (1):  57-75.  doi: 10.12082/dqxxkx.2020.190462 Abstract ( 21 )   HTML ( 1 )   PDF (31390KB) ( 4 )   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.
 A Tentative Study on System of Software Technology for Artificial Intelligence GIS SONG Guanfu, LU Hao, WANG Chenliang, HU Chenpu, HUANG Kejia 2020, 22 (1):  76-87.  doi: 10.12082/dqxxkx.2020.190701 Abstract ( 26 )   HTML ( 1 )   PDF (12875KB) ( 3 )   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.
 Anchor-Free Traffic Sign Detection FAN Hongchao, LI Wanzhi, ZHANG Chaoquan 2020, 22 (1):  88-99.  doi: 10.12082/dqxxkx.2020.190424 Abstract ( 18 )   HTML ( 2 )   PDF (12889KB) ( 1 )   Traffic signs are essential elements in High Definition (HD) maps and hence very important for vehicles in autonomous driving. Real-time and accurate detection of traffic signs from street level images is of great significance for the development of autonomous driving. Conventional algorithms detect traffic signs based on image color and shape features, and can only work for specific kinds of traffic signs. Algorithms based on image feature and machine learning classifier need artificial designed features, and the detection speed is slow. To date, many approaches using deep learning methods have been developed based on anchor boxes, which introduce extra hyper parameters in network design. When switching to a different detection task, anchor boxes need to be redesigned. Anchor-based methods also generate massive redundant anchor boxes during model training, which easily cause imbalance between positive and negative samples. Inspired by the idea of anchor-free and YOLO, this paper proposed a real-time traffic sign detection network called AF-TSD, which regresses object boundary directly. AF-TSD adopts an effective convolution module named deformable convolution to enhance the feature expression ability of convolutional neural networks. This module adds 2D offsets to the regular grid sampling locations in the standard convolution. It also modulates input feature amplitudes from different spatial locations/bins. Both the offsets and amplitudes are learned from the preceding feature maps, via additional convolutional layers. In addition, AF-TSD introduces attention mechanism. It is inserted after fusion of the feature pyramid, and adaptively recalibrates channel-wise feature responses by explicitly modeling the interdependencies between channels. This module first squeezes global spatial information into a channel descriptor. Then the excitation operator maps the input-specific descriptor to a set of channel weights. The attention mechanism in this paper is lightweight and imposes only a slight increase in model complexity and computational burden. To test the superiority of AF-TSD, extensive comparative experiments were carried out. We first evaluated the influence of different modules on detection precision. The experimental results show that the deformable convolution and attention mechanism can help extract features of traffic signs. Then, AF-TSD was compared with mainstream detection networks, including Faster R-CNN, RetinaNet, and YOLOv3. Our proposed AF-TSD traffic sign detection network achieved 96.80% of mAP on GTSDB traffic sign detection dataset, which was superior to mainstream detection algorithms. The average detection speed was 32ms per images, which can meet the requirements of real-time detection.
 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 2020, 22 (1):  100-112.  doi: 10.12082/dqxxkx.2020.190406 Abstract ( 27 )   HTML ( 2 )   PDF (27769KB) ( 2 )   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.