采用聚类技术探测空间异常

提出了一种基于聚类的空间异常探测方法。该方法通过空间聚类获得局部相关性较强的实体集合,分别探测空间异常,给出了一种稳健的空间异常度量指标,提高了异常探测结果的可靠性。通过实例验证以及与SOM方法的比较分析,证明了该方法的正确性和优越性。     

Adaptive Zoning for Transport Mode Choice Modeling

Adaptive zoning is a recently introduced method for improving computer modeling of spatial interactions and movements in the transport network. Unlike traditional zoning, where geographic locations are defined by one single universal plan of discrete land parcels or ‘zones’ for the study area, adaptive zoning establishes a compendium of different zone plans, each of which is applicable to one journey origin or destination only. These adaptive zone plans are structured to represent strong spatial interactions in proportionately more detail than weaker ones. In recent articles, it has been shown that adaptive zoning improves, by a large margin, the scalability of models of spatial interaction and road traffic assignment. This article confronts the method of adaptive zoning with an application of the scale and complexity for which it was intended, namely an application of mode choice modeling that at the same time requires a large study area and a fine‐grained zone system. Our hypothesis is that adaptive zoning can significantly improve the accuracy of mode choice modeling because of its enhanced sensitivity to the geographic patterns and scales of spatial interaction. We test the hypothesis by investigating the performance of three alternative models: (1) a spatially highly detailed model that is permissible to the maximum extent by available data, but requires a high computational load that is generally out of reach for rapid turnaround of policy studies; (2) a mode choice model for the same area, but reducing the computational load by 90% by using a traditional zone system consisting of fewer zones; and (3) a mode choice model that also reduces the computational load by 90%, but based on adaptive zoning instead. The tests are carried out on the basis of a case study that uses the dataset from the London Area Transport Survey. Using the first model as a benchmark, it is found that for a given computational load, the model based on adaptive zoning contains about twice the amount of information of the traditional model, and model parameters on adaptive zoning principles are more accurate by a factor of six to eight. The findings suggest that adaptive zoning has a significant potential in enhancing the accuracy of mode choice modeling at the city or city‐region scale.     

Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land Imager), combined with Object-Based Image Analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis.     

一种基于场论的空间异常探测方法

从空间数据场的角度,借鉴高斯势函数发展了一种新的空间异常度度量指标。进而,提出了一种基于场论的空间异常探测方法。该方法通过空间聚类获得局部相关性较强的空间簇,并构建合理、稳定的空间邻近域。在此基础上,采用专题属性变化梯度修复策略减弱空间邻近域中潜在异常的影响,并利用空间异常度度量指标计算实体的异常度,从而探测空间异常。实验结果及实例证明了此方法的正确性。     

Exploring the relationships between perceived neighborhood boundaries and street network orientation

The neighborhood is a core unit of analysis in urban research, planning, and policy-making. However, perceptual and historical processes oftentimes result in neighborhoods that are not tied to sub-urban jurisdictions. For instance, historic neighborhoods might lack official spatial definitions, hampering neighborhood-based tasks in local offices. In this case, urban practitioners can benefit from readily available spatial proxies, such as the local street network. In this study, we conducted an exploratory analysis that combines neighborhood mapping and street network modeling. By retrieving participants' sketched boundaries and quantifying spatial orientations of sketched polygons and local network patterns, we were able to measure and compare the relationships between the urban fabric and the perceived extents of two historic neighborhoods in Lisbon, Portugal. The results provided insights for enhancing existing definitions of non-official neighborhoods, outlining new urban districts as well as for discussions about the role of the urban form in shaping people's perceptions.     

顾及尺度效应和景观格局的土地利用数据综合指标研究

从尺度效应和土地利用案观格局影响两个层面研究土地利用数据综合的宏观和微观综合指标,设计土地利用数据综合的指标体系和指标定量化描述模型.在全国各个土地利用分区采集不同尺度的具有典型代表性的土地利用数据样本集,通过空间统计分析等方法,得到土地利用数据综合中的宏观控制性指标和微观图形综合指标的尺度变化规律及其与土地利用格局的关系.以一个县为试验区,验证其合理性.

Abstract：

Indices for land use data generalization are critical for generating multiscale land use maps and databases. Previous research suffers from two major setbacks. Firstly, determination of threshold values for multi-scale land use data generalization in a large area, such as a nation, remains subjective. A second problem is general ignorance of landscape pattern. This paper studies the index system of land use data generalization from both scale and landscape pattern perspective. We discuss the macro and micro indices of land use data generalization with consideration of spatial scales and landscape pattern. To quantitatively relate the indices and scale and landscape pattern metrics, land use data samples have been collected at multiple spatial scales in various land use regions across China. Based on statistic analysis, we then generate both macro and micro control rules for land use data generalization at various spatial scales and land use patterns. Finally, we prove the proposed method and achieved results to be effective and reasonable with sample data at county level.     

Data matching of building polygons at multiple map scales improved by contextual information and relaxation

The aim of matching spatial data at different map scales is to find corresponding objects at different levels of detail (LODs) that represent the same real-world phenomena. This is a prerequisite for integrating, evaluating and updating spatial data collected and maintained at various scales. However, matching spatial data is not straightforward due to the ambiguities caused by problems like many-to-many correspondence, non-systematic displacement and different LODs between data sets. This paper proposes an approach to matching areal objects (e.g. buildings) based on relaxation labeling techniques widely applied in pattern recognition and computer vision. The underlying idea is to utilize contextual information (quantified by compatibility coefficient) in an iterative process, where the ambiguities are reduced until a consistent matching is achieved. This paper describes (1) a domain-specific extension to previous relaxation schemes and (2) a new compatibility coefficient that exploits relative relationships between areal object pairs in spatial data. Our approach were validated through extensive experiments using building data sets at 1:10k and 1:50k as an example. Our contextual approach showed superior performance against a non-contextual approach in general and especially in ambiguous situations. The proposed approach can also be applied to matching other areal features and/or for a different scale range.     

A Multiscale Object-Specific Approach to Digital Change Detection

Landscape spatial pattern is dependent not only on interacting physiographic and physiological processes, but also on the temporal and spatial scales at which the resulting patterns are assessed. To detect significant spatial changes occurring through space and time three fundamental components are required. First, a multiscale dataset must be generated. Second, a change detection framework must be applied to the multiscale dataset. Third, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce an object-specific multiscale digital change detection approach. This approach incorporates multitemporal SPOT Panchromatic (Pan) data, object-specific analysis (OSA), object-specific up-scaling (OSU), marker-controlled watershed segmentation (MCS) and image differencing change detection. By applying this framework to SPOT Pan data, image-objects that have changed between registration dates can be identified and delineated at their characteristic scale of expression. Results illustrate that this approach has the ability to automatically detect changes at multiple scales as well as suppress sensor related noise. This study was conducted in the forest region of the Örebro Administrative Province, Sweden.     

Network Density and the Delimitation of Urban Areas

This paper examines network analysis for urban areas. The research is focused on the problem of definition and visualisation of network geography and network spaces at different scales. The urban scale of analysis is examined and different spatial indices are considered. The built environment of the city is considered as a reference environment for a road network density index. The latter is implemented in order to study the spatial interactions between network phenomena and spaces and to provide further elements for the analysis of urban shape. The study is focused in particular on understanding spatial patterns drawn by networks and in helping with the delimitation of city centres. Different approaches are used to obtain the two indices: a grid–based analysis and a spatial density estimator based on Kernel Density Estimation. The two methodologies are analysed and compared using point data for the urban road network junctions and street numbers as house location identifiers in the Trieste (Italy) Municipality area. The density analysis is also used on road network junctions' data for the city of Swindon (UK) in order to test the methodology on a different urban area.     

点群多尺度表达不确定性评估方法研究

空间数据的不确定性评估是保证数据质量的重要手段.由于点群多尺度表达的不确定性非常复杂,评价指标和评估模型是点群多尺度表达不确定性评估需要亟待解决的关键问题.本文围绕点群的多尺度表达和空间分析流程,确立了点群多尺度表达不确定性的评估内容为位置不确定性和空间分析结论的不确定性,建立了点群及其多尺度表达位置不确定性的评价指标...     

基于小波分析的线状特征数据无级表达

利用小波分析提出了线状特征空间数据的多尺度一致性无级表达模型，研究了基于该模型获取整数尺度上的地理信息，以及通过增量、连续地增补细节来获取分数尺度上的过渡地理信息，从而获取任意尺度上不同详细程度的地理信息的方法，动态地实现了线状特征空间数据的无级表达，并给出了实验结果。     

Area-based tests for association between spatial patterns

 Edge effects pervade natural systems, and the processes that determine spatial heterogeneity (e.g. physical, geochemical, biological, ecological factors) occur on diverse spatial scales. Hence, tests for association between spatial patterns should be unbiased by edge effects and be based on null spatial models that incorporate the spatial heterogeneity characteristic of real-world systems. This paper develops probabilistic pattern association tests that are appropriate when edge effects are present, polygon size is heterogeneous, and the number of polygons varies from one classification to another. The tests are based on the amount of overlap between polygons in each of two partitions. Unweighted and area-weighted versions of the statistics are developed and verified using scenarios representing both polygon overlap and avoidance at different spatial scales and for different distributions of polygon sizes. These statistics were applied to Soda Butte Creek, Wyoming, to determine whether stream microhabitats, such as riffles, pools and glides, can be identified remotely using high spatial resolution hyperspectral imagery. These new “spatially explicit” techniques provide information and insights that cannot be obtained from the spectral information alone. Received 1 June 2001 / Accepted 25 October 2001     

Event pattern analysis: Spatial clustering of sequential events and temporal change of events over time

This article develops a method for analyzing spatial and temporal event patterns. Events in this article refer to zero-dimensional objects in the spatiotemporal dimension, which represent the occurrence of crimes, traffic accidents, earthquakes, and so forth. The spatial clustering of sequential events and the increase and decrease in events over time are discussed. These patterns are often observed and analyzed in various academic fields, such as criminology, epidemiology, and geography. However, analytical methods for these patterns have not yet been fully developed. To fill the research gap, this article proposes a new method for analyzing these event patterns. Two statistical measures are utilized, one represents the degree of the spatial clustering of sequential events, and the other evaluates the increase and decrease of events over time. The method is applied to the analysis of the spatial and temporal patterns of the openings of new shops and restaurants in Shibuya-ku, Tokyo. The results gave us interesting empirical findings and indicated the soundness of the method.     

Urban land use mapping and zoning of Bombay Metropolitan Region using remote sensing data

Bombay Metropolitan Region covering an area of about 4,360 sq. km. was selected for urban land use studies and for urban land use zoning. Urban land use mapping was carried out using SPOT multispectral linear array imagery on 1∶25,000 scale employing visual analysis tehcniques. Fifteen maps were prepared depicting the spatial distribution of various urban classes in the Greater Bombay and New Bombay regions. Sixteen urban land use maps were also prepared using Landsat TM data showing the distribution of land use pattern on 1∶50,000 scale for the entire metropolitan region. Urban land use zoning was carried out based upon suitability index on 1∶250,000 scale. This map provides information on the areas to be used for construction and areas to be kept under green belt in the metropolitan region. This study is a joint venture of Space Applications Centre with Bombay Metropolitan Development authority.     

纹理频谱分析的高分辨率遥感影像最佳尺度选择

基于对纹理频谱的分析提出了一种高分辨率遥感影像最佳尺度的选择方法。首先,分析四种典型地物在傅里 叶变换频域的频谱响应特性。然后,采用点扩散函数对原始影像进行尺度扩展,进而根据地物纹理的径向与角向曲线 随尺度扩展的变化选择最佳尺度。最后,通过分析四种地物在6个尺度下的纹理特征可分性,说明本文方法能客观反映 出地物的尺度效应,具备最佳尺度选择的可行性。利用支持向量机对QuickBird全色影像进行面向对象的分类,实验结 果表明在最佳尺度下可取得较高精度。     

A Space-Time GIS Approach to Exploring Large Individual-based Spatiotemporal Datasets

The increasing number of large individual-based spatiotemporal datasets in various research fields has challenged the GIS community to develop analysis tools that can efficiently help researchers explore the datasets in order to uncover useful information. Rooted in Hägerstrand's time geography, this study presents a generalized space-time path (GSTP) approach to facilitating visualization and exploration of spatiotemporal changes among individuals in a large dataset. The fundamental idea of this approach is to derive a small number of representative space-time paths (i.e. GSTPs) from the raw dataset by identifying spatial cluster centers of observed individuals at different time periods and connecting them according to their temporal sequence. A space-time GIS environment is developed to implement the GSTP concept. Different methods of handling temporal data aggregation and the creation of GSTPs are discussed in this article. Using a large individual-based migration history dataset, this study successfully develops an operational space-time GIS prototype in ESRI's ArcScene and ArcMap to provide a proof-of-concept study of this approach. This space-time GIS system demonstrates that the proposed GSTP approach can provide a useful exploratory analysis and geovisualization environment to help researchers effectively search for hidden patterns and trends in such datasets.     

基于ArcGIS的土地用途分区研究

土地用途分区是土地利用总体规划的重要组成部分,是确定土地用途和采取具体土地利用措施的主要依据之一。GIS的空间分析功能可以辅助土地利用变化分析,完成土地适宜性评价以及土地利用结构和布局调整等规划任务,并根据未来土地用途管制要求,划定和调整土地用途分区。文章以葫芦岛市龙港区土地资源作为研究对象,应用GIS的空间叠置统计分...     

A spatial error model with continuous random effects and an application to growth convergence

We propose a spatial error model with continuous random effects based on Matérn covariance functions and apply this model for the analysis of income convergence processes (\(\beta \)-convergence). The use of a model with continuous random effects permits a clearer visualization and interpretation of the spatial dependency patterns, avoids the problems of defining neighborhoods in spatial econometrics models, and allows projecting the spatial effects for every possible location in the continuous space, circumventing the existing aggregations in discrete lattice representations. We apply this model approach to analyze the economic growth of Brazilian municipalities between 1991 and 2010 using unconditional and conditional formulations and a spatiotemporal model of convergence. The results indicate that the estimated spatial random effects are consistent with the existence of income convergence clubs for Brazilian municipalities in this period.     

连续运行卫星定位服务系统--城市空间数据的基础设施

城市连续运行卫星定位服务系统是城市空间数据基础设施之一 ,它需要向用户提供包括实时和事后在内的精密定位服务 ,满足各领域对位置、时间及实时性的要求。着重论述了城市连续运行卫星定位服务系统的构成模式 ,并以我国第一个实用CORS系统———深圳连续运行卫星定位服务系统为例 ,介绍了系统的构成和在CORS系统下的作业情况