A statistical test on the local effects of spatially structured variance

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.     

开封市商品住宅价格的探索性空间数据分析

商品住宅价格空间分布是房地产价格研究的热点。为了探测商品住宅价格的空间分布特征,以开封市商品住宅交易均价为例,利用探索性空间数据分析(ESDA)的理论与方法分析开封市商品住宅价格空间自相关性和变异性。研究表明:开封市商品住宅价格全局空间自相关性显著,总体上呈空间集聚格局;开封市大部分商品住宅价格呈现局部空间集聚格局,少量存在空间异质性而呈现局部离散格局;开封商品住宅价格的地域分异规律明显。     

区域旅游交通服务功能对旅游效率的空间溢出效应及其影响机理--以云南省为例

在构建交通服务功能模型和旅游效率评价指标基础上,综合运用熵权TOPSIS法、Bootstrap-DEA模型、双变量LISA和空间面板杜宾模型,探究2000-2016年云南省交通服务功能与旅游效率的空间关联特征,解析交通服务功能对旅游效率影响的空间效应及机理。结果表明:1)2000-2016年云南省交通服务功能整体呈上升态势,呈现典型“核心—边缘”空间格局;旅游效率总体水平呈增长趋势,在空间上呈集聚分布格局。2)云南省交通服务功能与旅游效率总体呈现空间集聚和依赖特征,双变量局部Moran散点主体落点呈现“高—高”和“低—低”空间聚类模式,表明评价交通服务功能与旅游效率关系时不能忽略空间效应。3)云南省交通服务功能对旅游效率具有明显的正向直接效应和空间溢出效应,佐证了交通是区域旅游效率增长不可或缺的先决条件;本地交通服务功能每提升1%,将会带动本地和邻近地区旅游效率分别增长0.2752%和0.1456%。4)经济实力、信息化水平、产业结构高级化、市场规模和对外开放度对本地旅游效率具有显著正向直接效应,经济实力、信息化水平、产业结构高级化与对外开放度对邻近地区旅游效率具有空间溢出效应。     

Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization

An origin-destination (OD) flow can be defined as the movement of objects between two locations. These movements must be determined for a range of purposes, and strong interactions can be visually represented via clustering of OD flows. Identification of such clusters may be useful in urban planning, traffic planning and logistics management research. However, few methods can identify arbitrarily shaped flow clusters. Here, we present a spatial scan statistical approach based on ant colony optimization (ACO) for detecting arbitrarily shaped clusters of OD flows (AntScan_flow). In this study, an OD flow cluster is defined as a regional pair with significant log likelihood ratio (LLR), and the ACO is employed to detect the clusters with maximum LLRs in the search space. Simulation experiments based on AntScan_flow and SaTScan_flow show that AntScan_flow yields better performance based on accuracy but requires a large computational demand. Finally, a case study of the morning commuting flows of Beijing residents was conducted. The AntScan_flow results show that the regions associated with moderate- and long-distance commuting OD flow clusters are highly consistent with subway lines and highways in the city. Additionally, the regions of short-distance commuting OD flow clusters are more likely to exhibit ‘residential-area to work-area’ patterns.     

基于空间自相关的中国省际人口迁移模式与机制分析

人口迁移具有空间指向性,表现为迁入地和迁出地在地域上呈现一定的空间集聚特征。然而,大部分针对我国人口迁移进行分析和建模的研究忽视了这一空间指向性及其影响。该文利用全国第五次人口普查省际人口迁移数据和相关资料,以空间自相关分析为基础,对1995-2000年我国省际人口迁移的空间模式与动力机制进行了初步分析。首先,运用全局空间自相关统计量(Moran′s I)对人口迁移流中的空间自相关程度进行了测度,发现研究期间我国省际人口迁移的空间指向性明显:从一个区域出发(或抵达一个区域)的人口迁移流均受到周边地区人口迁移的影响。为了进一步研究这种空间指向性对人口迁移规模的影响,分别采用重力模型(仅用距离变量捕捉人口迁移过程中的距离衰减效应)和空间OD模型(采用因变量空间滞后的不同形式对迁移流的空间指向性加以考虑)研究中国省际人口迁移的动力机制,对比两种模型的估计结果发现:1)空间OD模型在参数估计和模型拟合等方面均优于传统的重力模型;在选取相同解释变量的情况下,空间OD模型的残差平方和仅为传统重力模型的47%,模型拟合指标AIC值也大大缩小。2)在对中国人口迁移动力机制的定量分析中,如果不考虑人口迁移流之间的空间自相关(空间指向性)现象,会导致对社会、经济等变量作用和距离衰减效应的过高估计。     

地方饮食到全国小吃：兰州拉面和沙县小吃空间扩散的案例研究

地方小吃的空间集聚与扩散是饮食文化空间传播的主要内容之一,也是人口迁移与地域文化交流的重要表现。论文以兰州拉面和沙县小吃2大中国知名地方小吃为研究案例,以小吃门店兴趣点(POI)大数据为主要数据源,基于密度、距离与加权平均中心方法分析地方小吃空间集聚与扩散格局,然后采用空间自相关、聚类和异常值分析以及热点分析方法研究地方小吃的空间集聚关联特征以及空间扩散模式和路径。结果表明：① 兰州拉面和沙县小吃门店呈现非均衡的凝聚型空间分布特征,兰州拉面空间扩散范围和距离均大于沙县小吃;② 兰州拉面和沙县小吃的空间扩散主要依赖于人口的跨区流动,从民族饮食和在地小吃逐渐演化为外出人口的生存手段,形成品牌效应以后进一步在全国扩张;③ 沙县小吃空间扩散的集聚性特征强于兰州拉面,兰州拉面和沙县小吃的空间扩张具有邻域扩散和等级扩散相融合的特征,跳跃式扩散是小吃从业者在信息时代市场竞争下的新选择。地方小吃空间扩散格局反映出中国饮食习惯的空间分异,小吃的空间扩散与中国流动人口的分异格局具有空间匹配性。     

塔里木河流域荒漠河岸林胡杨群落的空间格局研究

 以塔里木河流域荒漠河岸林胡杨群落为研究对象,研究优势种胡杨与灰胡杨种群径级结构、空间分布格局与空间关联性。结果表明,胡杨和灰胡杨种群径级结构均呈倒金字塔型,属衰退型种群。胡杨种群在1～25 m尺度内呈聚集分布,灰胡杨种群在2～23 m尺度内呈聚集分布,胡杨种群聚集强度大于灰胡杨。随种群生长发育,胡杨和灰胡杨聚集尺度范围及聚集规模逐渐减弱,大树趋于随机分布。灰胡杨不同生长阶段在0～25 m空间尺度上相互独立,胡杨小树与中树在5～10 m空间尺度上相互依存。胡杨中树与灰胡杨中树在大尺度空间(＞23 m)上相互排斥; 胡杨与灰胡杨种群在小尺度空间(2～3 m)上相互依存,中尺度空间上相互独立。     

Multi-level method for discovery of regional co-location patterns

Regional co-location patterns represent subsets of feature types that are frequently located together in sub-regions in a study area. These sub-regions are unknown a priori, and instances of these co-location patterns are usually unevenly distributed across a study area. Regional co-location patterns remain challenging to discover. This study developed a multi-level method to identify regional co-location patterns in two steps. First, global co-location patterns were detected, and other non-prevalent co-location patterns were identified as candidates for regional co-location patterns. Second, an adaptive spatial clustering method was applied to detect the sub-regions where regional co-location patterns are prevalent. To improve computational efficiency, an overlap method was developed to deduce the sub-regions of (k + 1)-size co-location patterns from the sub-regions of k-size co-location patterns. Experiments based on both synthetic and ecological data sets showed that the proposed method is effective in the detection of regional co-location patterns.     

基于城市流空间的城市群变形结构识别——以长江三角洲城市群为例

全球化、信息化时代,城市群空间结构在城市流的作用下发生剧烈的变形。基于长三角城市流的测度和关系矩阵构建,通过流要素分析、多维尺度分析,从“节点-联系-格局”3个方面识别流空间下城市群变形结构。研究表明：流空间下城市中心性层级特征变化明显,中间层级的城市数量大幅增加;局域范围内城市流网络仍受地理空间距离的制约,但高等级城市流具有跨地理空间流动特征;中心城市和边缘城市呈“异化”发展趋势,表现为中心城市的集聚组团以及边缘城市的分散偏离。     

Network-constrained and category-based point pattern analysis for Suguo retail stores in Nanjing,China

The distribution of many geographical objects and events is affected by the road network; thus, network-constrained point pattern analysis methods are helpful to understand their space structures and distribution patterns. In this study, network kernel density estimation and network K-function are used to study retail service hot-spot areas and the spatial clustering patterns of a local retail giant (Suguo), respectively, in Nanjing city. Stores and roads are categorized to investigate the influence of weighting different categories of point events and network on the analysis. In addition, the competitive relation between Suguo and foreign-brand retail chains was revealed. The comprehensive analysis results derived from the combination of the first-order and second-order properties can be further used to examine the reasonability of the existing store distribution and optimize the locational choice of new stores.     

基于空间面板计量模型的雾霾对中国城市旅游流影响的空间效应

以中国342个市域单元为研究对象,借助双变量LISA模型、空间面板杜宾模型等方法,探究了1998-2016年雾霾与中国城市旅游流的空间关联特征,分析了雾霾对旅游流的影响及其空间溢出效应。结果表明,在中国雾霾PM_2.5与城市旅游流有东高西低的分布特点,在胡焕庸线两侧的空间分布呈现出与地形和城市发展等因素的空间耦合性;雾霾与城市旅游流（含国内和入境旅游流）均表现出显著的空间集聚和空间依赖特征,雾霾污染对旅游流产生明显的影响并形成相应的空间效应;雾霾对旅游流的抑制区域在不断扩大,H-L型城市数量的增加、L-H型集聚区的片状扩张和华北、华中地区的L-H型集聚的“空心化”现象均表明旅游流具有低雾霾指向性;雾霾污染与旅游流的倒“U”型曲线关系表明经典的EKC假说对中国城市旅游流同样适用,且雾霾污染的显著负向影响主要存在于入境旅游方面;雾霾和旅游流均具有明显的正向空间溢出效应,将雾霾治理同经济发展、对外联系、旅游开发、生态保护和交通建设等因素结合起来进行综合治理,才能为旅游发展创造美好的环境,实现国际、国内旅游健康、协调、可持续的高质量发展。     

Inferring spatial interaction patterns from sequential snapshots of spatial distributions

Spatial interactions underlying consecutive sequential snapshots of spatial distributions, such as the migration flows underlying temporal population snapshots, can reflect the details of spatial evolution processes. In the era of big data, we have access to individual-level data, but the acquisition of high-quality spatial interaction data remains a challenging problem. Most research has been focused on distributions of movable objects or the modelling of spatial interaction patterns, with few attempts to identify hidden spatial interaction patterns from temporal transitions of spatial distributions. In this article, we introduced an approach to infer spatial interaction patterns from sequential snapshots of spatial population distributions by incorporating linear programming and the spatial constraints of human movement. Experiments using synthetic data were conducted using four simple scenarios to explore the characteristics of our method. The proposed method was used to extract interurban flows of migrants during the Chinese Spring Festival in 2016. Our research demonstrated the feasibility of using discrete multi-temporal snapshots of population distributions in space to infer spatial interaction patterns and offered a general analytical framework from snapshot data to spatial interaction patterns.     

An agent-based model for optimal land allocation (AgentLA) with a contiguity constraint

Spatial optimization is complex because it usually involves numerous spatial factors and constraints. The optimization becomes more challenging if a large set of spatial data with fine resolutions are used. This article presents an agent-based model for optimal land allocation (AgentLA) by maximizing the total amount of land-use suitability and the compactness of patterns. The essence of the optimization is based on the collective efforts of agents for formulating the optimal patterns. A local and global search strategy is proposed to inform the agents to select the sites properly. Three sets of hypothetical data were first used to verify the optimization effects. AgentLA was then applied to the solution of the actual land allocation optimization problems in Panyu city in the Pearl River Delta. The study has demonstrated that the proposed method has better performance than the simulated annealing method for solving complex spatial optimization problems. Experiments also indicate that the proposed model can produce patterns that are very close to the global optimums.     

基于站点观测数据的气温空间化方法评述

基于统计学的插值方法是地理学、生态学领域研究气温空间化的主要方法之一,对获取精细化气温数据进行生态模拟具有重要意义。结合国内外气温空间插值的主要研究成果,对常用气温空间化方法进行了归纳、对比,探讨各种方法的适用性和不足之处,从而为涉及气温空间化的具体研究提供一定的参考,并探讨了各类方法优化的方向。不同方法的对比分析结果表明:各种气温空间化方法各有所长,在具体的应用中都取得过较好的效果,但并不存在普适性的方法,在实际应用时必须针对研究区域具体的地理特征进行方法适用性验证或对各类方法中的具体参数进行改进,才能实现区域气温的空间最优化模拟。根据气温场的物理分布特征,结合GIS技术,考虑地形等更多的相关因子以提高气温分布微观细节的模拟精度是未来重要的发展趋势。     

知识密集型服务业集聚对城市群旅游创新影响的空间效应

知识密集型服务业作为国家创新体系的重要组成部分,已经成为促进区域旅游创新发展的关键因素。以中国长三角、珠三角和京津冀城市群为例,运用区位商、数据包络分析法、探索性空间数据分析法和空间计量模型等方法,对知识密集型服务业集聚和旅游创新的时空演变特征与两者之间的空间关联性深入分析。结果表明：① 三大城市群知识密集型服务业集聚特征明显,集聚程度由强到弱依次为珠三角、长三角和京津冀,且城市群内部各城市间的集聚存在不均衡特征。② 三大城市群旅游创新的生产效率变化主要由技术进步推动,旅游创新均表现出一定的空间集聚特征,长三角城市群具有明显空间集聚性,但空间集聚模式存在差异,珠三角和京津冀旅游创新集聚不明显。长三角城市群内部各城市大多属于高高和低低空间集聚模式,珠三角和京津冀城市群内部各城市大多属于高低和低高的空间集聚模式。③ 知识密集型服务业集聚对城市群旅游创新水平的提高具有一定的促进作用但存在地区差异性,三大城市群旅游创新水平呈现空间溢出效应,但其对周边城市的辐射带动作用有待加强。     

降雨量地面观测数据空间探索与插值方法探讨

空间插值方法广泛应用于气象数据产品的制作,其精度与气象要素的空间变异特征、气象观测站分布和插值方法选择有关。选择美国得州599个地面观测站30年平均降雨量记录,设计了27个观测站样本方案,选择全年、1月和8月数据,利用空间统计、空间自相关、半变异函数等方法探索降雨量的空间变异特征,并采用5种常规方法进行空间插值,比较和解释插值结果,在此基础上讨论基于知识的气象要素空间插值方法。案例研究发现:①降雨量地面观测数据通常具有明显的空间趋势、较强的空间自相关特征和较稳定的空间变异规律,但针对不同时段或采样方案,其空间自相关强度和半变异函数模型会有一定的差异。②增加气象观测站数,空间插值误差有减小的趋势;但观测站数目达到一定数值后,增加观测站数,插值精度提高并不明显。③在观测站较少时,不同插值方法间的精度差异较大,而在观测站充足的情况下,其差异有减小的趋势。④探讨气象要素与地理环境要素之间的关系,获得定量化的先验知识,开发基于知识的空间插值模型,是高精度气象要素插值的关键;线性加权回归和地理加权回归方法的初步试验验证了这一思路的有效性。     

GML数据的整体同构压缩算法研究

在分析传统的GML,数据压缩技术特点的基础上,提出GMI数据整体同构压缩方法:以GMI,文档中的Fea-ture元素为基元,利用标签索引编码实现语义的同构压缩;以GMI空间数据的聚类分区结果建立分组局部坐标参考系,利用坐标参考系转换实现空间内容的同构压缩.实验证明:该压缩算法的存储空间消耗、查询处理速度等性能均优于传统方法,对研究GMI数据的存储、查询与传输等技术具有一定的参考价值.     

An empirical study on the intra-urban goods movement patterns using logistics big data

ABSTRACT

Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning.     

丘陵地貌区高程内插适宜方法选择——以长春净月潭为例

对不同的地貌区进行空间内插时选择的适宜方法不同,以典型丘陵地貌区长春市净月潭地区的高程为研究对象,用ArcGIS 9.2分别作反距离权重法、最近邻点法、趋势面分析和克里金法做高程内插。应用交叉检验方法对其内插的精度作出分析,检验其科学性和可靠性,得出反距离权重法、最近邻点法、趋势面法、样条函数法和克里金法高程内插的不同精度,并对各种方法的适用性进行讨论。结果表明最近邻点法精度最高,以下依次为普通克里金插值法、样条函数、反距离权重法、趋势面法。为类似于净月潭地区的丘陵地貌区高程内插提供选择参考。     

Incorporating spatial association into statistical classifiers: local pattern-based prior tuning

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.