Minimum geocoding match rates: an international study of the impact of data and areal unit sizes

ABSTRACT

The analysis of geographically referenced data, specifically point data, is predicated on the accurate geocoding of those data. Geocoding refers to the process in which geographically referenced data (addresses, for example) are placed on a map. This process may lead to issues with positional accuracy or the inability to geocode an address. In this paper, we conduct an international investigation into the impact of the (in)ability to geocode an address on the resulting spatial pattern. We use a variety of point data sets of crime events (varying numbers of events and types of crime), a variety of areal units of analysis (varying the number and size of areal units), from a variety of countries (varying underlying administrative systems), and a locally-based spatial point pattern test to find the levels of geocoding match rates to maintain the spatial patterns of the original data when addresses are missing at random. We find that the level of geocoding success depends on the number of points and the number of areal units under analysis, but generally show that the necessary levels of geocoding success are lower than found in previous research. This finding is consistent across different national contexts.     

Statistical analysis of spatial point patterns on deep seismic reflection data: a preliminary test

Spatial point patterns generated from bitmaps of images of processed reflection seismic profiles are analysed to quantify the reflectivity patterns. The point process characteristics for two different regions of a deep seismic reflection profile in northwestern Canada demonstrate that in both cases the points are not randomly distributed and that the point pattern distribution is different between the regions. The cluster effects for small point pair distances are stronger for the region of data where there is strong sedimentary layering than for the region where the layering is less distinct. As a result, it appears that future developments in point pattern analysis may provide a new tool for analysing spatial variations in reflection data.     

Massively parallel spatial point pattern analysis: Ripley’s K function accelerated using graphics processing units

This study presents a massively parallel spatial computing approach that uses general-purpose graphics processing units (GPUs) to accelerate Ripley’s K function for univariate spatial point pattern analysis. Ripley’s K function is a representative spatial point pattern analysis approach that allows for quantitatively evaluating the spatial dispersion characteristics of point patterns. However, considerable computation is often required when analyzing large spatial data using Ripley’s K function. In this study, we developed a massively parallel approach of Ripley’s K function for accelerating spatial point pattern analysis. GPUs serve as a massively parallel platform that is built on many-core architecture for speeding up Ripley’s K function. Variable-grained domain decomposition and thread-level synchronization based on shared memory are parallel strategies designed to exploit concurrency in the spatial algorithm of Ripley’s K function for efficient parallelization. Experimental results demonstrate that substantial acceleration is obtained for Ripley’s K function parallelized within GPU environments.     

The spatial stability of alcohol outlets and crime in post‐disaster Christchurch,New Zealand

The devastating Canterbury Earthquakes of 2010 and 2011 left an indelible mark on the city of Christchurch. The social and economic upheaval that immediately followed the Earthquakes has, in time, been replaced with a period of rebuild and transformation. In this study we investigate the effects that the Canterbury Earthquakes had on two important and inter‐related phenomena in the city: alcohol availability and crime. More specifically, we investigate how alcohol outlets and crime across six different categories changed in magnitude and spatial distribution pre‐ (end‐2009) and post‐ (end‐2014) earthquake. We do this using a variety of geospatial techniques including a relatively new method: the spatial point pattern test which allows for the identification of changes in spatial patterns at the local level. Results indicate that both alcohol outlets and crime have decreased in magnitude since the Canterbury Earthquakes. Using the spatial point pattern test we found statistically significant differences in spatial point patterns for both alcohol outlets and all crime types pre‐ to post‐earthquake. The similarity in the differences of the spatial distributions of alcohol outlets and crime provides a first empirical clue of their potential association in the city post‐earthquake.     

Spatial Pattern and Order in Sunbelt Retailing: Shopping in Phoenix in the Twenty-First Century

Understanding the complexity of store location in sprawling polycentric cities requires exploitation of new spatial analysis methods that can decipher patterns in georeferenced point data. This article shows how the intrametropolitan location of retailing is best understood as a series of interconnected spatial distributions with varying order-based characteristics. A scattered pattern, which initially appears random or chaotic, is a web of differentiated spatial regimes containing wide-ranging order. A variety of clustering and colocation methods are used to uncover spatial patterns of retailing in Phoenix, Arizona. The analysis simultaneously identifies establishment associations and disassociations within and across sectors. Results show that clothing and motor vehicles are the most likely to cluster next to establishments in the same sector. These sectors also have strong intersectoral relationships across retailing. We find limited evidence that the size of establishments significantly increases with distance from sectoral mean centers. Geospatial technologies are increasingly used by individual retailers to locate and manage their facilities. It is important that scholarly analysis of retailing spatial patterns keeps pace, especially as cities grow and land use and land value patterns become more complex.     

基于空间点模式分析的城市管理事件空间分布及演化——以武汉市江汉区为例

城市网格化管理系统经过多年运行积累了大量历史事件数据, 这类事件数据在空间上呈现明显集聚分布。确定事件发生的空间分布以及衡量空间分布的集聚程度, 能够为城市管理资源的合理调配、划分提供重要的决策支持。本文应用空间点模式分析方法, 对2011 年1-8 月间武汉市江汉区城市网格化管理系统中的两类主体事件(占道经营和垃圾处理类)进行分析, 研究发现:占道经营事件的“热点”区域1-8 月总体呈减少趋势, 而垃圾处理事件的“热点”区域整体呈递增趋势;两类事件呈现明显的空间集聚, 其特征空间尺度都为1000 m左右。研究表明, 空间点模式分析方法能够为城市管理者提供一种针对城市事件空间集聚模式的直观的可视化分析手段, 以及对空间集聚程度的定量分析方法, 并可为进一步统计建模分析奠定基础。     

Temporal Point Pattern Analysis of Human Activities Using GIS Methods: A Case Study of Library Visiting Activities in Chengdu City,China

This article aims to develop a new type of temporal pattern analysis—temporal point pattern analysis (TPPA)—by treating the distribution of activities as a point pattern on a two-dimensional plane using the start time and end time of activities as axes. Geographic information systems (GIS) methods, which are originally used in spatial point pattern analysis in GIS, are introduced to support TPPA. This article presents a case study to understand the temporal patterns of the library visiting activities of university students using a four-week smart card data set in Chengdu City, China. Several methods from GIS are applied, including the measurement of mean centers, kernel density, nearest neighbor distances, and optimized hot spot analysis. Results show that the GIS methods can reveal a lot of information on the temporal pattern of activities, thereby proving the reasonability of the proposed TPPA of activities. Key Words: GIS, human activities, library visiting, smart card data, visualization.     

中国国家级经济技术开发区及其产业空间格局演化——基于地级及以上市面板数据实证研究

选取1989~2015年地级市国家级经济技术开发区数量及主导产业面板数据,运用探索性空间数据分析（ESDA）技术,探讨国家级经济技术开发区及其产业空间格局演化特征。研究发现：① 国家级经济技术开发区整体经历探索阶段、推广阶段、完善阶段和升级阶段等4个阶段;空间格局演化体现出“点轴结构、T型结构、π型结构和网状结构”4种空间形态;空间扩散方式有“点-线-面”扩散、梯度扩散、等级扩散、邻近扩散、集聚扩散和升级扩散6种扩散模式;空间相关性整体呈波动起伏且逐渐上升态势,高-高集聚区从青岛-烟台逐渐演变为长三角、京津冀和福建沿海。② 五大主导产业集中稳定,装备制造始终处于集聚态势,其余4个主导产业呈“分散-集聚”态势;三大类型产业空间布局演化与上述空间格局演化态势基本一致,空间集聚性整体呈“N型”变化态势,技术密集型产业集聚特征最为明显。     

空间分析方法在房地产市场研究中的应用--以北京市为例

基于北京市2003年普通住宅数据,利用空间分析中点模式分析、空间自相关分析和空间插值方法等,对北京市房地产,尤其是普通住宅的空间格局进行分析。研究表明,北京市房地产发展在空间上具有强烈的集聚特点,而房价的空间自相关特性也非常明显。空间分析方法提供了准确认识、评价和综合理解空间位置和空间相互作用的手段,为定量研究空间格局提供了支持。在房地产发展等社会经济现象研究中,空间分析方法强调了“位置”因素的重要性,是刻画房地产空间格局的理想工具。     

SECOND-ORDER ANALYSIS OF BIVARIATE POINT PATTERNS

Second-order methods of point pattern analysis have only recently received attention from geographers. This paper demonstrates the use of these methods in a spatial analysis of two interrelated categories of objects in a finite region. Two contrasting methods of analyzing the spatial structure of bivariate point data provide insight into the potential of second-order methods. The first is the conventional “within” group analysis that compares a spatial pattern of points to a Poisson random pattern. The second, a “between” groups analysis, allows a distribution-free investigation of locational interdependencies between two categories of points. The map pattern of a sample of commercial investments in Cincinnati illustrates the utility of this approach.     

2004-2008年北京城区商业网点空间分布与集聚特征

本文以北京城区内的8 个行政区作为研究对象,选取批发和零售业、住宿和餐饮业、居民服务与其他服务业作为具体的商业类别,利用北京第一次、第二次全国经济普查数据,采用核密度(Kernel)、标准差椭圆、Ripley's K(r)函数相结合的GIS 点模式分析方法,对比研究了2004 年和2008 年北京市商业网点分布与空间集聚特征。研究结果表明:① 北京商业网点呈现相对集中分布态势,具有向心性并形成明显的集聚区,集聚中心主要分布在五环内,且在两次普查期间有所改变,商业网点空间偏向性差异明显;② 以CBD、金融街、王府井、中关村、亚运村和奥运村等为代表的典型商圈对北京商业网点的布局影响十分显著,商业网点在典型商圈周围分布密度较高,呈现集聚中心状态;③ 北京商业网点Ripley's K(r)曲线随距离的变化总体呈现“先增后减”态势,其中受居民小区影响较大的居民服务与其他服务业网点两次普查期间变化剧烈,反映了居民由市中心向外扩散的过程。     

A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data

Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data.     

Relationships between String Bag Craft Distributions,Language and Geographical Distance in the Upper Sepik and Border Mountains of Papua New Guinea

This paper describes analyses involving patterned string bags collected in the upper Sepik in Papua New Guinea. The Mantel test and correspondence analysis were used to explore whether variability in craft repertoires exhibits any covariance with the region's complex linguistic picture, and if so, whether this relationship is more significant than any spatial autocorrelation the data may exhibit. Bag construction techniques exhibited strong spatial autocorrelation, while for colour patterns the effect was weaker. An effect for language remained for both dependents after statistical control, but colour pattern characteristics had a slightly stronger association with language overall. The weaker spatial autocorrelation for colour pattern variability is argued to be due to higher rates of dissemination facilitated by the visibility of the patterns and their compatibility with a broad range of construction techniques. The effect for language, on the other hand, is argued to have resulted from of a higher rate of inter-settlement migration along a particular stretch of the Sepik where people speak the same language.     

网络约束点格局分析研究综述

随着信息通信技术高速发展,地理相关大数据使得传统的空间分析面临着机遇与挑战。平面空间分析方法在分析网络空间点要素空间过程、揭示其空间分布时的弊端日益凸显,由此对探索网络约束点要素特定的分析方法的关注度不断提高。本文定义了网络约束的思想,详细阐述了网络约束点格局分析方法体系,并分别从网络约束点格局的理论和应用出发,梳理了网络约束点格局分析的一阶方法、二阶方法及其他方法上的研究进展,总结了网络约束点格局分析在交通安全领域、犯罪学领域和其他城市点要素中的典型应用,最后在方法与应用层面,讨论近年重要的研究动向及未来的发展趋势。     

武汉市城市空间集聚要素的分布特征与模式

不同城市要素的集聚现象具有不同的空间模式,定量化研究城市要素集聚模式的差异和联系对于理解城市发展机理、合理制定城市规划具有重要意义。本文以武汉市为例,获取不透水表面、商业服务业网点（POI）、人口、容积率、城市道路等城市要素数据;采用核密度估计法识别城市主次中心,从城市要素的分布形态、集聚程度、集聚模式等来研究城市要素的空间分布格局特征。研究发现,武汉市呈现“一主七副”的多中心结构,各城市要素从城市中心向外呈反S型的圈层递减。采用集聚度指数衡量城市要素的集聚水平,结果显示商业POI、人口密度的集聚程度最大,其次是容积率、道路密度、不透水表面占比。高度集聚的商业POI、人口密度呈现出点状模式与带状模式相结合的分布模式,中度集聚的道路密度、容积率呈现点状模式、环状模式与轴状模式相结合的分布模式,低度集聚的不透水表面密度主要表现为环状模式。城市中心的吸引、立体空间开发等促进了城市要素的向心集聚,交通干线、稀缺景观资源的廊道效应等重塑了城市要素的空间分布形态。     

居住形态分异视角下乡村聚落格局的演化机制

系统探究不同地域乡村聚落的演化机制是构建乡村聚落转型实践逻辑和理论体系的基础。居住形态是涵盖居住空间形态、生活形态和文化形态的综合概念,立足居住形态视角有望揭示乡村聚落格局演化机制及其地域差异的深层逻辑。本文遵循“现象描述—机制分析—典型区对比”的思路,以乡村聚落地域分异为切入点,从“人-地”和“人-人”关系视角探究了人类发展进程中乡村聚落格局演化的一般规律,据此构建居住形态的形成及其对乡村聚落格局演化的作用机制,并以散居和聚居形态主导的样区为对照进行实证研究。研究表明,居住形态是衍生于宏观乡村聚落格局演化过程的多尺度、多维性因子,二者均可从“人-地”关系和“人-人”关系双重视角进行阐释;居住文化形态具有较强的传承性,为乡村聚落格局演化提供了路径遵循和内生机制,乡村聚落格局可视为居住文化形态在乡村地域空间解译和扩散的结果;生产力进步与偶发性事件可导致居住形态发生变革,从而诱发乡村聚落格局演化机制的改变。面向乡村振兴战略,需重视居住形态的研究视角,系统探讨不同地域传统乡村聚落的运行机制、城镇化阶段的演化机制以及乡村振兴背景下治理策略的本质差异,为制定差异化的村落振兴方案提供参考。     

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。     

A visualization approach for discovering colocation patterns

Colocation mining is one of the major spatial data mining tasks. When discovering colocation patterns, spatial statistics or data mining approaches are commonly used. Colocation mining results are typically presented in a textual form and do not provide any spatial information; thus, the results lack an intuitive approach to obtain cognition of colocation rules. Here, we propose a visualization approach to discover colocation patterns for two independent point distributions and generate visual results. This approach makes use of the ability of human color perception. For two geographic features, our approach first generates density surfaces of the input features and then visualizes the density surfaces using a red or green light with different intensities. Then, based on the law of additive color mixing, our approach mixes the colors of the two density surfaces to generate a colocation rule map. The visualization approach can also provide local details of colocation and be used for local colocation analysis. Users can detect colocation patterns and their distribution from the colocation rule maps. We use both synthetic data and real data to test the performance of our approach.     

“一带一路”倡议下欧亚大陆陆海运输的空间均衡分析

在“一带一路”背景下,解析欧亚大陆陆海运输成本竞合基础上形成的空间均衡格局,是兼具理论与现实意义的重大经济地理问题。以北京和柏林为欧亚大陆两端终点,基于现有铁路路网,采用可达性分析方法,构建了陆海运输空间均衡分析模型,勾划出了欧亚大陆陆海运输平衡线,理清了“一带一路”背景下欧亚大陆的陆海运输空间竞合格局。研究发现,仅比较运费成本时,海运运输方式具有绝对优势,陆海运输平衡线沿俄罗斯近波罗的海区域—哈萨克斯坦—土库曼斯坦—塔吉克斯坦—中国西北部—蒙古—俄罗斯远东地区,将欧亚大陆划分为占比约1∶2的陆海运输空间竞合格局;考虑时间成本后,陆海运输空间竞合的平衡线向沿海地区明显推移,以德国柏林—黑海北岸—里海南岸—土耳其—青藏高原—中国宝鸡—大兴安岭—俄罗斯丘米坎为界,陆海运输基本形成了占比约1∶1的空间均衡态势。上述基于陆海运输成本角度的欧亚大陆陆海运输平衡线,与地缘政治学中陆权论和边缘地带论的相关结果在空间上高度吻合。基于此,本文进一步给出了欧亚大陆内陆区（陆运优势区）、海洋区（海运优势区）和陆海复合区的空间分区方案。由此,陆海运输空间均衡关系的综合分析可以成为探讨欧亚大陆地缘政治与经济空间格局的新视角,并为“一带一路”倡议的实施提供扎实的地理学科学基础。