This paper takes Chang-Zhu-Tan Urban Agglomeration as study area. Based on the vegetation index and land use data interpreted from Landsat TM images and combined with the population, economic and climate data, a framework model was established. The results show that: (1) the mean values of wetland ecological security index in 2000, 2005 and 2010 for Chang-Zhu-Tan Urban Agglomeration were 0.7268, 0.7151 and 0.7196, respectively. The status of regional wetland ecological security was good, and the ecological security degree was relatively safe. In the recent decade, the overall performance of the regional ecological security index had decreased, and the decrease of the corresponding area was 21577 km², which accounted for 22.28% of the total land area; (2) in this study area, there is obvious difference of regional ecological security, that the first-class wetland ecological safety area mainly distributed in the surrounding regions of Dongting Lake, the second-class ecological safety region distributed along the major rivers, the third-class ecological safety area mainly distributed in the border area of two or three cities, and the fourth-class wetland ecological safety area mainly distributed in Yueyang city, Xiangtan city, Changsha city and Hengyang city. (3) During the study period, the area of wetland in Chang-Zhu-Tan Urban Agglomeration has changed obviously, that the total area of wetland has decreased year after year. The fractal dimension of forest swamp, herbaceous swamp, lake, river and paddy field showed an increasing trend. The fragmentation index of ponds/rivers was significantly higher than that of other types of landscape, and the value of wetland ecosystem services was decreasing in general. Finally, the main factors affecting the ecological security of wetland were analyzed from the aspects of land use change and transition, and wetland landscape structure and function.

Being the primary data source,3D points cloud is also an important means to describe and express the geographic objects and phenomena in 3D GIS as well as to perform model building.And the effective organization of the points is the basis for its operation and analysis.Therefore,in this paper,3D points are arranged and sorted according to a specified rule,and then organized by a compound structure of spatial octree and balanced binary tree,which greatly speeds up the query process based on the 3D coordinate,and lays a solid foundation for the further analysis of 3D points data.This paper also unifies the compound structure in both memory and database.And a case study has proved its validity.

Comparing with other satellite sensors, AVHRR has the capability to analyze more than 10 years of medium-resolution satellite imagery on a daily basis. AVHRR thereby holds a great potential to detect, map and quantify long-term environmental changes. However, different satellites use different retrieval algorithms, wavelength bandwidths and atmospheric validations. So it is important to compare different snow cover products retrieved by different satellites. Here, we describe and extensively validate the snow cover products of the historical 0.05°×0.05° AVHRR data. The spatial and seasonal validation includes a comparison with IMS and MOD10A1. It is found that the AVHRR snow products are in good accordance with the MODIS snow products. The influence of acquisition geometry and the sensor-to-sensor consistency will be discussed in future.

Three-dimensional GIS (3D GIS) is one of the primary and typical contents of GIS technology at present and in the future, which overcomes the constraints of representing 3D GIS spatial information in two-dimensional map, as well as provides a more effective decision-making support for people's daily life. This paper focuses on the research progress and its key technologies of 3D GIS, including the data model, database management and visual analysis. The pilot applications of 3D GIS in Wuhan are also illustrated. The entire 3D space of the city is represented by 3D GIS. Then construction of the large-scale city digitalization is enabled with the improvement of city management. Finally, the applications of 3D GIS for spatio-temporal information bearing engine and spatial intelligence in smart city and city safety are investigated.

This article discusses about the thereunder of choosing the optimum semivariogram models and setting the key parameters based on ARCGIS and GS+software from characteristics and laws of data through understanding the ordinary Kriging interpolation theory, and carries through an in-depth exploratory spatial data analysis taking the spatial interpolation to January average temperature of Fujian province as an example, using the obtained parameters and semivariogram models to simulate the spatial distribution of January average temperature of Fujian province. The aticle offers a clear way for reasonable spatial interpolation.

Space-Air-Ground integrated earth observation network (SAGIEON) is not only the most promising high-tech area, but also a fundamental infrastructure closely related to national security and economic/social development. Firstly, the scientific concept, key technologies, current situation and tendency of SAGIEON are comprehensively represented. Secondly, an integrated data processing system for native remote sensing satellites is introduced, including its objectives and key technologies. Thirdly, the connotation of generalized spatial information grid are proposed on the basis of the above mentioned discussions. Finally, some conclusions are drawn. For the propose of providing fast, precise and real-time spatial information service to everyone, it is very important to carry out research on the theories and technologies of SAGIEON.

Human mobility has received much attention in many research fields such as geography, sociology, physics, epidemiology, urban planning and management in recent years. On the one hand, trajectory datasets characterized by a large scale, long time series and fine spatial-temporal granularity become more and more available with rapid development of mobile positioning, wireless communication and mobile internet technologies. On the other hand, quantitative studies of human mobility are strongly supported by interdisciplinary research among geographic information science, statistical physics, complex networks and computer science. In this paper, firstly, data sources and methods currently used in human mobility studies are systematically summarized. Then, the research is comprehended and divided into two main streams, namely people oriented and geographical space oriented. The people oriented research focuses on exploring statistical laws of human mobility, establishing models to explain the appropriate kinetic mechanism, as well as analyzing human activity patterns and predicting human travel and activities. The geographical space oriented research focuses on exploring the process of human activities in geographical space and investigating the interactions between human movement and geographical space. Followed by a detailed review of recent progress around these two streams of research, some research challenges are proposed, especially on data sparsity, data skew processing and heterogeneous data mining, indicating that more integration of multidiscipline are required in human mobility studies in the future.

Leaf area index (LAI) is a primary parameter for charactering leaf density and vegetation structure. Since it could represent the capability of vegetation for photosynthesis, respiration and transpiration, LAI is used as a critical parameter for modeling water, carbon and energy exchanges among soil, vegetation and the atmosphere. Several regional and global LAI datasets have been generated from satellite observations. This paper reviews current status of theoretical background, algorithms, products and evaluation of LAI from optical remote sensing data. First, the definition of LAI and its effects in ecosystem modeling are introduced. Then, the radiative transfer processes of photon in canopy are described briefly. Based on these processes, vegetation presents its own spectral response characteristics, which are related to biophysical and biochemical properties of leaves, canopy and soil background, making it possible to derive LAI from optical remote sensing data. Two main methods which establish the relationships between LAI and satellite observed spectral canopy reflectance are widely used for LAI retrieval from remote sensing data, including vegetation index-based empirical regression method and physical model-based method. These two methods are presented subsequently, and their advantages and disadvantages are also discussed. Several major global LAI remote sensing products are reviewed, such as MOD15, CYCLOPES, GLOBCARBON and GLOBMAP LAI. The methods for LAI products evaluation and validation are presented, and several problems in LAI evaluation are also discussed. Finally, several problems in LAI retrieval are concluded, and directions for future research of LAI retrieval are then suggested.

Target classification and recognition (TCR) of high resolution remote sensing image is an important approach of image analysis, for the understanding of earth observation system (EOS), and for extracting information from the automatic target recognition (ATR) system, which has important values in military and civil fields. This paper reviews the latest progress and key technologies between domestic and international remote sensing image TCR in optical, infrared, synthetic aperture radar (SAR) and synthetic aperture sonar (SAS). The main research levels and the contents of high resolution remote sensing image TCR are firstly discussed. Then, the key technologies and their existing problems of high resolution remote sensing image TCR are deeply analyzed, from aspects such as filtering and noise reduction, feature extraction, target detection, scene classification, target classification and target recognition. Finally, combined with the related technologies including parallel computing, neural computing and cognitive computing, the new methods of TCR are discussed. Specifically, the main framework includes three aspects, which are detailed in the following. Firstly, the predominant techniques of high resolution remote sensing image processing are discussed based on high performance parallel computing. And the hybrid parallel architecture of high resolution remote sensing image processing based on Apache Hadoop, open multi-processing (OpenMP) and compute unified device architecture (CUDA) are also presented in this paper. Secondly, application prospects of TCR accuracy promotion are analyzed based on a thorough study of neuromorphic computing, and the method of multi-level remote sensing image target recognition based on the deep neural network (DNN) is introduced. Thirdly, the model and algorithm of big data uncertainty analysis for remote sensing images are discussed based on probabilistic graphical model (PGM) of cognitive computing, and the multi-scale remote sensing image scene description is given based on hierarchical topic model (HTM). Moreover, according to the related research of multi-media neural cognitive computing (MNCC), we discuss the development trend and research direction of TCR for remote sensing images big data in the future.

Multi-source remote sensing data fusion is the development trend of remote sensing technology in depth. This paper analyzes in detail algorithmic application characteristics of multi-source remote sensing data from three levels of pixel-based, feature-based and decision-based fusion processings. Take Fengning County for example, specific applications of remote sensing data fusion methods in information extraction are illuminated. The data used in this study is firstly pre-processed, then the principal components of Landsat TM data in 1999 are analyzed, the first three principal components account for 97.8% of the total information, the resulted image of inversed principal components transformation is clearer and has more abundant levels. To extract information from remote sensing image, we select the fusion image from Landsat TM pan and multi-spectral bands after principal components transformation, color composition scheme of bands 4, 3, 2 and bands 5, 4, 3, and vegetation index and greenness index after tasseled cap transformation are analyzed, the remote sensing image information fusion with DEM and spatial data of GIS database can also improve the accuracy of remote sensing information extraction. Problems to be resolved and future direction of multi-source remote sensing data fusion are put forward.

The gridding computation is a major model in current geoscientific research due to its simplicity in organizing data resources. However, because the gridding computation equally distributes computational resources, it brings redundancy to the computational process and neglects catastrophe points in geoscientific phenomena, which might overlook the important patterns and bring more uncertainties to the research result. To overcome this weakness, this paper proposes to use the spatial point process model in geoscientific research. The spatial point process model is used to model spatial point based geoscientific phenomenon, also is applied to most of the other geoscientific processes (because they can be transformed into spatial point processes). In this regard, the spatial point process is not only a data model, but also an analysis tool for geoscientific problems. Moreover, it provided a new angle of view for observing geoscientific problems. To extract patterns from point process data, the authors propose the frame of multilevel decomposition of spatiotemporal point process. This frame is similar to the basic idea of signal decomposition. We first assume that any point data set is the overlay of an unknown number of homogeneous point processes. Then, the points are transformed into a mixture probability density function of the Kth nearest distance of each point. After that, the optimization method is used to separate clustering points from noise. Finally, the patterns are extracted using the density connectivity mechanism. The theory can be used to any type of point process data. It can be considered as the "Fourier transform" of point process data.

Effective organization and management of vector geographic data is one of the key parts for spatial database application. The traditional vector geographic data service is usually based on magnetic disks and relational databases like Oracle Spatial. With the rapid development of wireless communication and mobile web technology, the performance of current vector geographic data service is declining dramatically under multi-user concurrent access, and can't meet the requirements of high performance and high concurrency. In order to improve the performance of vector geographic data service under multi-user concurrent access, we proposed a novel management approach of lightweight vector geographic data based on main memory database Redis. Redis is a main-memory lightweight key/value store. Its I/O performance is much better than traditional disk-based databases like Oracle and MS SQL server. At first, we analyzed Redis' key-value data model and data structure. Subsequently we designed a four level hierarchy organization structure of vector geographic database. We stored vector geographic data and its metadata based on Redis' plentiful data structures. Then, taking the grid spatial index as an example, we designed the storage method of spatial index and spatial query processing flow for Redis based on the hierarchy organization structure of vector geographic database. Our experimental results confirmed that compared to traditional relational spatial database-Oracle Spatial, our main memory style vector geographic data management approach greatly improves spatial query responding speed and its concurrent performance is excellent. The proposed approach can be used as a front end high performance cache of large spatial database or a high performance spatial indexes database.

The outlet of water body of Dianchi Lake is located in the southern area, but the main pollution load is in the northern area. The flow of Dianchi Lake is always rush from North to South. Because of the slow lake flow, over 90% of the pollutants in the waste water deposit on the bottom of Dianchi Lake.Hence there are mainly two factors causing the pollution in Dianchi Lake. One is the slow lake flow, the other is the intruding of the polluted water. There are two ways to deal with the pollution situation. One is to speed up the lake flow,the other is to stop the polluted water from intruding to the southern lake.In light with the situation that the northern lake polluted the southern lake of the Dianchi,the new solution identified is to change the original outlet of Dianchi Lake by digging up a new canal and putting Dianchi Lake flow backwards to the north. By so doing, the main pollution load can be closed to the new outlet and kept away from the original one, part of the polluted water in Kunming city will not directly discharge into the Dianchi Lake. Even if the polluted water discharged into the Dianchi Lake, it is possible to let it rapidly flow out of the northern Dianchi Lake.In a word, the elevated potential of high water pressure can effectively prevent the southward intrusion of pollution load from northern part, offering a basic solution to the pollution problems of the Dianchi Lake.

Web texts contain a great deal of implicit geospatial information, which provide great potential for the geographic knowledge acquisition and service. Geographic knowledge graph is the key to extend traditional geographic information service to geographic knowledge service, and also the ultimate goal of the collection and processing of implicit geographic information from web texts. This paper systematically reviews the state of the arts of the researches on open geographic semantic web, geographic entity and relation extraction, geographic semantic web alignment, and knowledge graph storage methods. The pressing key scientific issues are also addressed, including the quality evaluation of geospatial information collected from web texts, geographic semantic understanding, spatial semantic computing model, and heterogeneous geographic semantic web alignment.

At present there are two significant problems in the field of intelligent transportation systems,i.e.algorithmic efficiency and transfer routines.First of all,this paper describes route selection behaviors of passengers and the characteristics of city traffic networks,and then presents the public traffic network-transit matrix based on key stops.Secondly,based on the shortest path algorithm,a public traffic network-transit matrix and a non-transfer matrix are introduced to design the public traffic transfer algorithm.In this algorithm,the public traffic network transit matrix aims to decide which temp label notes are potential label notes,and non-transfer notes are always considered as the notes of the shortest path,in order to improve the performance of classical shortest path algorithm(Dijkstra).Finally,a case is used to evaluate the performance of this algorithm.The experimental results indicate that the proposed algorithm achieves better efficiency than the Dijkstra.And much more reasonable transfer frequency is obtained.It is believed that this algorithm can be used in general transit networks,especially high transfer-cost networks.

Resources and environmental sciences require greatly the spatialized socio-economic data sets which are always obtained from administrative regions at national or provincial level, and accurate estimates of the magnitude and spatial distribution of economic activity have many useful applications. Developing alternative methods for making estimates of gross domestic product (GDP) may prove to be useful when other measures are of suspect accuracy or unavailable. Based on the summary and analysis of existing economic activity spatialization approaches and nighttime imagery applications in economic activity, this research explores the potential for estimating the GDP using relationship between the spatial patterns of nighttime satellite imagery and GDP in China by correlation analysis and regression analysis using concerned data processing software. With the regional differences of China's economic development, logarithmic regression models have been established between different night light indexes and GDP, primary industry, secondary industry, tertiary industry and the sum of secondary industry and tertiary industry at the provincial level. A clear logarithmic linear relationship between nightlight imagery and GDP, especially the correlation coefficient of night light index and the sum of secondary industry and tertiary industry is 0.824 and R² of them is 0.679 at national level, suggests that this method is available and feasible to estimate the spatial distribution of economic activity such as GDP. The result, 1-km grid GDP map of China based on nighttime light data, by comparing with the other GDP spatialization approaches, shows the obvious advantage to reflect complete details and characteristics of the national secondary industry and tertiary industry distribution, which is extending the field of nighttime light data research and applications for the socio-economic data in resources and environmental sciences.

As an important indicator in measuring the economic development level of a region, GDP spatialization is of great significance to study the socio-economic heterogeneity. The ancillary spatial density data selection is the key technique in controlling the GDP spatialization′s accuracy. In this paper, the prefectural GDP statistics is distributed to grid cells according to the spatial distribution information of GDP such as the population density (LandScan, AsiaPop) and night light data in Beijing-Tianjin-Hebei. Moreover, the absolute errors and relative errors of the GDP disaggregation at county-level are both calculated in order to compare the errors among the three different ancillary data as mentioned above. These results can provide a reasonable reference to ancillary spatial density data selection in GDP disaggregation. The results show that, the spatial distributions of the three types of ancillary spatial density data for GDP have revealed their own advantages and disadvantages. Comparing with both of the night light and the LandScan data, the AsiaPop simulation generally has the smallest error, especially in the suburban districts and rural areas of Beijing where the GDP tends to be overestimated, while the GDP is often underestimated in the economically developed city centers. For the LandScan simulation, six counties have presented a relative error of more than 200%, as the LandScan data are concentrated in Beijing and Tianjin, while the suburban districts and counties have also been overestimated. The AsiaPop simulation has only three counties (which locate in Tianjin) presenting a relative error being more than 200%. Because of the spatial heterogeneity of the economic activities, the GDP disaggregation error will increase with respect to the refinement of the administrative units, therefore, using the single-generation data to reasonably reflect the spatial distribution of economic activities is difficult, we need to take advantage of the distribution data such as the night light, roads, housing distribution and cell phone signals to improve the GDP disaggregation′s accuracy in future, and to reflects the GDP distribution characteristics in a more detailed manner. High-quality exposure data not only provide the basic data for the study of spatial analysis of natural disaster risk, but also provide a reference for other multidisciplinary research fields; meanwhile, the comprehensive application of using both the multi-source remote sensing data and the statistics data is the trend for socio-economic data spatialization.

Traffic congestion in urban road network heavily restricts transportation efficiency. Detecting traffic congestions in the spatio-temporal sense and identifying network bottlenecks become an important task in transportation management. Up to now, many traffic congestion detection methods have been proposed, which have focused on the detection of momentary local congestions. Larger-scale, longer-time and regular congestions can't be detected using these methods. That is because congestions have different temporo-spatial scales, and a characteristic is not considered in those methods. This paper proposes a new kind of urban traffic congestion detection method that deals with spatio-temporal extension of congestion. It is based on spatio-temporal clustering analysis of real-time traffic data. By defining a proper spatio-temporal correlation, the classic DBSCAN algorithm is adapted to tackle spatio-temporal clustering. With it we can detect longer time and regular traffic congestion in the spatio-temporal sense. Experiments have been conducted using real traffic condition data of Chengdu to validate the effectiveness of the method. The experiment shows that the proposed method can detect the congestion areas and identify the spatio-temporal extent of congestions accurately. The detected congestion areas were compared with congestion report from local traffic management authority and found to be consistent with the later.

A geographic information system(GIS) integrates hardware,software,and data for capturing,managing,analyzing,and displaying all forms of geographically referenced information.Addresses are one of the most popular geographical reference systems in natural languages.Address geocoding is considered as the most effective approach to bridging the gap between business data in management information systems(MIS) and GIS,which supports geospatial information visualization and spatial analysis.Chinese address geocoding faces three significant problems,i.e.address models,address resolution and address matching,because of the un-standardization of Chinese place names and the shortage of national address databases.Address resolution aims to automatically split address strings in natural language into address units without semantic incompletion.It plays a fundamental role in address models and address matching.Previous research focuses on rule or gazetteer based approaches,which are easily implemented but with poor coverage and performance.In theory,Chinese address resolution is similar to word segmentation in Chinese natural language processing.Based on the investigation of large-scale Chinese place names and address syntactic patterns,this paper identifies primary and secondary general characters that represent a variety of address units.And then an address numerical representation method is presented to induce syntactical rules of Chinese addresses.Finally,we develop an RBAI algorithm for implementation Chinese address resolution and illustrate an example.The experimental results indicate that the proposed approach can achieve satisfactory efficiency and effectiveness for large-scale data processing,the accuracy ratio over 92% and the processing rate over 2,800 items per second.The proposed approach and system can be extended to such fields as land management,asset management,city plan,public security,postal system,taxation,public health management and other location-base services.

Based on the historic literatures and remotely sensed images, this paper studies the spatial-temporal characteristics of Guangzhou city's spatial morphologic evolution in recent one hundred years, and summarizes the historic characteristics and the laws of urban development since the formation of Guangzhou, especially the period since the implementation of reform and open policies. On the whole, Guangzhou experienced two major stages, i.e., traditional urban development period before 1923 and modern urbanization development period from 1923 to 2004, covering several sub-periods of stabilization period and fast development period. The total increased built-up area of the city from 1979 to 2004 is 385.56 km², an expansion of 3.46 times, and the average expansion rate is about 15.43 km² per year. Many factors have contributed to the urban spatial morphologic evolution. But four major driving forces, i.e., economic development, institution and policy change, city planning and transportation system are the most important factors. Physiographic environment is the base of the city expansion;economic development is the inherent impetus for the evolution of the spatial morphology, and city planning plays a vital guiding role to the construction and development.

The study area is located in Miller county,Yunnan province,China.An experiment to select the appropriate classification method for multi-temporal remote sensing images was done.Typical classification methods including Object-Oriented Classification(OOC),Back Propagation Neural Network(BPNN),Spectral Angle Mapper(SAM),Maximum Likelihood Classifier(MLC),and Comparison After Classification(CAC)were tested in this experiment.In this study,using two-phase remote sensing images of CBERS02B-CCD and Landsat-5 TM,the suitability and accuracy of typical methods to deal with multi-temporal images classification were compared,based on different phenological characteristics of sugarcane,corn and paddy.Using full sample test method,visual interpretation results were used as reference data to validate the accuracy of different classification methods.The experimental results show that the order of overall classification accuracy from high to low is OOC,BPNN,SAM,MLC,and CAC,and the Kappa accuracy of them is 0.655、0.635、0.631、0.601 and 0.577,respectively.As it is easy to identify paddy,its accuracy is higher than that of sugarcane and corn.The order of accuracy of paddy for different methods is as the same as the order of overall accuracy,the highest and lowest accuracy of paddy is 0.706 and 0.621,respectively.The accuracy curve position between the accuracy of various land covers and the overall accuracy are consistent for MLC and CAC,and the overall accuracy of CAC is the lowest one.The accuracy of corn for OOC is the highest one with Kappa of 0.611.The Kappa accuracy of sugarcane for OOC,SAM and BPNN is 0.594,0.575 and 0.575,respectively.In general speaking,for the remote sensing classification of Multi-temporal Images,OOC is the best,BPNN and SAM is better,MLC and CAC are the worst.The conclusions of this experiment have some guidance to select the appropriate classification method for multi-temporal remote sensing images.

The fast expansion of urban built-up land and accompanied sharp decrease in farm land have made timely monitoring of landuse changes become more important than ever before.The ability to monitor the built-up land dynamics in a cost-effective manner is highly desirable for local communities and decision makers alike.Fortunately,satellite remote sensing technique offers considerable promise to meet this requirement.Although the use of remote sensing technique in the monitoring of land use changes has become more and more popular and satellite imagery has been frequently used to discriminate built-up lands from non-built-up lands for the last few decades,the extraction of built-up land information from remote sensing imagery is still not an easy task due largely to the heterogeneous characteristics of the built-up land.Among many techniques developed for the extraction of built-up land information,the index-based built-up index(IBI) was created based on three existing thematic indices rather than original multispectral bands.The use of the three thematic indices-soil-adjusted vegetation index(SAVI),modified normalized difference water index(MNDWI) and normalized difference built-up index(NDBI)-greatly help the delineation of built-up land features in remote sensing imagery,because these three indices represent three major landuse components,which are vegetation,water and built-up land,respectively.Therefore,the IBI can significantly enhance built-up land information while suppressing background noise.Consequently,the built-up land can be effectively extracted from the IBI image with high accuracy.In order to quicken image processing,this built-up extraction technique has been programmed to form an easy-use module using the ER Mapper scripting language.The module was further integrated in the ER Mapper package by adding a button to the manual bar.This allows users to automatically perform the extraction procedure with high accuracy just in a few minutes.

Image segmentation is a key technique in image processing and computer vision field. From the point of view of geo-processing and application of remote sensing images, this paper emphasizes the importance of image segmentation for information extraction and targets recognition from remote sensing images and sets a classification system of common remote sensing image segmentation methods. In addition, this paper states the thoughts of high resolution RS image segmentation methods evaluation and tests it by evaluating four typical image segmentation algorithms based on features with six images qualitatively and quantitatively. The four typical image segmentation algorithms are Max-Entropy (ME), Split&Merge (SM), improved Gauss Markov Random Field(GMRF) and Orientation&Phase(OP). In the qualitative evaluation, this paper analyses these algorithms in terms of their rationale and gets a rough evaluation. In the quantitative evaluation, image complexity is taken into account firstly and five measures are employed. The five measures are removed region rumber, non uniformity within region measure, contrast across region measure, variance contrast across region measure and edge gradient measure. The qualitatively and quantitatively evaluation results are important to perform the optimal selection of segmentation algorithm in practical work. In the end, this paper draws some conclusions about high resolution remote sensing image segmentation and enumerates the flaws of image segmentation methods evaluation, especially it concludes the application prospect of high resolution RS image segmentation.

With the rapid development of information acquisition technology, the geographic information data is increasing at the magnitude of terabyte every day. As an important content of 3D GIS, 3D city model data plays an important role in the construction of digital city and smart city. Because the data structure of 3D city model is complex and the data volume is huge, how to efficiently divide and store large amount of 3D city model data in order to meet the long-term management of data, the rapid visualization of data scheduling and the requirement of spatial assistant decision-making of 3D GIS system, has become a research hotspot in recent years. Previous data partitioning methods have caused the changes of zoning frequently in the data scheduling, which makes the update and management of data become more difficult. So, it is necessary to find out a more stable and universal data partitioning method. In this paper, based on the research of the shortcomings for the existing 3D city model data partitioning methods, we proposed the large scale map partition method based on topology relation model, and then we designed a unified name encoding scheme for the 3D models data after splitting. With the help of the powerful massive data organization and efficient multiple concurrent access function of the non-relational database MongoDB, a MongoDB sharded cluster server is constructed. The 3D city model data was used in unit division, and the rules modeling software City Engine was applied to processing the divided units, thus producing the 3D city model. Afterwards, MongoDB was used for data storage experiments. The results show that the large scale map partition method based on topology relation model is capable and sutable for the data partition of 3D city model, and the storage efficiency of the divided data is obviously improved. Moreover, the MongoDB database has a good stability on multiple concurrent access.

The international relations are intricate and ever-changing since the 21^st century, and have brought profound changes to the world's economy, security, and diplomacy. These changes have had a major impact on China's internal and external policies. A comprehensive and timely analysis of international relations and its changing characteristics has important reference value for China's economic and diplomatic development planning. The analysis of international relations has spatio-temporal characteristics, and it needs real-time processing. Thus, it needs to introduce the methods of spatio-temporal big data analysis to analyze international relations. Traditional mass media such as news, radio, etc. record all kinds of events happening in the world. It contains a wealth of information. Compared with social media data recording personal activities, it is more suitable for large-scale and long-term analysis of human society. The Global Database of Events Language, and Tone (GDELT) is a free and open news database which monitors news from print, broadcast, and online media in the world, analyzes the texts and extracts the key information such as people, place, organization, and event. This paper researches the network characteristics of GDELT based on theory of complex network and further analyze the relations between countries. Firstly, this paper constructs national interaction networks using GDELT, then analyze the interaction relationship between countries through network characteristic statistics, and finally detect the time series changes of the national conflict event interaction network. The results show that: (1)The National interaction network has scale-free characteristics, the interaction between countries is unevenly distributed from a global and local perspective. Very few countries have lots of interactions while most countries have very few interactions, and one country has lots of interactions with a few countries while a few interactions with most countries. (2) Sudden changes in the national interaction network of conflict events often indicates some significant national conflict events. This paper can provide a new perspective for the exploration of international relations and a reference for the analysis of news media in the era of big data.

Research of urban growth has focused on the two-dimensional flat space, while the development of the modern city is three-dimensional. So the development and changes of the modern urban space could not be accurately described by two-dimensional method. Therefore, the research of three-dimensional urban growth has great significance to the future development of the city. Based on the theory of self-organization in urban development, the urban growth simulating model using three-dimensional cellular automata (3DCA) which proposed by Bengguigui was improved. The center distance parameter and the traffic distance parameter were added to the model. And new three-dimensional urban growth models were set up: the center distance model and the transport distance model. The two establishment steps of the model are as follows: Firstly, described the calculation methods and the economic interpretations of all the model parameters. Secondly, gave the potential development function of three-dimensional urban growth and the transition rules of the cellular state. The three-dimensional urban growth simulating experiments based on the given models was developed by NetLogo 3D. NetLogo 3D is a programmable modeling environment for natural and social phenomena simulation, which could show simulating results both in two dimension and three dimension view. The experiment results show that the three-dimensional urban growth simulating with the models proposed in this paper is more approximate to the reality city extension progress than Bengguigui's model, and also prove that the urban development is a self-organized process.

The trajectory datasets record a series of position information at different times, so they become the new data sources to study the laws of human mobility. As a main form of social remote sensing data, trajectory datasets also bring a new individual viewpoint to study geographical phenomena. With the emergence of big data, trajectory data mining becomes a hot topic in geographical information science, urban computing and other correlative disciplines. In this paper, we gave a brief review on trajectory data mining and its applications in cities. First, we listed the data sets frequently adopted by human mobility research, gave the classification and their typical applications using FCD data, mobile phone data, smart cards data, check-in data, etc. Then, we summarized its application in solving cities’ problems from four aspects: (1) the identification of urban spatial structure and function unit; (2) the patterns recognition of human activity and the behavior prediction of human movement; (3) the traffic time estimation and the anomaly detection of intelligent transportation; (4) other applications in urban computing such as in urban air and noise pollution, disaster prevention and rescue, even in intelligent tourism and information recommendation. At the end, we pointed out the challenges and further research directions of trajectory data mining.

The efficiency for querying complex spatial information resources is an important indicator to evaluate the performance of current spatial databases. Traditional single node relation spatial data management is difficult to meet the demand of high-performance in querying large amounts of spatial data, especially for the complex join query on multi-table. In order to solve this problem, we design and implement a spatial database middleware prototype system. This system takes full advantages of the massive parallel processing (MPP) and shared-nothing architecture. In consideration of the characteristics of spatial data, we design the spatial data parallel import, multi-spatial-tables join strategy, spatial data query optimization and other algorithms and models. This paper firstly introduces the development status of parallel database systems in recent years, and then elaborates its MPP architecture and its organizational model, and the strategy of the join query on multi-spatial-table. Finally, we made some query experiments on massive spatial data and analyzed the results of these inquiries. The experimental results show that this system indicates a good performance (nearly linear speedup) in processing the complex query of massive spatial data. Compared with the tradition single node database, this system can fully improve the efficiency of complex querying for large spatial data, and it is a more efficient solution to solve the complex spatial data queries.

Urban space and the behavior of human activities constantly interact with each other. Investigation on distribution of aggregated human activities and spatio-temporal change benefits data-driven policy-making in urban planning and urban governing. In the era of big data, with the development of information and communication technologies, it is possible to collect city-scale data with high resolution in space and time by various location-aware devices and sensors. Exploration of spatial-temporal activities attracts a lot of attention. By taking about 10 million one-day tracking data of mobile phone users in Shenzhen, China as an example, this paper firstly identified their stay locations according to spatial and temporal rules to generate stay trajectory for each individual and recovered activity semantic information by labelling activity types for each stay locations. Then, the significant differences in patterns of distributions of stay locations and their activities were analyzed. Spatial and temporal distributions of different human activities were explored, respectively. The study shows that the distribution of stay locations and activities is obviously heterogeneous. The average number of stay locations of an individual per day is 2.1, while the average number of activities an individual engaged in per day is 3.4. This study furthermore suggests that different types of activities have temporal variance and spatial heterogeneity. The temporal distribution fluctuates significantly over 24 hours, which is in accordance with daily routine. The spatial distribution overall obeys “space power law”, and the spatial distribution of social activity, which has a faster-down tail, shows a more obvious pattern of spatial segregation than the other two activities. The study revealed the diversity and heterogeneity of spatial and temporal distribution of human aggregated activities in urban space, which is meaningful in analyzing human activities research and facilitating urban traffic optimization and urban planning.

In the era of big data, the rapid growth of geographic spatial temporal data has challenged the conventional application concepts, technical framework and service modes. In this paper, the concept and features of geographic spatial temporal big data is elaborated firstly. Then, the characteristics and challenges of the geographic spatial temporal big data computation are analyzed. Particularly, the theory of collaborative computing and service for the geographic spatial temporal big data is developed, which includes four levels of collaboration: data collaboration, technology collaboration, service collaboration and producing collaboration. According to the demand of the market-oriented operation and platform-based service, the technical frameworks of the geographic spatial temporal big data collaborative computing are designed. Furthermore, four common key technologies are discussed, including the remote sensing data preprocessing, the geographic spatial temporal data storage and management, the high performance computing and the visualization of geographic spatial temporal big data. Next, the remote sensing data processing system is developed, and is taken as a case to illustrate the implementation of collaborative computing and service of geographic spatial temporal big data. At last, this paper forecasts the future application mode of geographic spatial temporal big data.