Assessing Similarity of Geographic Processes and Events

The increased availability of spatiotemporal data collected from satellite imagery and other remote sensors provides opportunities for enhanced analysis of geographic phenomena. Much of the new data includes regular snapshots of the environment. Comparison of these snapshots can provide information about changes to the phenomena of interest. However, conventional GIS data models and analytical tools lack capabilities to adequately handle massive multidimensional data. One of the fundamental tools necessary to meet such challenges is query support to retrieve and summarize data according to dynamic geographic phenomena, such as geographic events and processes, of interest. Such query support depends upon abilities to assess spatiotemporal similarity so that data representing geographic events that exhibit the spatiotemporal characteristics of interest can be identified in a GIS database. To this end, this paper introduces a method to assess similarity of geographic events and processes (such as storms) based on their spatiotemporal characteristics (such as distribution of precipitation). We developed six indices to capture static and dynamic characteristics of geographic events and applied the Dynamic Time Warping method to temporal sequences of the six indices to examine the similarity among these events. With a case study, we demonstrated the proposed indices and method capable of comparing spatiotemporal characteristics of events as recorded in a GIS database and categorizing spatiotemporal data into groups of events according to their behavior in space and time.     

Measuring segregation: an activity space approach

While the literature clearly acknowledges that individuals may experience different levels of segregation across their various socio-geographical spaces, most measures of segregation are intended to be used in the residential space. Using spatially aggregated data to evaluate segregation in the residential space has been the norm and thus individual’s segregation experiences in other socio-geographical spaces are often de-emphasized or ignored. This paper attempts to provide a more comprehensive approach in evaluating segregation beyond the residential space. The entire activity spaces of individuals are taken into account with individuals serving as the building blocks of the analysis. The measurement principle is based upon the exposure dimension of segregation. The proposed measure reflects the exposure of individuals of a referenced group in a neighborhood to the populations of other groups that are found within the activity spaces of individuals in the referenced group. Using the travel diary data collected from the tri-county area in southeast Florida and the imputed racial–ethnic data, this paper demonstrates how the proposed segregation measurement approach goes beyond just measuring population distribution patterns in the residential space and can provide a more comprehensive evaluation of segregation by considering various socio-geographical spaces.     

Consistency of accuracy assessment indices for soft classification: Simulation analysis

Accuracy assessment plays a crucial role in the implementation of soft classification. Even though many indices of accuracy assessment for soft classification have been proposed, the consistencies among these indices are not clear, and the impact of sample size on these consistencies has not been investigated. This paper examines two kinds of indices: map-level indices, including root mean square error (rmse), kappa, and overall accuracy (oa) from the sub-pixel confusion matrix (SCM); and category-level indices, including crmse, user accuracy (ua) and producer accuracy (pa). A careful simulation was conducted to investigate the consistency of these indices and the effect of sample size. The major findings were as follows: (1) The map-level indices are highly consistent with each other, whereas the category-level indices are not. (2) The consistency among map-level and category-level indices becomes weaker when the sample size decreases. (3) The rmse is more affected by error distribution among classes than are kappa and oa. Based on these results, we recommend that rmse can be used for map-level accuracy due to its simplicity, although kappa and oa may be better alternatives when the sample size is limited because the two indices are affected less by the error distribution among classes. We also suggest that crmse should be provided when map users are not concerned about the error source, whereas ua and pa are more useful when the complete information about different errors is required. The results of this study will be of benefit to the development and application of soft classifiers.     

Multidimensional Similarity Measuring for Semantic Trajectories

Most existing approaches aiming at measuring trajectory similarity are focused on two‐dimensional sequences of points, called raw trajectories. However, recent proposals have used background geographic information and social media data to enrich these trajectories with a semantic dimension, giving rise to the concept of semantic trajectories. Only a few works have proposed similarity measures for semantic trajectories or multidimensional sequences, having limitations such as predefined weight of the dimensions, sensitivity to noise, tolerance for gaps with different sizes, and the prevalence of the worst dimension similarity. In this article we propose MSM, a novel similarity measure for multidimensional sequences that overcomes the aforementioned limitations by considering and weighting the similarity in all dimensions. MSM is evaluated through an extensive experimental study that, based on a seed trajectory, creates sets of semantic trajectories with controlled transformations to introduce different kinds and levels of dissimilarity. For each set, we compute the similarity between the seed and the transformed trajectories, using different measures. The results showed that MSM was more robust and efficient than related approaches in the domain of semantic trajectories.     

矿区开采沉陷时空信息动态模拟研究

开采沉陷所涉及的信息具有多维性、复杂性和动态变化的特点,为实现其动态模拟,首先在研究开采沉陷地理空间实体的时空动态过程特征的基础上,结合地理信息系统、虚拟现实、多媒体及动画等技术,反演矿山开采沉陷这一时空地学现象和过程,进行可视化表达与动态模拟。模拟的结果有助于研究地表、岩层等地理实体以及生态环境,因为煤层的开采所造成的移动变形和破坏的规律,以便采用更好的控制模式,减少经济损失和开采损害。     

On the accuracy assessment of celestial reference frame realizations

The scatter of the celestial pole offset (CPO) time-series obtained from very long baseline interferometry (VLBI) observations is used as a measure of the accuracy of celestial reference frame (CRF) realizations. Several scatter indices (SI), including some proposed for the first time, are investigated. The first SI is based on residual analysis of CPO series with respect to a free core nutation (FCN) model. The second group of SIs includes Allan deviation and its extensions, which allow the treatment of unequally weighted and multidimensional observations. Application of these criteria to several radio source catalogues (RSCs) showed their ability to perform a preliminary assessment of the quality of each RSC. The 2D Allan deviation estimate seems to be the most sensitive measure. The proposed extensions of Allan deviation, weighted and multidimensional, can also be used for the statistical analysis of other time-series.     

辐射特征支持下的城市高分影像阴影校正

大量城市建筑使得高分影像中含有许多阴影区。这些阴影区在土地利用分类、植被绿度调查等遥感应用中会较大地影响结果精度,降低数据使用效率并增加研究成本。基于同一地物阴影区与临近非阴影区反射率相等这一辐射特征关系,通过建立辐射传输方程,发展了一种新的城市高分遥感影像阴影校正方法 RERB(Reflectance Equality Relationship Based Method)。利用RERB对不同城市(北京和荷兰Enschede)不同高分多光谱影像(Geo Eye-1和Quick Bird)进行阴影校正,并对比分析其与被广泛采用的均值方差变换法MVT(Mean and Variance Transformation)的校正结果,通过定性和定量精度评价发现:(1)RERB能很好地将城市阴影区影像视觉特征(颜色、纹理、色调等)信息恢复到与非阴影区同一水平上;(2)RERB恢复后的阴影区具有丰富的细节信息且在视觉上与临近非阴影区具有良好的一致性;(3)RERB恢复后的城市柏油路面和水泥路面阴影区辐射信息具有较低的误差,可见光-近红外波段的平均误差分别为7%和9%。同时RERB能较好地恢复城市阴影区植被波谱特征信息。     

Sequence-based mapping approach to spatio-temporal snow patterns from MODIS time-series applied to Scotland

Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99%) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60% of explained deviance) with environmental information such as morphology supporting their use as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position-wise information (e.g. importance of short term fall/melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models.     

Geo-scape,a granularity depended spatialization tool for visualizing multidimensional data sets

Recently, the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data, on the basis of methods called spatialization methods. Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques. Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of granularity. The present paper makes an investigation on how the issue of granularity is handled in the context of representative examples of spatialization methods. Furthermore, this paper introduces the prototyping tool Geo-Scape, which provides an interactive spatialization environment for representing and exploring multidimensional data at different levels of granularity, by making use of a kernel density estimation technique and on the landscape “smoothness” metaphor. A demonstration scenario is presented next to show how Geo-Scape helps to discover knowledge into a large set of data, by grouping them into meaningful clusters on the basis of a similarity measure and organizing them at different levels of granularity.     

The effectiveness of richness,diversity and dominance textural indices when applied on digital ground photographs using a GIS for landscape visual analysis purposes

This study aims to investigate the effectiveness of three main textural indices for the automatic evaluation and classification of landscape scenic quality using digital ground photographs. Based on the textural indices that are commonly used in landscape ecology, the richness, diversity and dominance indices were selected to be examined, and the results from the application of these indices to a sample of ground photographs are described in this paper. The application of the textural indices to the sample of photographs takes place in two different ways, using a geographic information system (GIS) that aims to describe the general and partial heterogeneity of the landscape appearing in each photograph. Furthermore, we investigate the effectiveness of the indices selected as to the classification of the landscape's scenic quality, with the results being compared to other results derived from a research programme of the National Technical University of Athens. Comments and suggestions are presented for further investigation. The main conclusion of the investigation is that the texture indices are sensitized in the landscape's scenic quality, a fact that is positive and encourages the pursuit of further research.     

Using geoinformatics and geomorphometrics to quantify the geodiversity of Crete,Greece

The geodiversity of Crete is quantified in this study, based on the classification of geomorphometric, geological and climatic factors. A number of geomorphometric variables, extracted from the ASTER Global Digital Elevation Model (ASTER G-DEM) in conjunction with geological and climatic information, are evaluated through various algorithms incorporated into Geographical Information System (GIS) software’s. The derived geoinformatic data sets are then analyzed to produce the geodiversity of Crete. The geodiversity map is used to quantify the geodiversity, by calculating landscape diversity and other spatial pattern indices. Those indices are evaluating the richness, evenness, fragmentation and shape of the landscape patch types. The outcome of this study has highlighted that western Crete is characterized by complex geodiversity with more irregular, elongated and fragmented landscape patterns relative to the eastern part of the island. The geodiversity indices provide insights into the processes shaping landscapes, particularly the “battle” between neotectonic landscape deformation and erosion/deposition. The methodology presented can be useful for decision makers when evaluating a regions geological heritage, planning the management of natural resources, or designating areas for conservation.     

Using Ontologies for Integrated Geographic Information Systems

Today, there is a huge amount of data gathered about the Earth, not only from new spatial information systems, but also from new and more sophisticated data collection technologies. This scenario leads to a number of interesting research challenges, such as how to integrate geographic information of different kinds. The basic motivation of this paper is to introduce a GIS architecture that can enable geographic information integration in a seamless and flexible way based on its semantic value and regardless of its representation. The proposed solution is an ontology-driven geographic information system that acts as a system integrator. In this system, an ontology is a component, such as the database, cooperating to fulfill the system's objectives. By browsing through ontologies the users can be provided with information about the embedded knowledge of the system. Special emphasis is given to the case of remote sensing systems and geographic information systems. The levels of ontologies can be used to guide processes for the extraction of more general or more detailed information. The use of multiple ontologies allows the extraction of information in different stages of classification. The semantic integration of aerial images and GIS is a crucial step towards better geospatial modeling.     

关于线状地理特征空间关系的自然语言描述的形式化表达

目前的地理信息系统都使用定量的方法存储和查询空间信息,不能反映人们的定性思维方式,因此它表达地理空间信息的能力受到限制。在关于空间关系的自然语言描述的调查的基础上,本文定义了一组能反映线状物体空间关系的度量指标,结合反映拓扑关系的定量指标,使用决策树的数据挖掘算法,对调查结果进行分析,形式化了描述空间关系的自然语言词汇,建立了模糊的自然语言表达和图形的几何特征之间的联系。自然语言形式化后得到的规则可用于空间关系的自然语言查询系统。     

Hyperspectral image noise reduction based on rank-1 tensor decomposition

In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed based on high-order rank-1 tensor decomposition. The hyperspectral data cube is considered as a three-order tensor that is able to jointly treat both the spatial and spectral modes. Subsequently, the rank-1 tensor decomposition (R1TD) algorithm is applied to the tensor data, which takes into account both the spatial and spectral information of the hyperspectral data cube. A noise-reduced hyperspectral image is then obtained by combining the rank-1 tensors using an eigenvalue intensity sorting and reconstruction technique. Compared with the existing noise reduction methods such as the conventional channel-by-channel approaches and the recently developed multidimensional filter, the spatial–spectral adaptive total variation filter, experiments with both synthetic noisy data and real HSI data reveal that the proposed R1TD algorithm significantly improves the HSI data quality in terms of both visual inspection and image quality indices. The subsequent image classification results further validate the effectiveness of the proposed HSI noise reduction algorithm.     

利用MERIS数据植被指数分析福建省植被长势季节变化

监测植被长势动态变化可以提供生态系统状况有价值的信息,可以检测到人类或气候作用引起的变化。本研究利用2004—2005年间10期MERIS影像数据,以福建省为例,探讨MERIS数据在区域植被长势季节变化监测中的应用效果;分析了MERIS数据用于区域植被季节变化监测时的数据处理方法;比较了MERIS数据几种植被指数,提出了利用10和8波段组合改进MERISNDVI的建议;利用多时相合成的NDVI简单分析了2004年夏季—2005年夏季三个季节的植被长势状况。结果表明,MERIS植被指数的时空变化有效反映了气候变化对植被长势的影响。     

区域统计指标空间化技术及应用研究

社会数据的空间化是现代地图制图学研究的主要内容之一。本文提出了区域统计指标的概念并将其分为区域平均指标与区域总量指标两种类型,成功地将网格单元面积权重内插(GCAW I)法引入到区域统计指标的空间化工作中,应用GCAW I法实现了新沂市耕地标准亩指标的空间化表达,结果表明:运用GCAW I法进行区域统计指标的空间化不仅可以很好地避免传统方法的同一区域均匀分布、不同区域突变分布的缺点,而且经kriging插值后的地图很好地符合区域统计指标空间分布的特点,生成的3维可视化地图更加切合实际。     

A numerical technique for delineation of soil mapping units using multi-spectral remote sensing data

A numerical technique for transformation of ground based sail spectral information into soil mapping — unit information, in terms of the total information content index has been proposed. The study carried out on 14 surface soil samples. widely differing in their physical appearance of colour and collected from different parts of India, revealed that total information content index could distinctly discriminate between the contrasting soil physiographic units with black cotton, red and sandy soil types. A comparison of the proposed index with the conventionally used two or three waveband specific indices (e.g. NIR/Red and NIR-Red/Red-Green) showed that the proposed index was more characteristic of the various soil types studied. Further, unlike the conventional 2-D indices, the proposed, numerical technique lead to the complete compression of the information contained in the entire reflectance spectrum (irrespective of the number of wavebands) to a single characteristic value in 1-D Space and a simplified 1-D clustering analysis.     

Geo-scape,a granularity depended spatialization tool for visualizing multidimensional data sets

Recently,the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data,on the basis of methods called spatialization methods.Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques.Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of gran...     

A Comparison of Aspatial and Spatial Measures of Segregation

The modifiable areal unit problem arises when the boundaries that define neighborhoods affect perceived levels of segregation. Scholars postulate that this problem is exacerbated when one uses a definition of neighborhoods that is based on administrative units; doing so leads to an aspatial measure of segregation, which may or may not adequately account for the spatial relationships among residential locations. In this article, we assess whether aspatial and spatial definitions of neighborhoods produce different perceived levels of income segregation. Using an original individual‐level dataset on income in San Mateo County, California, we define each individual's neighborhood in three ways – two aspatial and one spatial. On the basis of these definitions of neighborhoods, we then estimate residential income segregation using the local Moran's I statistic. We report two primary findings. First, the three measures generate different perceived levels of income segregation. Specifically, we observe less income segregation when using the aspatial measures as compared with the spatial one. Second, the inconsistencies between these measures are systematic in such a way as to lead to different inferences when used to predict individual voter turnout.     

An Information fusion approach for PALSAR data to retrieve soil moisture

Estimation of vegetation covered soil moisture with satellite images is still a challenging task. Several models are available for soil moisture retrieval in which water cloud model (WCM) is most common. But, it requires an estimation of accurate vegetation parameterization. Thus, there is a need to develop such an approach for soil moisture retrieval which minimize these limitations. Therefore, this paper deals with the soil moisture retrieval using fully polarimetric SAR data by fusing the information from different bands. Various polarimetric indices and observables were critically analysed, and found that the index; SPAN (total scattered power) gives better information of vegetation cover as compared to other indices/observables. Based on this, WCM model has been modified using SPAN as parameter and soil moisture content were retrieved.