网格GIS及其在数字油田中的应用探讨

数字油田是解决当前油田智能化管理问题的最好途径。该文分析了当前油田建设中所面临的问题,结合油田在勘探开发中的实际需要,特别就当前数字油田建设中所面临的共性问题,在分析现有油田技术及发展需求的基础上,指出了基于中间件的分布式网格GIS技术是解决当前数字油田领域中所存在的问题的最佳方式,并讨论了其实现过程。     

基于企业服务总线的网格GIS集成研究

在面向服务的分布计算架构中,企业服务总线(ESB)提供了服务间智能化集成、管理、中介和交互功能。在利用网格架构的面向服务体系结构中,总结如何利用ESB灵活、开放的集成异构多源的GIS平台和数据源。整体架构基于OGSA,服务集成采用ESB,网格门户和实际GIS平台通过ESB连接,形成基于总线消息驱动的智能化服务集成网格系统。该系统将在Internet平台上广泛互联现有的GIS资源,实现GIS"单系统映射"。通过细粒度服务搭建和配置,生成GIS实例,直接而简单地满足具体的业务需求。     

Computational improvements to multi-scale geographically weighted regression

ABSTRACT

Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different bandwidths for each covariate. However, the multiscale property of MGWR brings additional computation costs. The calibration process of MGWR involves iterative back-fitting under the additive model (AM) framework. Currently, MGWR can only be applied on small datasets within a tolerable time and is prohibitively time-consuming to run with moderately large datasets (greater than 5,000 observations). In this paper, we propose a parallel implementation that has crucial computational improvements to the MGWR calibration. This improved computational method reduces both memory footprint and runtime to allow MGWR modelling to be applied to moderate-to-large datasets (up to 100,000 observations). These improvements are integrated into the mgwr python package and the MGWR 2.0 software, both of which are freely available to download.     

Computational reproducibility in geoscientific papers: Insights from a series of studies with geoscientists and a reproduction study

Reproducibility is a cornerstone of science and thus for geographic research as well. However, studies in other disciplines such as biology have shown that published work is rarely reproducible. To assess the state of reproducibility, specifically computational reproducibility (i.e. rerunning the analysis of a paper using the original code), in geographic research, we asked geoscientists about this topic using three methods: a survey (n = 146), interviews (n = 9), and a focus group (n = 5). We asked participants about their understanding of open reproducible research (ORR), how much it is practiced, and what obstacles hinder ORR. We found that participants had different understandings of ORR and that there are several obstacles for authors and readers (e.g. effort, lack of openness). Then, in order to complement the subjective feedback from the participants, we tried to reproduce the results of papers that use spatial statistics to address problems in the geosciences. We selected 41 open access papers from Copernicus and Journal of Statistical Software and executed the R code. In doing so, we identified several technical issues and specific issues with the reproduced figures depicting the results. Based on these findings, we propose guidelines for authors to overcome the issues around reproducibility in the computational geosciences.     

A Decade of Experience Using a New Zealand Geographic Information System

The experience of converting the NZLRI, a national physical land resource data base at the 1:63,360 scale comprising 320 maps, to a GIS has illustrated many of the problems and advantages associated with the use of this software system. Experience has shown that commercial (rather than custom built software), is the most effective and that a close physical relationship between the mappers and the interpreters of large data sets provides the most effective interpretive system.     

我国地理信息产业的发展现状及思考

文章讨论了地理信息和地理信息产业的涵义,以及发展我国地理信息产业的重要性,对我国地理信息产业的现状进行了思考与分析,并提出了建议。认为我国地理信息产业面临机遇与挑战,通过扶持国产GIS软件,制定空间数据标准和交换格式标准,扩大地理信息技术服务的应用领域,珍惜人才,重视教育,我国的地理信息产业将有很大的发展空间。     

地理空间分布整体统计研究进展

利用统计学方法研究社会经济与自然等要素的地理空间分布在最近几十年来已引起许多学者的关注.地理空间分布的整体性就像它的局部性一样,是空间统计分析的主要对象.本文目的是综述各种地理空间分布整体性特征的统计研究进展.在这里,地理空间分布的整体性特征是指它在二维空间上的中心和方位以及它自身的地理范围、空间密集度和形状.文中介绍了对这些整体性特征进行统计描述、预测和估计的方法.已有近20个空间统计指标用于描述社会经济和自然地理空间分布的各种整体性特征,分析和预测它们的时空演化过程.如空间移动、扩张、加密、旋转,并确定不同空间分布的相关关系.文中探讨了地理空间分布整体统计目前存在的主要科学问题.如空间独立性假设、空间随机抽样、时空过程分析(包括时空稳态随机过程1、空间统计描述方法和空间分布函数等.     

National Center for Geographic Information and Analysis

Abstract

Abstract. The growth of the remote sensing field has caused and explosion in the size and complexity of Earth satellite image databases. The explosion of data is already challenging current satellite-based GIS databases. New technologies for information management will be required in the future to maintain these large geographically-oriented systems created by projects such as NASA's Earth Observing System (EOS). This paper presents a technology adapted from the planning/scheduling field in Artificial Intelligence (AI) that promises to automate and facilitate the process of creating and storing satellite images and their associated data products.     

基于数据分治与双层索引的并行点面叠加分析方法研究

地图叠加分析是一种计算密集型算法,并行化计算是加快算法执行速度的一种有效方法。该文研究分布式环境下的点面图层并行化叠加分析方法与实现。首先根据点面叠加的特点设置并行数据分解的方式,基于分治法分解空间数据,在并行系统下将地理要素分而治之。然后引入双层索引的并行叠加机制,一是对面图层根据Hilbert空间索引的排序方式分发数据,二是对点图层建立四叉树索引,对每一个进行相交运算的多边形进行快速过滤和求交。最后在Linux集群系统下实现该并行算法,其一利用MPI分布式计算环境实现在整体计算框架下的消息通讯模式的并行,其二在每个子节点中实现基于多核OpenMP工具的本地并行化。结果表明,利用双层空间索引分治的方法可实现并行数据分块,各子节点实现独立计算,减少并行系统中的I/O冲突,并行加速比明显。该方法对矢量地图运算的并行化进行了有益的尝试,为大数据时代的空间数据分析提供一种有效的途径。     

Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations

Geographically Weighted Regression (GWR) is a widely used tool for exploring spatial heterogeneity of processes over geographic space. GWR computes location-specific parameter estimates, which makes its calibration process computationally intensive. The maximum number of data points that can be handled by current open-source GWR software is approximately 15,000 observations on a standard desktop. In the era of big data, this places a severe limitation on the use of GWR. To overcome this limitation, we propose a highly scalable, open-source FastGWR implementation based on Python and the Message Passing Interface (MPI) that scales to the order of millions of observations. FastGWR optimizes memory usage along with parallelization to boost performance significantly. To illustrate the performance of FastGWR, a hedonic house price model is calibrated on approximately 1.3 million single-family residential properties from a Zillow dataset for the city of Los Angeles, which is the first effort to apply GWR to a dataset of this size. The results show that FastGWR scales linearly as the number of cores within the High-Performance Computing (HPC) environment increases. It also outperforms currently available open-sourced GWR software packages with drastic speed reductions – up to thousands of times faster – on a standard desktop.     

Place matters in sports: effects of the length of championship series on winning probabilities for the team with home field advantage

In this paper, I address the question of how the length of championship series between two teams can affect the probability that a team wins the series. The geographical element of the question enters through consideration of ‘home-field advantage’. This refers to the fact that in championship series, one of the teams has the benefit of one additional game that is scheduled to be played at home. This, coupled with the fact that teams typically have a higher likelihood of winning at home, leads to an interesting and surprisingly complex relationship between the length of a series and the probability of winning that series. Conclusions include the facts that (1) when the team with home-field advantage has a relatively small probability of winning away from home, it will fare relatively better in shorter series, and (2) the probability of winning a series can first become lower, but then become higher, as one progresses from 1-game series to 3-, 5- and 7-game series.     

Establishing a framework for Open Geographic Information science

When conducting research within a framework of Geographic Information Science (GISc), the scientific validity of this work can be argued as highly dependent upon the extent to which the methods employed are reproducible, and that, in the strictest sense, can only be fully achieved by implementing transparent workflows that utilize both open source software and openly available data. After considering the scientific implications of non-reproducible methods, we provide a review of both open source Geographic Information Systems (GIS) and openly available data, before describing an integrated model for Open GISc. We conclude with a critical review of this embryonic paradigm, with directions for future development in supporting spatial data infrastructure.     

基于网络K函数法的地理对象分布模式分析——以香港岛餐饮业空间格局为例

很多地理对象的空间分布与空间上呈现网状结构的地理现象高度相关,分析这些地理对象的分布模式,在地理研究中有重要意义.该文采用由平面空间扩展到网状结构空间的网络K函数法,以香港岛餐饮业地理空间格局为例开展研究.应用单变量K函数法分析餐饮店在网状结构空间中的分布模式,应用双变量交叉K函数法分析餐饮店分布是否受交通站点及旅游景点影响,并对不同尺度下餐饮店地理选址和空间分布规律进行探索与分析.     

Mapping Systems and GIS: A Case Study using the Ghana National Grid

The problem of incompatible projections and conversion between mapping systems is of general concern to those involved in the collection of natural resources data. The Ghana National Grid (GNG) is an example of a mapping system that is not defined in image processing and GIS software and for which the transformation parameters are not readily available in the literature. Consequently, integrating GNG topographic map data within a GIS with data derived from other sources can be problematic. In this paper a practical solution for deriving the required transformation parameters to convert from the World Geodetic System of 1984 (WGS84) to the GNG system is demonstrated. The method uses a single geodetic control point, available 1:50 000 topographic maps and a SPOT satellite panchromatic image geo-referenced to GNG. The resultant parameters are applied to road survey data in Universal Transverse Mercator (UTM) format for overlay with the SPOT image. Despite the approximations made in applying the method, when compared against official estimates of the datum transformation parameters, this relatively simple procedure resulted in estimates that appear acceptable in regard to combining data sets at a nominal scale of 1:50000.     

Using adaptively coupled models and high-performance computing for enabling the computability of dust storm forecasting

Forecasting dust storms for large geographical areas with high resolution poses great challenges for scientific and computational research. Limitations of computing power and the scalability of parallel systems preclude an immediate solution to such challenges. This article reports our research on using adaptively coupled models to resolve the computational challenges and enable the computability of dust storm forecasting by dividing the large geographical domain into multiple subdomains based on spatiotemporal distributions of the dust storm. A dust storm model (Eta-8bin) performs a quick forecasting with low resolution (22 km) to identify potential hotspots with high dust concentration. A finer model, non-hydrostatic mesoscale model (NMM-dust) performs high-resolution (3 km) forecasting over the much smaller hotspots in parallel to reduce computational requirements and computing time. We also adopted spatiotemporal principles among computing resources and subdomains to optimize parallel systems and improve the performance of high-resolution NMM-dust model. This research enabled the computability of high-resolution, large-area dust storm forecasting using the adaptively coupled execution of the two models Eta-8bin and NMM-dust.     

A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

A general-purpose parallel raster processing programming library (pRPL) was developed and applied to speed up a commonly used cellular automaton model with known tractability limitations. The library is suitable for use by geographic information scientists with basic programming skills, but who lack knowledge and experience of parallel computing and programming. pRPL is a general-purpose programming library that provides generic support for raster processing, including local-scope, neighborhood-scope, regional-scope, and global-scope algorithms as long as they are parallelizable. The library also supports multilayer algorithms. Besides the standard data domain decomposition methods, pRPL provides a spatially adaptive quad-tree-based decomposition to produce more evenly distributed workloads among processors. Data parallelism and task parallelism are supported, with both static and dynamic load-balancing. By grouping processors, pRPL also supports data–task hybrid parallelism, i.e., data parallelism within a processor group and task parallelism among processor groups. pSLEUTH, a parallel version of a well-known cellular automata model for simulating urban land-use change (SLEUTH), was developed to demonstrate full utilization of the advanced features of pRPL. Experiments with real-world data sets were conducted and the performance of pSLEUTH measured. We conclude not only that pRPL greatly reduces the development complexity of implementing a parallel raster-processing algorithm, it also greatly reduces the computing time of computationally intensive raster-processing algorithms, as demonstrated with pSLEUTH.