Query plan enumeration and query cost estimation are two important steps in a query optimizer of DBMS. The query plan enumeration is responsible for enumerating some candidate query plans with best or better plan. The query cost estimation is used to choose the best plan for execution. However, if the candidate query plans in the first step are too many, the second step has to spend more time on estimating them. In order to save the cost of estimation time and improve execution efficiency of the system, spatial heuristic rules are used to eliminate some impracticable query plans. This paper firstly explained some basic concepts, i.e. query graph, joined tree, and query plan. Then, we put forward three heuristic rules for spatial equal classes and spatial constrained pairs. The first is that spatial join operators should be established on spatial equal classes or spatial constrained pairs. The second is that the orders of join operators should be equal classes, spatial equal classes, non-Cartesian products of ordinary attributes, spatial constrained pairs and Cartesian products of ordinary attributes. The last one is a recursion rules about spatial equal classes. It means, only the attributes in spatial equal classes of a query plan could be transmitted each other. After that, this paper puts forward two rules for spatial indexing tables. The first is that it's better to replace a spatial table with its spatial indexing, when there is a spatial predicate on some spatial attributes. The second is the spatial indexing table must be on the top of its original table in a query plan and there should be a TID join between spatial index table and its original table. In the following sections, we explains the rules mentioned above and analyses how to improve query efficiency by using low cost operation as soon as possible and how to filter out candidate data as few as possible. At last, we present a sample to show how to eliminate some impracticable query plans by those spatial heuristic rules. Those rules are not only for query optimizer, but also for SQL programmer.
Aiming at the difficulties of massive laser point cloud data organization and management, a new spatial index structure-3DOR*-tree is proposed. This is constructed by fully integrating Octree with fast convergence capability of 3D space and 3D R*-tree with the advantages of stable performance of irregularly distributed data. Firstly, laser point cloud data is divided using Octree, and then 3D R*-trees are built on the octree leaves. The laser point cloud data is stored only on the leaves of 3D R* tree and 3DOR*-tree index structure is constructed. Then, we analyze feature of laser point cloud to achieve laser point cloud storage and management based on 3DOR*-tree. At last, the point cloud data of the library of JiangXi University of Science and Technology are used for experiment and comparative analysis. The result shows that the improved storage structure of laser point cloud data based on 3DOR*-tree has an advantage of efficient space storage and query compared to other tree structure, such as 3D R* tree, integrated tree of Octree and R tree and so on. Thus, 3DOR*-tree can be applied to management and analysis applications of massive laser point cloud data storage.
Efficient and precise discovery of geoscience data on data sharing websites has been a challenge for years. This study applied Web mining techniques for National Earth Science Data Sharing Platform to derive user searching and visit behaviors using clustering algorithm. We proposed cluster-based approaches for search recommendation and visit recommendation. At data preprocessing stage, data cleaning, user identification, session identification and search terms extraction were performed. At user behavior mining stage, DBSCAN algorithm was employed for session clustering with Jaccard distance metric, considering the binary nature of session vectors. To mine user search patterns, we regard the collection of search term in each cluster as a document of text, and the collection of the whole historical search terms as corpus. Thereby, TF-IDF value of each search term in each cluster was then generated. In the scenario of online search recommendation, the real-time search term is taken to index the TF-IDF values in the clusters, and return the cluster with highest TF-IDF value. The items with top frequency is generated as recommendation list. As in the scenario of online visit recommendation, real-time visit vector is taken to query the clusters by the distance between the visit vector and cluster centroids. The nearest cluster is selected to generate most frequent items in the cluster as recommendation. Results of the experiment revealed the hot research topics of geoscience in recent years. The proposed search recommendation has a fair precision and recall, and visit recommendation was considerably improved compared to frequency-based approach. It can be concluded that: (1) web users of geoscience data sharing are more professional and predictable compared with normal web users; (2) DBSCAN is density-based clustering algorithm. It is vital to specifically define user behavior and chose a proper distance metric; (3) TF-IDF-based approach to predict users' search needs is feasible. The resulted search recommendation could be complementation to keyword-based searching. The outcome of this study would potentially contribute to the development of National Earth Science Data Sharing Platform, and even other science data sharing platform.
Data was the basis of carrying out the research on environmental quality of the soil. However, in the experimental process, the systematic errors and artificial errors may lead to some outliers, which may reduce the data quality and cause erroneous judgement for pollution assessment and management decision. At present, there was a lack of thorough study and exploration in this respect. Based on this, a method for detecting outliers of soil heavy metal data was put forward in this study. The soil Cd concentration of Beijing in China was taken as an example to verify the validity of the method. The results show that there are 34 outliers for Cd concentration in Beijing. The detected outliers in Beijing were re-analysed. The results showed that 76.5% of the outliers were found to be caused by the systematic errors and artificial errors and 20.6% of the outliers existed, objectively. After the correction, the interpolation accuracy was improved significantly. The mean relative error and mean square error of the outliers were reduced by 44.56% and 33.33%, respectively. Also, the mean relative error and mean square error of the nearest neighboring points which are influenced by the outliers were reduced by 20.59% and 17.33%, respectively. Results indicated that the outliers of soil heavy metal could be effectively detected by the proposed method. Under the premise of adding finite sample size and analysis time, the quality of the survey data was improved and an effective tool was provided to carry out soil investigation at regional scale and guarantee the data quality.
Science and technology innovation is not only the core driving force of the development of the tourism industry but also an important part of tourism innovation system. It affects the quality and speed of economic development of the tourism. Science and technology innovation can improve the tourism industrial structure and change the development mode of the tourism industry. This paper builds the structural model and evaluation index system of scientific and technological innovation ability of tourism industry from four respects including base, input, output and potential. Then, we analyzed and evaluated the overall level, space-time dynamics and influencing factors of scientific and technological innovation ability of China in 2004, 2008 and 2014. The results are as follows: from 2004 to 2014, the overall level of scientific and technological innovation ability of China had been growing. But there were obvious regional differences. All the three time sections presented the distributional pattern, namely East-West direction increasing and South-North direction having the inverted“U-shaped”pattern. The spatial autocorrelation analysis showed that there were significant and stable agglomeration features and some polarization features in the scientific and technological innovation ability of China. The type of spatial correlation mainly appeared to “HH” type and “LL” type. Specifically, they were the eastern coastal areas HH cluster and the western inland areas LL cluster. Simultaneously, its spatial structure presented characteristic of lock-in and road dependence. The spatial hot spots of scientific and technological innovation ability of China mainly concentrated in Beijing, Tianjin and a few eastern coastal provinces. And in 2014, Henan, Anhui, Fujian and Guangdong became the spatial hot spots. The spatial cold spots were mainly concentrated in the Midwest hinterland. There were spatial spillover effect of scientific and technological innovation activity among adjacent areas. The spatial error regression model and the geographically weighted regression analyses indicated that the tourism industry base, spatial spillover effect and policy system were three core driving forces of the change of time and space pattern of the scientific and technological innovation ability in China.
The monitoring of γ radiation dose rate is an essential part in the field of natural radioactivity level research. In addition, it relates to the public health and radiation environment safety. The evaluation methods of spatial distribution level are mostly based on expert experience knowledge, which cannot explicitly express the quantitative relationship between the γ Radiation dose rate and the environmental factors. In order to understand the spatial distribution and the influencing mechanism of γ Radiation dose rate, we proposed a new evaluation method of revealing the environmental influence mechanism of γ Radiation dose rate, based on geographical detector (Geogdetector). A total of 6 environmental factors were selected for the evaluation of spatial distribution of γ Radiation dose rate, including the elevation, ecology, topography, climate, vegetation, and LUCC. Firstly, we collected the data of automatic monitoring station of national radiation environment in 2015, and calculated the annual average value in each city. Then, we studied the spatial distribution of γ Radiation dose rate in 44 cities to discover the spatial pattern. Finally, we used the geographical detector to reveal the environmental influence mechanism of γ radiation dose rate. The results show that: (1) the spatial distribution of γ radiation dose rate in China has the following pattern: the low value distribute along the buffer zone from the city Heihe to Nanning, the high value distribute on both sides of the buffer zone. (2) The explanatory power of environmental factors is sorted into: Elevation (0.846)>Climate (0.741)>Vegetation (0.691)>LUCC(0.427)>Ecology (0.419)> Topography (0.101). On the one hand, the elevation works through the appearance of the geological structure to influence the crust in the natural radiation level. On the other hand, through the impact of the distribution of the natural environment, the elevation also determines the distribution of human activities, and the role of artificial ionizing radiation source. Therefore, the distribution of γ radiation dose rate was significantly affected by elevation classification, which is comprehensively considered as the human economic factors and the natural environment factors.
Snow cover on the Tibet is a vital water source in western China and Himalayan regions. In addition, it is very sensitive to climate change and closely related to hydrological processes in the Tibet and downstream areas. Understanding snow cover dynamics and spatial distribution pattern is very important for regional water resources, environment management and climate change. Therefore, in this study, the spatial and temporal distribution patterns of snow cover and the impact of topography (elevation, aspect and slope) on snow cover distribution of the Tibet are analyzed based on snow cover fraction (SCF) derived from MODIS 8-day snow cover product （MOD10A2） from 2000 to 2014 and digital elevation model (DEM) using GIS spatial analysis and statistic methods. Results are as follows: (1) the spatial distribution of snow cover on the Tibet is spatially uneven, which is generally characterized by rich snow and high SCF on Nyainqentanglha Mountain ranges and surrounding high mountains and less snow and low SCF in southern valley and middle part of northern Tibet. Annual mean SCF is 16%, with 22% of SCF in spring and winter, 16% in autumn and 5% in summer. (2) Snow cover on the Tibet presents that the higher altitude the higher the SCF and the longer the snow cover duration and the more stable in intra-annual variations. Average SCF below 2000 m above sea level (masl) is less than 4% while it reaches to 75% above 6000 masl. (3) Intra-annual snow cover distribution below 4000 masl is characterized by unimodal patterns with the higher the altitude the more obvious single-peak type. Above 4000 masl, it is characterized by bimodal patterns with the higher altitude the more obvious double-peak types. The lowest SCF below 6000 masl occurs in summer while above 6000 m which occurs in winter. (4) For different slopes, monthly mean snow cover presents bimodal patterns with generally the higher slope the higher SCF. (5) For different aspects, SCF is highest in north aspect and lowest in south aspect, and is between the two of them in east and west aspects. The distribution pattern of intra-annual snow cover in different aspects is double-peak type, whereas it is single-peak type in flat terrain and its SCF is less than that in mountains with aspects.
Against the one-way or two-way causality between urban housing development and economic growth, different conclusions are obtained by the relevant scholars via macro statistical analysis methods. In this paper, I explained the relationship between the two mentioned above at the fine scale, through construction of their temporal and spatial data sets and I analyzed their coupling relation and spatial difference at micro level of the city. Taking Zhengzhou City as an example, I proposed an estimation method of grid GDP (Gross Domestic Product) based on luminous index data to generate the spatial-temporal GDP data sets. Also, the spatial correlation analysis is carried out between the spatial-temporal GDP data sets and the spatial-temporal residential quarter data sets which are estimated by the Point of Interest (POI) of residential quarters. Different from previous macro studies, coupled analysis shows that the relationship between the development of housing industry and economic growth has spatial differences in the inner city. There are mutual influence areas in some places and uncorrelated areas in some other places. The grids of significant coupling coordinative relationship account for about 20%, and mainly are located in the municipal district and county central district while the grids that is not significant and not related account for over 70%, and are located in the counties mostly. In this paper, the spatial distribution of urban residence was made by the density estimation of the residential POI with construction area attribute, which is not available in the traditional social economic statistical data. I found that this kind of spatial distribution data of real estate can be used to study the spatial and temporal changes of urban housing, and make up for the deficiency of macro analysis.
In April 2016, Chengdu-Chongqing Urban Agglomeration Development Plan firstly officially confirmed the connotation and specific boundary of Chengdu-Chongqing urban agglomeration. It's conducive to optimize and adjust the trend of development in future and help understanding the spatial patterns and dynamic changes of Chengdu-Chongqing urban agglomeration. Firstly, during the pretreatment process of DMSP/OLS night-time light data, the traditional methods calibrated images by a representative invariant region and those frequently selected regions were far away from the study area, causing uncertainties and errors. We calibrated all pixels of the DMSP/OLS night-time light data within the boundaries of urban-district in 2013. Next, we designed a rule to calibrate the statistical data. On the basis of these two kinds of data, we calculated out the best thresholds of the urban built-up areas in 5 stages of Chengdu-Chongqing urban agglomeration from 1997 to 2013. Comparing extractive areas with statistical areas, the total average relative error is only -0.38%. The result was more stable and accurate than other methods. We tested the extracted built-up polygons (2013) with the certain built-up polygons that sketched on Google Earth historical images. The results showed that the accuracy of extraction had reached 98.29%. Furthermore, we selected the barycenter index and landscape isolation index to analyze the extraction results. We studied the shifting processes of barycenter and the integrating processes of cities and found that: (1) the level of integration was highly consistent with planning and Chengdu-Chongqing urban agglomeration had entered a stage of rapid development; (2) Chongqing metropolitan and Chengdu metropolitan gradually formed and the former achieved greater developments and it also deteriorated regional imbalance. Overall, our research results can be used to optimize the development of Chengdu-Chongqing urban agglomeration, and we provided a template for the study on the space forming processes of urban agglomeration.
Spatio-temporal patterns of NDVI and possible relations between NDVI and climate factors in Xinjiang Province have been analyzed based on daily precipitation and temperature data covering the period of 2003-2010 from 50 meteorological stations. Results show that inter-annual variations of NDVI and climate factors are not evident and apparent annual changes are well identified. Abundant precipitation and high temperature was observed in summer and autumn with good coverage of vegetation. However, the adverse conditions can be found in winter and spring. The northern Xinjiang as well as the north slope of the Tianshan Mountains are dominated by abundant precipitation and flourishing vegetation. The south slope of Tianshan Mountains and the eastern Xinjiang take second place. The southern Xinjiang, however, is characterized by scarce precipitation, high temperature and poor vegetation coverage. The vegetation condition in northern Xinjiang and the north slope of Tianshan Mountains has no obvious time-lag response to climate factors. Nevertheless, it has obvious time-lag response to climate factors and the growth of vegetation is restricted by climate factors in southern Xinjiang and the south slope of Tianshan Mountains. As the main factor, restriction of temperature is more prominent. Overall, the vegetation varied continuously in Xinjiang and the area of improvement tendency is less than those of degradation tendency. To some extent, human activity has a great influence on the vegetation condition. Exploration of vegetation changes and carrying out of timely adjustments, not only can offer a scientific basis for vegetation protection and restoration, but also can provide important theoretical guidance for crop production effectively.
The rapid development of highway transport networks has increased much work load to road maintenance departments in China, and consequently it is currently a pressing demand to develop new technical support to rapidly and accurately collect the health conditions of road pavements. In contrast to the conventional methods, previous research indicated that remote sensing might offer a new approach for the monitoring of pavement conditions of highway roads. This paper first tends to examine the spectral responses and features of the road pavements with different aging conditions and pavement materials based on field measurements of the pavement spectral reflectance. In addition, this study also tries to construct effective road pavement condition index from satellite remote sensing data for the monitoring of pavement health conditions. One of the findings shows that the slope of the spectral curves in the wavelength region of 400~900 nm grows bigger from negative to positive with the gradual aging of the asphalt pavements based on the field measurements of the pavement spectra. After that, several spectral index models were built up to monitor and evaluate the road pavement aging conditions by means of simple arithmetical calculation such as ratio and normalization. To demonstrate the applicability of the proposed indices to satellite remotely sensed data, a Worldview-2 image acquired on September 21, 2013 in the Liangxiang area near the sixth Ring Road south, Beijing City was used to analyze the road pavement health conditions, and to verify these models using the Munsell Scale Card values that were collected together with the spectral measurements and used to indicate the pavement aging conditions in our study. The image was first preprocessed in RSI ENVI software package, such as radiometric and geometric corrections, and subset. The four proposed indices were calculated from the Worldview-2 image in the study area and evaluated using the in-situ measurements of the pavement health conditions by visually comparing the performance of the proposed spectral indices in characterizing the asphalt pavement aging conditions. Furthermore, correlation analysis between spectral pavement condition indices and the Munsell Scale Card values shows that the logarithmic health index can achieve the biggest determinant coefficient （R2=0.72,n=23） and may be applicable in the monitoring of road pavement conditions. The case study indicates that road pavements with different materials and aging conditions have distinct spectral response in visible and near-infrared wavelength, and satellite remote sensing can be employed in the rapid mapping and assessment of large-range asphalt road pavement conditions. Thus, the study extends remote sensing applications and offers a new technique for the road maintenance departments. Future work may explore spectral mixture analysis in asphalt condition mapping from low-altitude unmanned aviation vehicle （UAV） hyperspectral imagery.
Scale transformation has been a research hotspot in the field of remote sensing information science, and its processing methods are now limited to the traditional statistical methods. They consider less spatial structural information of data and can't satisfy the requirements of multi-scale expression. In view of scale inconsistency problems of remotely sensed data, this paper proposed a scaling up method based on image blurring characteristic of Gaussian pyramid. Firstly, this method made Gaussian blur using different filtering parameters for remotely sensed data, and it created several Gaussian blurring data in each layer of the pyramid model. Then Gaussian blurring data were processed by down-sampling constantly, and we obtained a series of different resolution of remotely sensed data. Accordingly, the multi-scale remotely sensed data could meet the spatial resolution requirements of practical application. In order to prove the effectiveness of the proposed method, this paper chose Landsat 7 ETM images and ASTER GDEM data to achieve scaling up. Also, we put forward quantitative evaluation indices including mean, variance, mean absolute error, root mean square error and conducted experiments to compare with the general methods of scaling up, such as nearest neighbor method, bilinear method and cubic convolution method. Besides, the performance of Gaussian pyramid method was measured through the results of contour sets between ASTER GDEM and SRTM DEM with the same resolution. Experimental results showed that Gaussian pyramid method in this paper can effectively realize the scaling up for continuous remotely sensed data, and the results are superior to other traditional methods. Gaussian pyramid method can also keep local characteristics of the details and maintain amount of information, spatial structural features of remotely sensed data. In summary, the using of Gaussian pyramid method in the field of remote sensing is a new attempt, and can provide necessary data preparation for multi-scale visual expression.
The high-resolution DEM and DOM data is an accurate description of the topography and geomorphology, and it is also an important source data for landslide information extraction. At first,according to the requirement of landslide information extraction,we use the UAV platform equipped with mini SLR camera combined with the GPS data measured in the field, as the image acquisition method. According to the characteristics of the UAV images, we use the basic principle of photography measurement and computer vision algorithms to obtain the high-resolution DEM and DOM images, which greatly preserves the rich spectral and texture information. Then, with the help of the ESP auxiliary tool we get optimal segmentation scale of the DOM. Based on the fuzzy classification and SVM algorithm to construct a decision tree, which we used to achieve the object oriented classification and information extraction. Finally, according to the spatial feature and distribution of study area we determine the high risk area. By the morphology and texture analysis and accuracy assessment of the landslide area, we show that the producer’s accuracy and user's accuracy of the landslide area are 84.65% , 91.44%. The result proves that the UAV remote sensing has a high value in the field of landslide information extraction.
It is very important to search a methodology to extract surface water quickly, accurately and efficiently. Single band thresholding and water indices are commonly used water extraction methods because of ease of use and the fact that these methods are computationally less time-consuming than alternative approaches. However, in environments where the area is larger, the type of water is various and the influencing factors are complicated, simple classification methods such as two-band water indices and single-band thresholding may not sufficiently and accurately distinguish between water and non-water pixels. In this paper, in order to extract water accurately in the complex mountain area where all the types of water and interference factor exist, the threshold value of three indices is analyzed respectively, that are Moderate Normal Different Water Index (MNDWI), Automate Water Extraction Index (AWEI) and Normal Difference Three Band Index (NDTBI). Then, the zoning scheme based on the optimal threshold value is used to build three decision classifier based on MNDWI, AWEI and NDTBI respectively. After that, comparative analysis is executed among the effect of the three classifiers for different types of surface water. Lastly, according to the optimal principal, a decision tree classifier of three indices (MNDWI, AWEI and NDTBI) united is reconstructed to extracted water. The results show: the Kappa coefficients means of three methods on single index MNDWI, AWEI and NDTBI in three test sites are 0.863, 0.854 and 0.862 respectively. The Kappa coefficients means of the combined indices method is 0.881. Thus, the water extraction method based on the optimal partition and three indices combined possess have the highest accuracy and the best effects. According to the research, we find that the key factor of the precision improved is the optimal zoning method based on the index to achieve hierarchical extraction. No matter which method among the single index or the united indices, the accuracy is all within the permitted range. In comparison of three single indices methods, extraction of region water such as lake and reservoir is suited for the MNDWI, the line water such as river and stream works best with AWEI, the small, narrow river affected by the channel sandstone has good success with NDTBI. However, the combination decision tree classification of the optimal zoning and the optimal method of indices united will be satisfied for water extraction.
In the context of city expansion and raise of awareness of climate change, urban planners are looking for methods and tools to take the urban heat island （UHI） into account. Urban heat island intensity （UHII） is an important metric used in measuring UHI effect. Nevertheless, its quantitative measurement has not yet been clearly addressed. Due to the limitation of meteorological stations either in number or location, the traditional method of calculating the temperature difference between urban and rural areas based on the meteorological station data fails to accurately describe the UHII of a city. In order to solve this problem, a classification schema “Local Climate Zones” （LCZ） was proposed by Steward and Oke. Nowadays, the satellite remote sensing imagery is widely used to reveal urban heat island phenomenon. Therefore, this paper applied the new framework of LCZ to the study of UHII in Fuzhou City, located in the center of the Fuzhou basin, southeast China, using remote sensing technology. Fuzhou City has witnessed a rapid urban expansion since the late 1970s. The fast expansion of the city has caused severe UHI phenomenon in the city. Thus, it has become the top one furnace city in China. This study reveals that LCZ based on remote sensing technology can effectively distinguish the thermal contrasts among all LCZ classes. Such contrasts are governed largely by height and spacing of buildings, pervious surface fraction, trees density and soil wetness. In addition, the LCZ can fully disclose the distribution patterns of UHI. In this study, we revealed a UHIILCZ of 6.73℃ for Fuzhou city on 27 September 2015, which indicates a significant UHI in the city.
