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The production of Volunteered Geogra...

Parr, David A.

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The production of Volunteered Geographic Information: A study of OpenStreetMap in the United States.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The production of Volunteered Geographic Information: A study of OpenStreetMap in the United States./
作者:	Parr, David A.
面頁冊數:	178 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-12(E), Section: A.
Contained By:	Dissertation Abstracts International76-12A(E).
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3725344
ISBN:	9781339095790

The production of Volunteered Geographic Information: A study of OpenStreetMap in the United States.
Parr, David A.

The production of Volunteered Geographic Information: A study of OpenStreetMap in the United States. - 178 p.

Source: Dissertation Abstracts International, Volume: 76-12(E), Section: A.

Thesis (Ph.D.)--Texas State University - San Marcos, 2015.

The arrival of the World Wide Web, smartphones, tablets and GPS-units has increased the use, availability, and amount of digital geospatial information present on the Internet. Users can view maps, follow routes, find addresses, or share their locations in applications including Google Maps, Facebook, Foursquare, Waze and Twitter. These applications use digital geospatial information and rely on data sources of street networks and address listings. Previously, these data sources were mostly governmental or corporate and much of the data was proprietary. Frustrated with the availability of free digital geospatial data, Steve Coast created the OpenStreetMap project in 2004 to collect a free, open, and global digital geospatial dataset. Now with over one million contributors from around the world, and a growing user base, the OpenStreetMap project has grown into a viable alternative source for digital geospatial information. The growth of the dataset relies on the contributions of volunteers who have been labeled 'neogeographers' because of their perceived lack-of-training in geography and cartography (Goodchild 2009b; Warf and Sui 2010; Connors, Lei, and Kelly 2012). This has raised many questions into the nature, quality, and use of OpenStreetMap data and contributors (Neis and Zielstra 2014; Neis and Zipf 2012; Estima and Painho 2013; Fan et al. 2014; Haklay and Weber 2008; Corcoran and Mooney 2013; Helbich et al. 2010; Mooney and Corcoran 2012b; Haklay 2010b; Budhathoki and Haythornthwaite 2013; Mooney, Corcoran, and Winstanley 2010; Mooney and Corcoran 2011; Haklay et al. 2010; Mooney, Corcoran, and Ciepluch 2013; Stephens 2013).

ISBN: 9781339095790Subjects--Topical Terms:

524010
Geography.

The production of Volunteered Geographic Information: A study of OpenStreetMap in the United States.
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500 $a Adviser: Yongmei Lu.
502 $a Thesis (Ph.D.)--Texas State University - San Marcos, 2015.
520 $a The arrival of the World Wide Web, smartphones, tablets and GPS-units has increased the use, availability, and amount of digital geospatial information present on the Internet. Users can view maps, follow routes, find addresses, or share their locations in applications including Google Maps, Facebook, Foursquare, Waze and Twitter. These applications use digital geospatial information and rely on data sources of street networks and address listings. Previously, these data sources were mostly governmental or corporate and much of the data was proprietary. Frustrated with the availability of free digital geospatial data, Steve Coast created the OpenStreetMap project in 2004 to collect a free, open, and global digital geospatial dataset. Now with over one million contributors from around the world, and a growing user base, the OpenStreetMap project has grown into a viable alternative source for digital geospatial information. The growth of the dataset relies on the contributions of volunteers who have been labeled 'neogeographers' because of their perceived lack-of-training in geography and cartography (Goodchild 2009b; Warf and Sui 2010; Connors, Lei, and Kelly 2012). This has raised many questions into the nature, quality, and use of OpenStreetMap data and contributors (Neis and Zielstra 2014; Neis and Zipf 2012; Estima and Painho 2013; Fan et al. 2014; Haklay and Weber 2008; Corcoran and Mooney 2013; Helbich et al. 2010; Mooney and Corcoran 2012b; Haklay 2010b; Budhathoki and Haythornthwaite 2013; Mooney, Corcoran, and Winstanley 2010; Mooney and Corcoran 2011; Haklay et al. 2010; Mooney, Corcoran, and Ciepluch 2013; Stephens 2013).
520 $a This study aims to complement and contribute to this body of research on Volunteered Geographic Information in general and OpenStreetMap in particular by analyzing three aspects of OpenStreetMap geographic data. The first aspect considers the contributors to OSM by building a typology of contributors and analyzing the contribution quality through the lens of this typology. This part of the study develops the Activity-Context-Geography model of VGI contribution which uses three aspect dimensions of VGI contributions: the Activity (the amount and frequency of content creation, modification and deletion); Context (the technological and social circumstances that support a contribution); and Geography (the spatial dimensions of a contributor's pattern). Using the complete OpenStreetMap dataset from 2005 to 2013 for the forty-eight contiguous United States and the District of Columbia, the study creates twenty clusters of contributors and examines the differences in positional accuracy of the contributors against two datasets of public school locations in Texas and California. The second part of the study considers the questions of where mapping occurs by evaluating the spatial variability of OSM contributions and comparing mapping activity against population and socioeconomic variables in the US. The third part of the study considers the choices that OSM contributors make through the types of features that are most commonly mapped in different locations. Understanding the types of contributors, their differences in quality, the spatial variability in mapping activity, and their choices in types of features to provide data will provide insight into the credibility of users, the trustworthiness of their contribution, and where there are gaps in mapping activity and feature representation.
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