Why GPS makes distances bigger than they are

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.     

Unsupervised regionalization of the United States into landscape pattern types

We present a pattern-based regionalization of the conterminous US – a partitioning of the country into a number of mutually exclusive and exhaustive regions that maximizes the intra-region stationarity of land cover patterns and inter-region disparity between those patterns. The result is a discretization of the land surface into a number of landscape pattern types (LPTs) – spatial units each containing a unique quasi-stationary pattern of land cover classes. To achieve this goal, we use a recently developed method which utilizes machine vision techniques. First, the entire National Land Cover Dataset (NLCD) is partitioned into a grid of square-size blocks of cells, called motifels. The size of a motifel defines the spatial scale of a local landscape. The land cover classes of cells within a motifel form a local landscape pattern which is mathematically represented by a histogram of co-occurrence features. Using the Jensen–Shannon divergence as a dissimilarity function between patterns we group the motifels into several LPTs. The grouping procedure consists of two phases. First, the grid of motifels is partitioned spatially using a region-growing segmentation algorithm. Then, the resulting segments of this grid, each represented by its medoid, are clustered using a hierarchical algorithm with Ward’s linkage. The broad-extent maps of progressively more generalized LPTs resulting from this procedure are shown and discussed. Our delineated LPTs agree well with the perceptual patterns seen in the NLCD map.     

Estimating spatial efficiency using cyber search,GIS, and spatial optimization: a case study of fire service deployment in Los Angeles County

ABSTRACT

The efficiency of public investments and services has been of interest to geographic researchers for several decades. While in the private sector inefficiency often leads to higher prices, loss of competitiveness, and loss of business, in the public sector inefficiency in service provision does not necessarily lead to immediate changes. In many cases, it is not an entirely easy task to analyze a particular service as appropriate data may be difficult to obtain and hidden in detailed budgets. In this paper, we develop an integrative approach that uses cyber search, Geographic Information System (GIS), and spatial optimization to estimate the spatial efficiency of fire protection services in Los Angeles (LA) County. We develop a cyber-search process to identify current deployment patterns of fire stations across the major urban region of LA County. We compare the results of our search to existing databases. Using spatial optimization, we estimate the level of deployment that is needed to meet desired coverage levels based upon the location of an ideal fire station pattern, and then compare this ideal level of deployment to the existing system as a means of estimating spatial efficiency. GIS is adopted throughout the paper to simulate the demand locations, to conduct location-based spatial analysis, to visualize fire station data, and to map model simulation results. Finally, we show that the existing system in LA County has considerable room for improvement. The methodology presented in this paper is both novel and groundbreaking, and the automated assessments are readily transferable to other counties and jurisdictions.     

考虑地球曲率影响的重力中间层校正计算及其精度分析

介绍一种考虑地球曲率影响的“球冠模型” 重力中间层校正算法,并通过不同算例对比该算法与以往“圆柱体模型”算法之间的差值。结果表明,其差值的数量级最大可达0.4 mGal。     

Visualising 30 Years of Population Density Change in Australia’s Major Capital Cities

ABSTRACT

This study uses a novel spatial approach to compare population density change across cities and over time. It examines spatio-temporal change in Australia’s five most populated capital cities from 1981 to 2011, and documents the established and emerging patterns of population distribution. The settlement patterns of Australian cities have changed substantially in the last 30 years. From the doughnut cities of the 1980s, programs of consolidation, renewal and densification have changed and concentrated population in our cities. Australian cities in the 1980s were characterised by sparsely populated, low density centres with growth concentrated to the suburban fringes. ‘Smart Growth’ and the ‘New Urbanism’ movements in the 1990s advocated higher dwelling density living and the inner cities re-emerged, inner areas were redeveloped, and the population distribution shifted towards increased inner city population densities. Policies aimed at re-populating the inner city dominated and the resultant changes are now visible in Australia’s five most populated capital cities. While this pattern has been reported in a number of studies, questions remain regarding the extent of these changes and how to analyse and visualise them across urban space. This paper reports on a spatial method which addresses the limitations of changing statistical boundaries to identify the changing patterns in Australian cities over time and space.     

中国省际人口迁移的多边效应机制分析

区际人口迁移不仅与迁出地和目的地的要素特征以及距离有关,而且还受到周边迁移流的影响.基于网络自相关理论,利用"六普"省际人口迁移数据和相关统计资料,在重力模型的基础上考虑迁移流之间可能存在的几种空间依赖形式,构建中国省际迁移流的空间OD模型,初步揭示区域经济社会等因素及其空间溢出效应对省际人口迁移的影响,并就区域要素变化对整个省际人口迁移系统产生的"连锁反应"进行了模拟.结果表明:① 中国省际迁移流之间存在显著的网络自相关效应.目的地和迁出地的自相关效应皆为正,导致迁入和迁出流的空间效仿行为;迁出地和目的地周边则出现负的自相关效应,导致迁移流的空间竞争行为;② 区域经济社会等因素通过网络空间关系对周边地区产生的多边溢出效应导致迁移流在空间上集聚.其中,距离衰减效应位居各要素之首,其溢出效应进一步加剧距离的摩擦作用;对目的地而言,区域工资水平和迁移存量超过GDP的影响并产生正的溢出效应,促进周边地区吸引更多的外来人口;对迁出地而言,人口规模和迁移存量产生正的溢出效应,推动周边地区人口外迁;③ 区域要素变化潜在地对整个省际人口迁移系统产生一系列"连锁反应",震荡中心及其周边区域的迁移流波动较大.江苏省GDP增长5%的模拟结果表明,江苏迁往全国其他省份的人口数量都有不同程度地减少,而其他省份入迁人口均有所增加.相对而言,江苏周边省份的迁入或迁出流受到的波动较大,偏远省份波及较小,这是传统的重力模型所无法解释的.     

济宁许厂煤矿伴生分散元素铼的富集成矿探析

铼是一种分散金属,目前尚没有在煤层中富集成矿的案例。该文依据山东省济宁煤田许厂煤矿16上、17煤层光谱分析成果,结合铼元素的性质、物质来源和石炭-二叠纪太原组的聚煤环境等因素进行综合分析,论证了其富集可能性。简要阐述了铼富集成矿的意义,建议煤田地质勘查工作中加强对煤中伴生铼元素的分析和研究。     

临沂市城市地下空间资源地质调查评价研究

临沂市城市建设发展迅速,城市面貌日新月异,但高楼密集、交通拥挤等问题也逐渐显现出来,城市建设需要更多的空间,开发利用城市地下空间已是首要解决的问题。临沂市位于沂沭断裂带与临沂单斜、平邑-方城盆地多个地质单元的交会处,断裂构造发育,地震活动强烈,地质条件复杂,城市地下空间开发的影响因素众多,该文从水文及水文地质条件、工程地质条件、环境地质条件、人类工程活动等方面对临沂城市地下空间资源地质环境适宜性进行了分析研究,提出了其主要制约因素为极其发育的活动性断裂的看法,凡是活动性断裂对地下空间开发有影响的区域,均为地下空间资源开发制约区。评价结论对于提高城市发展格局、提高城市空间利用率、指导城市空间开发利用规划具有一定的参考意义。     

基于主成分分析的济宁市环境承载力研究

该文以济宁市环境承载力为研究对象,运用主成分分析法,从影响环境污染状况、环境治理状况、人口状况、经济发展状况、自然资源状况、物耗能耗状况等19个指标中,提取出影响环境承载力动态变化的3个主成分,对济宁市环境承载力年际变化进行了综合评价。结果表明:2006—2010年,济宁市环境承载力呈现逐年上升的趋势。该研究评价结果较好,可以为该地区的可持续开发规划提供理论参考。     

Vulnerability of Kuwait coast to sea level rise

Assessing coastal vulnerability to sea level rise (SLR) at local and regional scales is a fundamental step for designing successful long-term coastal management plans. This study was thus designed to assess Kuwait coastal vulnerability to SLR at four scenarios (.5, 1, 1.5 and 2 m). Potential inundated areas and the number of people at risk were estimated based on these SLR scenarios. A coastal vulnerability index (CVI) map of Kuwait was then computed based on the lowest scenario using eight parameters: elevation, coastal slope, geomorphology, distance to 20-m isobath, population, land use, cultural heritage and transportation. The geographic distribution of inundated areas at an SLR of .5 m revealed that the northern islands of Kuwait and coastal areas along Kuwait Bay would be highly impacted, whereas the coastal area near Shuaibah Port was the most influenced among the southern coasts. Most of the coastal area exhibited a moderate vulnerability to SLR, especially the northern islands. This study presented an initial vulnerability assessment for Kuwait coasts to SLR, which can be extended with more variables. The integrated remote sensing and geographic information system methodology demonstrated in this study can be applied in similar studies elsewhere.