OpenStreetMap (OSM) has evolved as a popular dataset for global urban analyses, such as assessing progress towards the Sustainable Development Goals. However, many analyses do not account for the uneven spatial coverage of existing data. We employ a machine-learning model to infer the completeness of OSM building stock data for 13,189 urban agglomerations worldwide. For 1,848 urban centres (16% of the urban population), OSM building footprint data exceeds 80% completeness, but completeness remains lower than 20% for 9,163 cities (48% of the urban population). Although OSM data inequalities have recently receded, partially as a result of humanitarian mapping efforts, a complex unequal pattern of spatial biases remains, which vary across various human development index groups, population sizes and geographic regions. Based on these results, we provide recommendations for data producers and urban analysts to manage the uneven coverage of OSM data, as well as a framework to support the assessment of completeness biases.

OpenStreetMap (OSM), the largest Volunteered Geographic Information project in the world, is characterized both by its map as well as the active community of the millions of mappers who produce it. The discourse about participation in the OSM community largely focuses on the motivations for why members contribute map data and the resulting data quality. Recently, large corporations including Apple, Microsoft, and Facebook have been hiring editors to contribute to the OSM database. In this article, we explore the influence these corporate editors are having on the map by first considering the history of corporate involvement in the community and then analyzing historical quarterly-snapshot OSM-QA-Tiles to show where and what these corporate editors are mapping. Cumulatively, millions of corporate edits have a global footprint, but corporations vary in geographic reach, edit types, and quantity. While corporations currently have a major impact on road networks, non-corporate mappers edit more buildings and points-of-interest: representing the majority of all edits, on average. Since corporate editing represents the latest stage in the evolution of corporate involvement, we raise questions about how the OSM community—and researchers—might proceed as corporate editing grows and evolves as a mechanism for expanding the map for multiple uses.

Today, disaster events are mobilizing digital volunteers to meet the data needs of those on the ground. One form of this crowd work is Volunteered Geographic Information. This peer-produced spatial data creates the most up-to-date map of the affected region; maintaining the accuracy of these data is therefore a critical task. Accuracy is one aspect of data quality, a relative concept requiring standards to measure against. The field of Geographic Information Sciences has developed standards for this comparison, achieving widespread acceptance. However, the peer production model of spatial data presents new opportunities—and challenges—to traditional methods of quality assessment. Through analysis of the OpenStreetMap database, we show that temporal editing patterns and contributor characteristics can provide additional means of understanding spatial data quality. Drawing upon experiences from Wikipedia, we offer and evaluate three intrinsic quality metrics of peer-produced spatial data to assess the quality of contributions to OpenStreetMap for crisis response.

Dominated by quantitative data science techniques, social media data analysis often fails to incorporate the surrounding context, conversation, and metadata that allows for more complete, accurate, and informed analysis. Here we describe the development of a scalable data collection infrastructure to interrogate massive amounts of tweets-including complete user conversations-to perform contextualized social media analysis. Additionally, we discuss the nuances of location metadata and incorporate it when available to situate the user conversations within geographic context through an interactive map. The map also spatially clusters tweets to identify important locations and movement between them, illuminating specific behavior, like evacuating before a hurricane. We share performance details, the promising results of concurrent research utilizing this infrastructure, and discuss the challenges and ethics of using context-rich datasets.

An important area of work in big data software engineering involves the design and development of software frameworks for data-intensive systems that perform large-scale data collection and analysis. We report on our work to design and develop a software framework for analyzing the collaborative editing behavior of OpenStreetMap users when working on the task of crisis mapping. Crisis mapping occurs after a disaster or humanitarian crisis and involves the coordination of a distributed set of users who collaboratively work to improve the quality of the map for the impacted area in support of emergency response efforts. Our paper presents the challenges related to the analysis of OpenStreetMap and how our software framework tackles those challenges to enable the efficient processing of gigabytes of OpenStreetMap data. Our framework has already been deployed to analyze crisis mapping efforts in 2015 and has an active development community.