Responsive geo-referenced content visualization based on a user interest model and level of detail

With steady growth in the deployment of sensor networks and widespread usage of mobile devices, the production of geo-referenced data is increasing exponentially, while analysis of the data typically uses a variety of application services on the Internet for activities such as logging and driving. This geo-referenced content needs to be classified and organized according to user interest for location-based decision-making. In this study, we define a user interest model and propose a method for organizing and presenting content based on the model. Our approach reconstructs geo-referenced content from a variety of sources based on points of interest organized by the user interest model. We present typical cases of using the proposed method, including a theme park resort, personal weather stations, and travel planning to show how the proposed method facilitates user decision-making based on the geo-referenced content.     

Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin,Paris, and Tallinn

This paper proposes a methodology for using mobile telephone-based sensor data for detecting spatial and temporal differences in everyday activities in cities. Mobile telephone-based sensor data has great applicability in developing urban monitoring tools and smart city solutions. The paper outlines methods for delineating indicator points of temporal events referenced as ‘midnight’, ‘morning start’, ‘midday’, and ‘duration of day’, which represent the mobile telephone usage of residents (what we call social time) rather than solar or standard time. Density maps by time quartiles were also utilized to test the versatility of this methodology and to analyze the spatial differences in cities. The methodology was tested with data from cities of Harbin (China), Paris (France), and Tallinn (Estonia). Results show that the developed methods have potential for measuring the distribution of temporal activities in cities and monitoring urban changes with georeferenced mobile phone data.     

Integration of multi-source remote sensing data for land cover change detection

The objective of this study is to develop a methodology to integrate multi-source remote sensing data into a homogeneous time series of land cover maps in order to carry out change detection. We developed a method to increase the comparability between land cover maps coming from panchromatic aerial photographs and SPOT XS (multi-spectral) data by equalizing their levels of thematic content and spatial details. The methodology was based on the hypotheses that: (1) map generalization can improve the integration of data for change detection purpose, and (2) the spatial structure of a land cover map, as measured by a set of landscape metrics, is an indicator of the level of generalization of that map. Firstly, the methodology for data integration was developed by using land cover maps generated from near-synchronous data. Results revealed that, by controlling successively the parameters that influence the level of map generalization, the percentage of agreement between the near-synchronous land cover maps can be increased from 42% to 93%. The computation of five landscape metrics for a set of generalized land cover maps and for the target map allowed us to optimize the level of generalization by measuring the similarity in landscape pattern of the maps. The optimum level of generalization of the land cover map obtained from the aerial photographs for comparison with a land cover map derived from SPOT XS data was found at a resolution of 41m for two generalization levels of the thematic content. The spatial structure of a land cover map, as measured by a set of landscape metrics, is thus a good indicator of the level of generalization of this map. Secondly, the method was applied by integrating a land cover map obtained from aerial photographs of 1954 with a land cover map obtained from a SPOT XS image of 1992.     

利用ASTER遥感数据反演陆面温度的算法及应用研究

陆面温度是地气交换过程的一个重要参数,在生态环境研究中应用很广。传统方法只能进行点上的观测和计算,遥感的出现则使得计算区域陆面温度成为可能。ASTER遥感数据具有较高的空间分辨率和光谱分辨率,能够提供比NOAA/AVHRR和Landsat等遥感数据更丰富的陆面信息,有助于提高反演陆面温度的精度。本文以新疆自治区阿瓦提县典型研究区域为例,根据ASTER遥感数据的特点,基于温度/比辐射率分离算法的思想,运用ADE(AlphaDerivedEmissivity)、比值法和MMD(Maximum-MinimumDiffer-ence)三个模块计算陆面温度,并简要分析了模型的主要误差来源。分析结果表明本文所采用的算法是可行的,ASTER遥感数据用于反演陆面温度可以取得比较理想的结果,具有良好的应用前景。     

Near real-time geoprocessing on the grid: A scalable approach to road traffic monitoring

The geospatial sensor web is set to revolutionise real-time geospatial applications by making up-to-date spatially and temporally referenced data relating to real-world phenomena ubiquitously available. The uptake of sensor web technologies is largely being driven by the recent introduction of the OpenGIS Sensor Web Enablement framework, a standardisation initiative that defines a set of web service interfaces and encodings to task and query geospatial sensors in near real time. However, live geospatial sensors are capable of producing vast quantities of data over a short time period, which presents a large, fluctuating and ongoing processing requirement that is difficult to adequately provide with the necessary computational resources. Grid computing appears to offer a promising solution to this problem but its usage thus far has primarily been restricted to processing static as opposed to real-time data sets. A new approach is presented in this work whereby geospatial data streams are processed on grid computing resources. This is achieved by submitting ongoing processing jobs to the grid that continually poll sensor data repositories using relevant OpenGIS standards. To evaluate this approach a road-traffic monitoring application was developed to process streams of GPS observations from a fleet of vehicles. Specifically, a Bayesian map-matching algorithm is performed that matches each GPS observation to a link on the road network. The results show that over 90% of observations were matched correctly and that the adopted approach is capable of achieving timely results for a linear time geoprocessing operation performed every 60 seconds. However, testing in a production grid environment highlighted some scalability and efficiency problems. Open Geospatial Consortium (OGC) data services were found to present an IO bottleneck and the adopted job submission method was found to be inefficient. Consequently, a number of recommendations are made regarding the grid job-scheduling mechanism, shortcomings in the OGC Web Processing Service specification and IO bottlenecks in OGC data services.     

圣保罗大都市区社会空间分异研究——多元统计方法在城市连绵区的应用

空间数据和地理信息系统在城市规划和决策中应用的重要性日见凸显。主要原因在于：重要的人口数据和社会变动经常表现出一定的空间特性,这种特性可以通过空间分析和统计方法被认识和解释。应用多元分析的空间分类方法编制圣保罗大都市区社会分异地图并进行相关分析。研究的主要数据来自2000年巴西全国人口普查,其中包括了圣保罗大都市的所有行政区和39个自治市的21774个人口普查区。为了把都市连绵区从数据全集中分离出来,我们采用混合技术进行互补分析,即在2000年4月30日的陆地卫星7号图像中绘制一个个多边形,这些被识别出来的多边形就是人口普查区。然后,通过目视解译出假彩色多边形集合。应用空间分类评分程序将这些多边形分成五类,并建立人口普查区的数目、覆盖的面积和都市连绵区之间的关系。这种多元分析方法是基于变量的均衡化来生成易于用分级统计图描述平均值,以促进可视化和后续的空间分布分析。基于多元分析的空间分类方法研究,清楚地展现了圣保罗大都市最重要的社会特征,也说明城市社会地图方法和多元分析的空间分类方法在大都市区的管理、公共政策规划和复杂决策中具有重要的应用价值。     

The impact of interactive visualization on trade-off-based geospatial decision-making

In a previous work we developed GISwaps, a novel method for geospatial decision-making based on Even Swaps. In this paper, we present the results of an evaluation of a visualization framework integrated with this method, implemented within a decision support system. This evaluation is based on two different studies. In the quantitative study, 15 student participants used GISwaps with no visual features, and 15 participants used GISwaps with the integrated visual framework, as the tool in a solar farm site location case study. The results of the quantitative evaluation show positive impact of the visualization in terms of increased coherency in trade-offs. The results also show a statistically significant difference in average trade-off values between the groups, with users from the non-visual group setting on average 20% higher trade-off values compared with the users in the visual group. In the qualitative study, we had one expert in GIS, two experts in decision-making and two experts in solar energy as a focus user group. Data in this study were obtained by observations and semi-structured interviews with the participants. The impact of the visualization framework was assessed positively by all participants in the expert group.     

Spatiotemporal changes in the ecological status of water bodies on the mining territories of the Syunik Province (Republic of Armenia)

We discuss the changes in hydrological characteristics of water bodies caused by freshwater withdrawals to meet the interests of various users on the territory of the Syunik Province of the Republic of Armenia. An analysis is made of the possibilities of combining the water withdrawal for mining enterprises and for agriculture. The problems with water supply of settlements located on this territory are highlighted. The temporal dynamics of water withdrawals is presented for separate rivers as well as its relationship with the minimum and ecological flow. It is found that the anthropogenic load on water bodies is more clearly pronounced in the case of a degradation of the quality of water resources as a result of the industrial waste water diversion, and wash-out of pollutants from the territory of enterprises during floods and in the case of emergency input of wastes from tailings pools, rock spoil heaps and waste storage areas. Results of these authors’ observations, data of routine observations and material from published sources were used in analyzing the water quality in the rivers of the Syunik Province. We calculated the coefficients of ecological quality and determined the river water quality classes according to physicochemical and biological indicators. Assessments of the river water quality class were made in accordance with the methodology of Directive 2000/60/EC. We identified the main pollutants that are responsible for the water quality class, and the factors of impact on natural waters governing the ecological status of water bodies. A comprehensive analysis is made of the intensity of anthropogenic impact on surface waters. The ecological status of water bodies is determined. Spatial changes in the quality indicators of the water of the region’s main rivers are demonstrated. Special emphasis is placed on the need to implement immediate measures to improve technological processes and bring the operation of enterprises into compliance with ecological normative standards.     

Automatic change detection in lane-level road networks using GPS trajectories

Lane-level road network updating is crucial for urban traffic applications that use geographic information systems contributing to, for example, intelligent driving, route planning and traffic control. Researchers have developed various algorithms to update road networks using sensor data, such as high-definition images or GPS data; however, approaches that involve change detection for road networks at lane level using GPS data are less common. This paper presents a novel method for automatic change detection of lane-level road networks based on GPS trajectories of vehicles. The proposed method includes two steps: map matching at lane level and lane-level change recognition. To integrate the most up-to-date GPS data with a lane-level road network, this research uses a fuzzy logic road network matching method. The proposed map-matching method starts with a confirmation of candidate lane-level road segments that use error ellipses derived from the GPS data, and then computes the membership degree between GPS data and candidate lane-level segments. The GPS trajectory data is classified into successful or unsuccessful matches using a set of defuzzification rules. Any topological and geometrical changes to road networks are detected by analysing the two kinds of matching results and comparing their relationships with the original road network. Change detection results for road networks in Wuhan, China using collected GPS trajectories show that these methods can be successfully applied to detect lane-level road changes including added lanes, closed lanes and lane-changing and turning rules, while achieving a robust detection precision of above 80%.     

雅砻江上游径流及影响因素关系研究

为研究雅砻江上游径流的变化及其积雪、气温和降水对径流的影响,首先根据相关性将年径流周期分为枯水期、融雪影响期和汛期,其次,结合MODIS 8天积雪产品、研究区气温和降水数据,采用相关分析和归因分析法分析了径流与影响因素的相关性以及各影响因素对径流变化的影响程度,最后用逐步多元回归分析法得出枯水期和融雪影响期径流的预测方程。结果表明：2000-2014年间雅砻江上游径流整体呈上升趋势,冬季积雪面积的减少导致径流减少了24.89%,汛期降水增加导致径流增加了79.38%,采用相关分析和逐步多元回归方法可有效分析径流与影响因素的关系及影响程度。     

Assessing physical vulnerability for multi-hazards using an indicator-based methodology

Globally, many built-up areas are threatened by multiple hazards which pose significant threat to humans, buildings and infrastructure. However, the analysis of the physical vulnerability towards multiple hazards is a field that still receives little attention although vulnerability analysis and assessment can contribute significantly to risk reduction efforts. Indicator-based vulnerability approaches are flexible and can be adjusted to the different hazards as well as to specific user needs. In this paper, an indicator-based vulnerability approach, the PTVA (Papathoma Tsunami Vulnerability Assessment), was further developed to be applicable in a multi-hazard context. The resulting multi-hazard version of the PTVA consists of four steps: the identification of the study area and relevant hazards as well as the acquisition of hazard information, the determination of vulnerability indicators and collection of data, the weighting of factors and vulnerability assessment and finally, the consideration of hazard interactions. After the introduction of the newly developed methodology a pilot application is carried out in the Faucon municipality located in the Barcelonnette basin, Southern French Alps. In this case study the vulnerability of buildings to debris flows, shallow landslides and river flooding for emergency planning and for general risk reduction purposes is assessed. The implementation of the methodology leads to reasonable results indicating the vulnerable buildings and supporting the priority setting of different end-users according to their objectives. The constraints of the presented methodology are: a) the fact that the method is not hazard-intensity specific, thus, vulnerability is measured in a rather qualitative and relative way and b) the high amount of data required for its performance. However, the advantage is that it is a flexible method which can be applied for the vulnerability analysis in a multi-hazard context but also it can be adjusted to the user-specific needs to support decision-making.     

基于大数据的极端暴雨事件下城市道路交通及人群活动时空响应

随着全球气候变化加剧,极端降雨增多,暴雨内涝灾害频发,严重威胁城市的可持续发展。快速掌握暴雨给城市交通及人群的影响,有助于提高灾害应急管理水平和事件响应能力。利用实时动态的交通路况信息和手机定位请求数据,通过一种融合STL时序分解技术与极端学生化偏差统计检验的时间序列异常探测方法,监测和分析暴雨内涝灾害事件中,城市道路交通和人群活动的时空响应特征,并以2018年7月16日发生在北京的极端暴雨事件为例开展实证研究。研究结果显示,在降雨集中的早、晚高峰两个时段（8—9时、18—19时）,市区的拥堵道路数量超出往常水平最高可达150%,异常检测分析显示拥堵道路数量和交通拥堵指数均达到异常甚至极端水平。人群活动的异常响应分析结果显示,暴雨事件引起定位请求量异常升高、异常点增多,且异常点的空间分布与1 h前的降雨量分布存在较高相关性。以上结果不仅证明了大数据及异常检测方法对于快速洞察暴雨事件对城市交通及人群影响的有效性,也为城市暴雨内涝灾害的应急响应与管理提供了新的技术手段。     

DS18B20温度传感器在地下盐湖遥测系统中的应用与改进

介绍了DS18B20温度传感器在地下盐湖遥测系统中实现远距离温度检测应用的关键编程、传感器铠装壳体的封装工艺2个方面的改进与实施方法,实现了DS18B20在地下盐湖遥测系统中卤水温度参数的长距离、低成本、高质量的数据采集。     

Evaluating the fitness for use of spatial data sets to promote quality in ecological assessment and monitoring

This article proposes and illustrates a practical methodological framework to evaluate the fitness for use of spatial data sets for environmental and ecological applications, focusing on user requirements for specified application contexts. The methodology is based on the use of metadata to analyze similarity between the data characteristics and the user’s needs or expectations for several quality indicators. Additionally, the concept of ‘critical factors’ is introduced in this framework, allowing users to define which quality indicators have greater importance given their own requirements or expectations and the specified application contexts. The proposed methodology further allows integrating and interconnecting the spatial data quality (SDQ) evaluation methodology with metadata geoportals in WebGIS platforms, facilitating its operation by users from non-spatial disciplines and with often limited expertise on this subject. Examples of the evaluation of fitness for use for specific application contexts within the project BIO_SOS (‘Biodiversity Multi-SOurce Monitoring System: From Space To Species’ FP7 project) are presented. By providing a prompt and straightforward evaluation tool, the proposed methodology can encourage the implementation of SDQ evaluation routines in ecological assessment and monitoring programs, promoting a more adequate use of geospatial data and ultimately contributing to well-supported policy and management decisions.     

北京中心城区内涝成因

城市化的发展改变了原始的自然水文循环系统,打破了水量的均衡而导致了城市内涝。本文梳理了应对雨水的基本模式,并总结出与城市规划相关的三方面主要致涝因素——城市土地利用、水系统和基础设施。以北京中心城区为案例,综合考虑城市用地变化过程,从城市基本汇水区单元的视角出发,分析城区内涝程度和上述三方面因素之间的关联规律。结果表明平均坡度、河网密度与城市内涝程度呈线性正相关;耕地比率、林地比率、荒地比率与城市内涝程度呈线性负相关;而不透水表面比率和建设用地比率与城市内涝程度呈指数正相关关系。     

基于MAS的农户土地利用模型构建与模拟——以陕西省米脂县孟岔村为例

Multi-Agent System(简称MAS)给进行多主体参与的LUCC研究提供一个概念性框架.本文试图通过对多主体决策的模拟,来探讨MAS在微观层面土地利用变化过程中的应用,揭示农户土地利用决策变化的机制.首先,简要说明MAS在土地利用变化模拟中的研究进展和背景:然后,基于B、D、I的决策结构.建立基于MAS的农户土地利用框架;再次,以陕西省米脂县孟岔村为例进行研究:最后.探讨了MAS模型在微观层面模拟LUCC的潜力与不足.研究表明:①利用可清晰地表达决策产生过程的B、D、I决策框架,构建微观个体(农户)的土地利用决策模型:②利用连续函数,构建同类农户间的相互作用模型;③构建市场对农户决策的影响模型.定量分析市场对农户最终土地利用决策的影响,厘定市场、同类农户间相互作用对其土地利用决策的影响:④构建的基于MAS的农户土地利用决策模型为从微观视角研究土地利用变化提供了新的方法.同时.也为进一步探索宏观土地利用变化与微观土地利用决策间的互动机理积累理论与实践的经验.还需要进一步研究和探讨的问题包括:①决策的尺度变化问题,即单个农户决策与群体农户决策之间的转化问题;②探索在更长时间里进行农户土地利用决策研究的方法.以期在大量的土地利用资料与有限时间决策之间找到沟通的桥梁:③进一步探讨不同农户间相互作用的定量方法和模型,充实农户间相互作用的研究.     

Knowledge-guided consistent correlation analysis of multimode landslide monitoring data

A novel method called knowledge-guided spatio-temporal consistent correlation analysis (KSTCCA) was developed to discover reliable deformation features induced by multiple factors based on multimode landslide monitoring data. Compared to conventional approaches, KSTCCA integrates both temporal and spatial correlation analysis to improve the consistency of deformation patterns and capture the spatio-temporal heterogeneities in multimode monitoring data. KSTCCA considers both the landslide deformation mechanisms and the relationships between different influential factors as knowledge. Moreover, the method extracts the morphological structures of monitoring curves based on a seven-point approach and identifies knowledge rules using the k-means clustering method. Under the guidance of prior knowledge, a spatial correlation analysis is conducted based on support vector regression, and a temporal correlation analysis of the time lag is carried out based on the morphological structure features. Finally, three kinds of typical monitoring data, including deformation, rainfall, and reservoir water level data collected in the Baishuihe landslide area, China, are used for experimental analysis to verify the validity of the proposed method.     

NIGHT-FROST MODULATION OF NEAR-SURFACE SOIL-WATER ION CONCENTRATION AND THERMAL FIELDS

The modulation of the soil-water ion concentration and thermal fields resulting from night frost is illustrated with data from temperature-electric potential probes in the upper 15 cm of a sandy loam soil. The relative soil-water ion concentration, calculated from soil electric potential observations, aids in the detection of these modulation effects produced by non-conductive heat-mass transfer processes. The apparent thermal diffusivity, calculated from soil-temperature data, further facilitates the detection and analysis of these non-conductive processes. This new technology demonstrates the distorting effects of soil-water advection to the freezing zone, “ice purification” of soil water in the freezing zone and other coupled mass-heat transport effects produced by night frost. The conversion of electric potential observations to a relative index of soil-water ion concentration and the calculation of the apparent thermal diffusivity from the temperature data matrix are demonstrated as effective methods for the detection and analysis of the magnitude and direction of coupled-flow processes. [Key words: electric potential, frost, soil chemistry, soil temperature, thermal diffusivity.]     

Texture-based identification of urban slums in Hyderabad, India using remote sensing data

This paper outlines a methodology to identify informal settlements out of high resolution satellite imagery using the concept of lacunarity. Principal component analysis and line detection algorithms were applied alternatively to obtain a high resolution binary representation of the city of Hyderabad, India and used to calculate lacunarity values over a 60 × 60 m grid. A number of ground truthing areas were used to classify the resulting datasets and to identify lacunarity ranges which are typical for settlement types that combine high density housing and small dwelling size - features characteristic for urban slums in India. It was discovered that the line detection algorithm is advantageous over principal component analysis in providing suitable binary datasets for lacunarity analysis as it is less sensitive to spectral variability within mosaicked imagery. The resulting slum location map constitutes an efficient tool in identifying particularly overcrowded areas of the city and can be used as a reliable source in vulnerability and resilience assessments at a later stage. The proposed methodology allows for rapid analysis and comparison of multi-temporal data and can be applied on many developing urban agglomerations around the world.     

Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models

Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.