The continuous generalization of geographic information poses a huge challenge to the classic geographic information analysis modes. Networked knowledge services will gradually become a new mode for geographic information applications, facilitating to transform the form of geographic computing into social computing. Geographic knowledge services need to connect people, institutions, natural environments, geographical entities, geographical units and social events, so as to promote knowledge assisted data intelligence and computational intelligence. Facing the urgent need for spatiotemporal knowledge acquisition, formal expression and analysis, this paper firstly introduces the concepts and characteristics of spatiotemporal knowledge graph. The spatiotemporal knowledge graph is a directed graph composed of geographic spatiotemporal distribution or geo-locational metaphors of knowledge that is a knowledge graph centered on spatiotemporal distribution characteristics. Secondly we proposes a research framework for spatiotemporal knowledge graph. The framework includes various levels from multimodal spatiotemporal big data to spatiotemporal knowledge services that contain ubiquitous spatiotemporal big data layer, spatiotemporal knowledge acquisition technique layer, spatiotemporal knowledge management layer, spatiotemporal knowledge graph layer, software/tools layer, and industrial application layer. Thirdly this paper introduces relevant research progress from text implied geographic information retrieval, heterogeneous geographic semantic web alignment, spatiotemporal knowledge formalization and representation learning. Combined with application practice, we then enumerate the construction and application approaches of domain oriented spatiotemporal knowledge graph. Finally, it discusses the key scientific issues and technical bottlenecks currently faced in the research of spatiotemporal knowledge graph. It is argued that in the era of large models, constructing explicit spatiotemporal knowledge graph and conducting knowledge reasoning to meet domain needs is still the only way for spatiotemporal knowledge services.
Location description is the natural language expression of human spatial cognition. Since natural language is the primary and basic means of information transmission in human society, location description is an important medium for transmitting spatial location information in human communication. Spatial positioning based on spatial location description is the key to intelligent transformation of location-based services in the era of big data. To solve the problem that the vagueness of location description in different contexts is significantly different and results in difficulty in positioning, this paper proposes a representation method and reasoning mechanism for vague location description. Firstly, by combing the law of human spatial cognition, the types of elements concerned in the description of natural language are clarified. Based on the analysis of the sources of vagueness, a formal representation of vague location description is constructed. Different from the traditional spatial information modeling which focuses on spatial relationship, the formal representation proposed in this paper establishes the vagueness relation and influence among different information factors by the strategy of multi-factors representation. The formal representation also enhances the semantic analysis ability for the vagueness of location description. Secondly, based on supervaluation theory, the reasoning mechanism of vague location description is proposed from three aspects: spatial object, distance relation, and direction relation. Considering the context semantics of spatial location description, the threshold of observation value is used to carry out spatial reasoning. By being super-valued to different contexts, the reasoning results in different situations are obtained. The aim of the reasoning mechanism is to establish the mapping relationship between vague location description and real spatial location. Thirdly, a Question-Answering (Q&A) system is designed to collect contexts of location description, and a case study on the method is conducted. In the case study, a group of users' viewpoints from Q&A on spatial cognition are transformed into the spatial scope in the real world. These spatial scopes can establish the relationship between qualitative spatial concepts and quantitative spatial data, so as to realize the representation of vague location description in GIS. The results show that the proposed method in this paper can adjust the granularity of formal representation of location description in time according to actual application scenarios, and the spatial reasoning results fit intuitive cognition. In the future, knowledge graphs will be introduced to further improve the semantic reasoning ability and positioning accuracy for vague location description.
The new combat style places new requirements for battlefield environment service support. The intelligent service of battlefield environment urgently needs to improve knowledge based on the global multidimensional battlefield environment data. In view of the knowledge modeling problem of intelligent cognition of battlefield environment, this paper puts forward the classification method of battlefield environment knowledge and considers the battlefield environment knowledge graph as a new form of battlefield environment knowledge representation under the context of big data and artificial intelligence. To solve the fragmentation problem of triplet knowledge representation, a temporal hypergraph representation model of battlefield environment is constructed, a multi-level unified graph model combining entity knowledge, event knowledge, influence process knowledge, and service decision-making knowledge is realized, and all kinds of knowledge are represented as a unified knowledge hypergraph network with spatiotemporal and scene characteristics. Finally, the experimental verification is carried out based on the data of map, event, impact process, and combat impact effectiveness. The hypergraph network realizes the correlation of various battlefield environment knowledge from the semantic level, which can provide support for the further realization of intelligent reasoning and service decision-making based on hypergraph.
Natural language is an effective tool for humans to describe things, with diversity and ease of dissemination, and can contain human spatial cognitive results. How to use natural language to describe geographic spatial scenes has always been an important research direction in spatial cognition and geographic information science, providing important application values in personalized unmanned tour guides, blind navigation, virtual space scene interpretation, and so on. The essence of natural language description of geographic spatial scenes is the process of transforming the two-dimensional vector of geographic space into a one-dimensional vector in word space. Traditional models perform well in handling spatial relationships, but are somewhat inadequate in natural language description: (1) spatial relationship description models are one-way descriptions of the environment by humans, without considering the impact of the environment on the description; (2) spatial scenes emphasize traversal-based descriptions of spatial relationships, where each set of spatial relationships is equally weighted, which is inconsistent with the varying attention paid by humans to geographic entities and spatial relationships in the environment; (3) the spatial relationship calculation of traditional models is a static description of a single scene, which is difficult to meet the requirement of dynamic description of continuous scenes in practical applications; (4) the natural language style of traditional models is mechanical, lacking necessary knowledge support. This article proposes a spatial scene natural language generation framework Map2Text (M2T) that integrates multiple knowledge graphs. The framework establishes knowledge graphs for spatial relationships, language generation style, and spatial attention, respectively, and realizes the fusion of multiple knowledge graphs and the generation of natural language descriptions of spatial scenes within a unified framework. The spatial scene description knowledge graph solves the pruning problem of traversing spatial relationships, and establishes the relationship between spatial scenes by building a spatial relationship graph, supporting continuous expression of spatial scenes; the natural language style knowledge graph establishes the relationship between spatial expression and language style, achieving diversified language styles that are appropriate for spatial natural language expression; the spatial attention knowledge graph captures the nuances of natural language spatial expression by establishing an attention matrix based on the interaction state between the subject and object of the spatial scene. An experimental prototype system designed based on the Beijing Forbidden City demonstrates that the system-generated results are close to human travel notes, with more complete content coverage and more diverse styles, verifying the effectiveness of the M2T framework and demonstrating the potential value of natural language description of spatial scenes.
Geoscience Knowledge Graph (GKG) has strong capabilities of knowledge representation and semantic reasoning, thereby becoming a required infrastructure for the development of geoscience big data and geoscience artificial intelligence. However, existing studies on GKG were mainly conducted under the experimental scenarios. Because of a lack of research on the general framework of construction methods, sharing, and application of large-scale GKG for practical applications, it has not been used in practical applications in the geoscience field. For this reason, towards the needs of research and applications of geoscience big data and artificial intelligence for GKG, this paper first studied the construction techniques of large-scale GKG. Then, a general framework for covering the lifecycle of GKG including its construction, sharing, and application was proposed. Taking the big science program “Deep-Time Digital Earth (DDE)” as an example, the practice of developing GKG platform towards the practical application of DDE was carried out. Using this platform, this paper realized the construction of DDE large-scale GKG, the open sharing and application of built GKG, proving that the proposed framework can effectively support the construction, sharing, and application of large-scale GKG. This paper plays an important role in promoting the realization of the practical application value of GKG.
Digital city is one of the main requirements of three-dimensional (3D) real scene and leads the direction of future smart city construction. Digital city usually uses the 3D model of the real scene as the spatial data volume and integrates the object-linked data of various sensors to achieve virtual-real fusion. The integration of spatial data volume and object-linked perception data is the key to digital city applications. Visual sensor is an important sensor type which is widely used in urban life, such as surveillance cameras, vehicles, and other devices. The key to digital city application is registering the visual sensors with virtual 3D model accurately. The purpose of spatial registration for visual sensor is to estimate or optimize the position and orientation of the visual sensor and to get the accurate spatial position of any object in the image. It is one of the key technologies for applications such as Augmented Reality and Video GIS. Currently, the spatial registration methods for visual sensors can be divided into hardware-based and vision-based methods. Due to the popularity of vision sensors, vision-based registration methods have been widely used. However, in digital city applications, seasons and weather always change, there are often large differences in appearance between the real image taken by visual sensor and the image of virtual scene. Therefore, the accuracy of outdoor 6 Degree of Freedom (DOF) position obtained by existing methods is usually insufficient, resulting in low registration accuracy of the visual sensor. In order to improve the accuracy of visual sensor spatial registration in digital city scene, this paper presents a method of virtual-real registration for digital city scene with structural semantic information in urban area. Firstly, the virtual perspective image of digital city scene is obtained, the plumb line which contains structural semantic information is extracted from the target image, and the properties of global constraints of the plumb line is used to restore the camera's position accurately and achieve the registration of monocular image in the virtual digital scene. Experiments show that this method achieves accurate registration of virtual and real images with large differences in appearance. Compared with the existing methods, the position and orientation errors are reduced by 35.9% and 39.3%. This method can effectively optimize the initial pose and improve the registration accuracy of visual sensors in digital city scene. A lightweight cloud-edge registration framework is designed and can be used in image geolocation tasks based on portable devices.
It is of great significance to study how to set parameters of land use change simulation models more scientifically and objectively, in order to avoid the problem of poor simulation caused by improper parameters setting in a complex model. In this paper, the EA-FLUS model with parameter optimization function was constructed by coupling Evolutionary Algorithm (EA) and FLUS model. This model first optimized the parameters of the artificial neural network model in the FLUS model through evolutionary strategy to improve the prediction accuracy of the probability distribution of each land use type. On this basis, combined with geospatial partition, the parameters of the cellular automaton model in the FLUS model were adjusted by using the combination of elitist genetic algorithm and evolutionary strategy to improve the simulation accuracy. In the empirical study phase, taking Guilin as the study area, this paper analyzed the improvement of EA-FLUS model by partition simulation of land use change. In addition, the natural development scenario, cultivated land protection scenario, and ecological priority scenario were set up to simulate the land use change in Guilin from 2020 to 2030. The results show that: (1) Compared with the parameters setting based on experience and historical characteristics of land use change, the parameters optimization result using evolutionary algorithms was closer to the policy orientation in the study area, and better reflected the diversified development trends of various land use types in different geospatial partition; (2) Compared with the FLUS model, the EA-FLUS model had more advantages in land use change simulation with geospatial partition. The overall accuracy, Kappa coefficient, and FoM coefficient of the simulation result were increased by 0.56%, 0.011, and 0.009, respectively; (3) The construction land and cultivated land in Guilin showed a strong expansion trend, but the forested land showed a shrinking trend. Further strengthening the protection of ecological space would help to slow down the expansion of construction land and cultivated land. The research results not only enrich the existing land use change simulation techniques and methods, but also provide a certain theoretical basis and scientific basis for urban planning and sustainability research.
Understanding the urban spatial pattern from the perspective of the “Production-Living -Ecological” function not only paves the way to the optimization of land spatial structure, but also reflects the internal functional form and combination mode of urban land. However, in the past, the recognition of urban “Production-Living -Ecological Spaces” (PLES) mainly relied on remote sensing images, Point of Interest (POI), and land use data, and there was a lack of three-dimensional information within a city. Street View Images (SVI) can reflect the characteristics of the streets in the city and capture large-scale and high-resolution objective measurements of the physical environment within a street from a close-up view. Therefore, based on the semantic features of the scene extracted from the SVI, this paper proposes a method of identifying PLES in the central urban area and analyzing the importance of different features of the PLES. Taking the Fourth Ring Road of Chengdu as the study case, the classification system of PLES is constructed based on POI data, and the proportion of PLES is calculated at each SVI sampling point. The eXtreme Gradient Boosting (XGBoost) algorithm is used to identify the urban PLES, and a comparative test of model accuracy is also carried out. The spatial distribution of PLES in the study area is analyzed from three scales, i.e., road network, 500-m grid, and traffic analysis zone. The SHapley Additive exPlanation (SHAP) method is introduced to identify the important features that contribute to PLES. The results are as follows: (1) The proposed method of identifying PLES based on SVI in this paper has a high accuracy. The R2 of the model for identifying PLES reaches 0.6, indicating the feasibility of SVI for identifying PLES; (2) The spatial pattern of PLES reveals that the study area is dominated by production-living spaces, which are large in number and distributed in pieces in the study area. The number of units dominated by ecological space is small, and they are mainly distributed in large parks; (3) Among the semantic features of the seven types of scenes, street openness and motorization level have the greatest impact on the formation of the PLES. Based on PLES, this study uses SVI data to conduct a fine-scale analysis of land use in central urban areas and determine the types of urban land. This paper enriches the data and methods of PLES identification and provides a new tool for the optimization of urban spatial structure and development decision making.
As a forward-looking and proactive policing mode, predictive policing has been a major innovation of modern policing reforms across the USA and European countries since it was proposed in 2008. As it does not involve the use of personal privacy data and can be integrated with police patrolling and precise crime prevention strategies, place -based spatial -temporal crime prediction has been a hot research topic and main component of policing practices. This research presents a systematic review of the progress of spatial-temporal crime prediction across the world since 2013 when the RAND Corporation released its special report on predictive policing. It contributes to the literature with the following five aspects: (1) summarizing the new trends in the field of spatiotemporal crime prediction studies in terms of the number of papers, research topics, leading scholars, and academic journals. The studies on spatial-temporal crime prediction have received extensive attention from various countries in recent years, and the research themes have shown a diversified trend. The most productive scholars are mainly from China and the USA, with the main focus on spatial-temporal crime prediction model development; (2) describing the new dynamics and progress of six basic components involved in the spatial-temporal crime prediction research, which are the prediction target, temporal scale, spatial scale, prediction method, performance evaluation measure, and practical evaluation. The four most widely studied types of crimes are theft, robbery, burglary, and motor vehicle theft. For burglary crime, the typical temporal unit for spatial-temporal prediction is 1-month; For the other three types of crime, the typical temporal unit is 1-day. For these four types of crime, the typical spatial unit is 200-meter grid. The top three models with the best prediction performance are random forest model, spatial-temporal neural network model, and Hawkes process model; (3) introducing several main commercial softwares for spatial-temporal crime prediction and global predictive policing practices; (4) investigating the relevant ethical issues and potential challenges that are embedded in each stage of practical applications, including data & algorithm biases, lack of transparency and countability mechanism; (5) prospecting future research directions in spatial-temporal crime prediction areas. This research provides a brief and panoramic image of the field of spatial-temporal crime prediction and can act as a reference for researchers and practitioners in relevant fields including crime geography, smart policing, and Policing Geographic Information System (PGIS).
In the era of big data, the spatio-temporal category, information content, and application scenarios of Geographic Information Systems(GIS) have expanded unprecedentedly. GIS needs to transform from the passive adaptation mode of exception processing into an active kernel-supported mode, forming a new generation of spatio-temporal information system. Given that the essence of GIS is an information system with cartographic data model as the core, this paper summarizes the research paradigm of GIS from three aspects: research objects, basic principles, and technical methods, and analyzes the new requirements of spatio-temporal information expansion on the GIS research paradigm. Secondly, by analyzing the cognitive model of Pan-Spatial Information System (PSIS) and the multi-granularity spatio-temporal object data model, the theoretical and technical routes of the PSIS based on spatio-temporal entities are concluded, and its practice and application in many fields are summarized. Then, it systematically analyzes the specific extension mode of PSIS in GIS research object, basic principles, and technical methods, respectively, and proposes the PSIS research paradigm. Finally, this paper summarizes the basic content of the PSIS research paradigm, compares the core content with the GIS research paradigm, and looks forward to the impacts and changes that the advanced research paradigm of GIS would bring.
Blue-green space plays a prominent role in urban ecological security. This study built a blue-green database of 272 prefecture-level urban built-up areas in China using NDVI and MNDWI in 2005, 2010, 2015, and 2020 based on Google Earth Engine (GEE). Combining with the coverage rate, 300-meter service coverage rate, the fractal index distribution, and the landscape division index, the spatiotemporal pattern of the blue-green space and its climate factors were examined. The results show that: (1) The blue-green space in urban built-up areas in prefecture-level cities presented an overall pattern of “higher coverage in south than that in north”. While the south showed a pattern of “higher in west than east”, and the north had a pattern of “higher in east than west”. Particularly, the Bohai Rim area was marked as a basin of low coverage. The temporal trend of overall blue-green space was increasing except for a few cities in Central China; (2) In terms of different zones, the highest coverage rate (> 65%) of blue-green space in urban built-up areas occurred in Southwest China where the landscape division index was the lowest (< 0.60), and the coverage rate of Northwest China varied greatly. The North China indicated the lowest coverage (10%~30%) of blue-green space and a highest landscape division index (~0.98); (3) Based on the Multi-scale Geographically Weighted Regression (MGWR), the R-square value and the adjusted R-square value were 0.85 and 0.83, respectively. The impact of precipitation on the blue-green space coverage in urban built-up areas was significant and positive, while the temperature had negative impact on blue-green space. The impacts of climate factors were mostly equivalent to human activities but were stronger in certain periods.
Monitoring the spatiotemporal variations of the Earth Radiation Budget (ERB) could help to improve our understanding of global climate change. The Earth's reflected shortwave and emitted longwave radiation are important components of energy exchange between the Earth-Atmosphere system and outer space, and are main parameters to be measured by ERB sensors. The Earth radiation radiometer is intended for measuring the parameters of Earth radiation budget. The Angular Distribution Models (ADMs) refer to a series of factors for correcting the anisotropy of the Earth-Atmosphere radiation at the Top-of-Atmosphere (TOA), and it is an effective way to convert the broadband radiance measured by satellite-borne or Moon-based Earth radiation sensor to the Earth radiant flux. Therefore, the consistency between ADMs anisotropic factors and the anisotropy of TOA radiances would directly determine the accuracy of derived flux. This paper focused on the ADMs, reviewed the development progress of the ADMs over the past decades, introduced the current operational ADMs applied by the Clouds and the Earth's Radiant Energy System (CERES) onboard the Terra and Aqua satellites, and analyzed the advantages, problems, and potential of geostationary satellites and Moon-based Earth observations in the development of ADMs. Based on above reviews and analyses, the determinant factors for further improving the ADMs for satellite-borne and Moon-based Earth radiation measurements were discussed.
Land subsidence is an important factor that influences the sustainable development of a region. Due to the complexity of land subsidence, the uncertainty and risk caused by land subsidence disasters are increasing. Therefore, new methods need to be developed to quantify the nonlinear land subsidence processes, identify emerging risk, and improve urban resilience. In this paper, the necessity of introducing peridynamic to land subsidence modeling is discussed by analyzing the progress and shortcomings of current land subsidence modeling. For natural discontinuous structures such as fractures and faults, current deterministic models based on differential equations are insufficient to describe land subsidence. Therefore, the peridynamic theory which is suitable for discontinuous and nonlinear characteristics is introduced. The peridynamic theory (PD) describes the mechanical behavior of matter by solving integral equations and has advantages in analyzing discontinuous and multi-scale problems. The applicability of peridynamic in land subsidence is analyzed from the aspects of material properties and modeling methods, respectively. By establishing a peridynamic model of land subsidence, discontinuous disasters such as ground crack and ground collapse can be included, so as to realize the multi-field and multi-scale recognition of land subsidence under a unified framework. In the light of the “Higher-bigger-deeper” urban construction, combined with the CAS-ESM, the simulation of future evolution of ground subsidence and ground fractures can be carried out. However, there are still problems to be solved in the interdisciplinary research, such as the reasonable generalization of material properties, material structure, and the balance between operation accuracy and operation cost. Then, based on theoretical principles, the modeling method, solving process, and optimization method of peridynamic land subsidence model are given. Besides the establishment, solution and optimization of the model, a variety of spatial monitoring methods and data are also needed, e.g., subsidence data monitored by InSAR technology, the underground structure and density information obtained by Seismic Frequency Resonance Technology (SFRT), bedrock and stratified scale data, groundwater level data, building information data, and road network data. In this paper, a peridynamic land subsidence model with a range of 4km*6km and a depth of 0.2 km is established in Liyuan-Taihu -Zhangjiawan area in the eastern Beijing, and the evolution process of land subsidence is simulated by using the monthly average rate of groundwater level decline from 2007 to 2010 as the boundary condition. The mean absolute error between the simulated and the measured values is 18mm, which verifies the effectiveness of this interdisciplinary research. The peridynamic theory has superiority in the field of materials and the study of fatigue, damage, fracture, and so on. Our study provides new ideas and new methods for regional land subsidence modeling. Furthermore, with the support of big data, cloud computing platforms, and Geo-AI, new opportunities are emerging for preventing, controlling, slowing down, and avoiding land subsidence hazards.
An adaptive spatiotemporal data model and a spatiotemporal index structure are proposed to support efficient storage and querying of network-constrained trajectories. The proposed adaptive spatiotemporal data model extends the hierarchical Compressed Linear Reference (CLR) data model by establishing the adaptive route-based linear datum in road network with high-frequency network routes mined from the trajectory dataset. Network-constrained trajectories can be transformed from the link-based linear datum to the adaptive route-based linear datum, and the transformed trajectories consist of fewer sub-entities that can be stored with lower storage capacity. The proposed adaptive spatiotemporal index structure is an extension of the LRS-based index structure, which is constructed based on the adaptive route-based Linear Reference System (LRS). Fewer spatiotemporal sub-entities are saved in the adaptive spatiotemporal index structure, which allows for efficient spatiotemporal querying of network-constrained trajectories. In order to verify the effectiveness of the proposed adaptive data model and index structure, adequate experiments are conducted at the end of this paper using the real open-source T-Drive taxi trajectory dataset and the synthetic trajectory dataset. The experiments take two popular spatiotemporal intersection queries as an example, and the proposed adaptive data model and index structure with the conventional hierarchical CLR data model and the LRS-based spatiotemporal index structure are compared in terms of storage efficiency and query efficiency. The analysis results show that the proposed adaptive data model and index structure can improve storage efficiency by 40% and query efficiency by 50%, which confirms that the proposed method can provide a new solution for the management of network-constrained trajectory data.
The national land survey is a major component of evaluating national conditions and strength. Its main purpose is to master the detailed national land use status and natural resource changes. It is of great significance to cultivated land protection and sustainable social and economic development. With the development of remote sensing technology, investigating the status, quantity, and distribution of land resources has always been the focus of remote sensing applications. This article reviews the application of remote sensing in national land survey over the past four decades. Until now, remote sensing technology has shown broad prospects in national land survey. However, the remote sensing information extraction in national land survey still mainly relies on visual interpretation and is not automated enough. In recent years, the remote sensing data tend to have the characteristics of high-resolution, large-scale, multi-temporal, and multi-sensor. However, the existing automated information extraction technology does not fully integrate those characteristics, hindering the application in national land survey. This article first introduces the relevant progress in national land survey from four aspects: feature extraction using very-high-resolution images, samples acquisition from large-scale images, transfer learning in multi-temporal/multi-sensors images, and multi-source heterogeneous data fusion. Then, four challenges in the existing remote sensing information extraction technology in the national land survey are summarized: ① Image feature is the key to image classification. There are questions on how to define and select features. In addition, high-resolution images put forward higher requirements for advanced feature extraction; ② Remote sensing data in the national land survey are usually large in scale, and there are inter-class imbalance and intra-class diversity. Therefore, it is a challenge to obtain sufficient, balanced, and diverse sample sets from such complex data set; ③ Generally, the efficiency of sample collection cannot catch up with the accumulation speed of remote sensing data, thus the labeled samples are relatively small compared with the data. For multi-sensor/multi-temporal imagery, how to realize land use classification in a low-cost and timely manner is a question worth considering; ④ There is a semantic gap between land cover and land use. Since remote sensing images mainly reflect land cover information, how to properly introduce semantic information to bridge the semantic gap and realize land use classification is a problem. Finally, the future development and application of remote sensing technology in national land survey are prospected, such as transformation from visual interpretation to artificial intelligence technology, accuracy and consistency assessment of remote sensing classification products in land survey, crowdsourcing methods for large-scale land use production, and update of large-scale land use data.
Owing to the rapid advent of urbanization and the increasing demand for medical services by residents, the pressure on medical services in densely populated areas is surging. The analysis of the accessibility of medical service facilities is of primordial importance. In this study, the medical data was garnered from the Fuzhou Municipal Health Commission, and the crawler technology was used to yield the number of residential households to estimate the population. By use of the Baidu map to obtain the real time road condition information of the peak and non-peak time periods, the access time under the optimal route from the community residential districts to the hospital based on the real-time road condition was calculated, and the time zones of medical services were drawn. The accessibility of general hospitals in the main urban area of Fuzhou was analyzed using the two-step mobile (Ga-2SFCA) search method boosted by the Gaussian distance attenuation function, considering factors such as the travel mode, searching time threshold, and travel peak hours. The results yielded show that: (1) By integrating Baidu Map API into Ga-2SFCA model, multivariate and fine-grained analysis of accessibility was implemented, leading to the accurate measurement of urban medical service supply and demand; (2) The time cost of public transportation at different periods was less affected by traffic congestion, and reaching tertiary hospitals was faster. Under the premise of advocating green transportation, this mode of public transportation was recommended for medical treatment; (3) Under different conditions, the accessibility of medical facilities depended on the space of residential differentiation characteristics significantly, on the whole presenting a "single center" and "diminishing layer coil" distribution. High accessibility of residential areas was mainly distributed in urban core areas, and the lower level of accessibility settlement distribution was in the peripheral urban areas. However, other factors can also influence accessibility, such as the time threshold. The accessibility level of medical services markedly differed with the transportation mode, and the accessibility of medical services was significantly higher along the subway. The choice of off-peak travel time can effectively improve the level of medical service; (4) Due to the layout of urban expressways, the spatial distribution of medical accessibility in driving mode was consistent with that of roads, presenting a "loop level" pattern. However, the spatial distribution of accessibility under the public transport mode was affected by the urban bus microcirculation system, displaying the trait of "axial expansion." The method used in this paper provides a new scientific method for refined measurement and analysis of the accessibility of medical service facilities.
Territorial spatial planning is the spatial blueprint of high-quality social and economic development. With the rapid development of geo-information science and technology, geo-information science and tech- nology has changed the way of territorial spatial planning. Its powerful capability in data acquisition, analysis, prediction, and management provides support in data, method, and platform for territorial spatial planning, thus enabling territorial spatial planning to be more scientific, operable, and forward-looking. Based on literature review, summary, and comparative analysis, this study analyzes the technical requirements of territorial spatial planning compilation, implementation, supervision, public participation, and intelligent transformation, and systematically expounded the application of geo-information science and technology in territorial spatial planning. This study expounds the contributions of geo-information science on China's territorial spatial planning from the following three aspects: (1) Geospatial data, remote sensing data, and socio-economic big data provide data basis for territorial spatial planning; (2) Geographic Information System (GIS) analysis method, geographic simulation method, and artificial intelligence method provide method support for territorial spatial planning; (3) The application of GIS platform, cloud computing, and urban intelligent platform promotes the intelligent transformation of territorial spatial planning. This study also points out shortages of different technologies. However, there are still some problems that need to be further explored: (1) The generation of socio-economic big data and its application scenarios in territorial spatial planning are concentrated in urban space; (2) Both traditional and modern technologies in territorial spatial planning have advantages and disadvantages. These technologies need to be effectively integrated to prepare more scientific territorial spatial planning; (3) The construction of territorial spatial planning platform has not been organically combined with the construction of City Information Modeling (CIM) and other intelligent society platforms, there is a huge space for mining in the future. According to the maturity of its application in territorial spatial planning, these technologies can be divided into mature technology and promising technology. With the promulgation of territorial spatial planning at all levels and types and the initial establishment of Chinese territorial spatial planning system in 2021, attention should be paid to the application of intelligent planning methods in agricultural space and ecological space, technical system construction of intelligent territorial spatial planning, and the improvement of territorial spatial planning intellectualization.
Under the background of territorial spatial planning in the new period, delimiting the urban development boundary scientifically and reasonably and establishing a sound territorial space use control system are important measures to guide all kinds of territorial space development and protection. Taking Fuzhou City as an example, this paper constructs a global multi-dimensional territorial space control system. Management and control constraints are embedded in the future land use pattern simulation. At the same time, considering the regional spatial heterogeneity and spatial-temporal dependence, this paper designs the Spatial-temporal Cellular Automata (ST-CA) model which integrates geographical partition strategy, deep learning technology, and the functional module of FLUS model to delimit the urban development boundary. Based on the existing achievements, this study integrates three zones and three lines to carry out the application research of spatial management and control under the thinking of "combination of planning and control". The results show that: (1) The ST-CA model considering regional spatial heterogeneity and spatial-temporal dependence can effectively improve the accuracy of land use change simulation and achieve a more realistic and accurate geographical simulation process. The overall accuracy of the model increased from 95.95% to 98.34%; (2) Control constraints are embedded in the process of geographical simulation, which can guide the rational layout and controllable scale of urban, agricultural, and ecological spaces. Delimitation of urban development boundary based on simulation results can effectively avoid occupation on protected land; (3) The future simulation results combined with the control early warning value show that the urban expansion situation in the main urban area and surrounding districts and counties of Fuzhou City is relatively severe. In the future, it is urgent to reasonably regulate the territorial space pattern of Fuzhou City; (4) The characteristics of boundary change trend show that the delimitation results are consistent with the long-term development planning of Fuzhou City, which is in line with regional development demands. The territorial space pattern presents a multi-axis development trend. The research results can provide scientific planning for the development and protection of territorial space and practical reference for territorial space control and optimization in Fuzhou City.
Modeling urban land use change is important for future regional planning and sustainable development. Previous FLUS-based studies are mostly based on larger grid scales. How to simulate the complex land use change processes in rapidly developing cities and explore the driving mechanisms of land use change still need further exploration. This paper constructs an urban land use pattern simulation framework coupled with FLUS and Markov and innovatively introduces house price to characterize socio-economic attributes. We take Shenzhen as the study area to simulate future urban land use spatial patterns under different development scenarios based on small grid scale (30 m) land use classification data and multi-source spatial variables such as basic geography data, road and river networks, and point-of-interest data. Finally, we analyze the land use change drivers using random forest models. The results show that the coupled FLUS and Markov method proposed in this paper has higher accuracy (FoM = 0.22) and simulate the land use change processes more accurately in rapidly developing cities, compared to traditional CA models (RFA-CA and Logistic-CA). The mapping results of multi-scenario land use patterns verify the importance of ecological control lines in the process of urban development, further illustrating the reference value of the proposed simulation framework for future urban planning layout. Hospital infrastructure, entertainment venues, and bus stop, road network density have a greater impact on urban development than natural factors (e.g., elevation, slope), while the distance to coastline limits land use change processes to some extent within Shenzhen. The model constructed in this study and the fine mapping results can provide a reference basis and theoretical foundation for related research on urban regional planning and spatial pattern simulation.
Taking the iron tower as the basic platform and using the cameras or other sensors on it to conduct near-surface observations is called tower-based remote sensing. Tower-based remote sensing is not easily affected by natural factors such as cloud occlusion and can obtain video information in real time around the clock. Tower-based remote sensing effectively fills the scale gap between aerial remote sensing and ground observation network, so it has been widely used in various fields. However, when it faces a large area of observation and complex missions, its actual application is usually constrained by the complicated terrain and the spatial resolution. How to evaluate its imaging effectiveness has become the key to the optimization of tower-based remote sensing platform in the future. This research analyzes the imaging characteristics and main occlusion factors of the tower-based remote sensing cameras and constructs its actual coverage analysis method. This research establishes a resolution grading system relying on the spatial resolution requirements of the main applications and clarifies the applicable areas of main applications based on the actual coverage and camera parameters to evaluate the availability. Taking the "smart eyes guarding the land" tower-based remote sensing video system in Jiangning district, Nanjing City as an example, the experimental analysis shows that this method can efficiently calculate the actual coverage range of the tower-based remote sensing cameras in a larger observation area and analyze the main type and proportion of obstructions. This method can clarify the actual spatial resolution of imaging in each area considering the zoom ability of the camera and evaluate the application effectiveness of the system. The existing tower-based remote sensing camera is severely blocked by the surrounding terrain and the tower itself, and the average coverage rate within 5km is only 3.20%. The tower itself causes 47.66% of the viewing angle to be blocked. The actual coverage of applications with extremely high and high resolution is better, but the rest applications’ coverage needs to be further optimized. Increasing the height of the camera, improving the zoom capability of the camera, and adding a circular track can effectively optimize the application effectiveness of the tower-based remote sensing camera. This method can provide support for future data acquisition and practical applications and be a reference for system evaluation and location optimization in the future.
Geography is a comprehensive discipline that studies the spatial-temporal pattern, evolution process, and interaction mechanism of various geographic elements. With the evolution of the real world from binary space to the ternary world, it is urgent to deepen and expand the understanding, expression, and mining of geographic information connotation. The existing geographic information expression model of "location + geometry + attributes" is difficult to support the expression of various geographic elements and their laws. From the perspective of geography, based on the concept of the ternary world, we sort out the information elements and the process of their transformation into geographical information and form an information expression system with the "seven elements" of time, place, character, object, event, phenomenon, and scene, and from the geography "seven dimensions" perspective of semantic, spatial location, geometric structure, attribute, interrelationship, evolution process, mechanism of effect to interpret. It realizes the all-around classification and description of the connotation of geographic information from the perspective of geography and provides theoretical support for the multidimensional description and computational analysis of geographic information for comprehensive and integrated research in geography.
Updated spatiotemporal explicit data on cropping system is vital for ensuring the implementation of the national food security strategy and reasonable cropping structures. Time series analysis techniques are playing a more important role in agricultural remote sensing along with the continuously improved quality of remote sensing time series images. This paper analyzes main progresses and challenges in the field of cropping systems mapping using time series images from three aspects: mapping framework, remote sensing feature parameters, and data products. We find that: (1) The current cropping system mapping framework which mainly includes cropping intensity and agricultural planting structures, needs to cope with the problems of pre-requirements of cropland distribution data with high-quality. However, the existing land use/cover data could not fully fulfil this requirement due to the complex spectral characteristics of cropland introduced by multiple cropping systems over large regions. It is difficult to accurately derive information on cropping intensity using traditional time series vegetation indices datasets. Specifically, cropland fallow/abandonment in humid regions might be misclassified as single crop due to its corresponding high values of vegetation indices. Cropland abandonment and fallow are not negligible in recent decades and need further investigations, especially in China; (2) Novel multi-dimensional spectral indices based on red-edge and short-wave near-infrared bands are efficient in revealing the crop growth processes. Great progresses have been made in crop mapping in recent years. However, crop mapping at large scale is challenged by the minor differences among different crops as well as distinct heterogeneity within the same crop across different regions and multiple years; (3) There are increasing available remote sensing products of cropping intensity from national to global scale, however, the timeliness and spatiotemporal continuity need to be further improved; (4) Except for a few countries in North America and Europe, crop distribution maps at national scale are not fully available or limited to several staple crops with coarse resolution. There is a deficiency of finer datasets on cropping systems at large scale, especially in the complex multi-cropped regions. Fortunately, new technologies (i.e., cloud computing platform and deep learning algorithms) and emerging multi-sources remote sensing data with higher spatial, spectral, and temporal resolution provide great opportunities for spatiotemporally continuously detecting changes in cropping system at large scale. Future research should be focused on the following directions. First, we could improve the research strategy by developing an integrated mapping framework for directly deriving information on cropland and cropping patterns without relying on existing cropland distribution data. Second, we need to enrich the phenological features through exploring multiple-dimensional and less exploited spectral indices, such as the pigment indices, soil indices, nitrogen indices, and dry matter indices. Finally, we can develop spatiotemporal continuous change detection techniques for automatically tracking changes in cropping systems at multiple years and large scale.
With the rapid development of information technology, the world has entered an era of explosive growth of information, the Internet, the Internet of Things, and sensor networks have flooded with massive amounts of human society related information, providing us with a new way to solve urban governance and social management issues. The biggest challenge to further improve the ability of urban smart management is that information cannot be integrated and shared quickly and effectively. Therefore, the global location information superposition protocol and location-based service network technology have been proposed, based on the location-based fact of most social information, which become a key technology to break the barriers between systems in various fields, and to make the automatic collection, integration of computing, and intelligent services of massive heterogeneous information across networks, platforms, systems, and languages come true. This paper summarizes the current domestic and foreign research progress on the key technologies of the global location information superposition protocol and location-based service network, and then introduces the demonstration application of the global location-based service network. Finally, the technology and application of the global location-based service network on the future research directions are discussed, which can be the reference for the development of the global location-based service network in the future.
As a second-order analysis method of spatial point patterns, Ripley's K function (K function for short) uses distance as an independent variable to detect the distribution patterns of points under different spatial scales, which has been widely used in distinct fields such as ecology, economics, and geography. However, the applications of K function are limited due to the sharply increased computational cost of nested traversals on the point-pair distance measurements in both estimation and simulation phases when the point size getting larger. Therefore, the optimization of algorithm workflow and parallel acceleration have become the key technologies for tackling the performance bottleneck and computability problem of K function. Among these solutions, hash-based partitioning has been widely adopted in parallel computing frameworks for enabling data decomposition, while R-tree indexes have been proposed to reduce the computational cost of point-pair distance measurements by using spatial query instead. However, default hash-based partitioning methods ignore the spatial proximity of data distributions, while R-tree indexes fail to save query time of neighboring points under large spatial distance threshold comparing with pointwise distance calculation. In order to address these issues, this paper proposes an acceleration method for K function based on the space filling curves. Specifically, the Hilbert curve is adopted to achieve spatial partitioning, which reduces the data tilt and communication cost between partitions by better considering the spatial proximity. Upon the partition result, local indexing based on Geohash code is further developed to improve the spatial indexing strategy, which embeds spatial information in codes for achieving quick distance filtering, in turn accelerates the pointwise distance measurements. To verify the effectiveness of the proposed method, it is compared with two optimization methods adopted in previous studies, i.e., default partition without indexing, and KDB-tree partition with R-tree indexing, by analyzing the calculation time of K function for point of interests (POIs) of enterprise registration data in Hubei province, China under different data sizes, spatial distance thresholds, and computing nodes in a computing cluster. Experimental results indicate that the time cost of the proposed method is about 1/4 of that for default partition without indexing under the data scale of 300 000 points. Besides, the speedup ratio is larger than 3.6 times under 9 nodes. Therefore, the proposed method can improve performance of K function effectively in a distributed environment and has a promising scalability and could provide a reference for accelerating other spatial patterns analysis algorithms as well.
The Sky View Factor (SVF) is one of the most important indicators to characterize urban radiation fluxes and urban thermal environment. Therefore, it is a key morphological parameter to study the Urban Heat Island (UHI) effect. Studies have shown that SVF has a strong relationship with UHI intensity. Nevertheless, the relationships found can be contradictory. This is primarily due to the fact that the cases studied are often in different regions with different climatic conditions. In addition, the influences of trees are sometimes ignored due to the lack of vegetation data or the limitation of calculating methods. How to calculate SVF quickly and accurately is important to urban climate research. SVF is typically calculated by four types of methods: fisheye photo methods, 3D GIS methods, GPS methods, and street view image methods. Compared with the other types of methods, calculating SVF using street view images has many advantages, such as widely available data, low cost, high efficiency, and the ability to consider the influences of trees and other obstacles. On the one hand, street view images provide the possibility for fast and accurate calculation of SVF in large-scale areas. On the other hand, the street view image method is still at its developing stage and more work needs to be done to verify its application in various urban environments. In this study, we proposed an automatic SVF calculation method using street view images and deep learning algorithms, and then applied the method to the UHI study in the city center of Shanghai. Baidu static panoramas and Deeplabv3+ were used to detect sky range while MATLAB code was written to calculate SVF. A Landsat-8 OLI / TIRS image was also used to retrieve land surface temperature at street level in the study area. Based on the Local Climate Zones (LCZ) scheme, we combined large-scale SVF value with the land use and building morphology to examine the relationship between SVF and UHI intensity. The results showed that Deeplabv3+ can detect the sky and non-sky range effectively in different scenarios (MIOU=91.64%). The SVF calculated using the proposed method was in good agreement with that calculated using fish-eye photos (R2=0.8869). The LCZ scheme provides new insights for the relationship between SVF and UHI. For LCZ5 and LCZ1, the highest correlation coefficients were 0.68 and -0.79, respectively. The proposed method was shown to be applicable in high-density and complex urban environments. In addition, the calculation of large-scale continuous SVF provides the possibility for zonal understandings of the UHI effect based on the LCZ scheme.
The spread of infectious diseases is usually a highly nonlinear space-time diffusion process. Epidemiological models can not only be used to predict the epidemic trend, but also be used to systematically and scientifically study the transmission mechanism of the complex processes under different hypothetical intervention scenarios, which provide crucial analytical and planning tools for public health studies and policy-making. Since host behavior is one of the critical driven factors for the dynamics of infectious diseases, it is important to effectively integrate human spatiotemporal behavior into the epidemiological models for human-hosted infectious diseases. Due to the rapid development of human mobility research and applications aided by big trajectory data, many of the epidemiological models for Coronavirus Disease 2019 (COVID-19) have already coupled human mobility. By incorporating real trajectory data such as mobile phone location data at an individual or aggregated level, researchers are working towards the direction of accurately depicting the real world, so as to improve the effectiveness of the model in guiding actual epidemic prevention and control. The epidemic trend prediction, Non-pharmaceutical Interventions (NPIs) evaluation, vaccination strategy design, and transmission driven factors have been studied by the epidemiological models coupled with human mobility, which provides scientific decision-making aid for controlling epidemic in different countries and regions. In order to systematically understand this important progress of epidemiological models, this study collected and summarized relevant literatures. First, the interactions between the COVID-19 epidemic and human mobility were analyzed, which demonstrated the necessity of integrating the complex spatiotemporal behavior, such as population-based or individual-based mobility, activity, and contact interaction, into the epidemiological models. Then, according to the modeling purpose and mechanism, the models integrated with human mobility were discussed by two types: short-term epidemic prediction models and process simulation models. Among them, based on the coupling methods of human mobility, short-term epidemic prediction models can further be divided into models coupled with first-order and second-order human mobility, while process simulation models can be divided into models coupled with population-based mobility and individual-based mobility. Finally, we concluded that epidemiological models integrating human mobility should be developed towards more complex human spatiotemporal behaviors with a fine spatial granularity. Besides, it is in urgent need to improve the model capability to better understand the disease spread processes over space and time, break through the bottleneck of the huge computational cost of fine-grained models, cooperate cutting-edge artificial intelligence approaches, and develop more universal and accessible modeling data sets and tools for general users.
The Qinghai-Tibet Plateau is sensitive to climate change. At present, relevant researches mostly focus on the dynamic changes of ice and snow in the Qinghai-Tibet Plateau, and seldom pay attention to the dynamic changes of the rocky desert left by the melting ice and snow. Through the earth-atmosphere interaction, rocky desert may change the regional heterogeneity of climate at a large scale. This paper sorted out the extraction methods of remote sensing monitoring of ice and snow melting and rocky desert dynamic changes in the Qinghai-Tibet Plateau, and analyzed the advantages, disadvantages and applicability of various remote sensing data and extraction methods. We also summarized the data and research methods of the dynamic monitoring of ice and snow and the dynamic changes of the rocky desert in the Qinghai-Tibet Plateau. At present, the remote sensing monitoring data of the snow and ice dynamic changes in the Qinghai-Tibet Plateau are diverse and the research methods are mature. However, the remote sensing monitoring of the rocky desert dynamic changes left by the melting ice and snow has not yet formed a systematic study. Besides, under the condition of insignificant human disturbance, the dynamic changes of the rocky desert in the ice and snow melting area can also be used as a supplement to remote sensing monitoring of ice and snow dynamic changes.
With the rapid growth of remote sensing data, greater challenges arise in raster data efficient processing and value mining. Traditional map services focus on content sharing and visualization, but lacking real-time image analysis and processing functions. In this study, the real-time analysis and processing capabilities of raster tile data are realized in the form of map service. The cloud optimized GeoTIFF (Cloud Optimized GeoTIFF, COG) is used as the data organization method. The distributed collaborative prefetching strategy is designed to realize the raster tile loading in a cold or hot way, which optimizes the efficiency of reading image data from the cloud. Based on the efficient raster tile data loading, an expression-based raster tile processing model is proposed. By converting the expression into a calculation workflow, the raster tile is processed in the request of the map service in real time. The massive remote sensing data stored in the cloud is quickly analyzed to realize the direct visual conversion from raw data to products. For scenarios where full data are involved, use appropriate resampling data to simplify calculations to meet the real-time performance of map services. Three types of different complexity models, NDVI, ground object classification, and fractional vegetation cover, are used to perform real-time calculation and analysis on Landsat 8 images in the map service. Experimental results show that the processing model can effectively analyze raster tiles, and can be extended in a distributed manner. It can provide stable map service capabilities in high-concurrency scenarios, adapt to calculations at various levels and scales, and contribute a new idea to the future development of map service.
During the development of COVID-19 virus's global epidemic, the fundamental research and various applications of crowd dynamics-oriented observation theories have attracted much attention from many researchers and people all over the world within some related disciplines, such as public health, clinical medicine, geography, public management, etc. Researchers conducted many interdisciplinary explorations in theories and methods of monitoring epidemic dynamics scientifically, preventing and controlling spatial transmission precisely, predicting accurately, and responding effectively. However, no crowd dynamics-oriented observation theories have been proposed in literature so far. This paper revisits the concept and introduces a theory framework of crowd dynamics-oriented observation, which tries to include the core theories of observation from geospatial big data and to support diverse potential developments. Firstly, this article introduces the research background of crowd dynamics-oriented observation, and then summarizes its three core questions (how to observe its change, how to analyze its change, and how to control its change). From the inter-discipline view of geographic information science, surveying and mapping science, this paper explains the research significance and disciplinary value of crowd dynamics-oriented observation theories. Secondly, this paper introduces a framework of crowd dynamics-oriented observation and its spatiotemporal application, and then elaborates on the bottleneck problems of the key observation theories of crowd dynamics, such as fundamental space-time framework theory, space-time quantification and comprehensive observation theory, spatiotemporal process optimization theory, etc. Thirdly, this paper preliminarily introduces some changes of crowd dynamics-oriented observation theories, for example, refined observation driven by the application needs of digital society governance and public safety/health emergency, personal privacy protection and personalized observations by balancing the public interest and personal privacies, the development of integrated observation theories for human-oriented observation and earth-oriented observation, and the theory of crowd dynamics-oriented observation for high-level management and service. Finally, this article points out the potential directions of crowd dynamics-oriented observation theory and methods, such as, the development of big data-driven crowd perception, multi-space refined crowd dynamics observation, and human-land systematical interaction modeling, so as to realize some differentiated, integrated, and hierarchical crowd dynamics-oriented observations. All potential theories are helpful to the scientific decision-making of public management and public service. The crowd dynamics-oriented observation theory should focus on the fundamental research questions related to studying, analyzing, and servicing human beings, which has become a research frontier in geospatial information science, and could play very important roles in supporting national development strategies, such as "New urbanization", "beautiful China", "artificial intelligence", and "new infrastructure", so as to contribute to a green, efficient, smart, and sustainable regional and urban development.
In order to avoid crowd trampling, it is very important to accurately obtain information on the number of crowds from surveillance images. Early crowd counting studies used a feature engineering approach, in which human-designed feature extraction algorithms were used to obtain features that represented the number of people to be counted. However, the counting accuracy of such methods is not sufficient to meet the practical requirements when facing heavily occluded counting scenes with large changes in scene scale. In recent years, with the development of neural network, breakthroughs have been made in image classifications, object detections, and other fields. Neural network methods have also advanced the accuracy and robustness of dense crowd counting. In view of the difficulty of counting dense crowds, small crowd targets, and large changes in scene scale, this paper proposes a new neural network structure named: VGG-ResNeXt. The features extracted by VGG-16 are used as general-purpose visual description features. ResNet has more hidden layers, more activation functions and has more powerful feature extraction capabilities to extract more features from crowd images. Improved residual structure ResNeXt expands on the base of ResNet to further enhance feature extraction capabilities while maintaining the same computing power requirements and number of parameters. Therefore, in this paper, the first 10 layers of VGG-16 are used as the coarse-grained feature extractor, and the improved residual neural network ResNeXt is used as the fine-grained feature extractor. With the improved residual neural network feature of "multi-channel, co-activation", the single-column crowd counting neural network obtains the advantages of the multicolumn crowd counting network (i.e., extracting more features from dense crowd images with small targets and multiple scales), while avoiding the disadvantages of the multicolumn crowd counting network, such as the difficulty of training and structural redundancy. The experimental results show that our model achieves the highest accuracy in the UCF-CC-50 dataset with a very large number of people per image, the ShangHaiTech PartB dataset with a sparse crowd, and the UCF-QNRF dataset with the largest number of images currently included. Our model outperforms other models in the same period by 7.5%, 18.8%, and 2.4%, respectively, in MAE in the above three datasets, demonstrating the effectiveness of the model in improving counting accuracy in dense crowds. The results of this research can effectively help city management, relieve the pressure on public security, and protect people's lives and property.
