See user, administrator, and developer documentation in the wiki
Geoanalytics. The simplest path to Geoanalytics is through Docker. Install Docker and fig. Once you have Docker and fig installed:
# boot2docker init # if this is the first time you are running docker
$ boot2docker up
# export DOCKER_HOST=<url given in the output of boot2docker up>
$ git clone https://github.com/JeffHeard/geoanalytics.git
$ cd geoanalytics/ga_base
$ docker build -t ga_base .
$ cd ..
$ git submodule init
$ git submodule update
$ fig build
$ fig up -d
# the following will create a root user for logging into the admin site
$ fig run --rm geoanalytics python manage.py createsuperuserOnce all this has completed, you should be able to go to http://localhost:8000 or http://192.168.59.103:8000 on a Mac or Windows, and you will get to a basic website. Logging in with the superuser you can add new data and layers by going to /admin on the website.
Geoanalytics is an Open Source spatial data and analytics infrastructure based around unifying many of the open source python geography projects into a content management and collaboration framework. Geoanalytics is meant to be a framework that handles both classical and exotic formats in a unified way, setting up a system of drivers that connect these data sources to web services.
From there, data can be catalogued, described, queried, and Geoanalytics' CMS can serve as a host to a myriad of domain-specific applications.
Geoanalytics was developed at RENCI to deal with various projects centered around providing application infrastructure around scientific geographic data. It has been used in 11 projects at RENCI in:
- Environemtnal sciences (hydrology, weather modeling)
- Earth science
- Epidemiology
- Public health
- Decision support
- Emergency management and mitigation
- Digital humanities
- Digital media
We are actively seeking collaborators and contributors that can help grow the usage and sustainability of the Geoanalytics endeavor. The biggest thing we need right now is documentation. If you can read Python code and write readable documentation either as developer or user documentation, we need your skills!
Geography used to be the exclusive domain of GIS systems such as ArcGIS and MapInfo. Web-mapping, data APIs such as those provided by Facebook, Twitter, and the like, and huge datasets like those that come from NASA have become far more important to the average researcher and are difficult to work with in these traditional platforms.
Geoanalytics' lofty goal is to solve this problem and provide a mechanism for analytists, experts, and researchers to use these data in a way that's fundamentally easier and more fluid than current tools provide.
We also aim to provide a way to publish map data in a way that treats maps, cartography, and visualization as "not special." We aim for publishing tools that are more like Wordpress than they are like Apache Server.
Geoanalytics is based around treating data sources as curated content, and on supporting the development of applications and data APIs that enable people to work with data through a unified, well-understood interface. Toward this, we embrace OGC standards for interoperability and will work actively to support and more fully implement these standards.
We also follow and embrace open source projects in geography and cartography that have gained community traction and where these are more widely used or more capable than current OGC standards, we adopt these instead. For example, rendering maps is provided by Mapnik and stylesheets are compiled through Carto.
Geoanalytics is deployed as a Django project (not an app - see Mezzanine's explanation as to why), incorporating a number of reusable apps that work in concert:
- ga_resources : Data publishing, semantics, and metadata. WMS and soon to be WFS web services that provide OGC standard ways of accessing and visualizing datasets on a map or providing them to be consumed GIS or analytics systems like Arc, SAS, or qGIS.
- ga_ows : WMS and WFS webservice code that can be adapted to projects.
Geoanalytics is licensed under an MIT-style license called the RENCI Open Source license and is © 2014 UNC Chapel Hill. See LICENSE.txt for details.