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As communication technology and smartphones develop, many indoor positioning applications based on Bluetooth low energy (BLE) beacons have emerged. However, in a complex BLE network, it can be challenging to select the optimal reference beacon, and accurate positioning becomes difficult. Fortunately, if the BLE network is displayed on a map, we can intuitively grasp the structure and density of the beacons in each area, which is important information for accurate positioning. Therefore, in this study we developed a map-aided indoor positioning algorithm to model the relationship between beacons in the positioning area in a parking lot. Specifically, the algorithm split all beacons into multiple cell areas to find the optimal reference beacon in that area. Then, the optimal reference beacon is used to find the preferred reference beacons among the real-time beacons. Finally, the positioning results were calculated and evaluated according to the preferred beacons. According to the results, our method can optimize the selection of reference beacons in different areas. The average positioning accuracy was 2.09 m and the results can be scored accurately. The results verify that our algorithm can effectively use map information to guide the selection of reference beacons in complex environments.

Geoportals have been the primary source of spatial information to researchers in diverse fields. Recent years have seen a growing trend to integrate spatial analysis and geovisual analytics inside Geoportals. Researchers could use the Geoportal to conduct basic analysis without offline processing. In practice, domain-specific analysis often requires researchers to integrate heterogeneous data sources, leverage new statistical models, or build their own customized models. These tasks are increasingly being tackled with open source tools in programming languages such as Python or R. However, it is unrealistic to incorporate the numerous open source tools in a Geoportal platform for data processing and analysis. This work provides an exploratory effort to bridge Geoportals and open source tools through Python scripting. The Geoportal demonstrated in this work is the Urban and Regional Explorer for China studies. A python package is provided to manipulate this platform in the local programming environment. The server side of the Geoportal implements a set of service endpoints that allows the package to upload, transform, and process user data and seamlessly integrate them into the existing datasets. A case study is provided that illustrated the use of this package to conduct integrated analyses of search engine data and baseline census data. This work attempts a new direction in Geoportal development, which could further promote the transformation of Geoportals into online analytical workbenches.

There is a critical need to develop a means for fast, task-driven discovery of geospatial data found in geoportals. Existing geoportals, however, only provide metadata-based means for discovery, wi...

Homonymous entity matching is an important part of multi-source spatial data integration, automatic updating and change detection. Considering the low accuracy of existing matching methods in dealing with matching multi-scale settlement data, an algorithm based on Attributed Relational Graph (ARG) is proposed. The algorithm firstly divides two settlement scenes at different scales into blocks by small-scale road network and constructs local ARGs in each block. Then, ascertains candidate sets by merging procedures and obtains the optimal matching pairs by comparing the similarity of ARGs iteratively. Finally, the corresponding relations between settlements at large and small scales are identified. At the end of this article, a demonstration is presented and the results indicate that the proposed algorithm is capable of handling sophisticated cases.

Recent advances in earth observation technology have led to the generation of large numbers of remote sensing (RS) observations. As RS applications continue to increase in magnitude, major problems include how to discover and make quick and robust use of such data in spatial data infrastructures (SDIs). In this paper, a new RS image discovery method is proposed for discovering observational data based on task, location, and time. This method approaches location and time not only as filters but also as spatial–temporal constraints in the discovery process, and exploits the relationships between tasks and RS data sources under spatial–temporal constraints through case-based reasoning (CBR). In this method, cases are past experiences which comprise task, time, location, and image parameters, describing what images were used to satisfy a particular task, and at what time and place each discovery was made. CBR, given its similarity to assessment and result reasoning models, finds past cases that satisfy a user’s query and generates image parameters for specific RS data needed to satisfy that request. A prototype called iGeoportal was developed to evaluate the effectiveness of the proposed method. Experiments show that it performs efficiently when discovering RS images and can be easily integrated into current SDIs through a service-oriented architecture.

Recognition and understanding of ship mobility pattern have great significance for intelligent maritime applications, i.e. route discovery and anomaly detection. Besides a number of pattern discovery techniques currently derived from ship trajectory, topic modeling popular in the field of Natural Language Processing may provide a novel way to detect implicit patters underlying massive ship trajectories treated as documents. This paper is motivated to apply a semantic analysis method to explore potential mobility patterns from ship trajectories in inland river by combining semantic transformation and topic model. A coarse-grained semantic transformation model is firstly defined to translate each ship trajectory into a document containing a series of sequential motion words. A motion word is generally a synthetic semantic representation of three trajectory features (location, course and speed). All ship trajectories can then be examined and analyzed as a document corpus. A classic topic model (i.e. Latent Dirichlet Allocation, LDA) is employed to explore hidden ship mobility patterns from trajectory document corpus. The effectiveness of this approach is illustrated through a case study at Wuhan waterway, located at middle stream of Yangtze River in China.

The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.

Detection of spatial clusters among geographic events in a planar space often fails in real world practices. For example, events in urban areas often occurred on or along streets. In those cases, objects and their movements were limited to the street network in the urban area. This deviated from what a set of freely located points could represent. Consequently, many of the spatial analytic tools would likely produce biased results. To reflect this limitation, we developed a new approach, weighted network Voronoi diagrams, to modeling spatial patterns of geographic events on street networks whose street segments can be weighted based on their roles in the events. Using kernel density estimation and local Moran’s index statistics, the frequency of events occurring on a street segment can be used to produce a weight to associate with the street segment. The weights can then be normalized using a predefined set of intervals. The constructed weighted Voronoi network explicitly takes into account the characteristics of how events distribute, instead of being limited to assessing the spatial distribution of events without considering how the structure of a street network may affect the distribution. This approach was elaborated in a case study of Wuhan City, China. Constructing weighted network Voronoi diagrams of these partitioned networks could assist city planners and providers of public/private services to better plan for network-constrained service areas.

The Knox test is a common method to analyze the spatio-temporal interaction patterns of events. It has been used in many fields such as criminology, medical and public health, epidemiology, etc. However, this method has been criticized for arbitrary selection of critical distances. The same set of events may manifest different spatio-temporal interaction patterns under different critical distances, so the determination of critical distances is crucial for the Knox test. This paper examines and compares three existing critical distances choosing methods, and then proposes a new way to determine critical distances for the Knox test. Two data sets of crimes, burglary and electric vehicle theft in a city of central China are used as experimental data to demonstrate the validity and effectiveness of the method. Results show that compared with three common methods, the method presented in this study can be more effective at detecting the spatio-temporal interaction patterns of different events. This new method can provide a reference for selecting critical distances for the Knox test.

The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data sparsity. This paper proposes a novel location prediction model called CMoB (Crime Multi-order Bayes model) based on the spatiotemporal semantics to enhance the prediction performance. In particular, the model groups suspects with similar spatiotemporal semantics as one target suspect. Then, their mobility data are applied to estimate Markov transition probabilities of unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by integrating the total transition probabilities, which are derived from the multi-order property of the Markov transition matrix, into a Bayesian-based formula, it is able to realize multi-step location prediction for the individual suspect. Experiments with the mobility dataset covering 210 suspects and their 18,754 location records from January to June 2012 in Wuhan City show that the proposed CMoB model significantly outperforms state-of-the-art algorithms for suspect location prediction in the context of data sparsity.

Many geoportals are now evolving into online analytical environments, where large amounts of data and various analysis methods are integrated. These spatiotemporal data are often distributed in different databases and exist in heterogeneous forms, even when they refer to the same geospatial entities. Besides, existing open standards lack sufficient expression of the attribute semantics. Client applications or other services thus have to deal with unrelated preprocessing tasks, such as data transformation and attribute annotation, leading to potential inconsistencies. Furthermore, to build informative interfaces that guide users to quickly understand the analysis methods, an analysis service needs to explicitly model the method parameters, which are often interrelated and have rich auxiliary information. This work presents the design of the spatial data linkage and analysis services in a geoportal for China urban research. The spatial data linkage service aggregates multisource heterogeneous data into linked layers with flexible attribute mapping, providing client applications and services with a unified access as if querying a big table. The spatial analysis service incorporates parameter hierarchy and grouping by extending the standard WPS service, and data-dependent validation in computation components. This platform can help researchers efficiently explore and analyze spatiotemporal data online.

The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient–boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target links. This method can deliver high prediction accuracy by combining simple regression trees with poor performance. It corrects the error found in existing models for improved prediction accuracy. Our spatiotemporal gradient–boosted regression tree model was verified in experiments. The training data were obtained from big data reflecting historic traffic conditions collected by probe vehicles in Wuhan from January to May 2014. Real-time data were extracted from 11 weeks of GPS records collected in Wuhan from 5 May 2014 to 20 July 2014. Based on these data, we predicted link travel time for the period from 21 July 2014 to 25 July 2014. Experiments showed that our proposed spatiotemporal gradient–boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches. Furthermore, these results indicate the advantages of our model for urban link travel time prediction.

A Sensor Web registry acts as a broker in a service-oriented environment to publish and discover Sensor Web resources (e.g. sensors, sensor services, observations, and alerts). The Catalogue Service for the Web (CSW) developed by the Open Geospatial Consortium (OGC) defines a standard interface and protocol for publishing and discovering geospatial resources. This article adopts the CSW for the development of a Sensor Web registry. Metadata for Sensor Web resources are registered into the catalogue information model – the ebXML Registry Information Model (ebRIM). In the Sensor Web environment, sensor observations can be available in real-time or near-real-time, and thus metadata registered in CSW need to be updated frequently. This article proposes an incremental harvesting approach, which can harvest the updated metadata efficiently. A prototypical implementation is provided to demonstrate the applicability of the approach.

The growth of the Web has resulted in the Web-based sharing of distributed geospatial data and computational resources. The Geospatial Processing Web (GeoPW) described here is a set of services that provide a wide array of geo-processing utilities over the Web and make geo-processing functionalities easily accessible to users. High-performance remote sensing image processing is an important component of the GeoPW. The design and implementation of high-performance image processing are, at present, an actively pursued research topic. Researchers have proposed various parallel strategies for single image processing algorithm, based on a computer science approach to parallel processing. This article proposes a multi-granularity parallel model for various remote sensing image processing algorithms. This model has four hierarchical interfaces that are labeled the Region of Interest oriented (ROI-oriented), Decompose/Merge, Hierarchical Task Chain and Dynamic Task interfaces or sub-models. In addition, interfaces, definitions, parallel task scheduling and fault-tolerance mechanisms are described in detail. Based on the model and methods, we propose an open-source online platform named OpenRS-Cloud. A number of parallel algorithms were uniformly and efficiently developed, thus certifying the validity of the multi-granularity parallel model for unified remote sensing image processing web services.

Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk model, and the distribution of extracted trips shows a distance decay effect. To identify the spatial similarity of trips at a finer scale, this paper investigates the distribution of trips through topic modeling techniques. Firstly, trip origins and trip destinations were identified from raw GPS data. Then, different trips were given semantic information, i.e., link identification numbers with a semantic enrichment process. Each taxi trajectory was composed of a series of trip destinations corresponding to the same taxi. Subsequently, each taxi trajectory was analogous to a document consisting of different words, and all taxi’s trajectories could be regarded as document corpora, enabling a semantic analysis of massive trip destinations. Finally, we obtained different trip destination topics reflecting the spatial similarity and regional property of human mobility through LDA topic model training. The effectiveness of this approach was illustrated by a case study using a large dataset of taxi trajectories collected from 2 to 8 June 2014 in Wuhan, China.

The existing Spatial Data Infrastructure applications or virtual globes allow users to experiment with these tools for sharing and exploration of geospatial data. A typical application is the recently launched MapWorld, an interactive geospatial data browser developed based on GeoGlobe. Geospatial Service Web goes further by providing a geospatial infrastructure that integrates various geospatial-related resources through the Web and service technologies. The development of the Geospatial Service Web will be the establishment of a platform where data, information, and knowledge can be shared and exchanged in an interoperable and interworkable way. The fulfilment of this platform requires the integration of geospatial data acquisition system, data transformation system, distributed spatial data collection, high-capability server system, and large volume storage system etc., and exploration and extraction of useful information and knowledge from oceans of data. These are together known as Web-based Geospatial Intelligence (i.e. Web GeoIntelligence), which is an important content of the Geospatial Service Web.

Currently, the top-down spatial data update mechanism has made a big progress and it is wildly applied in many SDI (spatial data infrastructure). However, this mechanism still has some issues. For example, the update schedule is limited by the professional department’s project, usually which is too long for the end-user; the data form collection to public cost too much time and energy for professional department; the details of geospatial information does not provide sufficient attribute, etc. Thus, how to deal with the problems has become the effective shortcut. Emerging Internet technology, 3S technique and geographic information knowledge which is popular in the public promote the booming development of geoscience in volunteered geospatial information. Volunteered geospatial information is the current "hotspot", which attracts many researchers to study its data quality and credibility, accuracy, sustainability, social benefit, application and so on. In addition to this, a few scholars also pay attention to the value of VGI to support the SDI updating. And on that basis, this paper presents a bottom-up update mechanism form VGI to SDI, which includes the processes of match homonymous elements between VGI and SDI vector data , change data detection, SDI spatial database update and new data product publication to end-users. Then, the proposed updating cycle is deeply discussed about the feasibility of which can detect the changed elements in time and shorten the update period, provide more accurate geometry and attribute data for spatial data infrastructure and support update propagation.

Direction relations, as an important spatial relationship, is simply expressed as object prosperity in traditional geo-ontology. The lacking of explicit specifications and reasoning rules of direction relations in geo-ontology result in the difficult or inflexible of spatial reasoning. Also, digital gazetteers provide information on named features, linking the feature's name with its location and its type. Although the location information is incomplete and not exact, the implicit spatial information, for example spatial relationships and spatial scale, can be extract using the appropriate models based on geo-ontology. In this paper, we proposed a novel conceptual framework of direction relations in order to formalize the semantics and implicit information of direction relations, and present an extraction algorithm of implicit information based on previous researches, which will produce a complete query instance of direction relations. At last, the most suitable direction physical model is recommended to calculation module according to relevant rules. And the experimental results show that this direction query model not only extracted the implicit information effectively, but also made a reasonable interpretation for the user's intention.

Existing implementations of collaborative image interpretation have many limitations for very large satellite imageries, such as inefficient browsing, slow transmission, etc. This article presents a KML-based approach to support distributed, real-time, synchronous collaborative interpretation for remote sensing images in the geo-browser. As an OGC standard, KML (Keyhole Markup Language) has the advantage of organizing various types of geospatial data (including image, annotation, geometry, etc.) in the geo-browser. Existing KML elements can be used to describe simple interpretation results indicated by vector symbols. To enlarge its application, this article expands KML elements to describe some complex image processing operations, including band combination, grey transformation, geometric correction, etc. Improved KML is employed to describe and share interpretation operations and results among interpreters. Further, this article develops some collaboration related services that are collaboration launch service, perceiving service and communication service. The launch service creates a collaborative interpretation task and provides a unified interface for all participants. The perceiving service supports interpreters to share collaboration awareness. Communication service provides interpreters with written words communication. Finally, the GeoGlobe geo-browser (an extensible and flexible geospatial platform developed in LIESMARS) is selected to perform experiments of collaborative image interpretation. The geo-browser, which manage and visualize massive geospatial information, can provide distributed users with quick browsing and transmission. Meanwhile in the geo-browser, GIS data (for example DEM, DTM, thematic map and etc.) can be integrated to assist in improving accuracy of interpretation. Results show that the proposed method is available to support distributed collaborative interpretation of remote sensing image

The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran's I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions.

An important issue is to check and identify the inconsistency of multi-representation data, when building multiscale spatial database. The representations of the same geographic entity in real world are different in terms of location, shape, and spatial relationships. In order to efficiently integrate multi-scale spatial data, we present a seamless representation model of spatial data based on its innate characteristics. Meanwhile, we put forward a complete consistency evaluation model for multi-scale representation. Vertically, it is divided into three levels, including macro, medium, and micro consistencies. Horizontally, it involves location, spatial relationship, and semantics, etc. The quantitative similarity measurement methods of spatial relationship are presented, which are applicable to the case of collapse in generalization. As case study, three spatial scenes with different scales are used to prove the completeness and rationality of our assessment system.

With the advent of Sensor Web, the satellite data acquired by sensor systems could be shared among users immediately. Our research has led to an implementation of natural language queries such that users without particular knowledge of satellite imagery can describe easily for what they need. We use a rules-based method to retrieve named entities, with the help of a knowledge base and uses existing Sensor Web services for acquiring stored or real time satellite data. We use rule-based methods to align time, location and domain task entities in natural language queries with Sensor Web services with standard times, geographical coordinates, and satellite attributes. To evaluate our system, we wrote a series of natural language queries in the domains of surveying and mapping, forestry, agriculture, and disaster response. Our queries and satellite data retrieved by the queries were corrected by a group of experts to create a gold standard. Using their remarks as correct, we scored our system results using precision and recall metrics standard for information retrieval. The results of our experiment demonstrate that the proposed method is promising for assisting in Earth observation applications.

This paper proposes a novel approach to decompose two-person interaction into a Positive Action and a Negative Action for more efficient behavior recognition. A Positive Action plays the decisive role in a two-person exchange. Thus, interaction recognition can be simplified to Positive Action-based recognition, focusing on an action representation of just one person. Recently, a new depth sensor has become widely available, the Microsoft Kinect camera, which provides RGB-D data with 3D spatial information for quantitative analysis. However, there are few publicly accessible test datasets using this camera, to assess two-person interaction recognition approaches. Therefore, we created a new dataset with six types of complex human interactions (i.e., named K3HI), including kicking, pointing, punching, pushing, exchanging an object, and shaking hands. Three types of features were extracted for each Positive Action: joint, plane, and velocity features. We used continuous Hidden Markov Models (HMMs) to evaluate the Positive Action-based interaction recognition method and the traditional two-person interaction recognition approach with our test dataset. Experimental results showed that the proposed recognition technique is more accurate than the traditional method, shortens the sample training time, and therefore achieves comprehensive superiority.

Spatial data services are increasingly available on the Internet, including OGC (Open Geospatial Consortium) web services and proprietary mapping services (e.g. Google Maps). It makes sense to integrate these heterogeneous services to produce the information demanded in complex applications. For integration, unified descriptions of services are required. In this study, the virtualization idea used in cloud computing is employed to conceal interface differences by translating physical resources into logical resources. Correspondingly a description method for spatial data service integration by resources virtualization is presented. Finally, this article implements a virtual file system using the proposed method. Experiments were conducted to overlay multi-source spatial data services by operating virtual files, including MapWorld, Google Maps and OGC standard services. The effectiveness of the proposed description method is verified by experimental results.

This paper aims at providing a more convenient approach for remote sensing images retrieval based on sematic-based user demand modeling. The semantic user demand model is a two-layer model that bridges the gap between users' satellite image demand of natural language description and satellite images. Natural language process and semantic inference are involved to generate the semantic user demand model. A knowledge database consists of ontologies, rules and dictionaries is developed to support natural language process and semantic inference. Semantic similarity and confliction-resolution are also adopted in inference. Finally, the model is validated by a prototype system based on protege-owl and JESS. The results show that the model and the approach are available.

Numerous geospatial models have been developed. Development of technologies that facilitate sharing geospatial model becomes highly desirable. Although geospatial infrastructures built using standards-based distributed services have become the default computing paradigm adopted, most of them have been built following a top-down approach that only official providers are entitled to deploy and preserve geospatial resources including geospatial models. Since the mechanisms to deploy geospatial model as a web service in geospatial infrastructures are technologically complex, there has been limited participation from users, resulting in a scarcity of sharing geospatial model resources. Furthermore, the poor efficiency of geospatial model online implement remains the pains in most of geospatial infrastructures for transmitting and computing geodata in standard OGC service. Nowadays, the development of Cloud computing including associated spatial computing becomes the promising technology which is essential for enabling computing-intensive and data-intensive geospatial research and applications. Citizen sensing activities are accumulating data, information and models at a comparable or faster pace. To utilize these emerging technologies for addressing the limitations mentioned above, we present a distributed framework based on OGC specification and extend it with a service deploy component. This component improves ad hoc integration, deployment and register of geospatial model resources by two distinguishing approaches. Geospatial models can then be deployed, shared and accessed through tools and systems compliant with WPS by end users. Moreover, we modified the interface specification of WPS and WFS conforming to the MapReduce parallel computing mode and designed a self-adaptive task regulating mechanism for Hadoop and achieved great acceleration of the data-intensive computing in geospatial model. Finally, we illustrate our technique by the Geospatial Data and model platform addresses availability and efficiency of geospatial model auto-deploy mechanism.

Researchers from urban and regional studies often find it difficult to integrate and analyze data from multiple sources efficiently. Spatial intelligence, supported by spatial technology, provides an effective way to integrate information, methodology and knowledge together. In this paper, we present the Urban and Regional Explorer based on China Geo-Explorer, which is a web based platform for spatial intelligence, acting both as a data provider and a service provider. Large amount of data are added to meet research needs, and several functions are enhanced to make the usage more efficient. Ten sets of reports are prepared for easy information extraction, a new Point of interest (POI) module is integrated into the platform to assist users to find interesting or helpful data, and the ESDA methods in the platform are further enhanced to make the user's exploration more straightforward and interactive.

Due to the vagueness of spatial direction relations and the limitation of current models, a fuzzy description model of direction relations based on adaptive sampling is presented. According to fuzzy theory, a new membership function is proposed as the reference model which is a basic fuzzy description model of 4 directions. And two objects are adaptively partitioned and sampled, thus the detailed fuzzy description of direction relations between two objects and the cardinal direction relations are obtained by calculating the direction relations between two sample point sets. At last, the validity of this model is verified by an illustration. Moreover, the direction relations between overlapping and contained region can also be effectively described using this model.

Geographic information coding is a fundamental enabling technology for spatial data organization, which is critical to the geological information query, retrieval and data mining [1]. Currently, the demands of geographic data sharing and update become increasingly urgent, while there is no unified geographic information coding standard and application fields are chaos. Based on the insufficiency of traditional coding methods, this paper discusses the characteristics of geographic information from the views of scale, space, semantics and time, puts forward a new coding model, which expands the current coding methods in scale, spatial, semantic and temporal dimensions. This paper illustrates settlement places fundamental geographic information as the example, which applies SGST coding model in the settlement places geographic database and establishes the multi-scale representation update mechanisms.

Given that conventional spatial information user profile models have not a clear classification because of lack of theoretical basis, the comprehensive information theory is introduced into spatial information use profile modeling area. From the point of view of linguistic, the classification method of user profile models based on comprehensive information theory is proposed in this paper. According to syntactic, semantic and pragmatic information, user profile models are classified into four categories: syntactic information based models, semantic information based models, pragmatic information based models and composite elements based models, which make a clear distinction of the classification of user profile models. Based on these researches, the adaptability of all models respectively is analyzed in-depth, and the application conditions of every kind of user models are pointed out definitely.

This paper aims at providing an incremental harvesting model used in distributed geospatial data registry centers, which is more efficiency than overall harvesting model. Compared with overall harvesting model, it pays more attention to the incremental items which are created, modified and deleted. As incremental items are only a few of the totals, they save the time on fetching and processing changed items. The whole harvest process is mainly composed of three steps. Firstly, retrieve time and incremental items from remote server. Secondly, processing fetched items with UUID. Finally, post updates and log the harvest time obtained from step 1. The model is implemented by extending CSW protocol and GeoNetwork. The experiment of comparing the time cost by incremental harvesting model and overall harvesting model shows that incremental harvesting model is much more efficient and applicable.

Conventional information retrieval doesn't meet users' personalized requirements. Furthermore, personalized retrieval in recent few years focuses on mainly personalization of web information retrieval regardless of the difference between traditional information and spatial information. In this paper, a personalized retrieval strategy of spatial information is further put forward, which combines user profile with query request, and consists of following stages: Firstly, original data collected by system is cataloged into different levels, and useful information is refined from the levels; Secondly, user profiles are constructed on the basis of refined information; Thirdly, the search for spatial information from query conditions is implemented and the candidates are got accordingly; Lastly, utility degrees of the candidates aren't identical for different users in spite of the same query conditions, and so the utility degrees are achieved based on user profiles and personalized retrieval results are available after descending the candidates by utility degrees.

With the emergence of technologies and standards supporting the development of spatial web services, more and more services are becoming available on the Internet. In this paper, aiming at the automatic service composition of spatial information processing service, a temporal correlation based processing service composition method was presents. The temporal-correlation spatial data are integrated for the temporal — spatial analysis service by the procedure of searching spatial information service which provides the same extent of spatial data with different temporal-series. We adopt a semantic temporal approach to uniform the temporal property of spatial data brought by different spatial information services for the consistency in temporal analysis service. Moreover, the technology of case-based reasoning is employed to improve the intelligence of service searching. These issues are illustrated with an example — calculation of the changing vegetation coverage rates across multiple temporal-series.

In recent years, earthquake, typhoon, snowflake and other natural disasters happened frequently and cause large number of casualties and economic losses. It needs to be further improved under the circumstances of sudden natural disasters. The solution to the problem is to provide data support for emergency response and scientific decision-making to commanders by uploading accurate, reliable and instant information on disaster-occurred spot. This paper does some research on instant disaster information upload and remote disaster management using mobile terminals. Off-line processing, drawing tracks according to GPS information and other solutions are proposed. Finally, this paper designs the structure of disaster data collection and management prototype system and analyses the whole structure, functions and data processing workflow and implements the prototype at last.

A novel approach for proactive spatial information delivery is put forward. From the point of view of decision-making theory, spatial information delivery process can be seen as decision making and implementation. Note that the classic TOPSIS method in Multi-Attribute Decision-Making theory reveals two problems when applying in the delivery of spatial information and one is the difficulty of achieving ideal solution, and the other is the difficulty of getting spatial information utility degree. To solve the two problems, ideal solution is gotten through user profiles, and utility degree is obtained through the measurement of feature distances. So the proactive delivery decision-making based on the improved TOPSIS is proposed. The approach consists of following steps: data source collecting; user profile constructing; proactive delivery. Firstly, original data are collected by system, and useful information is refined; Secondly, user profiles are constructed on the basis of refined information; Thirdly, the utility degrees of spatial information for users and the pairs are achieved which are the information to be disseminated and the corresponding users. After proactive delivery decision making and subsequent delivery implementation, proactive delivery is consequently available.

With the advent of Web 2.0, new technologies for spatial data service, and demanding needs in spatial data sharing, and advanced analysis, both geographic information system and spatial statistics are facing challenges from different disciplines never met before. Due to their mutual beneficial roles in highlevel decision making, it's quite essential to make research on the integration of Web 2.0 based WebGIS with spatial statistics. Thus, to promote spatial intelligence, this study made an example of this integration by incorporating spatial cluster analysis seamlessly into China Geo-Explorer platform, a web-based platform for spatial & aspatial data integration and analysis. This integration made good use of benefits of WebGIS and spatial statistics, through data sharing, mapping and reporting, enabling spatial clusters identification with exploratory analysis and post-discovering knowledge from results of spatial pattern analysis. Some application scenario was introduced to demonstrate its great potential in spatial decision making.

More and more researchers from different fields find it essential to combine GIS data to do survey data analysis and multi-disciplinary studies. Spatial intelligence allows efficient data integration for spatial and non-spatial data, quick and accurate location analysis and spatial assessment. In this paper, we present an updated version of China Geo-Explorer, a web based platform for spatial intelligence, which integrates multi-source spatial data and non-spatial data from various fields including demography, business and religion, etc. The platform interacts with users through many flexible spatial selection methods to generate time-saving, easy-to-use, and pre-formatted reports as well as customized reports, provides different kinds of observations points on fields like economy and hydrology for spatial selection and time-series data visualization, comparison and analysis, and creates dynamic theme map on demand. Finally we discuss two case studies on business intelligence and multi-disciplinary studies as applications of the platform.

The integration of heterogeneous geospatial data offers possibilities to manually and automatically derive new information, which are not available when using only a single data source. This paper presents a three-level schema integration architecture which consists of local schemas, mapped schemas, and a global schema, for global heterogeneous geospatial systems. we describe a machine-learning based approach for GIS schema matching. Our approach extends existing machine-learning approaches for (traditional) data mapping but departs from them due to the nature of geographic data. Our solution reduces the complex mappings by identifying different values of a determining property.

Historically, cellular automata (CA) is a discrete dynamical mathematical structure defined on spatial grid. Research on cellular automata system (CAS) has focused on rule sets and initial condition and has not discussed its adjacency. Thus, the main focus of our study is the effect of adjacency on CA behavior. This paper is to compare rectangular grids with hexagonal grids on their characteristics, strengths and weaknesses. They have great influence on modeling effects and other applications including the role of nearest neighborhood in experimental design. Our researches present that rectangular and hexagonal grids have different characteristics. They are adapted to distinct aspects, and the regular rectangular or square grid is used more often than the hexagonal grid. But their relative merits have not been widely discussed. The rectangular grid is generally preferred because of its symmetry, especially in orthogonal co-ordinate system and the frequent use of raster from Geographic Information System (GIS). However, in terms of complex terrain, uncertain and multidirectional region, we have preferred hexagonal grids and methods to facilitate and simplify the problem. Hexagonal grids can overcome directional warp and have some unique characteristics. For example, hexagonal grids have a simpler and more symmetric nearest neighborhood, which avoids the ambiguities of the rectangular grids. Movement paths or connectivity, the most compact arrangement of pixels, make hexagonal appear great dominance in the process of modeling and analysis. The selection of an appropriate grid should be based on the requirements and objectives of the application. We use rectangular and hexagonal grids respectively for developing city model. At the same time we make use of remote sensing images and acquire 2002 and 2005 land state of Wuhan. On the base of city land state in 2002, we make use of CA to simulate reasonable form of city in 2005. Hereby, these results provide a proof of concept for hexagonal which has great dominance.

Keyhole Markup Language (KML) is an XML-based spatial data description language of modeling and storing geographic features such as points, lines, images, polygons, and models for display in earth browsers. It's very convenient to use KML for spatial data description, however, KML lacks of description of terrain information that has important value in geology, hydrology, nature disaster monitoring and many other fields. In consideration of such shortcomings, we expand some functions based on the KML and present Distributed Spatial Data Markup Language (DSDML). DSDML can describe DEM and raster data of large size, and support customed terrain information application. This paper mainly researches on how to describe terrain data using DSDML and implement visualization, including designing a data organization model using quad-tree model and pyramid model, utilizing external linked DEM dataset for terrain description of large volume and DEM tile for terrain description of small volume, realizing a DSDML data parser based on SAX model and building a terrain buffer pool for terrain visualization to obtain a high performance mechanism. We make experiments on three-dimensional visualization platform with different data size, and the results demonstrate that DSDML can be efficient and effective for terrain description and visualization.

The Web has become a platform where data, information and knowledge can be published, discovered and retrieved. The growth of the Web has resulted in the Web-based sharing of distributed resources. The trend to enhance the collaboration, creativity and rich user experiences has led the evolution of Web to the socalled Web 2.0, which is represented by a set of typical applications including blog, wiki, social networking (O’Reilly 2005). Another trend to increase the intelligence of the Web is the effort to move the Web to a universal medium for data, information and knowledge exchange, i.e. the Semantic Web, in which the semantics of information on the Web are defined, making it possible for the Web to understand (Berners et al. 2001). Grid computing, a blueprint for the computing infrastructure (Foster and Kesselman 2004), provides a set of middleware to back up the processing capabilities of Web.

Spatial data infrastructures are currently being set up within regions, countries and across national border. In SDIs, the data sharing is replaced by the service sharing gradually. Many geospatial services are provided on the internet, WPS-based remote sensing image processing is a special type in these services, it can differ widely in their functionality. Current (keyword-based) approaches to service discovery are inherently restricted by the ambiguities of natural language, which can lead to low precision and/or recall. In order to overcome this limitation, WSMO is chosen as the description framework of service, and a semantic discovery method based on logic programming is used to match the service description. In result, search results are more accurately, the semantic heterogeneity could be solved.

this paper integrates grid technology and OGC compliant services to design and implemented a geospatial web service platform for the western region's surveying and mapping project. It can make use of OGC common services and interoperability interfaces to supply efficient and secure data management, publishing and inquiries in the heterogeneous computer environment and as well as multi-scale seamless display in 3D GeoGlobe. As an integration of grid technology and the OGC Web Services, this platform has many advantages that traditional platform can't provide, including open, standardization, security and the ability to integrate heterogeneous services and support for heterogeneous data types, and having a good query and visualization effectiveness as well.

With great volumes of geospatial data and data from different fields distributed across Internet, the sharing and utilization of these data have presented great challenges for research and application in Geographical Information Science. Moreover, the new concepts and technologies of Web 2.0 represent new user demands for user-oriented interaction, and collaboration. There is an emerging need to provide users with an integrated tool for spatial exploration, and spatial analysis in supporting common advanced needs from multiple disciples of research and application. This paper introduced China Geo-Explorer - a new web based platform with advanced functions for spatial data integration, data selection, data analysis, and data service for research, education and decision support as a joint effort by the China Data Center at the University of Michigan, Wuhan University and All China Marketing Research. It demonstrated how space-time data of different formats and sources can be integrated, visualized, and reported in a web based system. Due to its flexibility in architecture, interoperability in data sharing, rich experience in user interaction, and deeper consideration in promoting spatial analysis, it was suitable for kinds of applications from various domains. Some case studies in disaster assessment, environment, health, and regional development as the application of the system were discussed.

In this paper, the spatial query models of multi platforms are analyzed. A 2-level MBR filter spatial join method (2FSJM) is proposed, which improves the performance of multi-platforms, including Oracle Spatial, ArcSDE and the open source platform OGR/GEOS. The experiments on multi-platforms are implemented. The results show that the improvement is valid.

A huge amount of geospatial and temporal data have been collected through various networks of environment monitoring stations. For instance, daily precipitation and temperature are observed at hundreds of meteorological stations in Northeastern China. However, these massive raw data from the stations are not fully utilized for meeting the requirements of human decision-making. In nature, the discovery of geographical data mining is the computation of multivariate spatio-temporal correlations through the stages of data mining. In this paper, a procedure of mining association rules in regional climate-changing databases is introduced. The methods of Kriging interpolation, fuzzy c-means clustering, and Apriori-based logical rules extraction are employed subsequently. Formally, we define geographical spatio-temporal transactions and fuzzy association rules. Innovatively, we make fuzzy data conceptualization by means of fuzzy c-means clustering, and transform fuzzy data items with membership grades into Boolean data items with weights by means of e-cut sets. When the algorithm Apriori is executed on Boolean transactions with weights, fuzzy association rules are derived. Fuzzy associ ation rules are more nature than crisp association rules for human cognition about the reality. Keywords: Temperature, precipitation, fuzzy associa tion rules, spatio-temporal data mining

Along with the development of earth observation technology, large amounts of geospatial information are accessible. There are also a lot of geospatial data and services which ar e shared on the Internet. Howe ver they vary in formats and are stored at various organizations leading to problems of data discovery, data interoperability and usability. The Open Geospatial Consortium (OGC) has developed standard service called catalogue in order to overcome this problem. The goal of a geospatial catalogue is to support a wide range of users in discovering relevant geographic data and services from heterogeneous and distributed repositories. But in most of geospatial catalogue services, the search functionality is limited to the direct match of keywords from metadata, the OGC catalogues may not return useful results as the used keywords often do not match with the meta-information stored in the catalogues. In this paper, we propose a geospatial semantic catalogue services that aims at overcoming this limitation. Keywords: catalogue service, semantic, ontology, inte roperability, geospatial data and service

Conventional spatial information delivery is system-oriented, and users have to adapt to the system and complete the delivery of spatial information. Two disadvantages exist in the delivery service mode: (1) users' personalized requirements cannot be satisfied; (2) Initiative delivery service cannot be provided for users. In this paper, we firstly classify conventional user profile models into four categories. After analyzing the limitations of these user profile models, we conclude that conventional user profile models are not suitable for the application of spatial information intelligent delivery. Then, aiming to the objectives of spatial information intelligent delivery, we propose a user profile model of spatial information, and give the building method of the model. Also, we put forward a similarity measure method between spatial information and user profile. In order to verify the availability of the user profile model, we present an example of spatial information intelligent delivery. The experimental results show that the proposed user profile model can reflect user profile quite well.

Geographic Information System (GIS) is moving towards distribution and sharing. Distributed Spatial Database Systems (DSDBS) has attracted the attention of many scholars. This paper introduces the prospects of Distributed GIS (DGIS), and describes the definition of DSDBS and the existing problems. The researches in related fields are analyzed, including the research results in the traditional distributed relational database fields, the distributed spatial database fields and the spatial query optimization aspect. Grid technologies are developing forward, and grid will be turned into the standard distributed computing platform, therefore the application of DSDBS will be much broader than ever. The present studies on distributed spatial query focus on spatial join optimization. Researches on query scheduling are rare. In the process of constructing our test system for distributed spatial query, we find there are some replication nodes after the step of data localization. These nodes cause redundant computing of query processing. This paper gives a method to solve it based on the Query Scheduling Tree Model (QSTM). It also gives a detailed scheduling algorithm, and analyzes the effectiveness of the model and the algorithm.