A taxonomical and ecological survey of rotiffer communities in Krottensee (Austria)

The composition and distribution of benthic, periphytic and planktonic rotifers in different habitats in Krottensee were investigated. Sixty-five rotifer taxa were identified, their relative abundance estimated. Classification of the data set by cluster analysis allowed the recognition of distinct habitat groups confined to acid bog ponds, macrophytes, sediments and open water. Five groups of rotifer taxa were identified on the basis of their habitat preferences and the occurrence of the taxa. Highest diversities were found on macrophytes and in acid bog ponds.     

一种基于双重距离的空间聚类方法

传统聚类方法大都是基于空间位置或非空间属性的相似性来进行聚类,分裂了空间要素固有的二重特性,从而导致了许多实际应用中空间聚类结果难以同时满足空间位置毗邻和非空间属性相近。然而,兼顾两者特性的空间聚类方法又存在算法复杂、结果不确定以及不易扩展等问题。为此,本文通过引入直接可达和相连概念,提出了一种基于双重距离的空间聚类方法,并给出了基于双重距离空间聚类的算法,分析了算法的复杂度。通过实验进一步验证了基于双重距离空间聚类算法不仅能发现任意形状的类簇,而且具有很好的抗噪性。     

基于遥感影像的青海省环境变化监测

本文通过对遥感影像的分析,针对土地利用及植被变化情况,利用优化的K-means算法对青海省环境变化进行检测,以此对土地的利用进行合理的规划,以期实现可持续发展。同时将试验结果与外业实际结果及当地政策规划进行比对,结果显示文中算法对土地的变化和未来走势预测准确率超过75%,从而验证了监测方法的可行性。这不仅保证了在经济发展的同时能够合理地使用土地资源,而且能对环境进行有效的保护。     

基于滚球法的面要素聚合

在自动制图综合中,面要素聚合较为常用的方法是利用聚类方法将面要素聚类为若干个类簇,进而将每个类簇合并为更大的多边形图斑。针对地图制图领域的前沿研究课题——地图自动综合,该文提出了一种基于滚球法的面状要素聚合的新方法,通过实验阐述了该方法在居民地等面要素综合中的应用,并与传统的聚合算法进行了效率上的对比分析。该方法较传统的聚合方法效率上有很大提高,在大数据量聚合中有一定的应用前景,当选择合适的滚球半径进行综合时,有较理想的结果。     

空间层次聚类显著性判别的重排检验方法

同时顾及空间邻近与专题属性相似的空间层次聚类是挖掘空间分布模式的一种有效手段。空间层次聚类方法虽然可以获得多层次的聚集结构,但聚类结果显著性的统计判别依然是一个尚未解决的难题。为此,本文提出了一种空间层次聚类结果显著性的统计判别方法,用于确定空间层次聚类的停止准则,减少聚类过程对参数设置的依赖。通过试验分析与比较发现,该方法能够有效判别空间层次聚类结果的显著性和确定层次聚类合并过程的停止条件,同时具有很好的抗噪性,避免随机结构的干扰。     

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.     

A density-based approach for detecting network-constrained clusters in spatial point events

Existing spatial clustering methods primarily focus on points distributed in planar space. However, occurrence locations and background processes of most human mobility events within cities are constrained by the road network space. Here we describe a density-based clustering approach for objectively detecting clusters in network-constrained point events. First, the network-constrained Delaunay triangulation is constructed to facilitate the measurement of network distances between points. Then, a combination of network kernel density estimation and potential entropy is executed to determine the optimal neighbourhood size. Furthermore, all network-constrained events are tested under a null hypothesis to statistically identify core points with significantly high densities. Finally, spatial clusters can be formed by expanding from the identified core points. Experimental comparisons performed on the origin and destination points of taxis in Beijing demonstrate that the proposed method can ascertain network-constrained clusters precisely and significantly. The resulting time-dependent patterns of clusters will be informative for taxi route selections in the future.     

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.     

模糊ISODATA聚类算法在声速剖面自动分类中的应用

依据中国海浅海区30′按月历史统计声速剖面数据,通过归一化处理和Akima差值采样得到梯度剖面,建立起各方区按月归化后的声速剖面分层梯度样本集,并采用模糊ISODATA聚类算法对声速剖面进行聚类分析.通过对分类结果和类内总方差和的分析表明,聚类参数m值在1.1～2.1之间,并以最远邻系统聚类法结果为初始类中心的模糊分类效果较好.应用该方法对海洋中的声速剖面进行自动分类和区划对海洋环境的战术应用意义重大.