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Studying the spatiotemporal distribution of SARS-CoV-2 infections among healthcare workers (HCWs) can aid in protecting them from exposure. Existing studies related to HCW infections have emphasized infection rates and protective measures. However, the spatiotemporal patterns and related external environmental factors of HCW infections remain unclear. To fill this gap, an open-source dataset of HCW diagnoses was provided, and the spatiotemporal distributions of SARS-CoV-2 infections among HCWs in Wuhan, China were explored. A geographical detector technique was then used to investigate the impacts of hospital level, type, distance from the infection source, and other external indicators of HCW infections. The results showed that the number of daily HCW infections over time in Wuhan followed a log-normal distribution, with and its mean observed on January 23, 2020 and a standard deviation of 10.8 days. The implementation of high-impact measures, such as the lockdown of the city, may have increased the probability of HCW infections in the short term, especially for HCWs in the outer ring of Wuhan. The infection of HCWs Wuhan exhibited clear spatial heterogeneity. The number of HCW infections was higher in the central city and lower in the outer city. Moreover, HCW infections displayed significant spatial autocorrelation and dependence. Factors analyses revealed that hospital level and type had an even greater impact on HCW infections; third-class and general hospitals closer to infection sources were correlated with especially high risks of infection. These findings can aid national epidemic prevention and control departments to understand the spatiotemporal distributions of viral transmission resulting in HCW infections, as well as external influencing factors, which can facilitate the protection of HCWs in China.

Fluorescence lifetime imaging provides more possibility of in vivo multiplexing in second near infrared (NIR-II) window. However, it still faces the obstacle that fluorescent probes with differentiable lifetime often exhibit quite different fluorescence intensity, especially the short lifetime usually accompanies with a weak fluorescence intensity, resulting in the difficulty for simultaneously decoding multiplexed lifetime information due to the interference of background noise. To facilitate high-fidelity lifetime multiplexed imaging, we developed a series of Er3+ doped double interface fluorescent nanoprobes (Er-DINPs): α-NaYF4 @NaErF4 : Ce@NaYbF4 @NaErF4 : Ce@NaYF4 with strong fluorescence intensity and easily distinguishable fluorescence lifetime. Both in vitro and in vivo experimental results confirmed the advantage of these probes with comparable fluorescence intensity for high-fidelity multiplexed lifetime bioimaging.

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.

In the big data era, robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making. Current cartographic and geographic information systems have limited capabilities for solving these problems. This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources. In this system, a new Semantic Location Model (SemLM) is established to present the semantic concepts and location feature and demonstrate how locations are interrelated. In the SemLM, various types of location descriptors in different application scenarios can be analyzed and understood. Additionally, considering the challenges involved in data-intensive computation and visualization, this paper implements a Place-based Pan-Information System (P2S) as an innovative 4D system that dynamically associates and visualizes place-based information, using public security as the case study.

Human detection is a popular topic and difficult problem in surveillance. This paper presents a research on human detection in complex indoor space utilizing a depth sensor. In recent years, target detection methods based on RGB-D data mainly include background learning, and feature detection operator. The former method depends on the initial background knowledge obtained from the first couple of frames in the video, and the length of frames decides detection quality. The latter method is more time intensive, and insufficient training samples will influence the detection result. Thus, in this paper we analyze the complex scene features thoroughly and integrate the color and depth information, proposing a RGBD+ViBe foreground extraction method. Based on the extraction outcome of the foreground, this research utilizes the 3D Mean-Shift method combined with depth constraints to handle multi-person partial occlusion problems. The experiment results indicate that the proposed RGBD+ViBe method outperforms the methods which only consider color or depth information, as well as the RGBD+MoG method. Furthermore, the proposed 3D Mean-Shift method achieves nearly 90% accuracy in multi-person detection result, and the false rate is merely 5%; while the accuracy of HOG, HOD and Comb-HOD methods are less than 75% and the false rate is around 10%.

The COVID-19 pandemic poses unprecedented challenges around the world. Many studies indicate that human mobility data provide significant support for public health actions during the pandemic. Researchers have applied mobility data to explore spatiotemporal trends over time, investigate associations with other variables, and predict or simulate the spread of COVID-19. Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks. We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective. We identified three major sources of mobility data: public transit systems, mobile operators, and mobile phone applications. Four approaches have been commonly used to estimate human mobility: public transit-based flow, social activity patterns, index-based mobility data, and social media-derived mobility data. We compared mobility datasets’ characteristics by assessing data privacy, quality, space-time coverage, high-performance data storage and processing, and accessibility. We also present challenges and future directions of using mobility data. This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

The U.S. has merely 4% of the world population, but contains 25% of the world&rsquo, s COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly.

The U.S. has merely 4% of the world population but 25% of the world’s COVID-19 cases. Massachusetts has been in the leading position of total cases since the outbreak in the U.S. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing sites access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: 1) residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COIVD-19 infections among minority; 2) The Black has the shortest drive time to testing sites, followed by Hispanic, Asian, and Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of testing location being accessed by all populations; 3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection; 4) Different from previous studies, elderly population rate is not statistically significant with incidence rate because the elderly population in Massachusetts is less distributed in the hot spot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.

Depth sensor is widely used today and has great impact in object pose estimation, camera tracking, human actions, and scene reconstruction. This paper presents a novel method for human interaction recognition based on 3D skeleton data captured by Kinect sensor using hierarchical spatial-temporal saliency-based representation method. Hierarchical saliency can be conceptualized as Salient Actions at the highest level, determined by the initial movement in an interaction; Salient Points at middle level, determined by a single time point uniquely identified for all instances of Salient Action; Salient Joints at the lowest level, determined by the greatest positional changes of human joints in a Salient Action sequence. Given the interaction saliency at different levels, several types of features, such as spatial displacement, direction relations, and etc., are introduced based on action characteristics. Since there are few publicly accessible test datasets, we created a new dataset with eight types of interactions named K3HI, using the Microsoft Kinect. The method was tested based on Support Vector Machine (SVM) multi-class classifier. In the experiment, the results demonstrate that the average recognition accuracy of hierarchical saliency-based representation is 90.29%, outperforming methods using other features.

Like outdoors, indoor security is also a critical problem and human action recognition in indoor area is still a hot topic. Most studies on human action recognition ignored the semantic information of a scene, whereas indoors contains varieties of semantics. Meanwhile, the depth sensor with color and depth data is more suitable for extracting the semantics context in human actions. Hence, this paper proposed an indoor action recognition method using Kinect based on the semantics of a scene. First, we proposed a trajectory clustering algorithm for a three-dimensional (3D) scene by combining the different characteristics of people such as the spatial location, movement direction, and speed. Based on the clustering results and scene context, it concludes a region of interest (ROI) extraction method for indoors, and dynamic time warping (DTW) is used to study the abnormal action sequences. Finally, the color and depth-data-based 3D motion history image (3D–MHI) features and the semantics context of the scene were combined to recognize human action. In the experiment, two datasets were tested and the results demonstrate that our semantics-based method performs better than other methods.

The outbreak of COVID-19 has constantly exposed health care workers (HCWs) around the world to a high risk of infection. To more accurately discover the infection differences among high-risk occupations and institutions, Hubei Province was taken as an example to explore the spatiotemporal characteristics of HCWs at different scales by employing the chi-square test and fitting distribution. The results indicate (1) the units around the epicenter of the epidemic present lognormal distribution, and the periphery is Poisson distribution. There is a clear dividing line between lognormal and Poisson distribution in terms of the number of HCWs infections. (2) The infection rates of different types of HCWs at multiple geospatial scales are significantly different, caused by the spatial heterogeneity of the number of HCWs. (3) With the increase of HCWs infection rate, the infection difference among various HCWs also gradually increases and the infection difference becomes more evident on a larger scale. The analysis of the multi-scale infection rate and statistical distribution characteristics of HCWs can help government departments rationally allocate the number of HCWs and personal protective equipment to achieve distribution on demand, thereby reducing the mental and physical pressure and infection rate of HCWs.

// Mingjie Shao 1 , Tingting Tian 2 , Xinyan Zhu 3 , Yingzi Ming 1 , Yasuko Iwakiri 4 , Shaojun Ye 5 and Qifa Ye 1, 5 1 Department of Transplant Center, Xiangya 3rd Hospital, Central South University, Changsha, China 2 Department of Radiology, Changsha 1st Hospital, Changsha, China 3 Digestive Disease, Dongfang Hospital, Tongji University, Shanghai, China 4 Department of Internal Medicine, Section of Digestive Diseases, Yale University School of Medicine, New Haven, Connecticut, USA 5 Department of Transplant, Zhongnan Hospital, Wuhan University, Wuhan, China Correspondence to: Qifa Ye, email: yqf_china@163.com Shaojun Ye, email: 86987100@qq.com Keywords: alemtuzumab, ATG, IL-2RAs, kidney transplantation, network meta-analysis Received: April 04, 2017 Accepted: July 12, 2017 Published: August 02, 2017 ABSTRACT To evaluate the efficacy and safety of antibody induction therapies in kidney transplantation. Systematic literature searches were undertaken using MEDLINE, Embase, and Cochrane Library database from 1980 to 2016. Randomized controlled trials (RCTs) comparing three antibody induction therapies (alemtuzumab, interleukin-2 receptor antibodies and antithymocyte globulin) between each other were identified. Bayesian network meta-analysis was used to combine both the direct and indirect evidence on treatment efficacy and its safety. Antibody induction therapy studies, comprising of 18 RCTs (3444 kidney transplant recipients), were included. Overall, alemtuzumab treatment was superior to the ATG group (OR: 0.49, 95% CI: 0.32 to 0.71) and IL-2RAs group (OR: 0.36, 95% CI: 0.25 to 0.52) for reducing the 1-year acute rejection in kidney transplant recipients. Although alemtuzumab treatment was nearly same with ATG group and IL-2RAs group in improving patient survival and renal function, it can reduce the adverse effects of cytomegalovirus infection more efficiently than ATG group (OR: 0.59, 95% CI: 0.32 to 0.95) and IL-2RAs group (OR: 1.08, 95% CI: 0.61 to 1.73). Alemtuzumab was not associated with increased other adverse effects. Alemtuzumab treatment is safe and effective for kidney transplant recipients. No serious adverse effects were observed in trials or in general populations.

This study investigates the association between urban vitality and street centrality in Wuhan, China. Urban vitality was measured with social network review data. Street centrality was evaluated in terms of closeness, straightness, and betweenness in walking and driving mode. We constructed a square mesh to convert datasets of street centrality (segments) and urban vitality (points) into one unit for analysis. Geospatial visualization, a chi-square test, and correlation analysis were first employed to obtain an initial understanding of the spatial coupling relationship between urban vitality and street centrality. Then spatial regression models were applied to evaluate the significances and directions of the influences of street centrality on urban vitality. A geographical detector technique was utilized to further evaluate the magnitudes of these influences. The results suggest that street centrality plays an important role in shaping the spatial organization of urban vitality, and various street centralities presented marked differences in their association with urban vitality. More specifically, when considering street centrality in walking mode, betweenness affected urban vitality the most, followed by closeness and straightness. When considering street centrality in driving mode, straightness had the greatest influence on urban vitality, followed by closeness and betweenness.

Image registration and fusion has always been an important part of computer vision, but most of the existing algorithms aim at the registration between two images, paying attention to accuracy, while sacrificing the requirements of speed The application scenario of this paper is real-time registration in image stitching under the microscope. That is, while moving the operating platform to observe the cell structure under the microscope, the stitching is performed in real time to obtain an entire spliced image. In order to speed up the image matching process and improve the accuracy, this paper creatively combines the phase correlation algorithm and the feature point matching algorithmfor image registration, and improves them, completing the registration of multiple images. This paper focuses on the real time ability and accuracy of the stitching program.

With the rapid development of indoor localization in recent years, signals of opportunity have become a reliable and convenient source for indoor localization. The mobile device cannot only capture images of the indoor environment in real-time, but can also obtain one or more different types of signals of opportunity as well. Based on this, we design a convolutional neural network (CNN) model that concatenates features of image data and signals of opportunity for localization by using indoor scene datasets and simulating the situation of indoor location probability. Using the method of transfer learning on the Inception V3 network model feature information is added to assist in scene recognition. The experimental result shows that, for two different experiment sceneries, the accuracies of the prediction results are 97.0% and 96.6% using the proposed model, compared to 69.0% and 81.2% by the method of overlapping positioning information and the base map, and compared to 73.3% and 77.7% by using the fine-tuned Inception V3 model. The accuracy of indoor scene recognition is improved, in particular, the error rate at the spatial connection of different scenes is decreased, and the recognition rate of similar scenes is increased.

With the certain regulation of unified real estate registration taken by the Property Law and the step-by-step advance of simultaneous development in urban and rural in China, it is the premise and foundation to clearly specify property rights and their relations in promoting the integrated management of urban and rural land. This paper aims at developing a cadastral domain model oriented at unified real estate registration of China from the perspective of legal and spatial, which set up the foundation for unified real estate registration, and facilitates the effective interchange of cadastral information and the administration of land use. The legal cadastral model is provided based on the analysis of gap between current model and the demand of unified real estate registration, which implies the restrictions between different rights. Then the new cadastral domain model is constructed based on the legal cadastral domain model and CCDM (van Oosterom et al., 2006), which integrate real estate rights of urban land and rural land. Finally, the model is validated by a prototype system. The results show that the model is applicable for unified real estate registration in China.

Before conducting domain-specific modelling, one task faced by researchers is trying to effectively explore the data. There has been much research done to put spatiotemporal data analysis functions online. However, there is still a gap between the spatial data and the analysis procedures. Manually matching the data sources and the geoprocessing services would be a troublesome job for normal users. Furthermore, geoprocessing services published online are still limited and mostly target only static spatial data. This paper investigates an integrated web-based environment, which incorporates several open source software packages including PySAL, R, JTS, and customized implementations for exploratory functions such as the gravity model and the center-of-gravity model. Users can navigate spatial data and perform spatial analysis directly on them, without knowing the complexities of data transformation and integration. Both data sources and the analytic functions can be dynamically added. The environment released users from the great burden of intermediate data processing unrelated to their work.

Most studies of spatial colocation patterns of crime and land-use features in geographical information science and environmental criminology employ global measures, potentially obscuring spatial inhomogeneity. This study investigated the relationships of three types of crime with 22 types of land-use in Wuhan, China. First, global colocation patterns were examined. Then, local colocation patterns were examined based on the recently-proposed local colocation quotient, followed by a detailed comparison of the results. Different types of crimes were encouraged or discouraged by different types of land-use features with varying intensity, and the local colocation patterns demonstrated spatial inhomogeneity.

Time is a crucial factor for many remote sensing applications such as emergency response. The traditional approach requires users to spend a lot of time downloading, processing, and viewing satellite images with specialized software. Realizing interactive real-time processing and visualization of satellite images online is our focus. This article presents an On-Demand computing schema for remote sensing images. A processing chain model is proposed for satellite images on a private cloud computing platform designed for the China Centre for Resources Satellite Data and Application (CCRSDA). The architecture, processing flow, optimization method, fault tolerance, and user interface are described in detail. To test the efficiency and scalability of the platform, 11 processing chains were created and three load balance experiments were executed. The results from these experiments show the validity of the proposed methods and architecture.

Crime does not occur randomly or uniformly across time, space, or social groups. The near-repeat phenomenon states that the risk of repeat victimization would increase at the nearby locations within a certain time period. The empirical evidence of near-repeat patterns has been widely reported in many different crime types across the world. Meanwhile, the growing ability to connect anywhere and anytime has led to the increasing use of social network analytics. In particular, social network analytics have been applied to the studies of criminal networks. This paper integrates near repeat and social network approaches on exploring spatiotemporal crime patterns. The spatiotemporal burglary data in five boroughs (the Bronx, Brooklyn, Staten Island, Manhattan and Queen) of NYC were analyzed and compared based on average clustering coefficient, degree centrality, closeness centrality, and closeness centrality. Furthermore, the implications and limitations of the findings are presented.

Indoor emergency response plays a critically important role in disaster management for cities, which must consider the evacuation of people in a dynamic indoor environment. The spatial model is the foundation for the specific analysis of indoor emergency responses, such as evacuations. The current spatial model for evacuation has three primary pitfalls: (1) it primarily focuses on static spatial information, such as rooms, doors, and windows, and lacks dynamic information, such as events and sensors; (2) it mainly focuses on the horizontal space and the static scene and lacks a multi-story component that considers the different properties of stairs compared to planar areas; and (3) it places emphasis on the indoor navigation calculation with a 2D/3D network, which lacks individual properties that can support more complicated analysis, such as congestion and stagnation. In this paper, we propose a dynamic indoor field model with three typical characteristics. (1) It includes not only static information but also dynamic information, such as outdoor and indoor building geometry, sensors, fire spread, and personnel behavior. (2) It supports multi-story buildings from the macro level (building level and floor level) to the micro level (room level and individual level) based on horizontal and vertical indoor space. (3) It supports spatial calculations based on a three-dimensional space grid and can analyze potential congestion and stagnation during evacuation. We design a corresponding evacuation method that supports individual evacuation route finding and evacuation assessment. We perform a series of analyses of the applicability of the proposed model and the efficiency of the designed evacuation method based on multi-story evacuation studies. The simulation includes a total evacuation population exceeding 7000 individuals, and the analysis suggests that the new model and algorithm are effective in planning indoor emergency routes that avoid potential congestion or stagnation.

Using the allele-specific diagnostic PCR for the rapid and efficient identification of Chinese herbal medicines and their adulterants. In this paper, we analyze the research object of P. kingianum and its adulterants. The total DNA of the samples to be identified was extracted, and the PCR was amplified by using universal primers. After the homology comparison, the specific sites were found out by using BioEdit software, and the specific PCR primers were designed by Premier primer 5.0 software, and the samples were amplified by the primers. The 331bp bands were amplified by P. kingianum of the specific primers, but the results of their adulterants were not amplified. The results show that the method can provide guidance for the allele-specific diagnostic PCR for quick and easy to select P. kingianum , which is not affected by environmental factors, affecting the growth period of the plant, were identified directly from the molecular level of DNA. This method having important application value in accurate introduction and clinical application can provide a model for other identification.

With the announcement of open information policy by the Chinese government, there has been lots of development in government statistics, Census data, and GIS data. That information provides rich reference for regional analysis in China. The primary challenges under rapid data growth include how to efficiently integrate those space-time data, provide methodology for different research targets, and promote their applications in different fields. This report will present some recent developments of spatial intelligence technologies for spatial data integration, data analysis, as well as their applications. The report will introduce the newly developed spatial explorer (China Geo-Explorer) as a joint effort by the University of Michigan China Data Center and Wuhan University. It will demonstrate how space-time data of different formats and sources can be integrated, visualized, analyzed and reported in a web based system. Some applications in disaster assessment, environment and health, cultural and religious studies will be discussed for China and global studies.

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 huge amount of information, including geographic, environmental, socio-economic, personal and social network information, has been generated from diverse sources. Most of this information exists separately and is disorderly even if some of it is about the same person, feature, phenomenon or event. Users generally need to collect related information from different sources and then utilize them in applications. An automatic mechanism, therefore, for establishing a connection between potentially-related information will profoundly expand the usefulness of this huge body of information. A connection tie is semantic location describing semantically concepts and attributes of locations as well as relationships between locations, since 80% of information contains some kind of geographic reference but not all of geographic reference has explicit geographic coordinates. Semantic location is an orthogonal form of location representation which can be represented as domain ontology or UML format. Semantic location associates various kinds of information about a same object to provide timely information services according to users’ demands, habits, preferences and applications. Based on this idea, a Pan-Information Location Map (PILM) is proposed as a new-style 4D map to associates semantic location-based information dynamically to organize and consolidate the locality and characteristics of corresponding features and events, and delivers on-demand information with a User-Adaptive Smart Display (UASD).

Spatio-temporal Bayesian modeling, a method based on regional statistics, is widely used in epidemiological studies. Using Bayesian theory, this study builds a spatio-temporal Bayesian model specific to urban crime to analyze its spatio-temporal patterns and determine any developing trends. The associated covariates and their changes are also analyzed. The model is then used to analyze data regarding burglaries that occurred in Wuhan City in China from January to August 2013. Of the diverse socio-economic variables associated with crime rate, including population, the number of local internet bars, hotels, shopping centers, unemployment rate, and residential zones, this study finds that the burglary crime rate is significantly correlated with the average resident population per community and number of local internet bars. This finding provides a scientific reference for urban safety protection.

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.

3D reconstruction of indoor environments based on vision has been developed vigorously. However, the algorithm's complexity and requirements of professional knowledge make it restricted in practical application. With the proposition of the concept of Volunteered Geographic Information (VGI), the traditional method is no longer suitable for VGI. So in this work we utilize consumer depth cameras – Kinect to enable non-expert users to reconstruct 3D model of indoor environment with RGB-D data. Considering the possibility of camera tracking failure we propose a method to perform automatic relocalization.

With the development of hydrographic work and information techniques, the traditional work mode is not suitable for the Chinese Marine Safety Administration Bureau (CMSAB). Aiming at the separation of charts production and management, a new idea that is the integration of production, management, and quality control is presented. Geographic Information System (GIS), Management Information System (MIS), Office Automation (OA) and Spatial Database Management (SDM) advanced techniques are introduced into the workflows in CMSAB. To realize information sharing, a mixed mode with Browser/Server (B/S) mode and Client/Serve (C/S) mode are adopted. Based on the above ideas and strategies, an integrated system named Hydrographic Production And Management Information System (HYPAMIS) was developed. The whole workflow and three subworkflows including survey management workflow, production workflow, and quality control workflow of are outlined after re-engineering the CMSAB organization. Some design principles and key ...

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.

Route backtracking has been applied in many fields, especially in tracing criminal cars. However, because of weak light, shading or shelter, plate numbers are unable to be recognized and the vehicle cannot be traced. So, this paper put forwards a route backtracking method based on video data and road network. the method get routes information by means of vehicles features extraction and matching method with time and spatial constraints. Time constraints include time records in each video and time range that car cost on every road, and spatial constraints include connectivity and topology of roads. Meanwhile, there are also vehicle constraints, such as vehicle's outline, color, type and plate number. and this paper uses Mean Shift algorithm to extract and match the candidate vehicles and target vehicle. Finally, an experiment is put forward to prove the availability and efficiency of the method.

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.

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.

Spatial information delivery is extremely basic and quite important in the field of geographic information science. Traditional spatial information delivery is system-oriented or system-centered. Accordingly users have to adapt to the system and complete the delivery of spatial information, and this delivery service mode suffers from personalization and proactivity problems. Aiming at the objectives of spatial information delivery, we give the process of spatial information delivery taking into count personalization and proactivity, and point out three main links: data source collecting, user profile modeling and information dissemination. Then, an analysis of delivery algorithms of spatial information is given subsequently. The adaptability of content-based delivery algorithms, collaborative delivery algorithms and hybrid delivery algorithms are analyzed in-depth respectively, and the application conditions of every kind of algorithms are given clearly.

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.

TSR (Traffic sign recognition) is an important research field in ITS (Intelligent traffic system), which is being paid more and more attention for realizing drivers assisting system and automated navigation etc. Author proposed a new method to detect traffic signs in outdoor scene. The method is divided into 2 step. Firstly, Two-way integration method including both methods drived by task and methods drived by data for coarse segmentation, which improves the accuracy of detection. And then edge extraction and morphological operations are used to obtain enclosed area. The shape of the enclosed area is determined by the circularity and central double cross shape measurement in order to detect the prohibition signs. The results showed the proposed method can effectively detect the prohibition signs.

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.

Featured as being knowledge-intensified, innovation-motivated and network-orientated, a modern service enterprise has to attach more importance on the organizational absorptive capacity of knowledge than a traditional one. Through external environment, internal management and knowledge attribute, this Paper analyses the impacts on the organizational absorptive capacity of knowledge by different factors, including the enterprise network, prior knowledge basis, employee's absorptive capacity of knowledge, supporting system, attributes of external knowledge and organizational learning mechanism, on which the Knowledge Absorptive Capacity is modeled for the modern service enterprise.

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.