GEOG 105 Lecture Notes - Lecture 13: Virtual Reality, Leapfrogging, Crowdsourcing
5 May 2018
School
Department
Course
Professor
Week 13
4/16 – Big Data and CyberGIS
• What makes data geospatial?
o Has location attached to it, spatial aspect
• What makes data big?
o Having lots of data → eough data that a desktop a’t hadle
o Requires extra step of processing
Volume
• Higher resolution = more storage space
• Geometric rate of increase
• Storage, retrieval, analysis issues
Variety
• Many different ways to store data
• Vector vs. raster
• Proprietary formats
o Different kinds of formats that need to be brought together
• Metadata (data about data)
Velocity
• Streaming data sources
• Constantly new data
• May be hard to compare across time or space
Veracity
• Accuracy, uncertainty, and completeness issues
o Esp. with crowdsourced or volunteered data
• Metadata
Big Data Issues
• Privacy, security
• People, not technology, design and implement algorithms
• Data is produced, not just collected
o Always a person behind whatever dataset you're using
• Data eer speak for theseles
4/18 – Knowing and Mapping the City
What is Urban?
• Non-rural, non-agricultural
find more resources at oneclass.com
find more resources at oneclass.com
Document Summary
4/16 big data and cybergis: what makes data geospatial, has location attached to it, spatial aspect, what makes data big, having lots of data e(cid:374)ough data that a desktop (cid:272)a(cid:374)"t ha(cid:374)dle, requires extra step of processing. Volume: higher resolution = more storage space, geometric rate of increase, storage, retrieval, analysis issues. Variety: many different ways to store data, vector vs. raster, proprietary formats, different kinds of formats that need to be brought together, metadata (data about data) Velocity: streaming data sources, constantly new data, may be hard to compare across time or space. Veracity: accuracy, uncertainty, and completeness issues, esp. with crowdsourced or volunteered data, metadata. Big data issues: privacy, security, people, not technology, design and implement algorithms, data is produced, not just collected, always a person behind whatever dataset you"re using (cid:862)data (cid:374)e(cid:448)er speak for the(cid:373)sel(cid:448)es(cid:863) What is urban: non-rural, non-agricultural, n # of people in one place, o(cid:448)er 50% of the (cid:449)orld"s populatio(cid:374, high density (specialization, social heterogeneity.
