共查询到20条相似文献，搜索用时 31 毫秒
1.
Manuel Ruiz Fernando López 《International journal of geographical information science》2013,27(3):413-439
Comparison of thematic maps is an important task in a number of disciplines. Map comparison has traditionally been conducted using cell-by-cell agreement indicators. More recently, other methods have been proposed that take into account not only spatially coincident cells in two maps, but also their surroundings or the spatial structure of their differences. The objective of this article is to propose a framework for map comparison that considers (1) the patterns of spatial association in two maps, in other words, the map elements in their surroundings; (2) the equivalence of those patterns; and (3) the independence of patterns between maps. Two new statistics for the spatial analysis of qualitative data are introduced that are based on the symbolic entropy of the maps. As well, all inferential elements to conduct hypothesis testing are developed. The framework is illustrated using real and simulated maps. 相似文献
2.
Rene Westerholt Bernd Resch Franz-Benjamin Mocnik Dirk Hoffmeister 《International journal of geographical information science》2018,32(3):571-600
Spatial variance is an important characteristic of spatial random variables. It describes local deviations from average global conditions and is thus a proxy for spatial heterogeneity. Investigating instability in spatial variance is a useful way of detecting spatial boundaries, analysing the internal structure of spatial clusters and revealing simultaneously acting geographic phenomena. Recently, a corresponding test statistic called ‘Local Spatial Heteroscedasticity’ (LOSH) has been proposed. This test allows locally heterogeneous regions to be mapped and investigated by comparing them with the global average mean deviation in a data set. While this test is useful in stationary conditions, its value is limited in a global heterogeneous state. There is a risk that local structures might be overlooked and wrong inferences drawn. In this paper, we introduce a test that takes account of global spatial heterogeneity in assessing local spatial effects. The proposed measure, which we call ‘Local Spatial Dispersion’ (LSD), adapts LOSH to local conditions by omitting global information beyond the range of the local neighbourhood and by keeping the related inferential procedure at a local level. Thereby, the local neighbourhoods might be small and cause small-sample issues. In the view of this, we recommend an empirical Bayesian technique to increase the data that is available for resampling by employing empirical prior knowledge. The usefulness of this approach is demonstrated by applying it to a Light Detection and Ranging-derived data set with height differences and by making a comparison with LOSH. Our results show that LSD is uncorrelated with non-spatial variance as well as local spatial autocorrelation. It thus discloses patterns that would be missed by LOSH or indicators of spatial autocorrelation. Furthermore, the empirical outcomes suggest that interpreting LOSH and LSD together is of greater value than interpreting each of the measures individually. In the given example, local interactions can be statistically detected between variance and spatial patterns in the presence of global structuring, and thus reveal details that might otherwise be overlooked. 相似文献
3.
Hexiang Bai Feng Cao M. Peter Atkinson Qian Chen Jinfeng Wang 《International journal of geographical information science》2020,34(10):2077-2114
ABSTRACT This paper proposes a new classification method for spatial data by adjusting prior class probabilities according to local spatial patterns. First, the proposed method uses a classical statistical classifier to model training data. Second, the prior class probabilities are estimated according to the local spatial pattern and the classifier for each unseen object is adapted using the estimated prior probability. Finally, each unseen object is classified using its adapted classifier. Because the new method can be coupled with both generative and discriminant statistical classifiers, it performs generally more accurately than other methods for a variety of different spatial datasets. Experimental results show that this method has a lower prediction error than statistical classifiers that take no spatial information into account. Moreover, in the experiments, the new method also outperforms spatial auto-logistic regression and Markov random field-based methods when an appropriate estimate of local prior class distribution is used. 相似文献
4.
Hexiang Bai Deyu Li Yong Ge Jinfeng Wang 《International journal of geographical information science》2019,33(2):240-268
When classical rough set (CRS) theory is used to analyze spatial data, there is an underlying assumption that objects in the universe are completely randomly distributed over space. However, this assumption conflicts with the actual situation of spatial data. Generally, spatial heterogeneity and spatial autocorrelation are two important characteristics of spatial data. These two characteristics are important information sources for improving the modeling accuracy of spatial data. This paper extends CRS theory by introducing spatial heterogeneity and spatial autocorrelation. This new extension adds spatial adjacency information into the information table. Many fundamental concepts in CRS theory, such as the indiscernibility relation, equivalent classes, and lower and upper approximations, are improved by adding spatial adjacency information into these concepts. Based on these fundamental concepts, a new reduct and an improved rule matching method are proposed. The new reduct incorporates spatial heterogeneity in selecting the feature subset which can preserve the local discriminant power of all features, and the new rule matching method uses spatial autocorrelation to improve the classification ability of rough set-based classifiers. Experimental results show that the proposed extension significantly increased classification or segmentation accuracy, and the spatial reduct required much less time than classical reduct. 相似文献
5.
基于空间化PageRank算法的人口流动空间集聚性分析 总被引：1，自引：0，他引：1
提出了一种基于空间化PageRank算法的人口流动空间集聚性分析方法。在PageRank算法的基础上增加空间节点间要素流量大小(F)的加权作用以及距离因子(Dst)所引起的流动成本和阻力效应,使该算法具备针对空间网络模型的分析能力,通过对人口流动网络模型中的节点进行集聚性排序,描述人口流动的空间特征。以华东六省一市人口流动状况为例,PR值、区域人口总流入量(RTI)和流动人口密度区位商(MLQ)的计算结果对比表明:空间化PageRank算法可以客观地评估空间节点吸引力,并弥补了总流入量等简单人口学统计指标对于现象背后驱动机制表达不足的缺点。 相似文献
6.
空间推理是空间信息智能化处理的难题之一,目前的空间推理过程缺乏柔性化与智能化的一个重要原因就是不能有效利用常识。该文中阐述常识、常识推理及常识的表示,研究空间推理、基于常识的空间推理的关键技术及其在空间信息处理中的作用。最后结合基于常识的空间推理的空间可视性分析示例,提出利用空间常识提高空间信息处理系统智能性的思路。 相似文献
7.
跨境教育基础设施对不同尺度空间的修复作用已成为学界的关注热点之一,但大部分研究仍缺乏对于中国经验案例的探讨。为进一步发挥跨境教育基础设施在城市和区域建设中的积极作用,推动中西方有关这一新兴现象的理论和实践对话,论文以粤港澳大湾区为例,探讨了跨境教育基础设施在中国的发展情况及其空间效应。基于多案例观察发现,跨境教育基础设施对城市空间的影响主要体现在物质性、象征性和情境性3个维度。研究发现,这些跨境办学机构只能被视为一种教育飞地,对城市空间所产生的物质影响仅仅是体现在对城市基础设施的改善和周边社区的绅士化等方面,对于城市深层的发展格局和肌理重构缺乏实质性帮助。相较于对城市的物质性修复,这些跨境教育机构对于城市空间的象征性修复作用显然更加明显,对城市品牌营建、城市发展愿景的扶持以及城市文化软实力提升都有比较明显的作用。从情境性来看,跨境教育基础设施除了在校园尺度上提供了独特的境外学习情景模式之外,在与迁出地的联系以及与城市和社区尺度上的社会文化联系等情境性交流方面都比较缺乏。研究提供了对非新自由主义社会语境下跨境教育基础设施流动所产生的(城市)空间效应的反思,对现有的有关这一新兴现象的理论和概念分析框架进行了补充和对话,对跨境办学及其政策也提供了非教育学层面的反思,对于面向教育的区域与城市发展决策也具有参考价值。 相似文献
8.
在传统缓冲区分析基本思想的基础上,提出基于空间对象缓冲区分析定义,区别在于邻域半径:前者为常量,后者为变量。设计实现基于空间对象缓冲区分析算法,该算法以空间对象为计算粒度,分为计算缓冲区边界点、生成缓冲区多边形、筛选缓冲区多边形内空间对象3个步骤,并从算法执行机理的角度对其做定性与定量评估。结果表明,该算法解决了传统缓冲区分析难以处理图层内以空间实体为分析粒度的问题,时间和空间复杂度亦优于传统缓冲区分析算法。 相似文献
9.
依据多中心空间结构的理想通勤模式构建通勤距离分布的理论模型,将实测结果与理论模型做比较,检验通勤效率。研究以上海和杭州为对象,发现：① 多中心空间结构确实有助于缩短通勤距离,但随着与就业中心距离增加,就业中心对缩短通勤距离的正效应逐渐减弱;② 社会经济发展水平更高的上海,其多中心空间结构的通勤效率比杭州发挥得更好。研究讨论了城市规模、多中心的实施时间、住房市场等对通勤效率的影响：上海的多中心空间结构发展更加成熟、租赁房源比例更高,为维持城市正常运转必须有更高效的交通组织,且居民确实经历了更长时间、有更多住房选择来调整职住空间以缩短通勤距离;杭州的城市功能尚处在完善中,多中心空间结构的通勤效率还未完全发挥作用。 相似文献
10.
基于PRISM和泰森多边形的地形要素日降水量空间插值研究 总被引：25，自引：5，他引：20
以黑河流域河西走廊中段地区为例,利用该研究区年、月降水与地形间较强的相关性特点,在PRISM方法的基础上对该地区日降水量进行了空间插值计算。文章提出了以月降水量的PRISM空间插值结果为该月逐日降水空间分布的参考本底,利用泰森多边形方法确定空间日降水的概率,从而实现黑河流域河西走廊中段地区日降水的空间制图方法,并对该方法得到的日降水时空数据集进行了误差分析和评估。分析结果表明,这一方法简单可靠,满足分布式水文模型或相关陆表过程分布式模拟对分布式日降水数据时空精度的要求。 相似文献
11.
土地利用变化的多尺度空间自相关分析——以内蒙古翁牛特旗为例 总被引：52，自引：5，他引：47
用传统统计学方法模拟和解释土地利用变化的前提条件是研究分析的数据在统计上必须独立且均匀分布。但是空间数据相互之间通常具有依赖性 (即空间自相关),某一变量的值随着测定距离的缩小而变得更相似或更为不同。由于经典线性回归方法未能抓住数据的空间自相关特征,而空间自相关包含一些有用的信息,为了克服这一缺点,利用Moran的I系数自相关图来描述研究区土地利用变化的空间自相关,并且建立了不仅考虑回归而且又考虑空间自相关的混合回归-空间自相关回归模型 (即空间滞后模型)。研究得到：① 研究区土地利用变化模型中不但自变量之间而且因变量之间存在空间正自相关,这表明土地利用变化数据的空间自相关很强;② Moran的I系数随着尺度的变粗而减小,这是由于数据平均时的滤波特性和Moran的I系数对距离的非线性特征造成的;③ 经典线性回归模型的残差也表现出正相关,这表明标准的多元线性回归模型未能考虑土地利用数据所存在的空间依赖性;④ 混合回归-空间自相关回归模型 (即空间滞后模型) 的残差未存在空间自相关,并且有更好的拟合度;⑤ 相对于经典线性回归模型,混合回归-空间自相关回归模型 (即空间滞后模型) 对于存在空间自相关性的数据来说有着统计上的合理性,而经典线性回归模型未能考虑这些因素。 相似文献
12.
The abstraction, representation, and computation of entity–space relationship are keystones of geographic information science (GIS). The newly proposed spatial chromatic tessellation (SCT) provides a novel model to explore this relationship. SCT has demonstrated a variety of potential applications in GIS, such as reasoning spatial topology, point pattern analysis, and Voronoi diagrams. This study aims to theoretically investigate SCT by focusing on two aspects: (1) extending SCT to higher dimensional spaces. Results show that cells missing in lower dimensional spaces are hidden in higher dimensional spaces; (2) exploring the uniqueness of chromatic codes, particularly the chromatic codes of 2-cell and 3-cell clusters: their codes are proved to be unique. In a mathematical perspective, the observed phenomena from the above two aspects bring some new thoughts into the first law of geography and spatial heterogeneity. Based on these new understandings of entity–space relationship, SCT is replaced by spatial chromatic model (SCM) in which spaces are created by entities themselves rather than by partitioning the space preexisted. This makes a change from an absolute geographic space to a relative geographic space. 相似文献
13.
Jonne Pohjankukka Tapio Pahikkala Paavo Nevalainen Jukka Heikkonen 《International journal of geographical information science》2017,31(10):2001-2019
In machine learning, one often assumes the data are independent when evaluating model performance. However, this rarely holds in practice. Geographic information datasets are an example where the data points have stronger dependencies among each other the closer they are geographically. This phenomenon known as spatial autocorrelation (SAC) causes the standard cross validation (CV) methods to produce optimistically biased prediction performance estimates for spatial models, which can result in increased costs and accidents in practical applications. To overcome this problem, we propose a modified version of the CV method called spatial k-fold cross validation (SKCV), which provides a useful estimate for model prediction performance without optimistic bias due to SAC. We test SKCV with three real-world cases involving open natural data showing that the estimates produced by the ordinary CV are up to 40% more optimistic than those of SKCV. Both regression and classification cases are considered in our experiments. In addition, we will show how the SKCV method can be applied as a criterion for selecting data sampling density for new research area. 相似文献
14.
零售活动的空间关系研究是城市地理学研究的一个热点问题.以上海市内环地区的星巴克、COSTA和瑞幸咖啡三家咖啡连锁公司的门店为研究对象,综合运用多种空间统计方法和实地调研分析三者之间的空间关联特征.结果表明:1)无论是传统零售还是新零售模式下的咖啡门店,其空间分布均大致表现出靠近消费市场的空间导向特征;2)星巴克门店的空... 相似文献
15.
在海洋经济质量转型之期,基于“认知—评价—建构—优化”理念,界定海洋经济增长质量内涵,通过中心-引力模型评价分析2000—2014年辽宁沿海地区海洋经济增长质量空间特征,构建了海洋经济增长质量模型,识别影响其空间特征的相关要素,并根据要素作用程度提出优化建议。研究发现：① 辽宁沿海地区海洋经济增长质量呈“核心—圈层”结构,形成以大连为“领头雁”的雁阵式相互继起模式;“钻石型”引力流结构促成辽宁沿海地区集中化连片发展格局;② 海洋经济增长质量空间特征主要受空间集聚效应影响,海洋资本、海洋基建和海洋产业结构成为推动海洋经济增长质量循环引力流的顺流机制,其中海洋产业结构成为影响辽宁海洋经济增长质量圈层空间集聚效应的关键流;海洋人才成为制约海洋经济增长质量循环引力流的逆流机制;③ 通过路径作用程度的象限划分,提出强化大连中心地职能,针对腹地城市定位及资源禀赋条件提出调控措施,推进沿海区域协调与一体化进程。 相似文献
16.
长春市作为东北地区的中心城市和特大型城市,产业结构正处在转型升级的关键时期。通过对长春市产业空间结构演化的历程和特征的分析研究,在工业化和城市化进程加速推进背景下,提出了产业空间结构调整与优化的基本思路,以保证长春可持续发展的客观要求。 相似文献
17.
在ArcGIS软件和云南基础地理数据支持下,对滇中城市群空间结构效益进行初步解析.结果表明：滇中城市群的突出优势在于人口规模大,经济发展水平较高,经济发展综合指数和城市化综合指数较大,其中昆明和玉溪的经济发展与城市化协调度较高,同时表现出空间差异;滇中城市群紧凑度较高,城市交通优势度相对较高,可达性较好,重要城市之间的空间联系较为紧密,但城市数量较少,规模效益相对欠缺.以实现城市群空间结构效益最大化为目标,在综合分析的基础上提出了滇中城市群方案. 相似文献
18.
When geographically aggregated data are included in hedonic models, the resulting coefficients are biased by the spatial scale and spatial configuration of variable measurement. We explore the effects of this modifiable areal unit problem (MAUP) within the context of hedonic price models with an individual-level dependent variable. Specifically, we developed standard and spatial hedonic regression models in order to examine the effects of the MAUP on model fit and coefficient estimates. Our empirical analysis documents several significant scale and zoning effects in the hedonic modeling framework. First, neighborhood characteristics are clearly important in efforts to improve model fit—and they are more significant contributors in the standard model than in the spatial hedonic model. For aggregation scale, the model fit change of the standard model is relatively large, whereas the change is more modest for spatial models. The patterns of change in model fit for standard and spatial hedonic models clearly diverge from one another, implying the existence of a scale level showing a maximum functional range of the submarket on which scale dependencies are expected to have an impact. Regarding the zoning effect, the model fits for both standard and spatial hedonic models vary according to the submarket systems. 相似文献
19.
基于空间互动模型的兰州市乡村旅游网络中心性分析 总被引：7，自引：4，他引：3
基于传统“引力模型”,引入通勤时间、旅游潜力等指标,构建了“旅游空间互动模型”,并以兰州市47个乡村旅游发展重点村为例,采用社会网络分析法剖析了兰州市乡村旅游网络中心性特征,发现：① 兰州市各村庄的度中心值均高于中介中心,中介中心值均高于向量中心,且主城区周边村庄的中心性普遍高于西北部永登县、东南部榆中县及其他县区所辖村庄的中心性;② 兰州市乡村旅游网络发育不成熟,空间结构呈现局部紧凑、整体稀疏且发展不均衡的特点;③ 当前兰州市乡村旅游发展以第二层级村庄为主,急需培育第一层级村庄以完善旅游网络中的核心节点。最后,提出优化兰州市乡村旅游网络结构的对策。 相似文献
20.
Braess模型与城市网络的空间复杂化探讨 总被引：2，自引：0，他引：2
Braess交通网络模型是经典的图论模型,但该模型同时具有很强的地理学色彩。Braess借助一个简单的网络揭示了出人意料的地理现象:增加交通路线有时反而降低运输效率。从理论地理学的角度对Braess网络进行了数学抽象,然后利用规划理论、图论和微分方程解析等方法揭示出区域-城市地理系统的空间复杂化两个重要动因:空间相互作用和宏观对称破坏。 相似文献