User Contribution Patterns and Completeness Evaluation of Mapillary, a Crowdsourced Street Level Photo Service

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Abstract

Mapillary is a Web 2.0 application which allows users to contribute crowdsourced street level photographs from all over the world. In the first part of the analysis this article reviews Mapillary data growth for continents and countries as well as the contribution behavior of individual mappers, such as the number of days of active mapping. In the second part of the analysis the study assesses Mapillary data completeness relative to a reference road network dataset at the country level. In addition, a more detailed completeness analysis is conducted for selected urban and rural areas in the US and part of northern Europe for which the completeness of Mapillary data will also be compared with that of Google Street View. Results show that Street View provides generally a better coverage on almost all road categories with some exceptions for pedestrian and cycle paths in selected cities. However, Mapillary data can be conveniently collected from any mobile device that is equipped with a photo camera. This gives Mapillary the potential to reach better coverage along off‐road segments than Google Street View.

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Most cited references 32

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Despite their importance for urban planning, traffic forecasting and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.

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Julien O Teitler, Kathryn Neckerman, Michael Bader … (2010)

Research indicates that neighborhood environment characteristics such as physical disorder influence health and health behavior. In-person audit of neighborhood environments is costly and time-consuming. Google Street View may allow auditing of neighborhood environments more easily and at lower cost, but little is known about the feasibility of such data collection. To assess the feasibility of using Google Street View to audit neighborhood environments. This study compared neighborhood measurements coded in 2008 using Street View with neighborhood audit data collected in 2007. The sample included 37 block faces in high-walkability neighborhoods in New York City. Field audit and Street View data were collected for 143 items associated with seven neighborhood environment constructions: aesthetics, physical disorder, pedestrian safety, motorized traffic and parking, infrastructure for active travel, sidewalk amenities, and social and commercial activity. To measure concordance between field audit and Street View data, percentage agreement was used for categoric measures and Spearman rank-order correlations were used for continuous measures. The analyses, conducted in 2009, found high levels of concordance (≥80% agreement or ≥0.60 Spearman rank-order correlation) for 54.3% of the items. Measures of pedestrian safety, motorized traffic and parking, and infrastructure for active travel had relatively high levels of concordance, whereas measures of physical disorder had low levels. Features that are small or that typically exhibit temporal variability had lower levels of concordance. This exploratory study indicates that Google Street View can be used to audit neighborhood environments. Copyright © 2011 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

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Author and article information

Journal

Title: Transactions in GIS

Abbreviated Title: Transactions in GIS

Publisher: Wiley

ISSN (Print): 1361-1682

ISSN (Electronic): 1467-9671

Publication date Created: December 2016

Publication date (Electronic): January 25 2016

Publication date (Print): December 2016

Volume: 20

Issue: 6

Pages: 925-947

Affiliations

[1 ] Geomatics Program, University of Florida

Article

DOI: 10.1111/tgis.12190

SO-VID: 241dc79f-93e1-4a4c-b0f4-04af86f783f4

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http://onlinelibrary.wiley.com/termsAndConditions#vor

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