Alexander Zipf

The Street Network Evolution of Crowdsourced Maps: OpenStreetMap in Germany 2007–2011

The OpenStreetMap (OSM) project is a prime example in the field of Volunteered Geographic Information (VGI). Worldwide, several hundred thousand people are currently contributing information to the “free” geodatabase. However, the data contributions show a geographically heterogeneous pattern around the globe. Germany counts as one of the most active countries in OSM; thus, the German street network has undergone an extensive development in recent years. The question that remains is this: How does the street network perform in a relative comparison with a commercial dataset? By means of a variety of studies, we show that the difference between the OSM street network for car navigation in Germany and a comparable proprietary dataset was only 9% in June 2011. The results of our analysis regarding the entire street network showed that OSM even exceeds the information provided by the proprietary dataset by 27%. Further analyses show on what scale errors can be reckoned with in the topology of the street network, and the completeness of turn restrictions and street name information. In addition to the analyses conducted over the past few years, projections have additionally been made about the point in time by which the OSM dataset for Germany can be considered “complete” in relative comparison to a commercial dataset.

DEEP MAP: Challenging IT Research In The Framework Of A Tourist Information System

Deep Map is a research framework that aims at building the prototype of an intelligent next generation spatial information system. Deep Map realizes the vision of a future tourist guidance system that works as a mobile guide and as a web-based planning tool. This long term research project addresses several challenging research aspects covering intelligent integration of information from different data sources and services including geographical information systems, multi-media databases, and interactive internet data sources such as reservation systems. On the basis of this complex information system the European Media Laboratory plans to build user interfaces that allow intuitive and easy access to information. Such a user interface including visual and natural language processing will be included into a mobile device that navigates the user through a city. Also virtual tours in a 3D-reconstructed city will be possible. In this paper, we present the current system that already covers a number of these aspects and demonstrates how tourists may be guided in the future.

Generating web-based 3D City Models from OpenStreetMap: The current situation in Germany

This paper investigates the prospects for the generation of interactive 3D City Models based on free geo-data available from the OpenStreetMap (OSM) project and public domain height information provided by the Shuttle Radar Topography Mission. In particular, the suitability and quality of the OpenStreetMap data for 3D visualizations of traffic infrastructure, buildings and points of interest (POIs) is reviewed. The diversity and quantity of the points of interest provide new opportunities and challenges in creating customized and detailed visualization of cities. Specialized web services were implemented to filter and display the data in an acceptable manner. All applied web services of the 3D spatial data infrastructure are based on standards and draft specifications of the open geospatial consortium (OGC). The service is available online at www.osm-3d.org.

Intelligent systems for tourism

The authors discuss travel recommender systems, adaptive context aware mobility support for tourists, tourism information systems, information delivery and travel planning information gathering agents.

A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis

OpenStreetMap (OSM) is one of the most popular examples of a Volunteered Geographic Information (VGI) project. In the past years it has become a serious alternative source for geodata. Since the quality of OSM data can vary strongly, different aspects have been investigated in several scientific studies. In most cases the data is compared with commercial or administrative datasets which, however, are not always accessible due to the lack of availability, contradictory licensing restrictions or high procurement costs. In this investigation a framework containing more than 25 methods and indicators is presented, allowing OSM quality assessments based solely on the datas history. Without the usage of a reference data set, approximate statements on OSM data quality are possible. For this purpose existing methods are taken up, developed further, and integrated into an extensible open source framework. This enables arbitrarily repeatable intrinsic OSM quality analyses for any part of the world.

Quality assessment for building footprints data on OpenStreetMap

In the past two years, several applications of generating three-dimensional (3D) buildings from OpenStreetMap (OSM) have been made available, for instance, OSM-3D, OSM2World, OSM Building, etc. In these projects, 3D buildings are reconstructed using the buildings’ footprints and information about their attributes, which are documented as tags in OSM. Therefore, the quality of 3D buildings relies strongly on the quality of the building footprints data in OSM. This article is dedicated to a quality assessment of building footprints data in OSM for the German city of Munich, which is one of the most developed cities in OSM. The data are evaluated in terms of completeness, semantic accuracy, position accuracy, and shape accuracy by using building footprints in ATKIS (German Authority Topographic–Cartographic Information System) as reference data. The process contains three steps: finding correspondence between OSM and ATKIS data, calculating parameters of the four quality criteria, and statistical analysis. The results show that OSM footprint data in Munich have a high completeness and semantic accuracy. There is an offset of about four meters on average in terms of position accuracy. With respect to shape, OSM building footprints have a high similarity to those in ATKIS data. However, some architectural details are missing; hence, the OSM footprints can be regarded as a simplified version of those in ATKIS data.

Comparison of Volunteered Geographic Information Data Contributions and Community Development for Selected World Regions

Volunteered Geographic Information (VGI) projects and their crowdsourced data have been the focus of a number of scientific analyses and investigations in recent years. Oftentimes the results show that the collaboratively collected geodata of one of the most popular VGI projects, OpenStreetMap (OSM), provides good coverage in urban areas when considering particular completeness factors. However, results can potentially vary significantly for different world regions. In this article, we conduct an analysis to determine similarities and differences in data contributions and community development in OSM between 12 selected urban areas of the world. Our findings showed significantly different results in data collection efforts and local OSM community sizes. European cities provide quantitatively larger amounts of geodata and number of contributors in OSM, resulting in a better representation of the real world in the dataset. Although the number of volunteers does not necessarily correlate with the general population density of the urban areas, similarities could be detected while comparing the percentage of different contributor groups and the number of changes they made to the OSM project. Further analyses show that socio-economic factors, such as income, can have an impact on the number of active contributors and the data provided in the analyzed areas. Furthermore, the results showed significant data contributions by members whose main territory of interest lies more than one thousand kilometers from the tested areas.

A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management

In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data (sensor data, hydrological data and digital elevation models). We apply this approach to examine the micro-blogging text messages of the Twitter platform (tweets) produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near (up to 10 km) to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring.

Toward mapping land-use patterns from volunteered geographic information

A large number of applications have been launched to gather geo-located information from the public. This article introduces an approach toward generating land-use patterns from volunteered geographic information (VGI) without applying remote-sensing techniques and/or engaging official data. Hence, collaboratively collected OpenStreetMap (OSM) data sets are employed to map land-use patterns in Vienna, Austria. Initially the spatial pattern of the landscape was delineated and thereafter the most relevant land type was assigned to each land parcel through a hierarchical GIS-based decision tree approach. To evaluate the proposed approach, the results are compared with the Global Monitoring for Environment and Security Urban Atlas (GMESUA) data. The results are compared in two ways: first, the texture of the resulting land-use patterns is analyzed using texture-variability analysis. Second, the attributes assigned to each land segment are evaluated. The achieved land-use map shows kappa indices of 91, 79, and 76% agreement for location in comparison with the GMESUA data set at three levels of classification. Furthermore, the attributes of the two data sets match at 81, 67, and 65%. The results demonstrate that this approach opens a promising avenue to integrate freely available VGI to map land-use patterns for environmental planning purposes.

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.