Obituary

Abstract

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In the area of volunteered geographical information (VGI), the issue of spatial data quality is a clear challenge. The data that are contributed to VGI projects do not comply with standard spatial data quality assurance procedures, and the contributors operate without central coordination and strict data collection frameworks. However, similar to the area of open source software development, it is suggested that the data hold an intrinsic quality assurance measure through the analysis of the number of contributors who have worked on a given spatial unit. The assumption that as the number of contributors increases so does the quality is known as ‘Linus’ Law’ within the open source community. This paper describes three studies that were carried out to evaluate this hypothesis for VGI using the OpenStreetMap dataset, showing that this rule indeed applies in the case of positional accuracy.     

Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks

In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R ²) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R ² ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.     

Fine-resolution population mapping using OpenStreetMap points-of-interest

Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.     

基于OpenStreetMap和90 m SRTM的地面站导航 电子地图实现

地面站导航电子地图是操作员用来监控无人驾驶装备的唯一平台,也是进行线路规划和自主决策的重要信息源。现有的导航电子地图一般存在价格昂贵、对高程数据支持不足以及依赖互联网等缺陷。本文提出了一种新的基于OpenStreetMap的地面站导航电子地图实现方法。首先,利用原始OSM数据搭建地图瓦片服务后台;然后将90 m SRTM高程数据处理成地形阴影和地形颜色渐变栅格文件;针对每个栅格文件,在瓦片服务后台的样式配置文件中添加相应图层并定义其渲染格式;最后,在地面站软件中实现地图客户端模块,向瓦片服务后台请求瓦片,并显示在地面站界面上。利用该方法实现的地面站导航电子地图可以提供0~18个缩放级别的中国范围的数据,显示0~13个缩放级别的立体地形地貌,并且无需互联网支持。本文使用该方法实现了某型无人机的地面站导航电子地图模块,并成功应用该无人机仿真系统。     

Quality assessment of OpenStreetMap data using trajectory mining

OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigations.     

GlobeLand 30和自发地理信息的对比分析研究

地表覆盖数据是关于土地利用信息的重要来源,在地理国情监测、生态环境保护等方面发挥着重要的作用,目前遥感影像解译、实地测量是该数据生产的主要手段,但是仍然存在一定的局限性。随着Web2.0、互联网技术以及各种GPS设备的快速发展传播,普通大众也可以参与公众制图,志愿者用户的参与能够有效判定地表类型的空间分布和属性特征,提高地表覆盖制图的分类精度。本文以自发地理信息中最成功的项目OpenStreetMap为例,与中国新研制的全球最高30m分辨率地表覆盖数据产品GlobeLand 30进行对比分析,首先对数据进行相应的预处理和拓扑检查,然后建立两种数据的要素对应关系,最后生成误差矩阵并分析两种数据的一致性。实验结果表明：① OpenStreetMap数据缺失的部分主要是耕地类型,其草地和水体要素比GlobeLand 30更加丰富;② 2种数据的一致性较好为75%左右,其中林地和人造地表的精度较高,耕地和水体次之,草地较差;③ 重点对不一致区域的地表类型进行判断验证,能够发现GlobeLand 30数据中的错误分类,为进一步修改和优化提供依据。本文研究表明,自发地理信息中包含丰富的地表覆盖信息,能够给地表覆盖制图及评价验证带来巨大的发展潜力。     

开放式街道地图在城市发展水平分析中的应用

针对城市路网的发展状况,能在一定程度上体现城市发展的水平,但传统路网数据难获取的问题,该文采用了易获取、成本低、更新快等优点的OpenStreetMap路网数据为主要研究对象,利用多元线性回归的分析方法,重点研究OSM路网与城市经济发展水平(城市GDP)间的相关关系。通过实验验证,OSM路网密度与城市经济发展水平之间存在着明显的相关关系,利用OSM路网密度对城市经济发展水平进行分析,具有一定的可行性,能够为日后城市经济水平评估与评价提供了一种新的辅助评价指标。     

浅论自发地理信息的数据管理

自发地理信息（volunteered geographic information,VGI）由大众志愿者自发标报,导致其可信度具有诸多不确定性,但目前的研究工作很少考虑用户信誉对于VGI质量的影响,因此提出基于用户信誉的VGI可信度计算模型。首先提出了VGI的用户信誉模型,模型综合考虑了用户的初始信誉和评价信誉两方面,在此基础上综合地理要素的编辑过程和贡献者的信誉等因素发展了地理对象的可信度计算模型。最后采用OpenStreetMap的真实历史数据中的线对象进行实验,实验结果表明,线要素的质量和可信度值呈正相关关系。本文从可信度的角度来评价自发地理信息,为VGI的质量评价和筛选提供了新的视角。