Big data sets are not only 'big' in size and/or number of records, but also in the variety of stories and insights they hold. Piling millions of points on a map can be beautiful and eye-catching, but what story does it tell? Each coordinate in the data provides the opportunity to extract meaningful patterns and observations at multiple scales over a variety of topics. The result? Maps driven by big data that are both beautiful and informative.
All Things Data
“Roads are the seeds of tropical forest destruction,” said the prominent ecologist Thomas Lovejoy. But until now, roads have been difficult to map and visualize, often discovered only after they have been abandoned and the damage to the forest around them has been done.
In 2005, Trimet (Portland, OR Transit System) and Google set off to create a computer readable format of Portland's multimodal transit system. Since then the GTFS documentation has been released to the public and hundreds of agencies all over the world have adopted this open data standard. While the intent of this data feed was originally to just be used in Google Maps other search engines, developers, planners, and GIS professionals have begun to use this data that many host on the open web.
Synthetic populations are a means of representing each individual household and person in an entire country. They are created from census data and several other government sources of information in such a way that a computer representation of every household and person can be created with characteristics such as household size, householder age, householder race, household income, age, gender, and location.
R is an open source statistical programming language. The R Shiny package provides a web application framework for R using the Twitter Bootstrap framework. Integrating leaflet into Shiny applications can lead to rapid deployment of interactive mapping tools. I will showcase three tools as we explore this topic.
Ordnance Survey has been managing geospatial information for 225 years. As part of LocationTech our TechLabs Team has been experimenting with GeoGig, an Eclipse project applying tried and tested Distributed Revision Control System techniques into geospatial data management.
This workshop will introduce attendees to GeoGig, a distributed version control system for geospatial data. We will start with a discussion of distributed version control as applied to the specific case of geospatial data, followed by a hands-on session with GeoGig. Then we will explore workflows suitable for data product generation and field collection using the graphical GeoGig interface in QGIS.
Let's condense 100,000+ satellite imagery scene vectors, each with 50+ unique metadata attributes, into a simple online vector tileset which displays imagery vintage.
There is currently no comprehensive search capability for online spatial information, a critical body of data which now represents an uncurated, “dark web” of data services and data files. One could argue this domain is not even a web since most of it does not link out and most is not linked-to from elsewhere.