Formal representation of qualitative direction

This paper reviews formal approaches to representing spatial knowledge about qualitative direction. Unlike geometric direction information, qualitative information does not employ numerical values but relies on comparison. The qualitative approach is often regarded as suitable for capturing commonsense concepts and thus is relevant to human-centered interfaces for spatial information systems. To establish a context for the work on qualitative direction, we preset a brief history of the development of qualitative temporal and spatial representations from different scientific perspectives. We identify main focal areas of these representations of spatial direction and propose a taxonomy. In the light of more than three decades of fruitful research, we obtain a map of formal representations that reveal interrelationships between different research strands in the field.     

Flow Cross K-function: a bivariate flow analytical method

Spatial flow data represent meaningful interaction activities between pairs of corresponding locations, such as daily commuting, animal migration, and merchandise shipping. Despite recent advances in flow data analytics, there is a lack of literature on detecting bivariate or multivariate spatial flow patterns. In this paper we introduce a new spatial statistical method called Flow Cross K-function, which combines the Cross K-function that detects marked point patterns and the Flow K-function that detects univariate flow clustering patterns. Flow Cross K-function specifically assesses spatial dependence of two types of flow events, in other words, whether one type of flows is spatially associated with the other, and if so, whether this is according to a clustering or dispersion trend. Both a global version and a local version of Flow Cross K-function are developed. The former measures the overall bivariate flow patterns in the study area, while the latter can identify anomalies at local scales that may not follow the global trend. We test our method with carefully designed synthetic data that simulate the extreme situations. We exemplify the usefulness of this method with an empirical study that examines the distributions of taxi trip flows in New York City.     

The Gendering of Philippine International Labor Migration*

This paper provides an institutional analysis of the use of gender as an organizing principle for labor migration flows. Through a case study of the marketing and recruitment strategies of Philippine government and private institutions, I examine how gendered representations contribute to divergent patterns of male and female labor migrants from the Philippines. Representations, or controlling images, are used by institutions to provide explanations or justification of a policy. Findings indicate that the global marketing of workers is organized around specific gendered assumptions of male versus female occupations. Recruitment strategies are also influenced by gendered representations of occupations, locations, and workers. The use of gender by government and private institutions as an organizing device provides a gendered context in which migrants and their households must subsequently operate.     

全球人才流动网络复杂性的时空演化——基于全球高校留学生流动数据

高质量的人才跨越地理的流动影响和驱动着全球范围内的创新活动,正在重塑世界经济格局。研究基于全球高校留学生流动数据,构建加权有向网络模型,对2001—2015年全球人才流动网络复杂性时空演化进行全面刻画。主要结论如下：① 全球人才流动网络规模迅速扩大,网络发育具有明显的小世界性,两级分化显著。② 全球人才流动网络呈典型“金字塔结构”特征,基本呈“东向西,南到北”的地理格局,新兴国家作为人才吸纳国的角色逐渐上升。③ 全球人才流动网络核心-边缘结构显著,核心国家间存在较强的关系流,边缘国家间联系较弱,其人才主要流向了核心国家和半边缘国家。④ 全球人才流动网络分化成美国社团、欧盟社团、中国社团、南美和南非社团、马来西亚社团、独联体社团等6个凝聚子群。     

Integrative representation and inference of qualitative locations about points,lines, and polygons

Qualitative knowledge representation of spatial locations and relations is popular in many text-based media, for example, postings on social networks, news reports, and encyclopedia, as representing qualitative spatial locations is indispensable to infer spatial knowledge from them. However, an integrative model capable of handling direction-based locations of various spatial objects is missing. This study presents an integrative representation and inference framework about direction-based qualitative locations for points, lines, and polygons. In the framework, direction partitions of different types of reference objects are first unified to create a partition consisting of cells, segments, and corners. They serve as a frame of reference to locate spatial objects (e.g., points, lines, and polygons). Qualitative relations are then defined to relate spatial objects to the elements in a cell partition, and to form the model of qualitative locations. Last, based on the integrative representation, location-based reasoning mechanism is presented to derive topological relations between objects from their locations, such as point–point, line–line, point–line, point–polygon, line–polygon, and polygon–polygon relations. The presented model can locate any type of spatial objects in a frame of reference composed of points, lines, and polygons, and derive topological relations between any pairs of objects from the locations in a unified method.     

An inconsistency measure of spatial data sets with respect to topological constraints

An inconsistency measure can be used to compare the quality of different data sets and to quantify the cost of data cleaning. In traditional relational databases, inconsistency is defined in terms of constraints that use comparison operators between attributes. Inconsistency measures for traditional databases cannot be applied to spatial data sets because spatial objects are complex and the constraints are typically defined using spatial relations. This paper proposes an inconsistency measure to evaluate how dirty a spatial data set is with respect to a set of integrity constraints that define the topological relations that should hold between objects in the data set. The paper starts by reviewing different approaches to quantify the degree of inconsistency and showing that they are not suitable for the problem. Then, the inconsistency measure of a data set is defined in terms of the degree in which each spatial object in the data set violates topological constraints, and the possible representations of spatial objects are points, curves, and surfaces. Finally, an experimental evaluation demonstrates the applicability of the proposed inconsistency measure and compares it with previously existing approaches.     

Spatial Structure and Development of Tourism Resources based on Point Pattern Analysis: A Case Study in Hainan Island,China

Tourism resources are the basic materials of tourism development, and they also provide the support for regional tourism spatial competition. The development of tourism depends on the degree to which tourism resources are utilized, and it is of great guiding significance for their development and utilization to study their spatial structure. Based on a large sample of data on tourism resources, and starting from the characteristics of multi-type, multi-level and multi-combination, this paper puts forward a framework and method for analyzing the spatial structure of tourism resources. Taking Hainan Island as an example, this paper describes the spatial structure of tourism resources in Hainan Island by using the method of point pattern analysis, identifies the tourism resource development zones, and puts forward some suggestions for the development of tourism resources. The results are as follows: (1) The characteristic scale of the spatial structure of tourism resources in Hainan Island is 30.5 km, and there are significant differences in the distributions of all kinds of tourism resources. (2) Through the spatial structure map of tourism resources, the tourism resource development zones are identified, including three tourist central city levels, “one horizontal and three vertical” tourist belts and four tourist combination areas. (3) By combing the distribution of tourism resources and the development zones in Hainan Island, the cross-border characteristics of the tourism resources and development zones are obvious. In order to give full play to the spatial combination and superposition effect of tourism resources, a change from a single isolated development mode to the overall combined development between regions is suggested. On the provincial scale, it is relatively accurate to describe the spatial structure of tourism resources for point data with a large sample size. However, the method of point pattern analysis can not only accurately describe the spatial structure of tourism resources, but it can also provide reference for other types of regional spatial analyses. The research results provide a scientific basis for the spatial planning of regional tourism resources and have practical significance for the development of regional tourism.     

A matrix representation of the potential second-rank gradient tensor for local modelling

Summary. This paper introduces a new matrix computational approach to the local determination of gravity gradients, convenient for comparing with gradient signals from moving base gradiometer systems or calculating topographic effects at instrument heights. The method represents a practical alternative to the more conventional spherical harmonics formulation, primarily global in nature, and it may be considered as an extension to other previously used local representations, such as point masses. Important characteristics of the analytical development outlined herein are its conceptual simplicity and the possibility of obtaining at once, up to a certain order n , and in an arbitrary Cartesian coordinate system, the symmetric point gradient tensor of second rank.     

近25年来塔里木河流域区域经济空间关联及演化特征分析

以塔里木河流域为研究对象,借助Arcgis8.5及Geoda等软件平台,引入空间自相关模型,对塔里木河流域42县(市)1980-2005年近25年的区域经济空间关联类型、动态演化特征及动力机制进行研究,结果表明:(1)25年来塔里木河流域经济全局空间自相关系数增长了近1.7倍,总体呈现出空间正相关特征,且为波动上升的动态过程,在5%的显著性水平下,经济在空间上表现为显著的正相关性.(2)局部区域空间自相关特征显著,区域经济显著负相关的区域逐渐消失,正相关显著区域日趋加强,最终演化为低一低及高一高两种类型区在东北和西南集中分布格局,且差距不断增大,各类型区域面积消长在起始,震荡和稳定三个阶段呈现出不同特点.(3)区域经济增长的近邻效应不断增强,2004年低-低和高-高两种类型区域面积是1980的1.66倍,但经济空间集聚效应主要是由于低-低区域面积的迅速增加所致.(4)工业化初期阶段,矿产资源条件和区位因素及交通条件两大因素是促进区域经济空间集聚与演化的动力.文章最后探讨了流域经济空间关联特点及演化特征对区域经济集聚扩散过程、区域政策选择、空间开发模式及区域协调发展等问题的影响.     

Watching the East: Constructions of ‘otherness’ in TV representations of East Germany

Coming from a socio-cultural geography perspective this paper sets out to question the ‘essentiality’ of East/West German differences, often said to persist in Germany despite (re)unification, by analysing the way in which they are socially constructed in German television reports and films today. Particular emphasis is placed upon the social and spatial positioning of the TV producers, of those represented, and of the audience. It is suggested that television representations such as those analysed in the paper effect a marginalization of East Germans and East Germany that can be partially resisted on the basis of contradictions ‘within’ and ‘outside’ filmic texts.     

PLACE INVADERS: CONSTRUCTING THE NOMADIC THREAT IN ENGLAND*

Over the centuries, the image of nomads threatening sedentary ways of life has been a common pejorative representation. In order to understand what geographies underpin narratives about nomads, I examine how social theory and media representations invoke the image of nomads. Both media and academic representations are buttressed by limited understandings of place and space, framing nomads as the quintessential “place invaders.” Focusing on nomadic Gypsies and Travelers in England provides a contemporary example of this process. British media representations construct nomadic Gypsies and Travelers in England as out‐of‐place and threatening. Deconstructing essentialist geographical conceptions allows us to avoid reproducing the common image of placeless nomads, reveals how people utilize place to render others inferior, and highlights the fact that conflicts between nomadic and sedentary ways of life are not intractable and natural. Adopting a more nuanced understanding of place can challenge the dominant trope of nomads as place invaders.     

基于空间自相关的耕地质量分布格局

耕地质量是保障粮食安全的基础。以广东省广宁县为例,采用空间自相关分析法,基于全局、局部和方向3个视角,对耕地质量空间分布特征进行分析。结果表明:全局来看,广宁县耕地质量在空间分布上具有较强的空间正相关性,且县级尺度空间自相关程度强于镇级尺度;局部区域,县域耕地质量存在聚集性分布规律,高质量耕地集中分布在排沙镇北部、江屯镇中部和螺岗镇南部,低质量耕地集中分布在赤坑镇中南部、古水镇东北部以及北市镇东北部;县域耕地质量在各方向上的结构性特征表现不一,在距离1 650 m范围内基本趋于一致,但随距离增加其结构性特征在各方向上呈现不同走势。     

中国经济权力空间格局演化研究——基于国家间相互依存的敏感性与脆弱性分析

在全球化时代,地缘政治逻辑发生了变化,不对称相互依存替代军事实力成为国家权力的重要来源,在此背景下和平崛起的中国正在以经济实力改写世界政治版图。本文基于国家间相互依存理论,利用双边贸易数据分析中国与世界各国经济相互依存的非对称性,以此构建中国经济权力评价模型,研究中国崛起过程中经济权力的空间演变格局。主要结论为：① 中国对外贸易的地理分布明显呈现由过度集中于东亚、西欧和北美的三极格局向更加均衡格局转变,但全球对华贸易依存度最高的国家依然集中在低纬度的亚非拉地区;② 世界各国对中国的贸易高敏感区呈现出由亚太向非洲、拉美地区推进的趋势,而高脆弱区域则呈现出由点状散布向连片生长转变的趋势;③ 中国经济权力空间已由周边扩展至全球,并向发达世界渗透,而同时期美国经济权力空间却呈现收缩趋势。     

Characterizing Content Distributions of Impurities in a Limestone Mine Using a Feedforward Neural Network

The factors determining the suitability of limestone for industrial use and its commercial value are the amounts of calcium oxide (CaO) and impurities. From 244 sample points in 18 drillhole sites in a limestone mine, southwestern Japan, data on four impurity elements, SiO₂, Fe₂O₃, MnO, and P₂O₅ were collected. It generally is difficult to estimate spatial distributions of these contents, because most of the limestone bodies in Japan are located in the accretionary complex lithologies of Paleozoic and Mesozoic age. Because the spatial correlations of content data are not clearly shown by variogram analysis, a feedforward neural network was applied to estimate the content distributions. The network structure consists of three layers: input, middle, and output. The input layer has 17 neurons and the output layer four. Three neurons in the input layer correspond with x, y, z coordinates of a sample point and the others are rock types such as crystalline and conglomeratic limestones, and fossil types related to the geologic age of the limestone. Four neurons in the output layer correspond to the amounts of SiO₂, Fe₂O₃, MnO, and P₂O₅. Numbers of neurons in the middle layer and training data differ with each estimation point to avoid the overfitting of the network. We could detect several important characteristics of the three-dimensional content distributions through the network such as a continuity of low content zones of SiO₂ along a Lower Permian fossil zone trending NE-SW, and low-quality zones located in depths shallower than 50 m. The capability of the neural network-based method compared with the geostatistical method is demonstrated from the viewpoints of estimation errors and spatial characteristics of multivariate data. To evaluate the uncertainty of estimates, a method that draws several outputs by changing coordinates slightly from the target point and inputting them to the same trained network is proposed. Uncertainty differs with impurity elements, and is not based on just the spatial arrangement of data points.     

Changes in the Geographical Distributions of Global Human Settlements

In recent decades, the continuous growth in the population has significantly changed the area of human settlements across the globe. The change of human settlements has brought great challenges to human development, environmental change, resource allocation, and disaster prediction and prevention. In the current paper, we integrate data products provided by the European Commission, Joint Research Centre with multi-source remote sensing data to analyze the changing trends of global human settlements under varying geographical distributions from 1990 to 2014. The results demonstrate that on the global scale, human settlements are generally distributed in Europe, East Asia, Southeast Asia, South Asia, the eastern United States, the Gulf Coast and the coast of Oceania, with most of them distributed in urban agglomerations and coastal areas. Global human settlements have continued to grow over the past 25 years, mainly in East Asia, Western Europe and the United States. The area of human settlements in eastern Europe has been slightly reduced. The distribution of human settlements is affected by climate, water and terrain conditions. Humans were more likely to have settled in temperate regions with wetter climates, and most of the human settlements are located within 500 km of the coastline and 30 km of land-based water sources. Our results can provide insights into further investigations of the spatio-temporal dynamics of human settlements and its connections to ecological and environmental issues in a changing world.     

国内外区域空间相互作用研究进展

区域空间相互作用是区域科学、经济地理学关注的重点。文章对国内外空间相互作用的相关理论、研究内容及测度模型进行梳理。从研究内容来看,区域联系特征及区域空间结构的关注较多,而针对区域间地缘经济关系的研究还有待深化,尤其是在测度方面需要更为深入的探讨,质性方法在研究中的分量也需要强化。经典的引力模型在实践应用中不断调整和深化,而对于城市流模型和地缘经济关系模型的支撑理论较为薄弱,模型应用过程中呈现出新技术、新方法运用的趋势。传统的研究还集中于地理意义上的区域空间及城市组织,针对虚拟空间中的信息技术流动和技术创新等因素的影响应作出积极回应。随着全球城市网络的显现,借助各种属性数据探索垂直层面的联系将是一个重要方向,不同尺度下的城市与区域间的网络结构、功能和关系研究将成为关注的重点。     

天津地区表层土壤多环芳烃类污染物污染源辨析

利用因子分析法及离散点曲面张力样条函数插值法对天津地区表层土壤多环芳烃类污染物污染源的类型及空间分布进行分析,表明研究区不同土地利用类型的污染源大多以混合源（包括燃煤与原油渗漏）为主,其中污灌农田、普通农田、荒地和城市绿地中混合源所占比重依次为61.26%、69.35%、52.86%、64.45%。而各污染源在空间分布上与其所处地理位置、地理条件及该区域内人们的生活习性都比较符合。     

A complete classification of spatial relations using the Voronoi-based nine-intersection model

In this article we show that the Voronoi-based nine-intersection (V9I) model proposed by Chen et al. (2001, A Voronoi-based 9-intersection model for spatial relations. International Journal of Geographical Information Science, 15 (3), 201–220) is more expressive than what has been believed before. Given any two spatial entities A and B, the V9I relation between A and B is represented as a 3 × 3 Boolean matrix. For each pair of types of spatial entities that is, points, lines, and regions, we first show that most Boolean matrices do not represent a V9I relation by using topological constraints and the definition of Voronoi regions. Then, we provide illustrations for all the remaining matrices. This guarantees that our method is sound and complete. In particular, we show that there are 18 V9I relations between two areas with connected interior, while there are only nine four-intersection relations. Our investigations also show that, unlike many other spatial relation models, V9I relations are context or shape sensitive. That is, the existence of other entities or the shape of the entities may affect the validity of certain relations.     

Modeling the scale dependences of topological relations between lines and regions induced by reduction of attributes

The scale dependences of topological relations are caused by the changes of spatial objects at different scales, which are induced by the reduction of attributes. Generally, the detailed partitions and multi-scale attributes are stored in spatial databases, while the coarse partitions are not. Consequently, the detailed topological relations can be computed and regarded as known information, while the coarse relations stay unknown. However, many applications (e.g., multi-scale spatial data query) need to deal with the topological relations at multiple scales. In this study new methods are proposed to model and derive the scale dependences of topological relations between lines and multi-scale region partitions. The scale dependences of topological relations are modeled and used to derive the relations between lines and coarse partitions from the relations about the detailed partitions. The derivation can be performed in two steps. At the first step, the topological dependences between a line and two meeting, covered and contained regions are computed and stored into composition tables, respectively. At the second step, a graph is used to represent the neighboring relations among the regions in a detailed partition. The scale dependences and detailed relations are then used to derive topological relations at the coarse level. Our methods can also be extended to handle the scale dependences of relations about disconnected regions, or the combinations of connected and disconnected regions. Because our methods use the scale dependences to derive relations at the coarse level, rather than generating coarse partition and computing the relations with geometric information, they are more efficient to support scale-dependent applications.     

Spatial interpolation using conditional generative adversarial neural networks

ABSTRACT

Spatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given a sample of data defined on point supports. However, the continuity and heterogeneity underlying spatial data are too complex to be approximated by classic statistical models. Deep learning models, especially the idea of conditional generative adversarial networks (CGANs), provide us with a perspective for formalizing spatial interpolation as a conditional generative task. In this article, we design a novel deep learning architecture named conditional encoder-decoder generative adversarial neural networks (CEDGANs) for spatial interpolation, therein combining the encoder-decoder structure with adversarial learning to capture deep representations of sampled spatial data and their interactions with local structural patterns. A case study on elevations in China demonstrates the ability of our model to achieve outstanding interpolation results compared to benchmark methods. Further experiments uncover the learned spatial knowledge in the model’s hidden layers and test the potential to generalize our adversarial interpolation idea across domains. This work is an endeavor to investigate deep spatial knowledge using artificial intelligence. The proposed model can benefit practical scenarios and enlighten future research in various geographical applications related to spatial prediction.