# Applications and tools

## Example open data-based applications in South Africa

[Wazimap](https://wazimap.co.za/) visualises Stats SA Census 2011 and Community Survey 2016 data

[South African Cities Open Data Almanac (SCODA)](https://scoda.co.za/scoda/#/) is launching a new version soon

[BioEnergy](https://bea.saeon.ac.za/) Atlas decision-support tool uses infrastructure and natural resource data

[Durban EDGE Open Data Portal](https://edge.durban/) runs regular data stories on economic topics

[Medicine Pricing Registry](https://medicineprices.org.za/) draws on medicine pricing data from the NDoH

[Planning for Informality](https://app.planning4informality.org.za/home/dataset/isustrat) uses planning and performance data from cities

[Municipal Money](https://municipalmoney.gov.za/) and [Vulekamali](https://vulekamali.gov.za/) visualises fiscal data from Treasury

Transport apps like [GoMetro](https://www.getgometro.com/) have drawn on open data from cities

[Open Gazettes](https://opengazettes.org.za/) and [Laws.Africa](https://laws.africa/) make it easier to view legal and legislative data

[SAAQIS](http://saaqis.environment.gov.za/) air quality index and dashboard draw on air quality data

[Regional eXplorer](https://www.ihsmarkit.co.za/Products/ReX/) and [EasyData](https://www.quantec.co.za/) economic intelligence tools draw on Stats SA data

## Visualisation and storytelling

For visualisation, there are many to try out like [Flourish](https://flourish.studio/) and [Datawrapper](https://www.datawrapper.de/). If you're more technical and using Python or R, have a look at this [summary of libraries](https://towardsdatascience.com/top-9-libraries-for-data-visualization-in-python-and-r-51bdf08e5d54).

Have a look at these storytelling [tools from Knightlab](https://knightlab.northwestern.edu/projects/) including [Timeline](https://timeline.knightlab.com/), [StoryMap](https://storymap.knightlab.com/), [Soundcite](https://soundcite.knightlab.com/) and [Juxtapose](https://juxtapose.knightlab.com/).

For creating infographics, there are tools like [Infogram](https://infogram.com/). 

For mapping relationships or networks as a story try [GraphCommons](https://graphcommons.com/), see [this example](https://graphcommons.com/stories/8de49ba6-68b4-4d8b-af6b-6336e7520742/slides/2) of three musicians in a recording ecosystem. [Kumu](https://kumu.io/) is also popular for network visualisation.

## Extracting and cleaning data

If you want to get data tables out of PDFs you can try [Tabula](https://tabula.technology/), [pd3f](https://pd3f.com/), or [Excalibur](https://excalibur-py.readthedocs.io/en/master/)/ Camelot.

[OpenRefine](https://openrefine.org/) is good for cleaning data.

If you want to extract and analyse text in articles or books (e.g. to identify people and places) there are lots of tools to try like [TextRazor](https://www.textrazor.com/demo), [Intellexer](http://demo.intellexer.com/) and [Google's Natural Language](https://cloud.google.com/natural-language).

## Mapping spatial data

For mapping, something like [Kepler](https://kepler.gl/) is easier to use. For more detail on other tools and working with spatial data see [this page](https://opendataza.gitbook.io/toolkit/open-data-resources/working-with-spatial-data).