Upcoming Events
Master's Thesis Defense - Department of Geography & Geoinformation Science
May 7, 2024, 3:30 - 5:00 PM
Location: GMU Exploratory Hall, Room 2312
Zoom: https://gmu.zoom.us/j/96567002385?pwd=Rkp1b1ZRakZNRDJ5OWxEMktRRUFkUT09
Meeting ID: 965 6700 2385
Passcode: 268955
Candidate: John Haumann
Master of Science in Geoinformatics and Geospatial Intelligence
Department of Geography and Geoinformation Science
TITLE: Improving Mobile Positioning Within an Image-Based Hybrid Geocrowdsourcing System
Committee:
Thesis Director: Dr. Matt Rice
Committee Members: Dr. Dieter Pfoser, Dr. Ruixin Yang
ABSTRACT:
This research examines the extent of technological improvements in modern smartphones and GIS
applications can overcome GcD data quality problems, with a focus on positional accuracy. The theme of
this research is pairing these technological improvements with an increased number of GcD contributors
to see whether the average positional error of data points matches is at the threshold cited by Haklay,
where positional error was observed to be around 6 meters, or possibly better such as the 3.89-meter
average recorded by Toby Williams in 2018. The field of Geo-crowdsourced data (GcD), also known as
volunteered geographic information (VGI), has grown exponentially within the past fifteen years, from a
mostly theoretical idea proposed by academics to a giant industry providing an indispensable service to
commercial and government interests alike. Many applications rely on crowdsourced data to provide
up-to-date traffic notifications and operation statuses of businesses. GcD is utilized extensively by
humanitarian efforts, non-government organizations (NGOs), and the intelligence and defense
communities to provide agencies with human geography data, topographic data, and human
intelligence data that would have been much harder to collect using traditional methods. Despite the
monumental growth and advances of GcD over the past decade, problems stemming from positional
accuracy of data sources affect the overall accuracy of GIS. This thesis research demonstrates
correlations between the number of GcD contributors and the level of positional accuracy of
information contributed to the GMU-GcT by using a mobile-phone, image-based data contribution tool.
By adding additional volunteer contributors, the level of spatial accuracy comparable to previously
studied accuracy thresholds are reached with a significantly more detailed and heavily moderated data
contribution workflow. This research will evaluate whether a fully moderated crowdsourced data
contribution process, used in previous incarnations of the GMU-GcT, is unnecessary for producing data
with adequate fitness-for-use, including common routing and obstacle avoidance algorithms.
The final discussion in the thesis is about strategies and methods to further build upon this research by
researching ways to overcome factors that negatively impact the data collection process and analyzing
the datasets captured in this research to extract data points as spatially accurate as possible.