Non-current data and geographical information systems A case for data retention

Inter-urban income disparities reflect differences between individual urban localities in the average incomes of their residents. The present paper discusses different ways of visualizing such disparities on thematic maps. The approach we propose is based on the transformation of distances between individual localities and a reference city (e.g. a major population centre of a country) in proportion to the actual differences in the income levels. The general principle of such a transformation is to bring closer to the reference city places with higher incomes, while moving away localities with low income levels. Three alternative approaches to the implementation of this transformation technique are discussed. According to the ‘actual distance’ method, the spatial ‘shift’ of a locality on the map is set proportional to both the relative difference in incomes and the aerial distance between a locality and the reference city. In the ‘proportional increment’ transformation, the distance between a locality and the reference city is adjusted by a parameter whose values are proportional to income disparities between the two. Lastly, according to the ‘concentric circle’ transformation, localities with identical levels of incomes are positioned at a certain distance from the reference city, forming concentric circles around it. Both advantages and disadvantages of these transformation techniques are discussed, and the ‘proportional increment’ method is chosen as the best-performing visualization technique. The performance of this technique is demonstrated using income data for urban localities in Israel in 1991 and 1999. As analysis indicates, the proposed method helps to illustrate both the existing patterns of inter-urban income disparities and their dynamics over time.     

Locality Attributes and Networks Serving to Reveal Amazonian Urbanization beyond the Cities

Beyond the Amazonian cities, distinct spatial units arranged in networks compose an extended urban space, usually invisible to government agencies and public polities. This article empirically reveals this space by studying the localities’ characteristics and connections. In southwestern Pará, Brazil, 236 communities were sampled using field expeditions. Variables related to the locality organization, history, health and education services, urban infrastructure, and land use were explored in a fuzzy cluster analysis. Local production and consumption flows were considered in the locality network analysis. The fuzzy analysis allowed identifying a hierarchy of localities for which the geographical location influences the spatial distribution patterns of five groups. On the other hand, consumption and production networks identified different locality's articulations, with persistent dependence on cities. These results characterize extensive urbanization at the local level and highlights the participation of the local populations configuring the Amazonian territory.     

Analysis of spatial patterns of public attention on housing prices in Chinese cities: A web search engine approach

Housing price has become one of the most pressing issues facing urban residents in China in recent years and received considerable attention. However, detailed housing price data are often ill-documented or unavailable for the public, thus posing a grand challenge for the study of housing prices in China. Because individuals' Internet search activities can be recorded by web search engines, the analysis of these web search activities in cyber-space may provide a means of better understanding public attention and associated concerns in real geographic space. In this study, we focus on exploring the spatial patterns of public attention on housing price through the analysis of web query activities based on Baidu Index, a Chinese keyword analysis tool from Baidu web search engine. We propose a new index based on keyword query outcome from Baidu search database to analyze spatially heterogeneous patterns of housing price attention from 19 large and medium-sized cities in China. We evaluate the spatial network structure of housing price attention, and develop a new index to measure the intensity of interaction relationships among cities of interest. Our results show that spatial interactions of housing price attention between cities evaluated using the new method are consistent with those from a gravity model. Meanwhile, as revealed from Baidu Index-based indicators, strong spatial association patterns exist among cities that form urban agglomerations. Further, our results demonstrate that the web search engine approach, based on the coupling of cyber-space and geographic space, provides solid support for the study of housing price attention and its spatially explicit patterns in China.     

地统计学方法在土壤学中的应用

地统计学目前在土壤科学中得到广泛的应用和发展,成为认识土壤特征的一个重要工具.地统计学的空间变异函数和克里格插值等方法是土壤性状分析的主要手段,空间变异函数主要用于描述土壤理化性状空间变异特性,不同的插值方法可优化田间试验设计和田间采样方案,克里格插值方法则特别适用于未测量点土壤属性值的估测等.近年插值方法又被广泛应用于确定区域土壤环境容量和土壤质量标准,随机模拟则用于对土壤特性进行不确定性估计等.因而,地统计学方法对我国大量土壤学资料的整合与分析具有极大的应用前景.     

Palaeomagnetism of some late Mesozoic dolerite sills of East Central Spitsbergen, Svalbard Archipelago

Summary. Two late Mesozoic dolerite sills, situated near Agardhbukta on the east coast of Vestspitsbergen and dated radiométrically at 100 ± 4 Myr BP, have been sampled in five localities and subjected to detailed mineralogical and rock magnetic studies to determine the direction and origin of their magnetization. Although the sills lie outside the Tertiary orogenic belt, one locality (no. 4) has undergone strong hydrothermal alteration and a small part of another locality (no. 3) has also been affected. A conventional procedure based on examination of Zijderveld diagrams, applied to specimens demagnetized by alternating fields and thermally, yielded similar remanence directions at all five localities, except at the altered part of locality 3. Using a least squares computer méthod of analysis of step demagnetization data, comparable directions were isolated from all localities, including the altered part of locality 3. Except in this last case, all directions were reversed. The adjusted mean direction obtained from this analysis is D = 159.0°, I = 62.2°, α₉₅= 9.0° yielding a palaeomagnetic pole situated at 225.0°, 54.3°N comparable with pole positions obtained from other late Mesozoic igneous rocks on Spitsbergen and distinct from palaeopoles derived from Mesozoic rocks in North America and Eurasia. This suggests that during the late Mesozoic Svalbard existed as a semi-independent microplate.     

Testing the effects of positional uncertainty on spatial decision‐making

There is now increasing agreement that the uncertainty associated with spatial information should be represented to users in a manner that is comprehensive and unambiguous. To assist with this task, researchers have developed a variety of methods to portray spatial uncertainty. While there has been some testing of the effectiveness of these displays, the possible effects of such representations on decision‐making have not yet been thoroughly investigated. Indeed, studies from the psychological literature indicate that people do not always make the same decisions when presented with the same information, and they can also be sensitive to the effects of presentation, task, and context. This paper examines how the use of four different methods to represent positional uncertainty can affect spatial decision‐making. The authors found that extremely significant differences in participants' responses were exhibited, depending on the manner in which positional uncertainty was displayed, although little difference was observed in the ability of the participants to comprehend the four display methods. In addition, strong preferences were recorded for certain representations over others.     

Quantification of spatial gradation of slope positions

Transition between slope positions (e.g., ridge, shoulder slope, back slope, foot slope, and valley) is often gradual. Quantification of spatial transitions or spatial gradations between slope positions can increase the accuracy of terrain parameterization for geographical or ecological modeling, especially for digital soil mapping at a fine scale. Current models for characterizing the spatial gradation of slope positions based on a gridded DEM either focus solely on the parameter space or depend on too many rules defined by topographic attributes, which makes such approaches impractical. The typical locations of a slope position contain the characteristics of the slope position in both parameter space and spatial context. Thus, the spatial gradation of slope positions can be quantified by comparing terrain characteristics (spatial and parametrical) of given locations to those at typical locations. Based on this idea, this paper proposes an approach to quantifying the spatial gradation of slope positions by using typical locations as prototypes. This approach includes two parts: the first is to extract the typical locations of each slope position and treat them as the prototypes of this position; and the second is to compute the similarity between a given location and the prototypes based on both local topographic attributes and spatial context. The new approach characterizes slope position gradation in both the attribute domain (i.e., parameter space) and the spatial domain (i.e., geographic space) in an easy and practicable way. Applications show that the new approach can quantitatively describe spatial gradations among a set of slope positions. Comparison of spatial gradation of A-horizon sand percentages with the quantified spatial gradation of slope positions indicates that the latter reflects slope processes, confirming the effectiveness of the approach. The comparison of a soil subgroup map of the study area with the maximum similarity map derived from the approach also suggests that the quantified spatial gradation of slope position can be used to aid geographical modeling such as digital soil mapping.     

Combining spatial transition probabilities for stochastic simulation of categorical fields

Categorical spatial data, such as land use classes and socioeconomic statistics data, are important data sources in geographical information science (GIS). The investigation of spatial patterns implied in these data can benefit many aspects of GIS research, such as classification of spatial data, spatial data mining, and spatial uncertainty modeling. However, the discrete nature of categorical data limits the application of traditional kriging methods widely used in Gaussian random fields. In this article, we present a new probabilistic method for modeling the posterior probability of class occurrence at any target location in space-given known class labels at source data locations within a neighborhood around that prediction location. In the proposed method, transition probabilities rather than indicator covariances or variograms are used as measures of spatial structure and the conditional or posterior (multi-point) probability is approximated by a weighted combination of preposterior (two-point) transition probabilities, while accounting for spatial interdependencies often ignored by existing approaches. In addition, the connections of the proposed method with probabilistic graphical models (Bayesian networks) and weights of evidence method are also discussed. The advantages of this new proposed approach are analyzed and highlighted through a case study involving the generation of spatial patterns via sequential indicator simulation.     

Spatially-explicit sensitivity analysis of an agent-based model of land use change

The complexity of land use and land cover (LULC) change models is often attributed to spatial heterogeneity of the phenomena they try to emulate. The associated outcome uncertainty stems from a combination of model unknowns. Contrarily to the widely shared consensus on the importance of evaluating outcome uncertainty, little attention has been given to the role a well-structured spatially explicit sensitivity analysis (SSA) of LULC models can play in corroborating model results. In this article, I propose a methodology for SSA that employs sensitivity indices (SIs), which decompose outcome uncertainty and allocate it to various combinations of inputs. Using an agent-based model of residential development, I explore the utility of the methodology in explaining the uncertainty of simulated land use change. Model sensitivity is analyzed using two approaches. The first is spatially inexplicit in that it applies SI to scalar outputs, where outcome land use maps are lumped into spatial statistics. The second approach, which is spatially explicit, employs the maps directly in SI calculations. It generates sensitivity maps that allow for identifying regions of factor influence, that is, areas where a particular input contributes most to the clusters of residential development uncertainty. I demonstrate that these two approaches are complementary, but at the same time can lead to different decisions regarding input factor prioritization.     

Spatial Modeling for Base-Metal Mineral Exploration Through Integration of Geological Data Sets

This study involves the integration of information interpreted from data sets such as LandsatTM, Airborne magnetic, geochemical, geological, and ground-based data of Rajpura—Dariba,Rajasthan, India through GIS with the help of (1) Bayesian statistics based on the weights ofevidence method and (2) a fuzzy logic algorithm to derive spatial models to target potentialbase-metal mineralized areas for future exploration. Of the 24 layers considered, five layers(graphite mica schist (GMS), calc-silicate marble (CALC), NE-SW lineament 0–2000 mcorridor (L4-NESW), Cu 200–250 ppm, and Pb 200–250 ppm) have been identified from theBayesian approach on the basis of contrast. Thus, unique conditions were formed based onthe presence and absence of these five map patterns, which are converted to estimate posteriorprobabilities. The final map, based on the same data used to determine the relationships, showsfour classes of potential zones of sulfide mineralization on the basis of posterior probability.In the fuzzy set approach, membership functions of the layers such as CALC, GMS, NE-SWlineament corridor maps, Pb, and Cu geochemical maps have been integrated to obtain thefinal potential map showing four classes of favorability index.     

地理空间中的空间关系表达和推理

针对地理空间中的应用,归纳了在空间关系的表达与推理中不同于人工智能领域研究的一些特点:在人工智能领域,更注重建立形式化的推理系统;而在地理信息科学中,则需更关注地理空间的特点以及地物的地理语义。该文基于地理空间和地理现象的本质且顾及地理空间认知,总结了地理空间中空间关系表达和推理的特点,具体包括空间的有限性、地球的球面特征、地物的地理语义、地物形状的复杂性、面状地物、特殊的空间关系、空间关系的层次性与尺度相应原则、不确定性、三维与时态特性九方面;进而介绍了地理空间关系表达的两个应用,即地理信息检索和基于对象的图像分析。该文的探讨可为地理信息科学中的相关研究提供方向性指导。     

Metrics for characterizing network structure and node importance in Spatial Social Networks

Social Network Analysis offers powerful tools to analyze the structure of relationships between a set of people. However, the addition of spatial information poses new challenges, as nodes are embedded simultaneously in network space and Euclidean space. While nearby nodes may not form social ties, ties may exist at a distance, a configuration ill-suited for traditional spatial metrics that assume adjacent objects are related. As such, there are relatively few metrics to describe these nuanced situations. We advance the burgeoning field of spatial social network analysis by introducing a set of new metrics. Specifically, we introduce the spatial social network schema, tuning parameter and the flattening ratio, each of which leverages the notion of ‘distance’ to augment insights obtained by relying on topology alone. These methods are used to answer the questions: What is the social and spatial structure of the network? Who are the key individuals at different spatial scales? We use two synthetic networks with properties mimicking the ones reported in the literature as validation datasets and a case study of employer–employee network. The methods characterize the employer–employee as spatially loose with predominantly local connections and identify key individuals responsible for keeping the network connected at different spatial scales.     

Guest editorial

The past decade has witnessed extensive development of measures that examine characteristics of spatial subsets (local spaces) defined with respect to a complete data set (global space). Such procedures have evolved independently in fields such as geography, GIS, cartography, remote sensing, and landscape ecology. Collectively, we label these procedures as local spatial methods. We focus on those methods that share a common goal of identifying subsets whose characteristics are statistically ‘significant’ in some way. We propose the concept of local spatial statistical analysis (LoSSA) both as an integrative structure for existing methods and as a framework that facilitates the development of new local and global statistics. By formalizing what is involved when a particular local statistic is used, LoSSA helps to reveal the key features and limitations of the procedure. These include a consideration of the nature of the spatial subsets, their spatial relationship to the complete data set, and the relationship between a given global statistic and the corresponding local statistics computed for the data set.     

Quantitative assessment of mineral resources with an application to petroleum geology

The probability of occurrence of natural resources, such as petroleum deposits, can be assessed by a combination of multivariate statistical and geostatistical techniques. The area of study is partitioned into regions that are as homogeneous as possible internally while simultaneously as distinct as possible. Fisher's discriminant criterion is used to select geological variables that best distinguish productive from nonproductive localities, based on a sample of previously drilled exploratory wells. On the basis of these geological variables, each wildcat well is assigned to the production class (dry or producer in the two-class case) for which the Mahalanobis' distance from the observation to the class centroid is a minimum. Universal kriging is used to interpolate values of the Mahalanobis' distances to all locations not yet drilled. The probability that an undrilled locality belongs to the productive class can be found, using the kriging estimation variances to assess the probability of misclassification. Finally, Bayes' relationship can be used to determine the probability that an undrilled location will be a discovery, regardless of the production class in which it is placed. The method is illustrated with a study of oil prospects in the Lansing/Kansas City interval of western Kansas, using geological variables derived from well logs.     

A framework for uncertainty assessment in simulation models

In this article, we introduce a conceptual framework for systematic identification and assessment of sources of uncertainty in simulation models. This concept builds on a novel typology of uncertainty in model validation and extends the GIScience research focus on uncertainty in spatial data to uncertainty in simulation modelling. Such a concept helps a modeller to interpret and handle uncertainty in order to efficiently optimise a model and better understand simulation results.

To illustrate our approach, we apply the proposed framework for uncertainty assessment to the TREE LIne Model (TREELIM), an individual-based model that simulates forest succession at the alpine tree line. Using this example, uncertainty is identified in the modelling workflow during conceptualisation, formalisation, parameterisation, analysis and validation. With help of a set of indicators we quantify the emerging uncertainties and assess the overall model uncertainty as a function of all occurring sources of uncertainty.

An understanding of the sources of uncertainty in an ecological model proves beneficial for: (1) developing a structurally valid model in a systematic way; (2) deciding if further refinement of the conceptual model is beneficial for the modelling purpose; and (3) interpreting the overall model uncertainty by understanding its sources. Our approach results in a guideline for assessing uncertainty in the validation of simulation models in a feasible and defensible way, and thus functions as a toolbox for modellers. We consider this work as a contribution towards a general concept of uncertainty in spatially explicit simulation models.     

DEM误差对滑坡危险性评价模型的影响

基于数字高程模型(DEM)计算得到的坡度、坡向等地形属性是滑坡危险性评价模型的重要输入数据, DEM误差会导致地形属性计算结果不确定性, 进而影响滑坡危险性评价模型的结果。本文选择基于专家知识的滑坡危险性评价模型和逻辑斯第回归模型, 采用蒙特卡洛模拟方法, 研究DEM误差所导致的滑坡危险性评价模型结果不确定性。研究区位于长江中上游的重庆开县, 采用5 m分辨率的DEM, 以序贯高斯模拟方法模拟了不同大小(误差标准差为1 m、7.5 m、15 m)和空间自相关性(变程为0 m、30 m、60 m、120 m)的12 类DEM误差场参与滑坡危险性评价。每次模拟包括100 个实现, 通过对每次模拟分别计算滑坡危险性评价结果的标准差图层和分类一致性百分比图层, 用以评价结果不确定性。评价结果表明, 在不同的DEM精度下, 两个滑坡危险性评价模型所得结果的总体不确定性随空间自相关程度的变化趋势并不相同。当DEM空间自相关性程度不同时, 基于专家知识的滑坡危险性评价模型的评价结果总体不确定随着DEM误差增加而呈现不同的变化趋势, 而逻辑斯第回归模型的评价结果总体不确定性随着DEM误差大小增加而单调增加。从评价结果总体不确定性角度而言, 总体上逻辑斯第回归模型比基于专家知识的滑坡危险性评价模型更加依赖于DEM数据质量。