The Coefficient of Localization—An Addendum

Geographical data sets sometimes contain missing observations that need to be estimated. A statistical approach to the problem is discussed for multivariate normal spatial data sets satisfying the first-order spatial Markov property with constant mean, where the information at neighboring or contiguous observed sites is used to estimate the missing values. The completed data are used to estimate the parameters of the distribution. The procedure is iterative. The approach is a special case of the Orchard and Woodbury missing information principle. The paper concludes with an illustrative empirical example using rainfall data from an area of Kansas and Nebraska. The quality of the estimates for different sites are compared.     

中国失踪人口的时空格局演变与形成机制

人口失踪作为一种复杂的社会问题,给家庭和社会造成了严重危害.在尊重生命与保障人权的现实背景下,对失踪人口开展深入研究具有重要意义.利用"中国儿童失踪预警平台(CCSER)"数据,综合运用文本分析、数理统计、空间分析等方法,管窥了2015-2019年中国失踪人口的基本特征、时空格局演变与形成机制.结果表明:①失踪人口中男...     

An object-oriented knowledge representation structure for exploration data integration

Knowledge representation structure and reasoning processes are very important issues in the knowledge-based approach of integrating multiple spatial data sets for resource exploration. An object-oriented knowledge representation structure and corresponding reasoning processes are formulated and tested in this research on the knowledge-based approach of integrating spatial exploration data. The map-based prototype expert system developed in this study has self-contained knowledge representation structure and inference mechanisms. It is important to distinguish between lack of information and information providing negative evidence for a map-based system because the spatial distribution of data sets are uneven in most cases. Error and uncertainty estimation is also an important component of any production expert system. The uncertainty propagation mechanisms developed here work well for this type of integrated exploration problem. Evidential bellef function theory provides a natural theoretical basis for representing and integrating spatially uneven geophysical and geological information. The prototype system is tested using real mineral exploration data sets from the Snow Lake area, northern Manitoba, Canada. The test results outline the favorable exploration areas successfully and show the effectiveness of the knowledge representation structure and inference mechanisms for the knowledge-based approach.     

一种融合时空特性的气温缺失记录重建方法

针对气象记录缺失的普遍现象以及现实中对完整记录的迫切需求,提出一种新的融合时空特性的气温缺失记录重建方法,并与线性插值、基于DEM的普通克里金以及标准比率法等进行对比。实验结果表明：线性插值法和基于DEM的普通克里金法仅考虑气温的时间或空间特性,重建精度较差;标准比率法部分地考虑气温的时空分布特性,因此在部分月份具有较高的重建精度;然该方法稳健性较差,致使其整体重建精度较低。     

Uncertainty management in integration of exploration data using the belief function

The evidential belief function (EBF) provides an adequate theoretical basis for managing uncertainties in exploration data integration. The EBF can be used to represent uncertainties in the reasoning process and provides the capability of distinguishing between lack of information and negative information. This capability is desirable when combining diverse data sets, which often vary in spatial resolution and spatial extent. The uncertainties associated with data and propositions can be represented naturally and consistently using belief functions. Hence, using the EBF approach can provide a realistic quantitative picture of the target proposition.     

Multidimensional visualisation of degrees of relevance of geographic data

The ever‐increasing number of spatial data sets accessible through spatial data clearinghouses continues to make geographic information retrieval and spatial data discovery major challenges. Such challenges have been addressed in the discipline of Information Retrieval through ranking of data according to inferred degrees of relevance. Spatial data, however, present an additional challenge as they are characteristically made up of geometry, attribute and, optionally, temporal components. As these components are mutually independent of one another, this paper suggests that they be ranked independently of one another. The representation of the results of the independent ranking of these three components of spatial data suggests that representation of the results of the ranking process requires an alternative approach to currently used textual ranked lists: visualisation of relevance in a three‐dimensional visualisation environment. To illustrate the possible application of such an approach, a prototype browser is presented.     

Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects,Abra, Philippines

Weights of evidence (WofE) modeling usually is applied to map mineral potential in areas with large number of deposits/prospects. In this paper, WofE modeling is applied to a case study area measuring about 920 km² with 12 known porphyry copper prospects. A pixel size of 100 m × 100 m was used in the spatial data analyses to represent in a raster-based GIS lateral extents of prospects and of geological features considered as spatial evidence. Predictor maps were created based on (a) estimates of studentized values of positive spatial association between prospects and spatial evidence; (b) proportion of number of prospects in zones where spatial evidence is present; and (c) geological interpretations of positive spatial association between prospects and spatial evidence. Uncertainty because of missing geochemical evidence is shown to have an influence on tests of assumption of conditional independence (CI) among predictor maps with respect to prospects. For the final predictive model, assumption of CI is rejected based on omnibus test but is accepted based on a new omnibus test. The final predictive model, which delineates 30% of study area as zones with potential for porphyry copper, has 83% success rate and 73% prediction rate. The results demonstrate plausibility of WofE modeling of mineral potential in large areas with small number of mineral prospects.     

美国失踪人口的时空格局演变与影响机制

基于Doe Network平台数据,综合运用数理统计、空间分析和地理探测器等方法,探讨了美国1996―2021年失踪人口的基本特征、时空格局与影响机制。结果表明：1）美国失踪多发生在青少年（13～18岁）与成年时期（19～59岁）,且失踪人口数量随年龄增长呈先增后减的趋势;男性失踪人数多于女性,但失踪高发年龄略滞后于女性;各种族中黑人面临着最大的失踪风险。2）时间上,自1996年以来,失踪人口的年际变化数量先呈现波浪式上升趋势,2017年达到峰值后大幅下降;受气温和节假日影响,夏季6―8月和冬季12月为失踪高发期,2―4月为失踪的低谷期。3）空间上,失踪人口在州尺度上呈由沿海边境地区向内陆递减的特征,失踪高发区域随时间推移,自东、西沿海地区与南部美墨边境同时向美国内陆推进;县尺度上呈边缘集中成片,内部零星分散的特征。4）失踪人口数量变化是多因素共同作用的结果,主要受地区人口流动性、人均GDP、生育率以及易失踪人群基数影响,人口环境因子与经济、社会因子结合后对美国失踪人口空间分异的解释力增强,达到80%以上。5）人口失踪可用“社会失范理论”解释,社会目标和手段的脱节导致社会失范,进而诱发...     

A visualization approach for discovering colocation patterns

Colocation mining is one of the major spatial data mining tasks. When discovering colocation patterns, spatial statistics or data mining approaches are commonly used. Colocation mining results are typically presented in a textual form and do not provide any spatial information; thus, the results lack an intuitive approach to obtain cognition of colocation rules. Here, we propose a visualization approach to discover colocation patterns for two independent point distributions and generate visual results. This approach makes use of the ability of human color perception. For two geographic features, our approach first generates density surfaces of the input features and then visualizes the density surfaces using a red or green light with different intensities. Then, based on the law of additive color mixing, our approach mixes the colors of the two density surfaces to generate a colocation rule map. The visualization approach can also provide local details of colocation and be used for local colocation analysis. Users can detect colocation patterns and their distribution from the colocation rule maps. We use both synthetic data and real data to test the performance of our approach.     

Integrating attribute and space characteristics in choropleth display and spatial data mining

This paper develops an interactive approach for exploratory spatial data analysis. Measures of attribute similarity and spatial proximity are combined in a clustering model to support the identification of patterns in spatial information. Relationships between the developed clustering approach, spatial data mining and choropleth display are discussed. Analysis of property crime rates in Brisbane, Australia is presented. A surprising finding in this research is that there are substantial inconsistencies in standard choropleth display options found in two widely used commercial geographical information systems, both in terms of definition and performance. The comparative results demonstrate the usefulness and appeal of the developed approach in a geographical information system environment for exploratory spatial data analysis.     

Toward mapping land-use patterns from volunteered geographic information

A large number of applications have been launched to gather geo-located information from the public. This article introduces an approach toward generating land-use patterns from volunteered geographic information (VGI) without applying remote-sensing techniques and/or engaging official data. Hence, collaboratively collected OpenStreetMap (OSM) data sets are employed to map land-use patterns in Vienna, Austria. Initially the spatial pattern of the landscape was delineated and thereafter the most relevant land type was assigned to each land parcel through a hierarchical GIS-based decision tree approach. To evaluate the proposed approach, the results are compared with the Global Monitoring for Environment and Security Urban Atlas (GMESUA) data. The results are compared in two ways: first, the texture of the resulting land-use patterns is analyzed using texture-variability analysis. Second, the attributes assigned to each land segment are evaluated. The achieved land-use map shows kappa indices of 91, 79, and 76% agreement for location in comparison with the GMESUA data set at three levels of classification. Furthermore, the attributes of the two data sets match at 81, 67, and 65%. The results demonstrate that this approach opens a promising avenue to integrate freely available VGI to map land-use patterns for environmental planning purposes.     

Spatial representation of economic and financial measures used in agriculture via wavelet analysis

A foundation is set forth for use of the wavelet transform as a spatial analysis tool for modelling the geographical representation of economic and financial measures used in agriculture. This provides a framework from which to estimate a smooth nonparametric function which describes complex, multivariate relationships embedded in spatial data, with the resulting maps conveying large amounts of information in a familiar format. We illustrate this approach for tasks which include the graphical presentation of information, density estimation and wavelet-based nonparametric regression. A redundant wavelet transform is used, and we detail the properties which make it particularly appropriate for these objectives.     

The GRASS GIS temporal framework

The availability of continental and global-scale spatio-temporal geographical data sets and the requirement to efficiently process, analyse and manage them led to the development of the temporally enabled Geographic Resources Analysis Support System (GRASS GIS). We present the temporal framework that extends GRASS GIS with spatio-temporal capabilities. The framework provides comprehensive functionality to implement a full-featured temporal geographic information system (GIS) based on a combined field and object-based approach. A significantly improved snapshot approach is used to manage spatial fields of raster, three-dimensional raster and vector type in time. The resulting timestamped spatial fields are organised in spatio-temporal fields referred to as space-time data sets. Both types of fields are handled as objects in our framework. The spatio-temporal extent of the objects and related metadata is stored in relational databases, thus providing additional functionalities to perform SQL-based analysis. We present our combined field and object-based approach in detail and show the management, analysis and processing of spatio-temporal data sets with complex spatio-temporal topologies. A key feature is the hierarchical processing of spatio-temporal data ranging from topological analysis of spatio-temporal fields over boolean operations on spatio-temporal extents, to single pixel, voxel and vector feature access. The linear scalability of our approach is demonstrated by handling up to 1,000,000 raster layers in a single space-time data set. We provide several code examples to show the capabilities of the GRASS GIS Temporal Framework and present the spatio-temporal intersection of trajectory data which demonstrates the object-based ability of our framework.     

城市计算视角下的空间粗糙关联规则方法研究

同位模式表示不同类型的实体在空间邻域内共同频繁出现的规律,是城市实体空间关联的主要表达形式,但不能挖掘出指定实体的空间关联,需要寻找新的计算方法。在城市计算的视角下,通过引入粗糙集研究城市空间关联问题发现：1）该方法能把复杂的地理空间关联问题转换成信息决策问题,在信息决策表中计算城市实体之间的空间关联等拓扑关系,计算过程和结果可以挖掘城市行业之间的空间集聚和关联问题。2）通过属性约简得到属性核可以把高维空间数据降维,找到影响空间关联的重要因子。3）该方法拓宽了城市计算的理论方法体系和粗糙集方法的行业应用。最后,通过Python爬取南宁市城市服务业数据,进行方法的验证,计算结果与成熟的Apriori算法结果,以及南宁市服务业空间关联实际情况基本一致,证明了粗糙空间关联方法的可行性和正确性。     

A spatially explicit methodology for a priori estimation of field survey effort in environmental observation networks

When establishing environmental monitoring programmes, it crucial to make reliable cost estimates, especially where a field survey is involved. This paper presents a methodology for creating a spatial measure of a field survey effort (SE). A set of relevant variables affecting a SE (e.g. areas with rough terrain, or distant from the main road network) was classified using fuzzy sets and then combined to produce spatially explicit effort indicators, which were integrated to a single measure using an analytic hierarchy process (AHP). To evaluate this approach and identify the limits for its application, three spatially nested case studies were used to test the spatial expression of SE and the scalable capacity of the method itself. The presented methodology could cope with variations in the scale and data resolution, retrieving a coherent estimate of SE across the different case studies. The presented methodology is therefore useful for (i) testing the network designs for sampling bias related to SE, (ii) comparing alternative sampling designs, (iii) assessing the sampling costs and (iv) supporting the human and logistical resource management.     

Identification of field attribute error under different models of spatial variation

Abstract

Recent developments in theory and computer software mean that it is now relatively straightforward to evaluate how attribute errors are propagated through quantitative spatial models in GIS. A major problem, however, is to estimate the errors associated with the inputs to these spatial models. A first approach is to use the root mean square error, but in many cases it is better to estimate the errors from the degree of spatial variation and the method used for mapping. It is essential to decide at an early stage whether one should use a discrete model of spatial variation (DMSV—homogeneous areas, abrupt boundaries), a continuous model (CMSV—a continuously varying regionalized variable field) or a mixture of both (MMSV—mixed model of spatial variation). Maps of predictions and prediction error standard deviations are different in all three cases, and it is crucial for error estimation which model of spatial variation is used. The choice of model has been insufficiently studied in depth, but can be based on prior information about the kinds of spatial processes and patterns that are present, or on validation results. When undetermined it is sensible to adopt the MMSV in order to bypass the rigidity of the DMSV and CMSV. These issues are explored and illustrated using data on the mean highest groundwater level in a polder area in the Netherlands.     

A GRASS GIS parallel module for radio-propagation predictions

Geographical information systems are ideal candidates for the application of parallel programming techniques, mainly because they usually handle large data sets. To help us deal with complex calculations over such data sets, we investigated the performance constraints of a classic master–worker parallel paradigm over a message-passing communication model. To this end, we present a new approach that employs an external database in order to improve the calculation–communication overlap, thus reducing the idle times for the worker processes. The presented approach is implemented as part of a parallel radio-coverage prediction tool for the Geographic Resources Analysis Support System (GRASS) environment. The prediction calculation employs digital elevation models and land-usage data in order to analyze the radio coverage of a geographical area. We provide an extended analysis of the experimental results, which are based on real data from an Long Term Evolution (LTE) network currently deployed in Slovenia. Based on the results of the experiments, which were performed on a computer cluster, the new approach exhibits better scalability than the traditional master–worker approach. We successfully tackled real-world-sized data sets, while greatly reducing the processing time and saturating the hardware utilization.     

Using a Bayesian estimator to combine information from a cluster analysis and remote sensing data to estimate high-resolution data for agricultural production in Germany

In Germany, a county-resolution data set that consists of 35 land-use and animal-stock categories has been used extensively to assess the impact of agriculture on the environment. However, because such environmental effects as emission or nutrient surplus depend on the location, even a county resolution might produce misleading results. The aim of this article is to propose a Bayesian approach which combines two sorts of information, with one being treated as defining the prior and the other the data to form a posterior, used to estimate a data set at a municipality resolution. We define the joint prior density function based on (i) remote sensing data, thus accounting for differences in county data and missing data at the municipality level, and (ii) the results of a cluster analysis that was previously applied to the micro-census, whereas the data are defined by official statistics at the county level. This approach results in a fairly accurate data set at the municipality level. The results, using the proposed method, are validated by the national research data centre by comparing the estimates to actual observations. The test statistics presented here demonstrate that the proposed approach adequately estimates the production activities.     

陆地生态系统碳循环及其机理研究的地球信息科学方法初探

针对陆地生态系统碳汇/源的时空格局、碳循环过程的驱动机制及未来情景等前沿科学问题,提出陆地碳循环研究的地球信息科学方法,采用陆地生态系统碳通量/储量与碳循环过程的综合网络观测、生物过程的适应性实验研究以及河流碳输运过程研究为支撑系统的自下而上途径、与以土地利用/土地覆被变化和对地观测数据生态参量反演为基础的自上而下研究途径、经相互验证和尺度转换模型实现有机结合的研究方法,开展综合观测、调查、比对分析、模拟和评价研究,把握陆地生态系统碳循环的格局与过程规律,辨析自然和人为因素对陆地生态系统碳循环过程的影响,探讨全球气候变化条件下陆地生态系统碳循环过程的演变趋势。