Two-stage permutation tests for determining homogeneity within a spatial cluster

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.     

A regionalization method for clustering and partitioning based on trajectories from NLP perspective

ABSTRACT

Regionalization attempts to group units into a few subsets to partition the entire area. The results represent the underlying spatial structure and facilitate decision-making. Massive amounts of trajectories produced in the urban space provide a new opportunity for regionalization from human mobility. This paper proposes and applies a novel regionalization method to cluster similar areal units and visualize the spatial structure by considering all trajectories in an area into a word embedding model. In this model, nodes in a trajectory are regarded as words in a sentence, and nodes can be clustered in the feature space. The result depicts the underlying socio-economic structure at multiple spatial scales. To our knowledge, this is the first regionalization method from trajectories with natural language processing technology. A case study of mobile phone trajectory data in Beijing is used to validate our method, and then we evaluate its performance by predicting the next location of an individual’s trajectory. The case study indicates that the method is fast, flexible and scalable to large trajectory datasets, and moreover, represents the structure of trajectory more effectively.     

国土空间规划:人口和城乡布局单幅总图的研制

研制人口与城乡布局是编制空间规划的必然要求和重点任务之一,旨在促进人口及城乡布局与资源环境承载能力相匹配,形成与“三区三线”相适应的人口和城乡布局,推进人口和城乡可持续发展。本研究提出一套“自上而下”与“自下而上”集成的人口与城乡布局研制方法。首先,总量控制,采用队列要素法、联合国法等完成基于行政区划单元的人口与城镇化水平预测;其次,因地制宜,以栅格为基本评价单元,实现城乡人口空间化,根据国土空间规划“三区三线”底图,核算现状超载和新增承载人口;最后,弹性集成人口与城镇化水平空间集疏态势以及地方国民经济发展诉求,划分人口增长地域类型、城镇增长类型、城镇规模等级等,完成人口与城乡布局。在此基础上,本研究以省级空间规划试点福建省为案例,将研制方法与研制实践相结合,实现福建省空间规划的人口与城乡布局总图绘制。以期为各尺度国土空间规划中的人口与城乡布局研制提供参考。     

多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用

对统计型人口数据进行格网形式的空间化可更直观地展示人口的空间分布,但不同的人口空间化建模方法和不同的格网尺度在表达人口空间化结果方面存在差异。本文在人口特征分区的基础上,引入DMSP/OLS夜间灯光对城镇用地进行再分类,采用多元统计回归和地理加权回归方法(GWR),开展人口统计数据空间化多尺度模型研究,生成1 km、5 km和10 km等3个尺度的2010年安徽省人口空间数据,并对3个尺度下2个模型结果进行精度评价与比较。结果表明：人口空间数据精度不仅与建模所用方法关系密切,还受到建模格网尺度大小的影响。基于多元统计回归方法的模型估计人口数与实际人口的平均相对误差值随着尺度的增加而降低,而基于GWR方法获得的人口空间数据误差值随着尺度的增加而升高。整体来看,基于GWR方法的1 km研究尺度的人口空间数据平均相对误差最低(22.31%)。区域地形地貌条件与人口空间数据误差有较强的关联,地貌类型复杂的山区人口空间数据误差较大。     

基于Voronoi模型的海南岛旅游资源集合体空间边界提取

旅游资源是旅游业发展的物质条件,是开展各项旅游活动的载体和基础。旅游资源分类方法和评价理论研究已取得了较大的进展,但在旅游资源调查与规划实践中,以有研究通常将一个景区或大规模地理实体与小规模实体在同一个标准下衡量与对比,未考虑旅游资源的地理空间尺度特征。不同尺度的旅游地域空间,旅游资源评价、规划方法及其开发方向都不同。本文目的是通过梳理不同尺度旅游资源空间单元概念,对最难界定的集合体进行空间识别。基于集合体的概念认知,利用空间语义关系构建本体概念模型,提出了不同类型旅游资源集合体的空间边界提取方法。鉴于此,以海南岛为例进行实证研究,运用空间语义关系构建3种不同类型的旅游资源本体概念模型,在此基础上对不同类型旅游资源集合体进行条件约束判断,并利用泰森多边形与缓冲区分析方法对其进行空间识别。与规划范围结果对比发现,该方法可较好地近似表达旅游资源集合体空间边界及空间关系。每种类型的集合体空间语义关系存在树状层次结构,包含2个层次,空间形态呈多边形和带状分布。研究方法具有可操作性,能够为旅游规划与管理提供科学参考。     

High-resolution spatial assessment of population vulnerability to climate change in Nepal

Current, spatially explicit, and high-resolution assessments of population vulnerability to climate change and variability in developing countries can be difficult to create due to lack of data or financial and technical capacity constraints. We propose a comparative, multiple-approach framework to assess the spatial variation of population vulnerability to climatic changes using several high-resolution variables related to climate, topography, and socioeconomic conditions with an objective to detect the spatial variability of climate vulnerability in Nepal. Nepal is one of the most vulnerable countries to the effects of climate change due to frequent climatic hazards and poor socio-economic capacity. We used a climate vulnerability index (CVI) approach to derive climate vulnerability maps at the one-kilometer resolution and test an additive and a principal components-based composite method of data aggregation. In this work, we attempt to answer three questions. 1) How do different methods of assessment inform the spatial variation of the climate vulnerability in Nepal? 2) How do different variables interact to shape climate vulnerability in Nepal? 3) What proportions of the population in Nepal are vulnerable to climatic disasters and why? Our analysis uncovered significant spatial variations in population vulnerability to climate change across Nepal, with the highest vulnerability being experienced by the High Mountain region followed by the regions in the lower elevations. We find that although the lack of adaptive capacity is the biggest cause of population vulnerability to climate change in Nepal, a resilient community is shaped by both biophysical and socioeconomic characteristics. By performing an iterative sensitivity analysis of our thirteen variables both at the aggregate level (nationally) as well as at the more disaggregated (physiographic region) level, we contribute to identifying important, multi-scalar driving factors for vulnerability that can be employed as leverage points for lowering vulnerability at different scales. After performing analyses at multiple regions, we conclude that region-specific variable selection is needed for more detailed assessments and in order to prioritize adaptation strategies at scales that go beyond the hierarchy of administrative divisions.     

Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.     

Sampling and mapping of heterogeneous surfaces: multi-resolution tiling adjusted to spatial variability

Abstract

Mapping by sampling and prediction of local and regional values of two-dimensional surfaces is a frequent, complex task in geographical information systems. This article describes a method for the approximation of two-dimensional surfaces by optimizing sample size, arrangement and prediction accuracy simultaneously. First, a grid of an ancillary data set is approximated by a quadtree to determine a predefined number of homogeneous mapping units. This approximation is optimal in the sense of minimizing Kullback-divergence between the quadtree and the grid of ancillary data. Then, samples are taken from each mapping unit. The performance of this sampling has been tested against other sampling strategies (regular and random) and found to be superior in reconstructing the grid using three interpolation techniques (inverse squared Euclidean distance, kriging, and Thiessen-polygonization). Finally, the discrepancy between the ancillary grid and the surface to be mapped is modelled by different levels and spatial structures of noise. Conceptually this method is advantageous in cases when sampling strata cannot be well defined a priori and the spatial structure of the phenomenon to be mapped is not known, but ancillary information (e.g., remotely-sensed data), corresponding to its spatial pattern, is available.     

代表性人口空间分布数据集的精度评价——以2010年广东省为例

以人口密度差异显著的广东省为研究区,比较Worldpop、GPW v4和2种中国公里网格人口分布数据集的空间分布一致性,并以第六次全国人口普查数据为真值,按人口密度分为高、中、低3组,从误差的数值分布和空间分布两方面定量评价4种数据集的精度,最后讨论估算误差的可能来源及数据适用性。结果表明,4种网格人口数据集中Worldpop整体精度最高,且在人口密集区的精度也是最高;GPW v4在低人口密度和中人口密度区域精度略高于Worldpop,但对镇街内人口分布细节刻画不够详细;2种中国公里网格人口分布数据集精度较前两者低,主要受空间化方法和模型变量的选择所限制。Worldpop适合用于人口密度中等及人口密度高区域的精细化研究,GPW v4适合用于长时序、最小研究单元大于镇街的研究,第一种中国公里网格人口分布数据集适合用于需要考虑镇街内人口分布和空间异质性的研究,第二种中国公里网格人口分布数据集适用于需要考虑人口分布细节和空间格局变化的长时序研究。     

Finding spatial outliers in collective mobility patterns coupled with social ties

ABSTRACT

Currently the increase in the variety and volume of data sources is demanding new data analytical workflows for exploring them concurrently, especially if the goal is to detect spatial outliers. In this paper, we propose a data analytical workflow for exploring Call Detail Records in conjunction with geotagged tweets. The aim was to investigate how massive data point observations can be analyzed to detect spatial outliers in collective mobility patterns that are coupled with social ties. This workflow consists of analytical tasks that are developed based on the a-priori assumption of two isometric spaces where Natural Language Processing techniques are used to find spatial clusters from geotagged tweets in a Social Space which are later used to aggregate the Call Detail Records generated by antennas located in the Mobility Space. The dynamic weighted centroids that are given by the mean location of the number of calls per hour of all antennas that belong to a particular cluster are used to compute Standard Deviation Ellipses. The longer the period of time a weighted centroid stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that they are spatial outliers. The workflow was implemented for the city of Dakar in Senegal. The results indicate that the further the hourly weighted centroids are skewed from the normal mean of an ellipse, the stronger the influence of a cluster is in finding spatial outliers. Furthermore, the longer the period of time the outliers stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that the outliers are genuine and can be associated to extraordinary events such as natural disasters and national holidays.     

基于GIS的新疆气温数据栅格化方法研究

以新疆99个气象台站1971-2010年年平均气温为数据源,采用多元回归结合空间插值的方法对新疆区域气温数据进行栅格化研究。建立了年平均气温与台站经纬度和海拔高度的多元回归模型,对于残差数据的插值采用了反距离权重法(IDW) 、普通克立格法 (Kriging)和样条函数法(Spline)3种目前应用广泛的空间插值方法,针对于这3种方法进行了基于MAE和RMSIE的交叉验证和对比分析,结果表明在新疆的年平均气温的GIS插值方案中,IDW方法精度总体要高于其他两种插值方法。     

A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

Georeferenced user-generated datasets like those extracted from Twitter are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multiscale nature. Such datasets cannot be properly dealt with using the most common analysis methods, because these are typically designed for single-scale datasets where all observations are expected to reflect one single phenomenon (e.g., crime incidents). In this paper, we focus on the popular local G statistics. We propose a modified scale-sensitive version of a local G statistic. Furthermore, our approach comprises an alternative neighbourhood definition that enables to extract certain scales of interest. We compared our method with the original one on a real-world Twitter dataset. Our experiments show that our approach is able to better detect spatial autocorrelation at specific scales, as opposed to the original method. Based on the findings of our research, we identified a number of scale-related issues that our approach is able to overcome. Thus, we demonstrate the multiscale suitability of the proposed solution.     

社会经济统计数据热点探测的MAUP效应

为探讨不同尺度下社会经济统计数据热点的变化规律及其影响因子, 本文基于2000 年全国县级农业统计数据和2008 年北京市第二次经济普查数据, 按照一定的聚合规则得到不同尺度的数据, 计算不同尺度下的局部空间自相关指标G 统计值并对其进行显著性检验得到热点分布, 分析不同聚合尺度下热点的变化规律。然后运用Logistic 回归分析探测了影响聚合前后热点变化的因素, 并根据探测结果建立了预测聚合前后热点变化的Logistic 模型。分析结果表明, 基于G 统计探测的热点分布具有明显的空间尺度效应, 聚合水平越高、空间尺度越大, 热点数目越少。Logistic 回归分析的显著性分析表明, 热点包含的面状单元数目和热点的平均G 统计值是影响热点探测尺度效应的主要因素。热点包含的面状单元越多, 热点的平均G 统计值越大, 热点探测结果受尺度效应的影响越小。研究建立的热点变化预测模型, 可以在细尺度热点分布状况已知时, 根据热点包含的面状单元数目和热点的平均G 统计值来预测聚合后热点的变化。对模型精度的交叉验证结果表明, 模型对全国县级农业统计数据热点变化预测精度可达到93.8%, 对北京市第二次经济普查数据热点变化预测精度达到94.2%。两套数据试验得到的结论一致, 说明热点探测的尺度效应变化规律和所选变量以及研究区域的大小无关。     

中国工业结对集聚和空间关联性分析

要推动形成优势互补高质量发展的区域经济布局,就需要从产业关联视角考察中国工业的空间格局。集聚是工业在空间的重要表现形式,通过把测度两两配对产业集聚的结对集聚指数和测度两两配对产业关联度的投入产出表相结合,首次构建集聚关联指数和关联集聚指数,以研究不同空间尺度下空间关联性的差异和出现差异的原因。通过整理中国工业企业数据库中的二位数行业数据发现,一个区域出现结对集聚的配对产业数多不意味着该区域的集聚关联度大。2003—2013年中国工业的集聚关联度先增加后下降;比较不同空间尺度发现,集聚关联度与研究空间大小正相关,与基本单元大小负相关;比较相近空间尺度发现,城市群和长江经济带内产业在区县和城市层次的集聚关联度较大。这种空间关联性的差异主要源于现有区域治理体系、区域内的产业构成和外部冲击,受区域与产业政策影响,不同的区域和产业将会演化出不同的产业空间格局。现阶段应继续以城市群和长江经济带为引领,补足城市间产业同构、空间关联性差的短板,增强产业在城市间的分工与合作,实现产业在空间的优化布局,推动区域协调发展。     

Developing a parallel computational implementation of AMOEBA

As geospatial researchers' access to high-performance computing clusters continues to increase alongside the availability of high-resolution spatial data, it is imperative that techniques are devised to exploit these clusters' ability to quickly process and analyze large amounts of information. This research concentrates on the parallel computation of A Multidirectional Optimal Ecotope-Based Algorithm (AMOEBA). AMOEBA is used to derive spatial weight matrices for spatial autoregressive models and as a method for identifying irregularly shaped spatial clusters. While improvements have been made to the original ‘exhaustive’ algorithm, the resulting ‘constructive’ algorithm can still take a significant amount of time to complete with large datasets. This article outlines a parallel implementation of AMOEBA (the P-AMOEBA) written in Java utilizing the message passing library MPJ Express. In order to account for differing types of spatial grid data, two decomposition methods are developed and tested. The benefits of using the new parallel algorithm are demonstrated on an example dataset. Results show that different decompositions of spatial data affect the computational load balance across multiple processors and that the parallel version of AMOEBA achieves substantially faster runtimes than those reported in related publications.     

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.     

Identifying American Beer Geographies: A Multiscale Core-Cluster Analysis of U.S. Breweries

The recent growth in the U.S. brewing industry is remarkable, and the prevailing number of breweries has not been seen since the late nineteenth century. Several studies have shown that beer-producing facilities are spatially uneven across the United States. These previous studies used spatial units, however, such as metropolitan statistical areas, that might bias conclusions. Using a multiscale core-cluster approach, we explicitly identify where significant agglomerations of brewers are located. Our approach offers two refinements to standard cluster detection methods. First, instead of using fixed spatial boundaries, our method allows us to measure the concentration of brewery point locations across a spectrum of spatial scales. Additionally, our approach enables us to account for important underlying factors that influence the location of beer production. We use point data for all U.S. breweries in 2014. Our results show that the localization of beer production is significant and strongest at small spatial scales and diminishes rapidly with increasing distance, after controlling for population. We map the results to show the spatial variation in brewery agglomeration across the United States.     

基于宜出行大数据的多中心空间结构分析——以重庆主城区为例

多中心已成为中国大多数城市的空间发展战略,多中心结构的有效识别对于规划效果评价、规划策略制定具有重要意义。相比于百度热力与手机信令数据,腾讯宜出行数据具有时空分辨率高、获取成本低的优点,可精细比较城市主副中心的人口集聚能力,为多中心结构的动态识别提供新的手段。论文以重庆主城区为例,基于连续一周的宜出行热力数据,利用核密度分析等方法,识别其多中心城市结构、影响范围与组团发育情况。结果表明：作为山地城市,重庆在自然限制、经济驱动与规划引导下主动选择了“多中心、组团式”结构。重庆内环以内的各个城市中心人口高度集聚、用地规模相近、发育相对成熟,并强于内环以外的副中心。研究指出,西永、茶园副中心及外围组团的发展与人口集聚能力有待提高。     

基于多源大数据的城市体征诊断指数构建与计算——以上海市为例

基于多源大数据,构建了整合城市活动-移动系统、城市人口系统、城市运行系统、城市环境系统4个系统的城市体征诊断指数体系。该指数体系分解为底力、动力、压力、活力4个维度,具有4个层次和12个时空间尺度。底力指数表征土地、人口等空间单元基本属性,用以把握区域总体特征;动力指数通过企业发展状况、环境质量等反映了空间单元的发展状态;压力指数用以监测城市系统运行状况,起到风险评判与预警的作用;活力指数以活动和流的时空特征进行活动动态展现,反映空间单元的真实活力。最后以2016年4月6日为例,计算和展示了上海各街道的综合和各维度体征诊断指数,说明了体征诊断指数的可应用性和指数计算结果的稳健性。城市体征诊断指数可以辅助于城市网格化管理、压力预警等治理需求。