The way to obtain the target data and relevant data efficiently and accurately has been a critical factor in data sharing and data mining during the era of BigData. The retrieval techniques which are currently in use could no more meet the increasing demands on quality and quantity for retrieving data, due to the unavailable usage of explicit and implicit relations among geodata. Current researches mainly focus on semantic retrieval, which is based on the theories and technologies of ontology. Taking consideration of time, an essential attribute of geodata, this paper constructed a geodata time-ontology model founded on the researches about the concepts and characteristics of temporal geodata. In addition, this article presented information about the temporal relations and time coordinate system, analyzed the functions for time position and time distance, and studied their formalization. In the end, a time-ontology base had been built up according to the time-ontology model, and an application had been developed using Apache Jena, a free and open source Java framework for building semantic web and linked data applications. After parsing the ontologis, extracting and annotating the time expressions from the metadata, the time ontology had been further applied to the retrieval of metadata from the data sharing infrastructure of earth system science. Results of these experiments show that the semantic geodata retrieval based on time-ontology has doubled the recall ratio, and it also performs much better than traditional information retrieval methods from the perspective of linked data recommendation and result sorting, which provides an effective approach for sharing geodata and finding linked data.
Recently, spatial-temporal K-anonymity has become a prominent method among a series of techniques for user privacy protection in Location Based Services (LBS) applications, because of its easy implementation and broad applicability. Analyzing spatial prediction scenarios based on spatial-temporal K-anonymity datasets is important in improving the utilization of LBS anonymity datasets for individualized services. In this paper, we present a spatial prediction method by combining the advantages of probabilistic statistics techniques and data mining techniques. The detailed process is divided into four phases: Phase 1, the predictive characteristics based on sequential rules and Markova chain are studied, and then an algorithm is designed to compute the n-step transition probability matrices of normalized sequential rules mined from sequences of spatial-temporal K-anonymity datasets; Phase 2, directly adopting the n-step transition probability matrices of example datasets, the simple predictions are performed; however, the drawback of this method is also found: the full path of the simple predictions cannot be learned, which is very important to the analysis of behavior patterns of LBS users; therefore in Phase 3, a precise predictive algorithm is designed, which recursively discovers the detailed k step path, its transition probability from the detailed k-1 step, and the simple k step that includes the start and the stop node only; and in Phase 4, simulation experiments are conducted, while the experimental results demonstrate that the proposed approach can build the predictive model faster than traditional methods, and can also adjust the accuracy of the predictions flexibly by setting different confidence thresholds for sequential rules of datasets.
With the development of technology, spatial datasets continue increasing in an incredible speed. Traditional data management based on single-node DBMS hardly meets the demands of high-concurrence in massive data. The rise of cloud computing brings brand new opportunities and challenges. Some researchers adopt a hybrid solution that combines the fault tolerance, heterogeneous cluster, and distributed computing framework together for efficient performances. Derived from the computing framework of Spark, Shark is a computing engine for fast data analysis. When a query is submitted, Shark compiles the query into an operator tree represented by RDDs, which will then be translated by Spark into a graph of tasks for execution. Shark does not support spatial query at the moment; therefore, we introduce an approach to enable Shark/Spark to support spatial query. With the APIs and UDFs that provided by Shark, Shark/Spark has the capability to process spatial data fetching from spatial databases and perform spatial queries according to the demands. Integrating Shark/Spark and relevant components which include mapping, loading, backup and querying of spatial data, and taking the advantages of the efficient spatial data management of spatial databases and high performance computing that involves the large-scale data processing of Spark, a framework of distributed spatial data analysis based on Shark/Spark has been implemented. During the implementation and testing process, we found that in order to achieve a better performance, some queries which had impacts on the returned dataset, should be pushed entirely into the database layer; while the other queries should be performed in Spark. In addition, we found that this system outperformed ArcGIS on Hadoop in some queries because the spatial index of spatial databases could improve its efficiency. Moreover, data management using a spatial database would be much more independent and convenient.
As the applications of geographic information services and a widespread of users expand, the demand for geographic information services becomes increasingly diverse. On one hand, the amount of registered and released geographic information services on the internet rises rapidly; on the other hand, people are confused about how to efficiently discover and combine interested services to satisfy their demands. However, benefiting from the convenience of geospatial resource search through keywords and combining spatial filtering conditions of WCS (Web Catalog Service), users still frequently encounter problems of low comprehension and precision. Facing an increasingly large group of services, how to find the target service automatically, quickly and accurately has become the bottleneck for further development and application of geographic information service. In order to solve this problem in geographical information service matching and discovery, the paper adopted the information retrieval technology, semantic web technology and semantic service technology to achieve a good approach. As a whole, four aspects are discussed in depth, including the semantic annotation algorithm based on the geographical concept matching, the strategy of service descriptions for SOA framework, the geospatial semantic similarity measurement model, and the integrated algorism of the geospatial semantic similarity measurement and the service semantic matching that based on subsumption reasoning. The article firstly introduces the geographic concept annotation of geo-ontology hierarchical model, and then introduces a new semi-automatic semantic annotation algorithm that is based on geographic concept matching and increases the annotation efficiency. Considering the application requirement of geographic information service discovery, this research illustrates a new geographic concept similarity measurement model according to the description logic, which sets up a foundation to solve the semantic service problem regarding to similarity measurement. By compromising the semantic service similarity measurement model and traditional subsumption reasoning engine, this research develops a new geographic information service discovery and matching method, which will increase the discovery and matching efficiency evidently. In the end, the article presents the necessity to build an evaluation index system to evaluate the resultant quality on both geographic information service semantic annotation and service discovery and matching. And an application framework for geographic information service is introduced, which integrates geographic information semantic service annotation, service registry, catalog management, and service matching and discovery all together. Through analysis of practical cases, the article provides useful new research ideas for improving current geographic information service matching methods.
Internet pages and microblog messages usually contain a great amount of road traffic information that can become an important data source for city road traffic collection. However, current information extraction technology for Chinese natural language text is not applicable to extract road traffic information from Internet texts for two reasons: (1) the location descriptions in these texts are usually in the form of linear reference methods; and (2) some information elements are missing or ignored in the expressions. In this paper, we propose a pattern matching method for extracting road traffic information from Internet texts. This method focuses on obtaining the location element and event element of road traffic information, due to the fact that these elements are often associated with the above issues. Firstly, extraction pattern is defined as a sequence in which each item contains two parts: part of speech (POS) of the road traffic feature words, and information attribute type. Then an extraction pattern library is established based on the linguistic features of the road traffic event description. Secondly, the Internet text after pre-progressing and the extraction patterns are both represented by POS sequences. Thirdly, the method of measuring similarity between sequences with dynamic time warping (DTW) theory is used in pattern matching to look for the most suitable extraction pattern for this text from the library. Finally, the elements and attributes of traffic information are extracted from the text under the guidance of the matching pattern. To add the missing or ignored elements, special filling rules based on the syntactic structure of information expression are introduced into this extraction process. In an experiment that takes relevant Internet texts for road traffic in Shanghai as the test data, whose sources are mainly from the official traffic information websites and Sina microblog platform, the precision and recall rate of road traffic information extraction is analyzed to be over 90% and 80% respectively. The result verifies the effectiveness of the presented approach. This method satisfies the requirement since the data accuracy is higher than average in real world public traffic service, and could effectively exact structure road traffic information from texts in any websites of different cities, by using the corresponding road lexicons.
Urban land use dynamically interacts with urban transportation. However, traditional urban space simulation models only focus on the simulation of the impacts of urban transportation on the dynamic changes of urban land use, by using a static road network. Therefore, they cannot express the dynamic interactions between urban land use and transportation. As a result, a simulation model for the dynamic growth of urban road network is required when developing an urban space simulation model with the capability to simulate the dynamic interactions between urban land use and urban transportation. In this paper, an urban road network growth simulation (URGS) model was proposed based on the observed relationship between urban road network density and land use intensity. It is known as the fact that road density is usually proportional to urban land use intensity. The city of Tangshan, which locates in Hebei province, China was taken as the study area. The road network of Tangshan within the City Ring road was reconstructed using the URGS model based on current land use intensity. Results show that the generated road network has a close similarity with the real road network. The URGS model can generate an intuitionistic road network automatically and quickly based on the spatial distributions of land use. Therefore, the impacts of urban land use on road network expanding can be reflected in the URGS model. The impacts of land use on road network would be simulated using the URGS model if the spatial distribution of land use were generated from the traditional urban land use simulation model. Moreover, in the future, the URGS model can be further improved to integrate with urban land use simulation model, thus to produce an urban space simulation model that can simulate the dynamic interactions between urban land use and transportation.
In order to solve spatial optimal allocation problem of water resource with multi-objective functions and multi-constrained conditions, Pareto ant colony algorithm (PACA) is used in this study. The model for spatial optimal allocation of water resources is established. Its objective function is the largest benefits from economy, society and environment. And its constraints include water supply, water demand and water quality. PACA is improved according to such strategies as limiting local pheromone scope and dynamically updating global pheromone. Then, GIS software is developed with the help of VB. NET 2008, ArcGIS Engine and Access. Zhenping County, Henan Province, China is selected as a study area. Data about water resources in the study area are handled using RS and GIS technology. The model is solved with PACA in the GIS environment. Spatial optimal allocation schemes of water resources, including surface water, groundwater and transfer water, are obtained. And spatial optimal benefit schemes of water resources, including economic, social and ecological benefits are also obtained. The optimal results obtained from PACA are compared with other intelligent optimization algorithms. Robustness performance, optimal performance and time performance of the improved PACA are 5.38, 0.398 and 21.6, respectively. The three performances of the ACA, however, are 8.16, 2.108 and 36.8, respectively. The results indicate that the integration of RS, GIS and PACA can effectively improve the performance of large-scale, multi-objective optimization model of water resources. This method can enhance the global search capability, the convergence speed and the result’s precision.
3D visualization is a key supporting technology of modern geographic information science. The spherical surface subdivision bricks were employed to build 2.5D static digital earth surface in the first generation of Digital Earth platforms represented by Google Earth. These platforms implemented earth surface data integration, model developments and applications, but did not involve the spaces above the surface and subsurface spaces. Therefore, the study of true 3D visualization technology based on spherical solid subdivision bricks is essential and necessary. The subdivision rules of Sphere Geodesic QTM Octree Grid, the geometric features, and the coding principle of brick system were described in this research. The transition algorithm between a brick code and its 3D Cartesian coordinate were designed and conducted. A prototype framework of true 3D digital earth visualization platform under C++ and OSG language environment was developed to achieve the arbitrary subdivision of a sphere, the visual modeling of underground, the surface and aerial entities, and the simple 3D spatial analysis. The advantages and prospect of the new generation Digital Earth platform in integration, management, model development and application of integrated space-ground spatial big data were demonstrated. The results indicate that the Sphere Geodesic Octree Grid has advantages such as simplicity, regularity, clear geometric features, being suitable as a global discrete space datum, and being conducive to the spatial entity modeling and visualization, etc. It can be used as the basis of data model to develop a new generation of Digital Earth platform.
Urban storm waterlogging has become one of the common serious “urban diseases” in China. The purpose of this study is to examine the spatio-temporal characteristics of urban storm waterlogging in Guangzhou from the 1980s and analyze the relationship of urban storm waterlogging and urban growth. Information about the urban storm waterlogging in Guangzhou was gathered from the newspaper of Guangzhou Daily and from the waterlogging census implemented by Guangzhou Bereau of Water affairs. Moreover, Landsat TM/ETM 30 m images for the years of 1990, 1999, and 2010 were collected to monitor the urban growth of Guangzhou. Density estimation method was used to quantify the density of these waterlogging spots and calculate the construction land ratio of Guangzhou. The relationship of urban storm waterlogging with urban growth was explored using Pearson correlation coefficient. The results show that the urban storm waterlogging spots in Guangzhou had increased greatly on the time scale and had sprawled since the 1980s on the spatial scale. In the 1980s, waterlogging disaster mostly took place in Yuexiu district, which is the core area of Guangzhou. However, the disaster had gradually spread into districts that having rapid urban growth such as Baiyun and Tianhe after the 1990s. Simultaneously, the urban area also had sprawled rapidly from 1990 to 1999 and to 2010. According to the results of Pearson correlation coefficient calculation, the density of waterlogging spots is positively correlated with the construction land ratio, and the correlation relationship gradually strengthens with respect to the rapid urbanization, implying the notable impact of construction land ration on urban storm waterlogging. The results suggest that more attention should be paid to the optimization of construction land ratio in urban planning procedure, and the government should improve the condition of drainage pipeline system and facilities to avoid urban storm waterlogging.
Precipitation is one of the most important factors of climate, and the analysis of spatio-temporal variability of precipitation is a primary way to understand the formation and development of regional water resources. Furthermore, the trend of the spatio-temporal variability of precipitation directly affects the structure, service function and spatial distribution of various ecosystems. Based on the monthly precipitation data from 1953 to 1987 and from 2007 to 2012, using regression analysis, anomaly, spatial autocorrelation, Mann-Kendall test, Co-kriging interpolation considering the influence of elevation (DEM), and cross-validation, we conducted an analysis on the spatial and temporal variability of precipitation in Dianchi basin. The results are stated in the following sections: (1) from 1953 to 1987, the precipitation tends to increase in spring, autumn and winter, but decrease in summer; from 2007 to 2012, however, the precipitation tends to decrease in all seasons except in autumn. (2) An increasing trend was discovered in the annual precipitation (11.12mm/10a) during the 35 years, and it generally revealed a fluctuation pattern of “down-up-down”; however, a downward trend of precipitation is significant from 2007 to 2012. (3) The indices of Moran’s I are negative during the 35 years, which reveals a main negative correlation that is different from the period of 2007 to 2012. The analysis of LISA shows that the spatial heterogeneity tends to change with respect to the geographical location and time. (4) The spatial distribution of annual rainfall is similar to the rainfall during the rainy season, which is a cross-distribution indicated by two high-value and two low-value areas. In addition, the spatial extent of heavy rain events is decreasing, and the spatial extent of light rain events is increasing.
The compartmentalization of urban rainwater catchment basin is an initial step in the application of spatially distributed hydrological models. Traditional watershed delineation approach has a better application in the plain region where less human activities are involved. However, in the flat areas of city it cannot get a correct watershed boundary and accurate watershed delineations. The realistic river network and watershed boundary may not always be derived from conventional DEM processing methods. In this paper, in order to reflect the city morphologies and represent the real situation of drainage network, a new method is proposed based on city classification and anthropogenic land cover features that influence the drainage patterns. According to the city drainage system and urban land classification, this method divides the city into the inner-city and suburban neighborhoods. According to the ability of river catchment, we take the rainwater catchment basins into lower divisions, and then find the anthropogenic land cover features that influence the convergence of the inner-city and suburban neighborhoods. To downscale the DEM and extract drainage structures and watershed boundaries with improved accuracy, we take the anthropogenic land cover features (i. e. roads and streams, buildings, some pond and drainage networks) into the DEMs, divide the catchment basin based on D8 algorithm and modify the catchment areas using Thiessen polygon. The results show that the method is effective and matches well with the real situation of city, which not only take the advantage of anthropogenic land cover features. but also combine with the spatial distribution of urban drainage facilities. The method is applied to a watershed in the Shanghai Jiading drainage basin based on high accuracy DEM and topographic river map, and we compare the proposed method with traditional methods. The results show that the proposed method has a good applicability for the plain urban areas and is able to produce more realistic results. Above all, we conclude that this method is effective and easy to implement.
Spatial interactions between multiple cities are important to the temporal and spatial evolution of urban expansion, and even significant to the carbon cycle. In this paper, an ELM-CA model was proposed by introducing extreme learning machine (ELM) into cellular automata (CA) to obtain the CA’s conversion rules. Taking Guangdong Province as an example, the effects of urban expansion on net primary productivity (NPP) were investigated by coupling Biome-BGC with the ELM-CA model. To represent the close interconnections between different cities, their spatial interactions were explicitly embedded in the ELM-CA model. Our results indicated that: the ELM-CA model could simulate the urban expansions in Guangdong Province at a high accuracy. In addition, the urban expansions exhibited crucial impacts on the NPP in Guangdong, which reduced the vegetation NPP evidently. According to the inertial trends of the urban expansion from 2000 to 2005, we found that the urban land development in 2020 may cause a reduction in NPP, which had taken up about 1.79% of the total provincial NPP of Guangdong. In summary, a reasonable guidance on the planning of future urban expansion is critical for the maintenance of carbon balance and climate change.
Observation scale research is one of the important subjects of scale research in remote sensing, and it is also one of the research focuses of information extraction from remote sensing images. Analysis of the scale properties in remote sensing image is normally based on geo-statistics, which mainly highlights the linear features of the remote sensing image. However, remote sensing image generally consists of both linear and non-linear features, it is insufficient and to use analysis that based on only geo-statistics. This paper discusses a fractal based method on selecting the optimal spatial resolution for remote sensing image (also known as optimal pixel-based observation scale) by analyzing the remote sensing image scale effect and study the mechanism of fractal characteristics. Three categories of study regions, covered by building, farmland and forest respectively, were cut from IKONOS panchromatic image and used as the experimental data. Then, a series of fractal dimensions based on Fractal Brown Motion, Double Blanket Method and Triangular Prism Method respectively were calculated along with the change of spatial resolution. The statistical analyses of the experimental results demonstrate that the fractal dimensions generally show a decreasing trend with the increase of spatial resolution, and some turning points emerged at certain spatial resolutions. According to the analysis, the spatial patterns or internal structures in remote sensing image vary among different scales. And with the decreasing of spatial resolution, the roughness of image will also decrease since many details are ignored. Nevertheless, the fractal dimension is the only basic quantitative value to describe the self-similarity and irregular degree of object, and it is intuitively consistent with the roughness. Therefore, the turning points at certain spatial resolutions indicate the significance for choosing the optimal spatial resolution. The experimental results show that the fractal based method on selecting the optimal observation scale is theoretically and practically significant to geo-applications, and it extends the research categories by analyzing remote sensing observation scales from different perspectives.
Land cover mapping is an important aspect in remote sensing (RS) researches, and the precision validation is a necessary procedure to describe the quality of land cover data interpreted from RS images. GPS pointing combined with the field photos are mainly used in traditional validation methods, which is easily and subjectively affected by the field work efficiency and sampling place selection. A new land cover data precision validation method is proposed in this study, which is based on the vehicle-mounted three dimensional (3D) camera. A series of detailed technical issues are addressed, which include the outputs and extraction of the validated GPS points from 3D videos, the identification and management of these points, the precision evaluation technology based on the grading system, and so on. 264 GB videos were used, which were gathered in the field validation task from 7 provinces of eastern Mongolia in 2013. Taking 1, 5 and 10 seconds, and 1 and 5 minutes respectively as the sampling intervals, collecting at an average car speed of 50. 95 kilometers per hour, 123396, 24679, 12339, 2056 and 411 points were recognized accordingly. The precision validation results of the study area were obtained based on the scoring methods. The top score of overall accuracy is 71.01% with the 1 minute interval, the accuracies for meadow steppe, real steppe, desert steppe, built area, barren, cropland and water are 49.29%, 86.09%, 32.71%, 80.65%, 87.5%, 1 and 0, respectively. The analysis shows that the 3D videos are suitable for land cover validation due to its highly automatic and continuous working features in the information and Big Data era. The testing results of the field sampling with various temporal intervals indicate that 1 minute interval is optimal for the precision validation in eastern Mongolia. For improving the future studies, sampling with variable temporal interval is the key issue, and it should be advanced thoroughly for the video data automatic processing technology.
Currently, defogging algorithms based on the physical model of a single image become the focus of defogging researches. Compare several classical single image defogging algorithms, the defogging algorithm based on the dark channel prior knowledge of a single image is the most effective and appropriate method. Since the dark channel prior defogging algorithm has high time complexity and space complexity, there are many researchers accordingly contributed significant improvements to reduce the complexity and improve its efficiency. Comparing these improved algorithms and studying the advantages and disadvantages of defogging, we proposed a new dark channel prior defogging fast algorithm for single image. First, through the introduction of the fast, efficient and low-pass Gaussian filter to substitute the soft matting algorithm or other wave filter, we achieved a smooth and refined transmittance figure. Next, during the process of defogging, since the dark colors in the image at the border of different depth of fields may appear a white border phenomenon, we proposed an area median filtering method to adjust its impact. Finally, the detailed algorithm adaptive to meet the requirements of a global atmospheric optical image were presented. Experimental results showed that the improved algorithm based on single image with the combination of the above mentioned three steps can quickly reduce the fog effect from the original image to ensure the quality of the image, while greatly improve the speed of dark channel prior defogging algorithms. The improved method is efficient in pratical, for example in engineering images defogging process and in video real-time defogging.
As one of the key biophysical parameters in the bamboo forest evaluation, the leaf area index (LAI) retrieval from remote sensing data has always been challenged by the topographic effect in mountainous area. In this paper, the topographic-adjusted vegetation index (TAVI) was proposed to eliminate the topographic influence for the bamboo forest LAI derivation in Yongan city, Fujian, based on the Rapideye high spatial resolution satellite imagery. Normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were also utilized in the statistical analysis with respect to LAI for comparison with TAVI. The regression results indicate: (1) LAI is more linearly correlated with TAVI than NDVI or RVI. R2 (coefficient of determination) of the linear regression between LAI and TAVI, NDVI, RVI are 0.6085, 0.3156, and 0.4092 respectively. For the optimal non-linear fitting model, the corresponding R2 had increased to 0.6624, 0.5280 and 0.6497 respectively. Although the quadratic polynomial regression model can well explain the relationship between LAI and NDVI or RVI, it can hardly illustrate the typical phenomenon of "same object with different spectra" and "different objects with same spectrum" that resulted from topographic effect. (2) Both the LAI-TAVI regression models and the in-situ measurement demonstrate that the proposed method can effectively avoid the above problems with a correlation coefficient (r) of 0.7674 between in-situ and the simulated LAI, and a RMSE of 0.3403. In conclusion, TAVI shows good capability to alleviate the topographic effect and can be effectively applied to the LAI retrieval of the bamboo forest in mountainous area.
