One of the impediments to citizen mapping is the line-of-sight cell tower limitations of mobile phones, or the wifi requirements for other mobile devices. Citizen mapping in urban and suburban environments is well-served by mobile devices, but what about natural areas, dense leaf cover, or extreme topography? Even if obtaining absolute mapping coordinates isn’t the issue, much crowdsourcing assumes an ability to connect back to a central data repository (e.g., a web database, ‘the cloud’). Equipment that can interact with GPS satellites and support data capture is typically expensive and generally requires proprietary software.
wq (https://wq.io/) is a framework that is ‘device first’ and ‘offline-enabled’. It attempts to leverage several open source technologies to build an entire mobile solution that can support citizen science data collection work, and then synchronize with a central repository once the device (and operator) return to an area served by cellular or wifi networks.
I’m stretching here, so if I get stuff wrong, please don’t yell. Still, I’ll take a pass at generally describing the framework and its related technology stack.
wq relies upon python, and a web framework called django for building offline-capable web apps that can run on iOS and Android devices. These web apps, then, rely very heavily upon javascript, particularly requirejs (http://requirejs.org/) and mustache (https://mustache.github.io/), for the templates that permit quick and (somewhat) painless web application development. Data visualization relies upon d3.js (http://d3js.org/), and geography makes heavy use of Leaflet (http://leafletjs.com/) — maybe the most pertinent layer of the stack for those of us in this course. If you’re not familiar withLeaflet.js, check it out!
Finally, wq extends several other open source technologies to enable synchronizing between a central data repository and multiple mobile devices in the hands of citizen mappers. Lastly, wq employs a set of tools to more easily build and distribute customized mapping apps that can be served from Apple’s app store, Google Play, etc.
What wq intends is to allow highly specialized citizen science/citizen mapping apps to be more easily and quickly built, based upon a solid collection of aligned F/OSS tools. Ideally, an app can spin up quickly to respond to a particular need (e.g., a pipeline spill), or a specialized audience (the run up to a public comment period for a development project), or even something like a high school field trip or higher ed service learning project.
Some examples of citizen mapping projects already built upon wq are here:
https://wq.io/examples/
—————–
Creating a walking tour map with Google Earth_2014
————-
Week 3
Podcast includes:
- Geocoding
- Georeferencing
- Spatial Data Formats
- Geospatial Data Online
- Discussion Question
Podcast and Powerpoint available from: http://www.lib.uwaterloo.ca/locations/umd/JuicyLibrarianMaterial.html
Tutorials: BatchGeo (optional); Google Fusion (optional)
https://en.batchgeo.com/
enter Xcel data, and export KLM file ready for google map and/or google earth
https://support.google.com/fusiontables/answer/2571232
http://en.wikipedia.org/wiki/Google_Fusion_Tables
store maps online, no latitude needed.
visualize geospatial data by map
spatial analysis by mapping different layers together
showing data by map, graph or chart
e.g. how many cars cross specific point
crowdsourcing: spotting butterflies, using fusion tables to map the spices and sightings
http://www.theguardian.com/news/datablog/2011/mar/31/deprivation-map-indices-multiple
students: journalism, history, geography.
Georeferencing (geocoding – data, geo referencing – image)
historical air maps or photos are much more useful when they are georeferenced.
Photos from different year is difficult to lay over one another without referencing. the only reference might be the river. usually reference the four corners, but sometimes river. Using GIS program to determine the longitute/latitude for each corner. sometimes only farmland and it is impossible
