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A Comparison of Feature Density for Large Scale Online Maps DOI: 10.14714/CP97.1707 PEER-REVIEWED ARTICLE A Comparison of Feature Density for Large Scale Online Maps Michael P. Peterson (he/him) University of Nebraska at Omaha [email protected] Large-scale maps, such as those provided by Google, Bing, and Mapbox, among others, provide users an important source of information for local environments. Comparing maps from these services helps to evaluate both the quality of the underlying spatial data and the process of rendering the data into a map. The feature and label density of three different mapping services was evaluated by making pairwise comparisons of large-scale maps for a series of random areas across three continents. For North America, it was found that maps from Google had consistently higher feature and label den- sity than those from Bing and Mapbox. Google Maps also held an advantage in Europe, while maps from Bing were the most detailed in sub-Saharan Africa. Maps from Mapbox, which relies exclusively on data from OpenStreetMap, had the lowest feature and label density for all three areas. KEYWORDS: Web Mapping Services; Multi-Scale Pannable (MSP) maps; OpenStreetMap; Application Programming Interface (API) INTRODUCTION One of the primary benefits of using online map Since the introduction of the technique in 2005 by services like those available from Google, Bing, and Google, all major online map providers have adopted the OpenStreetMap, is that zooming-in allows access to same underlying technology. Vector data is projected and large-scale maps. Maps at these large scales are not avail- divided into vector tiles at multiple scales. The tile bound- able to most (if any) individuals from any other source. aries are identical between all mapping services. The maps The features and labels that are included on these large- vary only as a result of differences in the underlying vector scale maps are an important indicator of both the com- database, and the generalization / symbolization used in pleteness of the underlying database and the conversion their rendering. process from data to map. MSP maps from the major online providers receive con- Online mapping services all rely on vector databases com- siderable use, and it is therefore important to evaluate the posed of point, line, and area features, along with feature quality of the maps provided by these services. In this attributes. When using these services, we view a version of paper, my evaluation method centers on examining the the vector data, rendered appropriately for the scale. The density of both features and labels at the 19th zoom level rendering process involves the scale-dependent generaliza- for three online map providers: Google, Microsoft, and tion and symbolization of the spatial data, and subsequent Mapbox. This zoom level was chosen because, when ex- tiling (Clouston and Peterson 2014). Rendered map tiles amining large-scale maps from these services, it can be can then be zoomed and panned from side-to-side pro- observed that no new features are added above the 19th ducing what we refer to as multi-scale pannable (MSP) zoom level. While features are made larger at larger scales, maps (Peterson 2015; 2019). additional features are not being added to the display. This © by the author(s). This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0. Cartographic Perspectives, Number 97, FORTHCOMING A Comparison of Feature Density for Large Scale Online Maps – Peterson | 23 seems to be true even in more densely populated urban By assessing the feature and label density for correspond- areas where a competition for map space would normally ing, randomly chosen locations, the findings reveal which result in a selection of displayable features. of these online map providers has made the greatest effort to offer detailed large-scale maps. ORIGINS OF THE MAP DATA As we are evaluating differences in the underly- United Kingdom. It was 2009 before their maps included ing vector databases between these online mapping ser- features for every country (Garfield 2012). Its spatial-da- vices, it is important to understand their origins. There are ta-capturing Street View vehicles have driven more than essentially three different types of sources that MSP map 10 million miles since 2007 (Mogg 2019), where it has providers can draw upon: (1) a governmental agency, such been allowed to do so. Maps from Google are now the as a city, state or federal entity; (2) a proprietary database, most used maps in the world. as created by Google, TomTom, HERE and others; or (3) a public-domain database based on crowdsourcing, as cre- In contrast to Google, HERE had its origins in the mid- ated by OpenStreetMap (OSM) and Wikimapia. The dis- 1980s as NavTeq, a pre-GPS car navigation company. tinctions between these are becoming increasingly fuzzy It was subsequently acquired by the Finnish company as local governmental agencies provide data at no cost to Nokia in 2007 to provide navigation for its phones, and commercial entities such as Google. The same agencies then by a consortium of German automobile compa- may also contribute to OSM, a service that itself relied nies, Audi, BMW and Daimler, in 2016 for autonomous initially on data made freely available by some govern- car navigation. TomTom has been building a proprietary ments, notably the United States. Some commercial map- map database for navigation since 2001. Its Automotive ping entities, like Mapbox, rely solely on data from OSM. and Licensing division, providing GPS-based naviga- tion for car manufacturers, represents a major part of its There are only a few sources of global geospatial data. business. Like HERE, the company is also based in the While governments generally restrict mapmaking to the Netherlands. area within their borders, some governments map foreign lands for military purposes. Not only are military maps After selling its mapmaking technology to Uber in 2015, kept secret but many governments also keep secret, or Microsoft no longer collects geospatial data. It has in- charge fees for, maps of their own territories, even in the stead built business relationships with both HERE and more developed parts of the world. On the opposite end TomTom (Stevenson 2016), even contracting with HERE are crowdsourcing platforms like OSM and Wikimapia for map rendering services. Microsoft has been using the that make their data available to anyone (Hall et al. 2010). Bing moniker for many of its services, including maps, Both platforms produce a large portion of their data using since 2009. satellite imagery, often from Google Earth—a proprietary source. GPS devices are also used to trace roads and path- Mapbox has become a major player in online mapping. ways. Of the two, OSM maintains a much larger crowd- It classifies itself as a developer platform only and does sourced database. This volunteered geographic information not provide a publicly available MSP map. The only way (VGI) is made available under the Open Database License to view Mapbox rendered tiles is to use the Mapbox (Feick and Roche 2013). Application Programming Interface (API). It is complete- ly reliant on OSM for its map data (Bliss 2015). Figure Only a handful of companies collect proprietary global 1 shows two pairwise comparisons between OSM and geospatial data, including Google, HERE, and TomTom. Mapbox generated maps. While the symbolization varies, Google is fairly new to the world of mapping, introducing the comparisons clearly demonstrate that they have iden- Google Maps in 2005 with acquired technology. Initially, tical features and labels and are based on the same vectors. Google only had spatial data for the United States and the Cartographic Perspectives, Number 97, FORTHCOMING A Comparison of Feature Density for Large Scale Online Maps – Peterson | 24 Figure 1. Two comparisons between OpenStreetMap (left) and Mapbox (right) demonstrating that Mapbox relies exclusively on data from OpenStreetMap. The maps are based on identical points. The symbolization and labeling vary only slightly. COMPARING VECTOR DATABASES While evaluations of the accuracy and complete- 2010; Wang et al. 2013). Jackson et al. (2013) propose a ness of vector databases are a major area of research, they method for quantifying the completeness and accuracy have unfortunately been limited to assessments of VGI. of volunteered geographic point datasets using a national Since it is not possible for researchers to obtain proprietary geospatial dataset as the reference. vector data, they have focused on comparing crowdsourced data with government sources, or have assessed the com- A number of studies have examined OSM data quality for pleteness and accuracy of OSM data using a variety of a particular country or region. Arsanjani and Vaz (2015) other indicators such as a ranking of its contributors. The assess the accuracy of its land use classifications in seven main purpose of this research has been to demonstrate the large European metropolitan regions. Siebritz and Sithole basic utility of crowdsourced spatial data (Flanagin and (2014) assess the quality of OSM data in South Africa Metzger 2007). This is analogous to previous research that with reference to national mapping standards. Zhao et al. examined the validity of Wikipedia pages as compared to (2015) implement a statistical analysis of OSM data for published encyclopedias (Okoli et al. 2012). The primary Beijing, China. Corcoran, Mooney, and Bertolotto (2013) comparisons that have been made are between OSM and examine the growth of OSM street networks in Ireland so-called “authoritative” spatial data, usually government and demonstrate that two elementary spatial processes of databases like Britain’s Ordinance Survey and Germany’s densification and exploration are responsible for increasing ATKIS (Haklay 2010; Fan et al. 2014; Zielstra and Zipf the density of information and expanding the network Cartographic Perspectives, Number 97, FORTHCOMING A Comparison of Feature Density for Large Scale Online Maps – Peterson | 25 into new areas.
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