bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiple Myeloma Includes Phenotypically Defined Subsets of Clonotypic CD20+ B Cells that Persist during Treatment with Rituximab
Authors: Pilarski Linda M. , Baigorri Eva , Mant Michael J. , Pilarski Patrick M. , Adamson Penelope , Zola Heddy , Belch Andrew R.
Published: September 2017

Potential progenitor B cell compartments in multiple myeloma (MM) are clinically important. MM B cells and some circulating MM plasma cells express CD20, predicting their clearance by treatment with anti-CD20. Here we describe two types of clonotypic CD20+ B cell in peripheral blood of myeloma patients, identified by their expression of CD19 and CD20 epitopes, their expression of CD45RA and their light scatter properties. Thus, the circulating component of the MM clone includes at least two distinct CD19+ CD20+ B cell compartments, as well as CD138+CD20+ plasma cells. To determine whether either or both B cell subsets and the CD20+ plasma cell subset were depleted by anti-CD20 therapy, they were evaluated before, during and after treatment of patients with rituximab (anti-CD20), followed by quantifying B cell subsets over a 5 month period during and after treatment. Overall, all three types of circulating B lineage cells persist despite treatment with rituximab. The inability of rituximab to prolong survival in MM may result from this failure to deplete CD20+ B and plasma cells in MM.
Radiation in fog: quantification of the impact on fog liquid water based on ground-based remote sensing
Authors: Wærsted Eivind G. , Haeffelin Martial , Dupont Jean-Charles , Delanoë Julien , Dubuisson Philippe
Published: September 2017

Abstract. Radiative cooling and heating impact the liquid water balance of fog and therefore play an important role in determining their persistence or dissipation. We demonstrate that a quantitative analysis of the radiation-driven condensation and evaporation is possible in real time using ground-based remote sensing observations (cloud radar, ceilometer, microwave radiometer). Seven continental fog events in midlatitude winter are studied, and the radiative processes are further explored through sensitivity studies. The longwave (LW) radiative cooling of the fog is able to produce 40–70 g m−2 h−1 of liquid water by condensation when the fog liquid water path exceeds 30 g m−2 and there are no clouds above the fog, which corresponds to renewing the fog water in 0.5–2 h. The variability is related to fog temperature and atmospheric humidity, with warmer fog below a drier atmosphere producing more liquid water. The appearance of a cloud layer above the fog strongly reduces the LW cooling relative to a situation with no cloud above; the effect is strongest for a low cloud, when the reduction can reach 100 %. Consequently, the appearance of clouds above will perturb the liquid water balance in the fog and may therefore induce fog dissipation. Shortwave (SW) radiative heating by absorption by fog droplets is smaller than the LW cooling, but it can contribute significantly, inducing 10–15 g m−2 h−1 of evaporation in thick fog at (winter) midday. The absorption of SW radiation by unactivated aerosols inside the fog is likely less than 30 % of the SW absorption by the water droplets, in most cases. However, the aerosols may contribute more significantly if the air mass contains a high concentration of absorbing aerosols. The absorbed radiation at the surface can reach 40–120 W m−2 during the daytime depending on the fog thickness. As in situ measurements indicate that 20–40 % of this energy is transferred to the fog as sensible heat, this surface absorption can contribute significantly to heating and evaporation of the fog, up to 30 g m−2 h−1 for thin fog, even without correcting for the typical underestimation of turbulent heat fluxes by the eddy covariance method. Since the radiative processes depend mainly on the profiles of temperature, humidity and clouds, the results of this paper are not site specific and can be generalised to fog under different dynamic conditions and formation mechanisms, and the methodology should be applicable to warmer and moister climates as well. The retrieval of approximate emissivity of clouds above fog from cloud radar should be further developed.
Temporal and spatial variability of Icelandic dust emissions and atmospheric transport
Authors: Groot Zwaaftink Christine D. , Arnalds Ólafur , Dagsson-Waldhauserova Pavla , Eckhardt Sabine , Prospero Joseph M. , Stohl Andreas
Published: September 2017

Abstract. Icelandic dust sources are known to be highly active, yet there exist few model simulations of Icelandic dust that could be used to assess its impacts on the environment. We here present estimates of dust emission and transport in Iceland over 27 years (1990–2016) based on FLEXDUST and FLEXPART simulations and meteorological re-analysis data. Simulations for the year 2012 based on high-resolution operational meteorological analyses are used for model evaluation based on PM2. 5 and PM10 observations in Iceland. For stations in Reykjavik, we find that the spring period is well predicted by the model, while dust events in late fall and early winter are overpredicted. Six years of dust concentrations observed at Stórhöfði (Heimaey) show that the model predicts concentrations of the same order of magnitude as observations and timing of modelled and observed dust peaks agrees well. Average annual dust emission is 4.3 ± 0.8 Tg during the 27 years of simulation. Fifty percent of all dust from Iceland is on average emitted in just 25 days of the year, demonstrating the importance of a few strong events for annual total dust emissions. Annual dust emission as well as transport patterns correlate only weakly to the North Atlantic Oscillation. Deposition amounts in remote regions (Svalbard and Greenland) vary from year to year. Only limited dust amounts reach the upper Greenland Ice Sheet, but considerable dust amounts are deposited on Icelandic glaciers and can impact melt rates there. Approximately 34 % of the annual dust emission is deposited in Iceland itself. Most dust (58 %), however, is deposited in the ocean and may strongly influence marine ecosystems.
REGIONAL DISPARITIES IN ONLINE MAP USER ACCESS VOLUME AND DETERMINING FACTORS
Authors: Li R. , Yang N. , Li R. , Huang W. , Wu H.
Published: September 2017

Abstract. The regional disparities of online map user access volume (use ‘user access volume’ in this paper to indicate briefly) is a topic of growing interest with the increment of popularity in public users, which helps to target the construction of geographic information services for different areas. At first place we statistically analysed the online map user access logs and quantified these regional access disparities on different scales. The results show that the volume of user access is decreasing from east to the west in China as a whole, while East China produces the most access volume; these cities are also the crucial economic and transport centres. Then Principal Component Regression (PCR) is applied to explore the regional disparities of user access volume. A determining model for Online Map access volume is proposed afterwards, which indicates that area scale is the primary determining factor for regional disparities, followed by public transport development level and public service development level. Other factors like user quality index and financial index have very limited influence on the user access volume. According to the study of regional disparities in user access volume, map providers can reasonably dispatch and allocate the data resources and service resources in each area and improve the operational efficiency of the Online Map server cluster.
AIRBORNE LIDAR POWER LINE CLASSIFICATION BASED ON SPATIAL TOPOLOGICAL STRUCTURE CHARACTERISTICS
Authors: Wang Y. , Chen Q. , Li K. , Zheng D. , Fang J.
Published: September 2017

Abstract. Automatic extraction of power lines has become a topic of great importance in airborne LiDAR data processing for transmission line management. In this paper, we present a new, fully automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) neighbourhood selection, (iii) feature extraction based on spatial topology, and (iv) SVM classification. In a detailed evaluation involving seven neighbourhood definitions, 26 geometric features and two datasets, we demonstrated that the use of multi-scale neighbourhoods for individual 3D points significantly improved the power line classification. Additionally, we showed that the spatial topological features may even further improve the results while reducing data processing time.
SRTM DEM-AIDED DEM EXTRACTION METHOD FOR ISLAND AND REEF
Authors: Chen X. W. , Zhao C. , Guo H. T. , Lin Y. Z. , Yu D. H.
Published: September 2017

Abstract. An SRTM DEM-aided DEM extraction method for island and reef is proposed to solve the problem of island and reef DEM extraction based on satellite imagery. The SRTM DEM is fully integrated into this method, namely, it is used to provide initial elevation for DEM and also to mark the sea area points in order to avoid the adverse effect of sea area image on DEM extraction. When determining elevations of grid points, only the valid land area points (VLPs) are taken into account. On the basis of initial elevation, the image coordinates of VLPs in multi-view images are determined and precise coordinates of conjugate points are obtained based on least square matching, then ground coordinates of VLPs are acquired by forward intersection. Finally, the elevations of VLPs are determined based on these object space points through data interpolation, and the sea area points are set as a uniform value. Experimental results show that the method can effectively solve the problem of island and reef DEM extraction. It can effectively extract DEM from island and reef satellite images regardless of the land area proportion, and island and reef can be completely extracted. Accuracy of the extracted DEM would improve with the increase of DEM resolution; when the resolution is relative high, the accuracy is consistent with SRTM DEM. The computational efficiency depends on the land area proportion and DEM resolution.
PHOTOGRAMMETRIC POINT CLOUDS GENERATION IN URBAN AREAS FROM INTEGRATED IMAGE MATCHING AND SEGMENTATION
Authors: Ye L. , Wu B.
Published: September 2017

Abstract. High-resolution imagery is an attractive option for surveying and mapping applications due to the advantages of high quality imaging, short revisit time, and lower cost. Automated reliable and dense image matching is essential for photogrammetric 3D data derivation. Such matching, in urban areas, however, is extremely difficult, owing to the complexity of urban textures and severe occlusion problems on the images caused by tall buildings. Aimed at exploiting high-resolution imagery for 3D urban modelling applications, this paper presents an integrated image matching and segmentation approach for reliable dense matching of high-resolution imagery in urban areas. The approach is based on the framework of our existing self-adaptive triangulation constrained image matching (SATM), but incorporates three novel aspects to tackle the image matching difficulties in urban areas: 1) occlusion filtering based on image segmentation, 2) segment-adaptive similarity correlation to reduce the similarity ambiguity, 3) improved dense matching propagation to provide more reliable matches in urban areas. Experimental analyses were conducted using aerial images of Vaihingen, Germany and high-resolution satellite images in Hong Kong. The photogrammetric point clouds were generated, from which digital surface models (DSMs) were derived. They were compared with the corresponding airborne laser scanning data and the DSMs generated from the Semi-Global matching (SGM) method. The experimental results show that the proposed approach is able to produce dense and reliable matches comparable to SGM in flat areas, while for densely built-up areas, the proposed method performs better than SGM. The proposed method offers an alternative solution for 3D surface reconstruction in urban areas.
STRATEGIES TO EVALUATE THE VISIBILITY ALONG AN INDOOR PATH IN A
POINT CLOUD REPRESENTATION
Authors: Grasso N. , Verbree E. , Zlatanova S. , Piras M.
Published: September 2017

Abstract. Many research works have been oriented to the formulation of different algorithms for estimating the paths in indoor environments from three-dimensional representations of space. The architectural configuration, the actions that take place within it, and the location of some objects in the space influence the paths along which is it possible to move, as they may cause visibility problems. To overcome the visibility issue, different methods have been proposed which allow to identify the visible areas and from a certain point of view, but often they do not take into account the user’s visual perception of the environment and not allow estimating how much may be complicated to follow a certain path. In the field of space syntax and cognitive science, it has been attempted to describe the characteristics of a building or an urban environment by the isovists and visibility graphs methods; some numerical properties of these representations allow to describe the space as for how it is perceived by a user. However, most of these studies are directed to analyze the environment in a two-dimensional space. In this paper we propose a method to evaluate in a quantitative way the complexity of a certain path within an environment represented by a three-dimensional point cloud, by the combination of some of the previously mentioned techniques, considering the space visible from a certain point of view, depending on the moving agent (pedestrian , people in wheelchairs, UAV, UGV, robot).
A STATISTICAL ANALYSIS ON THE SYSTEM PERFORMANCE OF A BLUETOOTH
LOW ENERGY INDOOR POSITIONING SYSTEM IN A 3D ENVIRONMENT
Authors: Haagmans G. G. , Verhagen S. , Voûte R. L. , Verbree E.
Published: September 2017

Abstract. Since GPS tends to fail for indoor positioning purposes, alternative methods like indoor positioning systems (IPS) based on Bluetooth low energy (BLE) are developing rapidly. Generally, IPS are deployed in environments covered with obstacles such as furniture, walls, people and electronics influencing the signal propagation. The major factor influencing the system performance and to acquire optimal positioning results is the geometry of the beacons. The geometry of the beacons is limited to the available infrastructure that can be deployed (number of beacons, basestations and tags), which leads to the following challenge: Given a limited number of beacons, where should they be placed in a specified indoor environment, such that the geometry contributes to optimal positioning results? This paper aims to propose a statistical model that is able to select the optimal configuration that satisfies the user requirements in terms of precision. The model requires the definition of a chosen 3D space (in our case 7 × 10 × 6 meter), number of beacons, possible user tag locations and a performance threshold (e.g. required precision). For any given set of beacon and receiver locations, the precision, internal- and external reliability can be determined on forehand. As validation, the modeled precision has been compared with observed precision results. The measurements have been performed with an IPS of BlooLoc at a chosen set of user tag locations for a given geometric configuration. Eventually, the model is able to select the optimal geometric configuration out of millions of possible configurations based on a performance threshold (e.g. required precision).
PERFORMANCE ANALYSIS OF CLASSIFICATION METHODS FOR INDOOR LOCALIZATION IN VLC NETWORKS
Authors: Sánchez-Rodríguez D. , Alonso-González I. , Sánchez-Medina J. , Ley-Bosch C. , Díaz-Vilariño L.
Published: September 2017

Abstract. Indoor localization has gained considerable attention over the past decade because of the emergence of numerous location-aware services. Research works have been proposed on solving this problem by using wireless networks. Nevertheless, there is still much room for improvement in the quality of the proposed classification models. In the last years, the emergence of Visible Light Communication (VLC) brings a brand new approach to high quality indoor positioning. Among its advantages, this new technology is immune to electromagnetic interference and has the advantage of having a smaller variance of received signal power compared to RF based technologies. In this paper, a performance analysis of seventeen machine leaning classifiers for indoor localization in VLC networks is carried out. The analysis is accomplished in terms of accuracy, average distance error, computational cost, training size, precision and recall measurements. Results show that most of classifiers harvest an accuracy above 90 %. The best tested classifier yielded a 99.0 % accuracy, with an average error distance of 0.3 centimetres.
A MAPPING METHOD OF SLAM BASED ON LOOK UP TABLE
Authors: Wang Z. , Li J. , Wang A. , Wang J.
Published: September 2017

Abstract. In the last years several V-SLAM(Visual Simultaneous Localization and Mapping) approaches have appeared showing impressive reconstructions of the world. However these maps are built with far more than the required information. This limitation comes from the whole process of each key-frame. In this paper we present for the first time a mapping method based on the LOOK UP TABLE(LUT) for visual SLAM that can improve the mapping effectively. As this method relies on extracting features in each cell divided from image, it can get the pose of camera that is more representative of the whole key-frame. The tracking direction of key-frames is obtained by counting the number of parallax directions of feature points. LUT stored all mapping needs the number of cell corresponding to the tracking direction which can reduce the redundant information in the key-frame, and is more efficient to mapping. The result shows that a better map with less noise is build using less than one-third of the time. We believe that the capacity of LUT efficiently building maps makes it a good choice for the community to investigate in the scene reconstruction problems.
REGIONAL SAR IMAGE SEGMENTATION BASED ON FUZZY CLUSTERING WITH
GAMMA MIXTURE MODEL
Authors: Li X. L. , Zhao Q. H. , Li Y.
Published: September 2017

Abstract. Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.
AUGMENTED CITIZEN SCIENCE FOR ENVIRONMENTAL MONITORING AND EDUCATION
Authors: Albers B. , de Lange N. , Xu S.
Published: September 2017

Abstract. Environmental monitoring and ecological studies detect and visualize changes of the environment over time. Some agencies are committed to document the development of conservation and status of geotopes and geosites, which is time-consuming and cost-intensive. Citizen science and crowd sourcing are modern approaches to collect data and at the same time to raise user awareness for environmental changes. Citizen scientists can take photographs of point of interests (POI) with smartphones and the PAN App, which is presented in this article. The user is navigated to a specific point and is then guided with an augmented reality approach to take a photo in a specific direction. The collected photographs are processed to time-lapse videos to visualize environmental changes. Users and experts in environmental agencies can use this data for long-term documentation.
AN IMPROVED AERIAL REMOTE SENSING IMAGE DEFOGGING METHOD BASED
ON DARK CHANNEL PRIOR INFORMATION
Authors: Zhang Z. , Feng W. , Wang T. , Zhang Y. , Ding L.
Published: September 2017

Abstract. Aerial remote sensing image is widely used due to its high resolution, abundant information and convenient processing. However, its image quality is easily influenced by clouds and fog. In recent years, fog and haze air pollution is becoming more and more serious in the north of China and its influence on aerial remote sensing image quality is especially obvious. Considering the characters that aerial remote image is usually in huge amount of data and seldom covers sky area, this paper proposes an improved aerial remote sensing image defogging method based on dark channel prior information. First, a 2 % linear stretching is applied to eliminate the haze offset effect and provide a better initial value for later defogging processing. Then the dark channel prior image is obtained by calculating the minimum values of r, g, b channels of each pixel directly. Subsequently, according to the particularity of aerial image, the adaptive threshold t0 is set up to improve the defogging effect. Finally, to improve the color cast phenomenon, a way called automatic color method is introduced to enhance the visual effect of defogged image. Experiments are performed on normal image in fog and on aerial remote sensing image in fog. Experimental results prove that the proposed method can obtain the defogged image with better visual effect and image quality. Moreover, the improved method significantly balances the color information in the defogged image and efficiently avoids the color cast phenomenon.
SURFACE SUBSIDENCE MONITORING AND ANALYZING IN BEIJING BASED ON INTERFEROMETRIC POINT TARGET ANALYSIS
Authors: Sun Y. , Lu L.
Published: September 2017

Abstract. Temporal and geometrical decorrelation often prevents SAR interferometry from being an operational tool for surface deformation monitoring. Moreover, atmospheric disturbances can strongly compromise the accuracy of the results. In order to solve the above problem. In this paper, Interferometric Point Target Analysis (IPTA) technology was used to obtain surface deformation information with an area of 700 km2 in Beijing. In the experiment, 20 TerraSAR-X images from October 2014 to June 2016 were selected to monitor the surface subsidence in Beijing and higher resolution and higher accuracy WorldDEM obtained by DLR was used to simulated terrain phase, so as to register the high-resolution TerraSAR-X image. The final results show the surface subsidence tendency of Beijing urban from October 2014 to June 2016: in the center of monitor area surface tends to be stable within approximately subsidence rate of 3 mm/a. In the east of monitor, the area of Chaoyang and Tongzhou, there are obvious subsidence phenomenon。This subsidence is affected by groundwater exploitation, flake subsidence it had an tendency of expanding to the east and south, and the maximum annual subsidence is more than 76 mm/year. The results of this monitoring are basically consistent with the published data of surface subsidence in Beijing.
MONITORING URBAN LAND COVER/LAND USE CHANGE IN ALGIERS CITY USING LANDSAT IMAGES (1987–2016)
Authors: Bouchachi B. , Zhong Y.
Published: September 2017

Abstract. Monitoring the Urban Land Cover/Land Use change detection is important as one of the main driving forces of environmental change because Urbanization is the biggest changes in form of Land, resulting in a decrease in cultivated areas. Using remote sensing ability to solve land resources problems. The purpose of this research is to map the urban areas at different times to monitor and predict possible urban changes, were studied the annual growth urban land during the last 29 years in Algiers City. Improving the productiveness of long-term training in land mapping, were have developed an approach by the following steps: 1) pre-processing for improvement of image characteristics; 2) extract training sample candidates based on the developed methods; and 3) Derive maps and analyzed of Algiers City on an annual basis from 1987 to 2016 using a Supervised Classifier Support Vector Machine (SVMs). Our result shows that the strategy of urban land followed in the region of Algiers City, developed areas mostly were extended to East, West, and South of Central Regions. The urban growth rate is linked with National Office of Statistics data. Future studies are required to understand the impact of urban rapid lands on social, economy and environmental sustainability, it will also close the gap in data of urbanism available, especially on the lack of reliable data, environmental and urban planning for each municipality in Algiers, develop experimental models to predict future land changes with statistically significant confidence.
STUDY ON ADAPTIVE PARAMETER DETERMINATION OF CLUSTER ANALYSIS IN
URBAN MANAGEMENT CASES
Authors: Fu J. Y. , Jing C. F. , Du M. Y. , Fu Y. L. , Dai P. P.
Published: September 2017

Abstract. The fine management for cities is the important way to realize the smart city. The data mining which uses spatial clustering analysis for urban management cases can be used in the evaluation of urban public facilities deployment, and support the policy decisions, and also provides technical support for the fine management of the city. Aiming at the problem that DBSCAN algorithm which is based on the density-clustering can not realize parameter adaptive determination, this paper proposed the optimizing method of parameter adaptive determination based on the spatial analysis. Firstly, making analysis of the function Ripley's K for the data set to realize adaptive determination of global parameter MinPts, which means setting the maximum aggregation scale as the range of data clustering. Calculating every point object’s highest frequency K value in the range of Eps which uses K-D tree and setting it as the value of clustering density to realize the adaptive determination of global parameter MinPts. Then, the R language was used to optimize the above process to accomplish the precise clustering of typical urban management cases. The experimental results based on the typical case of urban management in XiCheng district of Beijing shows that: The new DBSCAN clustering algorithm this paper presents takes full account of the data’s spatial and statistical characteristic which has obvious clustering feature, and has a better applicability and high quality. The results of the study are not only helpful for the formulation of urban management policies and the allocation of urban management supervisors in XiCheng District of Beijing, but also to other cities and related fields.
COMPARISON OF ADJACENCY AND DISTANCE-BASED APPROACHES FOR SPATIAL ANALYSIS OF MULTIMODAL TRAFFIC CRASH DATA
Authors: Gill G. , Sakrani T. , Cheng W. , Zhou J.
Published: September 2017

Abstract. Many studies have utilized the spatial correlations among traffic crash data to develop crash prediction models with the aim to investigate the influential factors or predict crash counts at different sites. The spatial correlation have been observed to account for heterogeneity in different forms of weight matrices which improves the estimation performance of models. But very rarely have the weight matrices been compared for the prediction accuracy for estimation of crash counts. This study was targeted at the comparison of two different approaches for modelling the spatial correlations among crash data at macro-level (County). Multivariate Full Bayesian crash prediction models were developed using Decay-50 (distance-based) and Queen-1 (adjacency-based) weight matrices for simultaneous estimation crash counts of four different modes: vehicle, motorcycle, bike, and pedestrian. The goodness-of-fit and different criteria for accuracy at prediction of crash count reveled the superiority of Decay-50 over Queen-1. Decay-50 was essentially different from Queen-1 with the selection of neighbors and more robust spatial weight structure which rendered the flexibility to accommodate the spatially correlated crash data. The consistently better performance of Decay-50 at prediction accuracy further bolstered its superiority. Although the data collection efforts to gather centroid distance among counties for Decay-50 may appear to be a downside, but the model has a significant edge to fit the crash data without losing the simplicity of computation of estimated crash count.
A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION
Authors: Lin X. , Li H. , Zhang Y. , Gao L. , Zhao L. , Deng M.
Published: September 2017

Abstract. Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by “learning” via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.
ASSESSMENT OF GLOBAL WIND ENERGY RESOURCE UTILIZATION POTENTIAL
Authors: Ma M. , He B. , Guan Y. , Zhang H. , Song S.
Published: September 2017

Abstract. Development of wind energy resource (WER) is a key to deal with climate change and energy structure adjustment. A crucial issue is to obtain the distribution and variability of WER, and mine the suitable location to exploit it. In this paper, a multicriteria evaluation (MCE) model is constructed by integrating resource richness and stability, utilization value and trend of resource, natural environment with weights. The global resource richness is assessed through wind power density (WPD) and multi-level wind speed. The utilizable value of resource is assessed by the frequency of effective wind. The resource stability is assessed by the coefficient of variation of WPD and the frequency of prevailing wind direction. Regression slope of long time series WPD is used to assess the trend of WER. All of the resource evaluation indicators are derived from the atmospheric reanalysis data ERA-Interim with spatial resolution 0.125°. The natural environment factors mainly refer to slope and land-use suitability, which are derived from multi-resolution terrain elevation data 2010 (GMTED 2010) and GlobalCover2009. Besides, the global WER utilization potential map is produced, which shows most high potential regions are located in north of Africa. Additionally, by verifying that 22.22 % and 48.8 9% operational wind farms fall on medium-high and high potential regions respectively, the result can provide a basis for the macroscopic siting of wind farm.
A WEB-BASED FRAMEWORK FOR VISUALIZING INDUSTRIAL SPATIOTEMPORAL DISTRIBUTION USING STANDARD DEVIATIONAL ELLIPSE AND SHIFTING ROUTES OF GRAVITY CENTERS
Authors: Song Y. , Gui Z. , Wu H. , Wei Y.
Published: September 2017

Abstract. Analysing spatiotemporal distribution patterns and its dynamics of different industries can help us learn the macro-level developing trends of those industries, and in turn provides references for industrial spatial planning. However, the analysis process is challenging task which requires an easy-to-understand information presentation mechanism and a powerful computational technology to support the visual analytics of big data on the fly. Due to this reason, this research proposes a web-based framework to enable such a visual analytics requirement. The framework uses standard deviational ellipse (SDE) and shifting route of gravity centers to show the spatial distribution and yearly developing trends of different enterprise types according to their industry categories. The calculation of gravity centers and ellipses is paralleled using Apache Spark to accelerate the processing. In the experiments, we use the enterprise registration dataset in Mainland China from year 1960 to 2015 that contains fine-grain location information (i.e., coordinates of each individual enterprise) to demonstrate the feasibility of this framework. The experiment result shows that the developed visual analytics method is helpful to understand the multi-level patterns and developing trends of different industries in China. Moreover, the proposed framework can be used to analyse any nature and social spatiotemporal point process with large data volume, such as crime and disease.
AMATCHMETHOD BASED ON LATENT SEMANTIC ANALYSIS FOR EARTHQUAKEHAZARD EMERGENCY PLAN
Authors: Sun D. , Zhao S. , Zhang Z. , Shi X.
Published: September 2017

Abstract. The structure of the emergency plan on earthquake is complex, and it’s difficult for decision maker to make a decision in a short time. To solve the problem, this paper presents a match method based on Latent Semantic Analysis (LSA). After the word segmentation preprocessing of emergency plan, we carry out keywords extraction according to the part-of-speech and the frequency of words. Then through LSA, we map the documents and query information to the semantic space, and calculate the correlation of documents and queries by the relation between vectors. The experiments results indicate that the LSA can improve the accuracy of emergency plan retrieval efficiently.
RELATIONSHIP MODEL BETWEEN NIGHTLIGHT DATA AND FLOOR AREA RATIO FROM HIGH RESOLUTION IMAGES
Authors: Yan M. , Xu L.
Published: September 2017

Abstract. It is a hotpot that extraction the floor area ratio from high resolution remote sensing images. It is a development trend of using nightlight data to survey the urban social and economic information. This document aims to provide a conference relationship model for VIIRS/NPP nightlight data and floor Area Ratio from High Resolution ZY-3 Images. It shows that there is a lineal relationship between the shadow and the floor area ratio, and the R2 is 0.98. It shows that there is a quadratic polynomial relationship between the floor area ratio and the nightlight, and the R2 is 0.611. We can get a conclusion that, VIIRS/NPP nightlights data may show the floor area ratio in an extent at level of administrative street.
ANALYSIS OF URBAN FOREST CARBON SEQUESTRATION CAPACITY: A CASE STUDY OF ZENGDU, SUIZHOU
Authors: Yu X. , Wang H. , Cai W. , Han Y.
Published: September 2017

Abstract. Carbon-fixing and oxygen-releasing is an important content of forest ecosystem serving in city. Analysis of forest ecosystem carbon sequestration capacity can provide scientific reference for urban forest management strategies. Taking Zengdu of Suizhou as an example, CITYGREEN model was applied to calculate the carbon sequestration benefits of urban forest ecosystem in this paper. And the carbon sequestration potential of urban forest ecosystem following the returning of farmland to forest land is also evaluated. The results show that forest area, percent tree cover, and the structure of forest land were the major factors reflecting regional carbon sequestration capacity.
GEOSPATIAL ANALYSIS OF ATMOSPHERIC HAZE EFFECT BY SOURCE AND SINK LANDSCAPE
Authors: Yu T. , Xu K. , Yuan Z.
Published: September 2017

Abstract. Based on geospatial analysis model, this paper analyzes the relationship between the landscape patterns of source and sink in urban areas and atmospheric haze pollution. Firstly, the classification result and aerosol optical thickness (AOD) of Wuhan are divided into a number of square grids with the side length of 6 km, and the category level landscape indices (PLAND, PD, COHESION, LPI, FRAC_MN) and AOD of each grid are calculated. Then the source and sink landscapes of atmospheric haze pollution are selected based on the analysis of the correlation between landscape indices and AOD. Next, to make the following analysis more efficient, the indices selected before should be determined through the correlation coefficient between them. Finally, due to the spatial dependency and spatial heterogeneity of the data used in this paper, spatial autoregressive model and geo-weighted regression model are used to analyze atmospheric haze effect by source and sink landscape from the global and local level. The results show that the source landscape of atmospheric haze pollution is the building, and the sink landscapes are shrub and woodland. PLAND, PD and COHESION are suitable for describing the atmospheric haze effect by source and sink landscape. Comparing these models, the fitting effect of SLM, SEM and GWR is significantly better than that of OLS model. The SLM model is superior to the SEM model in this paper. Although the fitting effect of GWR model is more unsuited than that of SLM, the influence degree of influencing factors on atmospheric haze of different geography can be expressed clearer. Through the analysis results of these models, following conclusions can be summarized: Reducing the proportion of source landscape area and increasing the degree of fragmentation could cut down aerosol optical thickness; And distributing the source and sink landscape evenly and interspersedly could effectively reduce aerosol optical thickness which represents atmospheric haze pollution; For Wuhan City, the method of adjusting the built-up area slightly and planning the non-built-up areas reasonably can be taken to reduce atmospheric haze pollution.
ANALYSIS OF SPATIAL PATTERN AND INFLUENCING FACTORS OF E-COMMERCE
Authors: Zhang Y. , Chen J. , Zhang S.
Published: September 2017

Abstract. This paper aims to study the relationship between e-commerce development and geographical characteristics using data of e-commerce, economy, Internet, express delivery and population from 2011 to 2015. Moran’s I model and GWR model are applied to analyze the spatial pattern of E-commerce and its influencing factors. There is a growth trend of e-commerce from west to east, and it is obvious to see that e-commerce development has a space-time clustering, especially around the Yangtze River delta. The comprehensive factors caculated through PCA are described as fundamental social productivity, resident living standard and population sex structure. The first two factors have positive correlation with e-commerce, and the intensity of effect increases yearly. However, the influence of population sex structure on the E-commerce development is not significant. Our results suggest that the clustering of e-commerce has a downward trend and the impact of driving factors on e-commerce is observably distinct from year to year in space.
ANALYZING THE VELOCITY OF URBAN DYNAMIC OVER NORTHEASTERN CHINA USING DMSP-OLS NIGHT-TIME LIGHTS
Authors: Zhou Y.
Published: September 2017

Abstract. Stable night-time lights (NTL) data from the Defense Meteorological Satellite Program Operational Line-scan System (DMSPOLS) can serve as a good proxy for anthropogenic development. Here DMSP-OLS NTL data was used to detect the urban development status in northeastern China. The spatial and temporal gradients are combined to depict the velocity of urban expanding process. This velocity index represents the instantaneous local velocity along the Earth’s surface needed to maintain constant NTL condition, and has a mean of 0.36 km/yr for northeastern China. The velocity change of NTL is lower in the urban center and its near regions, and the suburbs show a relatively high value. The connecting zones between satellite cities and metropolis have also a rapid rate of NTL evolution. The dynamic process of urbanization over the study area is mainly in a manner of spreading from urban cores to edges. The rank size of the velocity for the prefectures is analyzed and a long tail distribution is found. The velocity index can provide insights for the future pattern of urban sprawl.
WATER LEVEL MONITORING ON TIBETAN LAKES BASED ON ICESAT AND ENVISAT DATA SERIES
Authors: Li H. W. , Qiao G. , Wu Y. J. , Cao Y. J. , Mi H.
Published: September 2017

Abstract. Satellite altimetry technique is an effective method to monitor the water level of lakes in a wide range, especially in sparsely populated areas, such as the Tibet Plateau (TP). To provide high quality data for time-series change detection of lake water level, an automatic and efficient algorithm for lake water footprint (LWF) detection in a wide range is used. Based on ICESat GLA14 Release634 data and ENVISat GDR 1Hz data, water level of 167 lakes were obtained from ICESat data series, and water level of 120 lakes were obtained from ENVISat data series. Among them, 67 lakes contained two data series. Mean standard deviation of all lakes is 0.088 meters (ICESat), 0.339 meters (ENVISat). Combination of multi-source altimetry data is helpful for us to get longer and more dense periods cover water level, study the lake level changes, manage water resources and understand the impacts of climate change better. In addition, the standard deviation of LWF elevation used to calculate the water level were analyzed by month. Based on lake data set for the TP from the 1960s, 2005, and 2014 in Scientific Data, it is found that the water level changes in the TP have a strong spatial correlation with the area changes.
ICE VELOCITY MEASUREMENT IN EAST ANTARCTICA FROM 1960s TO 1980s BASED ON ARGON AND LANDSAT IMAGERY
Authors: Li R. , Ma X. , Cheng Y. , Ye W. , Guo S. , Tang G. , Wang Z. , Gao T. , Huang Y. , Li X. , Qiao G. , Tian Y. , Feng T. , Tong X.
Published: September 2017

Abstract. Ice flow velocity is a vital parameter for estimating the ice mass balance of glaciers in Antarctica. Especially long time serial observation of the surface velocity is of great significance to assessing the relationship between Antarctic ice materials and global climate change. However, the existing research on Antarctic ice velocity based on remote sensing data since 1970s due to the harsh climate in Antarctica. This paper presents an ice flow velocity estimating method includes image pre-processing, geometric model reconstruction, image ortho-rectification and feature matching by using ARGON images token in 1963 and Landsat images collected form 1973 to 1989.Considering the temporal-spatial distributions of ARGON images and Landsat images in Antarctica, two different methods respectively based on ortho-photos pair and Non-Ortho photos are adopted in this paper. More specifically, when there exist two stereo pairs taken in different time in the glacier region, after being ortho-rectified, the stereo pairs can be used to calculate ice flow velocity based on feature matching method. Otherwise, a parallax decomposition method that separates the effect of the terrain relief from the ice flow motion is applied when there only exists one stereo pair with a certain time interval. With this method, glacier surface velocity is available in the glacier region lacked enough stereo pairs. The methods mentioned above for estimating ice flow velocity are applied in Totten, Amery and Fimbul, etc. in eastern Antarctica. Furthermore, a 1960-80s ice flow speed map in the main glaciers of East Antarctica is produced for the first time.
STUDY ON VERTEX INTERVAL DETERMINATION FOR CONSISTENT CALCULATION OF COASTLINE LENGTH
Authors: Woo H. S. , Kwon K. S. , Kim B. G.
Published: September 2017

Abstract. Coastline extraction and decision have important implications for efficient land management and national policy formulation. However, coastlines extracted without certain criteria are difficult to obtain consistent results for the same region, and therefore, have a lot of restrictions on land management and application. For example, in some South Korea coastlines surveyed in this study, it was confirmed that there is a large difference in coastline lengths calculated for the same area depending on how the vertex interval is set. In this study, we propose and discuss the guidelines to enable consistent and accurate coastline extraction for the same area. The consistency of coastline output can be ensured only by the coastline calculation method using vertex interval criterion considering regional and coastline characteristics. This research content is expected to contribute to efficient land management and national policy establishment and progress.
A WEB-BASED INTERACTIVE PLATFORM
FOR CO-CLUSTERING SPATIO-TEMPORAL DATA
Authors: Wu X. , Poorthuis A. , Zurita-Milla R. , Kraak M.-J.
Published: September 2017

Abstract. Since current studies on clustering analysis mainly focus on exploring spatial or temporal patterns separately, a co-clustering algorithm is utilized in this study to enable the concurrent analysis of spatio-temporal patterns. To allow users to adopt and adapt the algorithm for their own analysis, it is integrated within the server side of an interactive web-based platform. The client side of the platform, running within any modern browser, is a graphical user interface (GUI) with multiple linked visualizations that facilitates the understanding, exploration and interpretation of the raw dataset and co-clustering results. Users can also upload their own datasets and adjust clustering parameters within the platform. To illustrate the use of this platform, an annual temperature dataset from 28 weather stations over 20 years in the Netherlands is used. After the dataset is loaded, it is visualized in a set of linked visualizations: a geographical map, a timeline and a heatmap. This aids the user in understanding the nature of their dataset and the appropriate selection of co-clustering parameters. Once the dataset is processed by the co-clustering algorithm, the results are visualized in the small multiples, a heatmap and a timeline to provide various views for better understanding and also further interpretation. Since the visualization and analysis are integrated in a seamless platform, the user can explore different sets of co-clustering parameters and instantly view the results in order to do iterative, exploratory data analysis. As such, this interactive web-based platform allows users to analyze spatio-temporal data using the co-clustering method and also helps the understanding of the results using multiple linked visualizations.