地名地址数据库多源集成方法研究

地名地址数据库属于基础地理信息数据库,是构成数字城市地理空间框架不可或缺的重要组成部分,随着我国数字城市的逐步推进,城市建设和管理工作对基础地理信息的需求日益增加,地名地址数据库的建设和更新工作显得尤为重要。面对当前城市发展日新月异的现状,如何有效地提升数据完整性、时效性以及权威性成为数据库更新工作中必须解决的问题。本文以德州市地名地址数据库更新项目为例,指出外业调查中存在的数据问题,在对多来源地名地址数据进行广泛收集,分析这些数据的空间特征、时间特征以及利用方式的基础上,提出基于多源数据集成的数据库建设思路,利用地名地址匹配技术及人机交互的方式将官方资料与调查数据进行有机融合,并详细地从数据体系化检核、地名地址拓展、名称标准化3个方面进行阐述。项目成果表明,数据的质量明显提升。     

多源地名地址数据治理技术的研究与实现

地名地址是实现跨部门业务协同和实现数据共享的关键纽带,本文通过分析各部门地名地址数据情况,提出数据库设计优化方案,运用自然语言处理等技术,研究并实现了一种多源地名地址数据治理技术方案,构建了标准统一、要素丰富、空间精准的地名地址库。     

浅析浙江省天地图地名地址数据库设计与数据处理实现

地名地址数据是浙江省天地图重要的组成部分,承担着天地图空间检索的任务,保障天地图高效、稳定运行。本文通过对地名地址数据库要素分类设计、数据库表结构设计规则的梳理,以及地名地址数据的提取、筛选、对象化等处理方法的归纳,提出了地名地址数据库设计的规则和数据处理实现的方式。     

地名就是地名

<正> 地名就是地名——一个特定的、有明确范围的空间的名称,它的主要功能就是一个地方或一个地域范围的代表或识别标志,因而应该尽可能保持稳定。这本来是一种常识,也是地名使用的惯例,近年     

基于Versant面向对象数据库的地名地址存储研究

地名地址数据是地理信息定位与交换的载体,是地理空间框架中的必要组成部分.本文基于Versant面向对象数据库,对地名地址的结构进行了分析及分类挂接到地址树中,设计了以现有关系数据库为基础,将地名地址数据导入到面向对象数据库的流程;同时,在Versant数据库中,设计了分布式存储过程,并进行了存储测试.结果表明,基于Versant数据库能够优化现有地址的存储,方便解决现有各种业务系统在地名地址数据使用中遇到的问题.     

北京市数字地名的发展现状

简要介绍北京市地址地名数据库建设的背景和北京市的地名发展史,从政府部门和地名信息提供商两个方面综述北京市数字地名发展的现状,并与兄弟城市的发展情况作对比。认为数字地名已引起全社会的重视,北京市的数字地名事业正处在一个大发展时期,需要政府出面很好地规划、协调、管理,实现地名信息资源的社会共享。地名服务开始走向多元化。     

基于POI数据的模糊地名空间范围估计

在信息检索中常用地名作为空间参照,因此确定模糊地名的空间范围备受关注。由于模糊地名不存在确切的边界,评估模糊地名空间范围十分困难,但人们对模糊地名的认知区域可以提供参考。本文分析了模糊地名空间的基本特征;以案例形式,讨论了利用POI数据和认知实验确定模糊地名空间范围的基本方法和特点,并分析这些案例地名空间的特性。从这些案例研究可以得出以下结论:影响模糊地名空间范围的因素有道路、熟悉程度和地名借用;用POI数据对模糊地名估计出的空间范围符合人们认知的空间范围。     

对城市门牌号空间定位功能的研究

门牌号是地名地址描述中的地址元素,地名地址是基础地理信息的一项重要内容,因此,研究门牌号有着十分重要的意义。本文主要介绍了门牌号的概念、存在形式和功能,并对门牌号空间定位功能的插值算法和存在的一些特殊情况进行探讨。     

汾河中游地名与流域文化研究

流域地名是时间和空间的统一、历史与地理的结合、自然与人文的融汇,是流域文化的重要组成部分。汾河中游的地名具有鲜明的流域性、历史性和多样性等特点。通过对汾河中游的地名与流域文化的研究,揭示出地名的形成、发展、变迁与流域的自然与人文环境变迁密切相关,对于保护地名和流域文化具有重要的现实意义。     

地名地址普查与建库研究

地名是人们赋予各个不同地理实体的专有名称,地址是对地名信息方位、地理位置的文字描述,地名地址是基础地理信息。地名地址普查与数据库的建立有利于地方经济社会协调发展,有利于社会交流交往,方便人民群众生产生活,对提高政府管理水平和公共服务能力具有重要的意义。笔者结合参与的地名地址普查项目的经验,对地名地址普查内容进行了简单介绍,就地名地址数据库设计、外业普查对象与数据采集方法作了简要探讨,以供同行参考。     

基于GIS的姓氏地名制图方法研究

地名研究能够揭示地名所固有的科学价值,为科学研究提供重要依据,地名制图法是地名研究中常用的方法,用它分析姓氏地名可以追述某姓氏起源,了解该姓氏人口的分布、迁移等.本文借助GIS软件,对张姓地名进行了分析,采用的格网制图数据分析,可以反映张姓村庄的密集程度、密度值连续变化状况、宏观分布规律;空间平滑插值制图数据分析,可以...     

广东西江流域地名调查工作特点与实例分析

地名调查工作涉及地域划分,地理空间,语言文化,历史文化等方面,运用"3S"技术,结合当地语言文化开展此项工作,能提高调查工作效率和准确度,有利于对存在尚未掌握的地理实体及地名信息的判断、界定和甄别。通过广东郁南县地名调查工作实例,探讨在"3S"技术背景下,根据具有地域性文化特征地区的地名调查工作所呈现的新问题、新特点,可作范例为粤语地区的地名调查提供可操作性建议。     

基于MAPGIS下的武汉市地名管理系统的研制与开发

地名管理系统是数字城市建设的重要组成部分。武汉市地名管理系统是基于MAPGIS平台开发的应用系统，包括武汉地名的按类查询显示、属性管理、图层管理、数据的处理及数据输出几个部分。该系统给武汉市地名管理带来极大方便。     

基于ArcGIS地名综合信息系统的设计及应用

地名信息系统是地名工作在信息时代的重要任务,也是一项复杂的基础工程。为了满足城市GIS中地名查询、规划设计、社会化服务的应用需求,主要采用地名数据库、管理及办理、系统综合管理三重架构,设计一个利于地名管理、业务办理、决策支持的应用系统。系统采用分布式数据库存储方式,同时实现了地名地理信息动态更新,为地名相关业务带来极大的方便。     

A Multiscale Approach for Spatio-Temporal Outlier Detection

A spatial outlier is a spatially referenced object whose thematic attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. It represents an object that is significantly different from its neighbourhoods even though it may not be significantly different from the entire population. Here we extend this concept to the spatio‐temporal domain and define a spatial‐temporal outlier (ST‐outlier) to be a spatial‐temporal object whose thematic attribute values are significantly different from those of other spatially and temporally referenced objects in its spatial or/and temporal neighbourhoods. Identification of ST‐outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability or deformation. Many methods have been recently proposed to detect spatial outliers, but how to detect the temporal outliers or spatial‐temporal outliers has been seldom discussed. In this paper we propose a multiscale approach to detect ST‐outliers by evaluating the change between consecutive spatial and temporal scales. A four‐step procedure consisting of classification, aggregation, comparison and verification is put forward to address the semantic and dynamic properties of geographic phenomena for ST‐outlier detection. The effectiveness of the approach is illustrated by a practical coastal geomorphic study.     

Mining Association Rules in Spatio-Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change

This research demonstrates the application of association rule mining to spatio‐temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A→B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio‐temporal association rule occurs when there is a spatio‐temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, USA region from 1970–1990. Geographic Information Systems (GIS)‐based data pre‐processing is used to integrate diverse data sets, extract spatio‐temporal relationships, classify numeric data into ordinal categories, and encode spatio‐temporal relationship data in tabular format for use by conventional (non‐spatio‐temporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatio‐temporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting’ rules.     

A case-based reasoning approach for task-driven spatial–temporally aware geospatial data discovery through geoportals

ABSTRACT

There is a critical need to develop a means for fast, task-driven discovery of geospatial data found in geoportals. Existing geoportals, however, only provide metadata-based means for discovery, with little support for task-driven discovery, especially when considering spatial–temporal awareness. To address this gap, this paper presents a Case-Based Reasoning-supported Geospatial Data Discovery (CBR-GDD) method and implementation that accesses geospatial data by tasks. The advantages of the CBR-GDD approach is that it builds an analogue reasoning process that provides an internal mechanism bridging tasks and geospatial data with spatial–temporal awareness, thus providing solutions based on past tasks. The CBR-GDD approach includes a set of algorithms that were successfully implemented via three components as an extension of geoportals: ontology-enhanced knowledge base, similarity assessment model, and case retrieval nets. A set of experiments and case studies validate the CBR-GDD approach and application, and demonstrate its efficiency.     

Improving Geocoding Match Rates with Spatially‐Varying Block Metrics

Address ranges used in linear interpolation geocoding often have errors and omissions that result in input address numbers falling outside of known address ranges. Geocoding systems may match these input addresses to the closest available nearby address range and assign low confidence values (match scores) to increase match rates, but little is published describing the matching or scoring techniques used in these systems. This article sheds light on these practices by investigating the need for, technical approaches to, and utility of nearby matching methods used to increase match rates in geocode data. The scope of the problem is motivated by an analysis of a commonly used health dataset. The technical approach of a geocoding system that includes a nearby matching approach is described along with a method for scoring candidates based on spatially‐varying neighborhoods. This method, termed dynamic nearby reference feature scoring, identifies, scores, ranks, and returns the most probable candidate to which the input address feature belongs or is spatially near. This approach is evaluated against commercial systems to assess its effectiveness and resulting spatial accuracy. Results indicate this approach is viable for improving match rates while maintaining acceptable levels of spatial accuracy.     

Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping

With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images.