Crowd Map Research Articles

A STUDY ON THE USE OF CROWDSOURCED INFORMATION FOR URBAN DECISION-MAKING

Social Media have become commonplace for citizens. The popularity of social media aff ords a great opportunity to investigate how citizens experience the landscape, their feelings towards it, and its perceived impact over citizens. Geographic Information Systems (GIS) have become an important tool to support decision making, as the use of geospatial technologies enables the construction of complex databases, quick access to data, and development of predictive scenarios. Combination of social media and GIS has been called crowdsourced mapping and represents a promising opportunity for their integrated study. In this paper we aim to evaluate contributed versus volunteered crowdsourced information. As a case study, we analyze information from the period in which the FIFA World Cup took place (Brazil, June and July 2014), obtained from social media services Twitter and Instagram, about the Pampulha Region in Belo Horizonte, Brazil, to show the analytical potential of contributed information. We seek to create a methodology to identify the possibilities for studying the landscape and its essential urban values, as reported by users of the analyzed area, and according to the stakeholders' point of view.

Searching for social justice in GIScience publications

ABSTRACTMaps are explicitly positioned within the realms of power, representation, and epistemology; this article sets out to explore how these ideas are manifest in the academic Geographic Information Science (GIScience) literature. We analyze 10 years of literature (2005–2014) from top tier GIScience journals specific to the geoweb and geographic crowdsourcing. We then broaden our search to include three additional journals outside the technical GIScience journals and contrast them to the initial findings. We use this comparison to discuss the apparent technical and social divide present within the literature. Our findings demonstrate little explicit engagement with topics of social justice, marginalization, and empowerment within our subset of almost 1200 GIScience papers. The social, environmental, and political nature of participation, mapmaking, and maps necessitates greater reflection on the creation, design, and implementation of the geoweb and geographic crowdsourcing. We argue that the merging of the technical and social has already occurred in practice, and for GIScience to remain relevant for contributors and users of crowdsourced maps, researchers and practitioners must heed two decades of calls for substantial and critical engagement with the geoweb and crowdsourcing as social, environmental, and political processes.

Design and Development of Framework for Identifying Errors in Crowdsourced Map Data

The development in the quantity of clients and the volume of data in OpenStreetMap (OSM) show the success of Volunteered geographical information(VGI) based task in attracting different types individuals from everywhere throughout the world. A huge quantity of data is produced every day by non-proficient clients and OSM faces the test of guaranteeing information quality. Since contributors have diverse perspectives about items, information integration in OSM might be considered as a type of multi-representation information combination. As due to freely availability of crowdsourced data quantity and quality assurance are two major areas to concern. This work introduces the design and development of a framework for identifying errors in crowdsourced map data, which enables volunteers to edit and tag geospatial and geographic data. Completeness, a quality parameter is used to investigate different types of errors. Initially Web-based framework is established, which includes a set of components to display the geospatial map data, indicators, markers to highlight or identify errors, as the establishment of the labeling framework. Based on this approach a prototype is developed and implemented in experiments. To actually fix the errors on OpenStreetMap, after completing the fix, it is sent back to OpenStreetMap. The result of this approach is to calculate the fixed errors by volunteers and graphically represent the stats of user contributions towards OpenStreetMap.

SEMANTIC LOCATION EXTRACTION FROM CROWDSOURCED DATA

Abstract. Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction.

SEMANTIC LOCATION EXTRACTION FROM CROWDSOURCED DATA

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction.

Mapping an alternative community river: The case of the Ciliwung

Dense, self-built settlements along riverbanks within Asian cities are often excluded from the planning realm, which ensures governments lack knowledge of how particular communities function. The magnitude of land area and population, dynamic local economies, organic policy making processes, and scarcity and consistency of data challenge research on flood impacts and possible solutions in Asian cities. Resultantly, a deeper understanding of alternative and more dynamic forms of environmental management is necessary. The focus of this paper is to analyze the usefulness and challenges of participatory mapping in relation to urban floods, particularly community mapping and crowd-sourced mapping. This analysis is based on the assumption of participatory mapping discourse that participatory mapping increases communities’ negotiation power to improve their livelihood. This paper employs participant observation and ethnographic interviews within the Ciliwung River corridor in Jakarta. Specifically it focuses on activists and residents in river communities in relation to participatory community mapping exercises conducted since 2012 and a new crowd-sourced flood mapping system launched in December 2014. Participatory community mapping and crowd-sourced flood mapping, as two forms of community-based mapping approaches to floods, are viewed as potential tools to overcome urban flood hazards while raising disaster awareness among city residents. Community mapping is a method of visualizing a neighborhood's communal memories and embedded power relations, while a crowd-sourced flood map visualizes vulnerabilities and may become a tool for information sharing for the betterment of the spatially and socially fragmented city.

Engaging Imperial County Communities In Participatory Hazard And Asset Mapping And Development Of Public Health Actions

INTRODUCTION: Imperial County, California, near the U.S.-Mexico border, is a low-income, primarily Latino community exposed to poor air quality, pesticides, and water contamination. A community-academic-state health agency partnership facilitated a participatory mapping process using mobile phones to document local hazards and assets. Participants used collected data in planning actions to improve community health. METHODS: A Community Steering Committee, representing diverse local stakeholders with experience in environmental health advocacy, guided the project and prioritized impacted neighborhoods. Residents of these neighborhoods were recruited and trained. An innovative crowd-sourcing mapping tool designed by and for local residents was adapted to allow data capture with cell phones. Using collected data and resident expertise, participants engaged in a process to prioritize issues and organize public health actions. RESULTS: 45 community residents received training and mapped sites over an 8-day period, collecting locations, photographs, and other details. Over 70 hazards were identified, including agricultural field burning, abandoned homes, and noise from the Naval Air Facility. Almost 70 assets were identified, including community parks, sports playing fields, and local organizations and clubs. Proposed community actions include advocating for local regulation enforcement, education/awareness campaigns, and enhancing school flag programs. CONCLUSIONS: Training of local residents in environmental science and geospatial data collection and adaptation of an existing community web tool are steps that 1) build capacity to address local environmental issues, 2) promote engagement with and use of data gathered, 3) strengthen relationships between local environmental health experts, and 4) foster collaboration with environmental regulatory agencies at all levels. Mobile phones can be a valuable tool in community-based participatory environmental health research.

The cartographic ambiguities of HarassMap: Crowdmapping security and sexual violence in Egypt

In December 2010, HarassMap was launched as a Cairo-based interactive online mapping interface for reporting and mapping incidents of sexual harassment anonymously and in real time, in Egypt. The project’s use of spatial information technologies for crowdmapping sexual harassment raises important questions about the use of crowdsourced mapping as a technique of global human security governance, as well as the techno-politics of interpreting and representing spaces of gendered security and insecurity in Egypt’s urban streetscape. By recoding Egypt’s urban landscape into spaces subordinated to the visual cartography of the project’s crowdsourced data, HarassMap obscures the complex assemblage that it draws together as the differentially open space of the Egyptian street – spaces that are territorialized and deterritorialized for authoritarian control, state violence, revolt, rape, new solidarities, gender reversals, sectarian tensions, and class-based mobilization. What is at stake in my analysis is the plasticity of victimage: to what extent can attempts to ‘empower’ women be pursued at the microlevel without amplifying the similarly imperial techniques of objectifying them as resources used to justify other forms of state violence? The question requires taking seriously the practices of mapping and targeting as an interface for securing public space.

Towards the production of digital terrain models from volunteered GPS trajectories

There currently exists a wide variety of online resources providing mapping infrastructures and geographic information. Most web-based map services, such as Google Maps, Yahoo! Maps and Bing Maps, are mostly based on data that is collected by authoritative mapping agencies. Alternatively, some relatively new web-map services, such as OpenStreetMap (OSM) and Wikimapia, are mostly based on volunteered data collected by the public (e.g. crowdsourced mapping). Although such volunteered-based map services platforms show an increasing planimetric (2D) accuracy, completeness and update-rate of their mapping infrastructure, surprisingly enough, there is a lack of comparable data and accuracy measures in respect to the third dimension, i.e. height; more specifically, the topographic representation that is based on the volunteered collected data. Most of these web services still rely on existing open-source authoritative topographic infrastructures, and not on data collected by the volunteers. Moreover, topographic information that is open to the public and is free to use, e.g. advanced spaceborne thermal emission and reflection radiometer and shuttle radar topography mission, is regularly available with relatively low height accuracy (not better than 5 m) and low planimetric resolution (over 30 m). Volunteered data, on the other hand, collected by individuals that are situated ‘all over’ the globe can offer with new capabilities and data characteristics having potentially higher qualities. This research proposes to examine the feasibility of using crowdsourced volunteered geographic information working paradigm for the task of producing a reliable digital terrain model (DTM) infrastructure for general use. This is achieved by collecting GPS observations that are available from VG data sources, while applying a 2D Kalman filter-based algorithm, aimed at reducing noise and ambiguities. This paper presents this methodology, with preliminary analysis results achieved by this implementation, showing the feasibility of this working methodology, having good results and accuracy of the DTM generated.

Mapping and measuring place attachment

The concept of place attachment has been studied extensively across multiple disciplines but only recently with empirical measurement using public participation GIS (PPGIS) and related crowd-sourcing mapping methods. This research trialed a spatially explicit method for identifying place attachment in a regional study in South Australia. Our research objectives were to (1) analyze and present the spatial results of the mapping method as a benchmark for future research, (2) compare mapped place attachment to the more common practice of mapping landscape values in PPGIS that comprise a values home range, (3) identify how participant socio-demographic and home location attributes influence place attachment, (4) provide some guidance for mapping place attachment in future research. We found large spatial variability in individual place attachment and mapped landscape values using both area and distance-based measures. The area of place attachment is influenced by occupational roles such as farming or conservation, as well as home location, especially in coastal versus non-coastal contexts. The spatial distribution of mapped landscape values or values home range is related to, but not identical to mapped place attachment with just over half of landscape values located outside the area of mapped place attachment. Economic livelihood values, as an indicator of place dependence, and social values, as an indicator of place identity, are more likely to be mapped within the place attachment area. Aggregated place attachment across participants in the region showed similar spatial intensity to aggregated values home range, but area-based assessment of place attachment and values home range are distorted by edge effects such as a coastline. To further develop the mapping of place attachment in PPGIS, we identify knowledge gaps from our study and offer suggestions for future research design.

Crowd-Sourced Mobility Mapping for Location Tracking Using Unlabeled Wi-Fi Simultaneous Localization and Mapping

Due to the increasing requirements of the seamless and round-the-clock Location-based services (LBSs), a growing interest in Wi-Fi network aided location tracking is witnessed in the past decade. One of the significant problems of the conventional Wi-Fi location tracking approaches based on received signal strength (RSS) fingerprinting is the time-consuming and labor intensive work involved in location fingerprint calibration. To solve this problem, a novel unlabeled Wi-Fi simultaneous localization and mapping (SLAM) approach is developed to avoid the location fingerprinting and additional inertial or vision sensors. In this approach, an unlabeled mobility map of the coverage area is first constructed by using the crowd-sourcing from a batch of sporadically recorded Wi-Fi RSS sequences based on the spectral cluster assembling. Then, the sequence alignment algorithm is applied to conduct location tracking and mobility map updating. Finally, the effectiveness of this approach is verified by the extensive experiments carried out in a campus-wide area.

An assessment of a collaborative mapping approach for exploring land use patterns for several European metropolises

Until recently, land surveys and digital interpretation of remotely sensed imagery have been used to generate land use inventories. These techniques however, are often cumbersome and costly, allocating large amounts of technical and temporal costs. The technological advances of web 2.0 have brought a wide array of technological achievements, stimulating the participatory role in collaborative and crowd sourced mapping products. This has been fostered by GPS-enabled devices, and accessible tools that enable visual interpretation of high resolution satellite images/air photos provided in collaborative mapping projects. Such technologies offer an integrative approach to geography by means of promoting public participation and allowing accurate assessment and classification of land use as well as geographical features. OpenStreetMap (OSM) has supported the evolution of such techniques, contributing to the existence of a large inventory of spatial land use information. This paper explores the introduction of this novel participatory phenomenon for land use classification in Europe's metropolitan regions. We adopt a positivistic approach to assess comparatively the accuracy of these contributions of OSM for land use classifications in seven large European metropolitan regions. Thematic accuracy and degree of completeness of OSM data was compared to available Global Monitoring for Environment and Security Urban Atlas (GMESUA) datasets for the chosen metropolises. We further extend our findings of land use within a novel framework for geography, justifying that volunteered geographic information (VGI) sources are of great benefit for land use mapping depending on location and degree of VGI dynamism and offer a great alternative to traditional mapping techniques for metropolitan regions throughout Europe. Evaluation of several land use types at the local level suggests that a number of OSM classes (such as anthropogenic land use, agricultural and some natural environment classes) are viable alternatives for land use classification. These classes are highly accurate and can be integrated into planning decisions for stakeholders and policymakers.

A Framework and a Tool for Semantic Annotation of POIs in OpenStreetMap

Current mobile systems for assisted navigation have limited effectiveness in satisfying user needs. The information content supporting location-based service discovery and path calculation is usually shallow. Semantic-based technologies can allow to overcome these limitations, by exploiting accurate and meaningful descriptions of locations, Points of Interest (POIs), road segments and environmental conditions. We present here a general framework leveraging an enriched cartography, which may be useful not only to enhance travel satisfaction and safety, but also to regulate vehicle efficiency, traffic and environmental impact.The availability of annotated map data is a crucial requirement to make practically viable such a proposal. Unfortunately, the majority of available systems is developed upon closed and proprietary solutions for both maps and software applications, so third parties cannot extend their functionality. To go beyond this restriction, the framework presented here is based on open standards and tools: in particular, it leverages Semantic Web technologies and crowd-sourced maps available from OpenStreetMap (http://www.openstreetmap.org/), enriching nodes and POIs with semantic annotations to enable innovative Location Based Services (LBSs) for traveling users. Particularly, the paper proposes a general method for storing semantic annotations into OpenStreetMap road nodes and POIs. A user-friendly software tool is also presented for editing annotations through a fully visual user interface, based on simple drag-and-drop operations, implemented as a plugin for the popular open source JOSM OpenStreetMap editor (http://josm.openstreetmap.de/), that will make any OpenStreetMap contributor capable of enriching maps with semantic information. Finally, a semantic-enhanced navigation tool is proposed, capable of exploiting the enriched cartography. Early users’ evaluation assesses the effectiveness of such a proposal.

Towards an Authoritative OpenStreetMap: Conflating OSM and OS OpenData National Maps’ Road Network

The quality aspects of OpenStreetMap (OSM), as the global representation of crowd-sourced mapping, have always been of priomary concern to academics. While the methodologies for checking its quality against the national maps have been implemented by a number of studies, there are minimal works on how to practically improve the quality of OSM towards being an authoritative map source. This paper presents a method for conflating road attributes, namely the name and reference code, of OSM with the Open Data provided by Ordnance Survey (the British national mapping agency). The added values in the proposed methodology include the daily updates and serving of the conflated maps via open Web Services. More importantly, the OSM crowd correction is facilitated by frequently highlighting and web-serving the individual differences. There are currently over 5,800 differences in matching road names and references between the two datasets. In addition to describing the conflation methodology, the different geographic distribution patterns of the identified differences are discussed. A negative effect of the road density on the ratio of the mismatched features between the two datasets is observable, evidenced by their different geographical distribution over the map. It is shown that the best correspondence between attributes exists in the very dense areas, followed by the very low density areas, and lastly in the middle to large sized cities.

Crowdsourcing techniques for augmenting traditional accessibility maps with transitory obstacle information

One of the most scrutinized contemporary techniques for geospatial data collection and production is crowdsourcing. This inverts the traditional top-down geospatial data production and distribution methods by emphasizing on the participation of the end user or community. The technique has been shown to be particularly useful in the domain of accessibility mapping, where it can augment traditional mapping methods and systems by providing information about transitory obstacles in the built environment. This research paper presents details of techniques and applications of crowdsourcing and related methods for improving the presence of transitory obstacles in accessibility mapping systems. The obstacles are very difficult to incorporate with any other traditional mapping workflow, since they typically appear in an unplanned manner and disappear just as quickly. Nevertheless, these obstacles present a major impediment to navigating an unfamiliar environment. Fortunately, these obstacles can be reported, defined, and captured through a variety of crowdsourcing techniques, including gazetteer-based geoparsing and active social media harvesting, and then referenced in a crowdsourced mapping system. These techniques are presented, along with context from research in tactile cartography and geo-enabled accessibility systems.

CROWDSOURCED MAPPING – LETTING AMATEURS INTO THE TEMPLE?

Abstract. The rise of crowdsourced mapping data is well documented and attempts to integrate such information within existing or potential NSDIs [National Spatial Data Infrastructures] are increasingly being examined. The results of these experiments, however, have been mixed and have left many researchers uncertain and unclear of the benefits of integration and of solutions to problems of use for such combined and potentially synergistic mapping tools. This paper reviews the development of the crowdsource mapping movement and discusses the applications that have been developed and some of the successes achieved thus far. It also describes the problems of integration and ways of estimating success, based partly on a number of on-going studies at the University of Nottingham that look at different aspects of the integration problem: iterative improvement of crowdsource data quality, comparison between crowdsourced data and prior knowledge and models, development of trust in such data, and the alignment of variant ontologies. Questions of quality arise, particularly when crowdsource data are combined with pre-existing NSDI data. The latter is usually stable, meets international standards and often provides national coverage for use at a variety of scales. The former is often partial, without defined quality standards, patchy in coverage, but frequently addresses themes very important to some grass roots group and often to society as a whole. This group might be of regional, national, or international importance that needs a mapping facility to express its views, and therefore should combine with local NSDI initiatives to provide valid mapping. Will both groups use ISO (International Organisation for Standardisation) and OGC (Open Geospatial Consortium) standards? Or might some extension or relaxation be required to accommodate the mostly less rigorous crowdsourced data? So, can crowdsourced data ever be safely and successfully merged into an NSDI? Should it be simply a separate mapping layer? Is full integration possible providing quality standards are fully met, and methods of defining levels of quality agreed? Frequently crowdsourced data sets are anarchic in composition, and based on new and sometimes unproved technologies. Can an NSDI exhibit the necessary flexibility and speed to deal with such rapid technological and societal change?

A Crowdsourcing Innovation Challenge to Locate and Map Automated External Defibrillators

Out-of-hospital cardiac arrest is a major public health problem that affects an estimated 300 000 people in the United States every year.1,2 The application of an automated external defibrillator (AED) to patients who have experienced cardiac arrest has saved many lives. AEDs coupled with cardiopulmonary resuscitation can significantly improve survival from cardiac arrest from 50%.3–5 AEDs can be used easily by untrained laypeople. When accessed and opened, most devices provide audible and visual instructions on use and how to perform cardiopulmonary resuscitation. However, AED effectiveness is extremely time dependent, and presently, in a crisis no comprehensive map of these devices exists to help bystanders find and use them.3,6–9 Previous work from a large database of out-of-hospital cardiac arrest in the United States suggests low (4%, 1166 of 31 689) use of AEDs by bystanders.10 An accurate, easily accessible map of AEDs could help people locate them in an emergency, either directly through smart phone applications (apps) or through communication with map-equipped 911 emergency responders.11 Creating such a map is challenging, and currently, there is no publicly accessible, accurate, comprehensive crowdsourced map for any region in the world. Although sending out an army of staff might allow canvassing of a geographic region, such an approach would be expensive and not clearly scalable. Furthermore, because AEDs can move locations and require maintenance, ensuring that a database of devices had valid, routinely updated data would be a difficult task. Crowdsourcing is increasingly used to address major creative and data gathering challenges.12–15 Crowdsourcing involves taking a task conventionally assigned to particular individuals and instead soliciting help for the task from a large group of diverse individuals (ie, the crowd). The task is often posted online to …

GO_Sync — A Framework to Synchronize Crowd-Sourced Mapping Contributors from Online Communities and Transit Agency Bus Stop Inventories

Proprietary formats and licensing restrictions limit individuals from sharing, viewing, or updating geographic (e.g., map) datasets and also restrict the use of these data to create innovative location-based services. Open geographic data repositories are now emerging that do not have these restrictions. The General Transit Feed Specification (GTFS) is a common open format for public transportation agencies’ schedules, routes, and geographic bus stop information. However, transit agencies struggle to internally maintain and update these very large official datasets. Meanwhile, OpenStreetMap.org (OSM), an online free-content “Wikipedia-like” repository of geographic data, currently has little information about U.S. public transportation, but does have a large number of users willing to freely contribute their efforts to improve geographic data for their communities. This paper presents GO_Sync, a framework and open-source software tool for synchronizing transit data between the transit agency’s official GTFS dataset and OSM. GO_Sync connects the wealth of data from GTFS datasets with the ability of the OSM community to contribute crowd-sourced improvements to large datasets. The GO_Sync tool therefore enables public transportation agencies to upload GTFS data to OSM and retrieve crowd-sourced data back, while the online community can edit and correct the bus stop locations and amenities based on existing GTFS data. Successfully translating GTFS data into the OSM format will enable over 125 transit agencies across the U.S. to share their public-domain data with the OSM community.

The Milky Way Project First Data Release: a bubblier Galactic disc

We present a new catalogue of 5,106 infrared bubbles created through visual classification via the online citizen science website 'The Milky Way Project'. Bubbles in the new catalogue have been independently measured by at least 5 individuals, producing consensus parameters for their position, radius, thickness, eccentricity and position angle. Citizen scientists - volunteers recruited online and taking part in this research - have independently rediscovered the locations of at least 86% of three widely-used catalogues of bubbles and H ii regions whilst finding an order of magnitude more objects. 29% of the Milky Way Project catalogue bubbles lie on the rim of a larger bubble, or have smaller bubbles located within them, opening up the possibility of better statistical studies of triggered star formation. Also outlined is the creation of a 'heat map' of star-formation activity in the Galactic plane. This online resource provides a crowd-sourced map of bubbles and arcs in the Milky Way, and will enable better statistical analysis of Galactic star-formation sites.

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.