Add text under Architecture

docs/technical-overview.rst

@@ -95,17 +95,20 @@ Below is an example of how the model is used to record information on one deed (
 
 Workflows
 ^^^^^^^^^
-The Django app imports data from the the CSV files (derived from project Google Sheet), adds geographic locations to places, and provides an admin interface to manage the data (with simple filtering). After the curation and cleaning up is done in the Django admin, the app exports data into GeoJSON format to support map visualisations.
+The Django app imports data from the the CSV files (derived from project Google Sheet), adds geographic locations to places, and provides an admin interface to manage the data (with simple filtering). After the curation and cleaning up is done in the Django admin, the app exports data into GeoJSON_ format to support map visualisations.
 
 .. figure:: _images/data-workflow.png
     :align: center
     :alt: Data workflow
     :figclass: align-center
     :width: 100%
+
+.. _GeoJSON: https://tools.ietf.org/html/rfc7946
 
 Architecture
 ^^^^^^^^^^^^
-TODO: Extract high level description from PQ
+KDL built the resource using Django_, an Open Source web publishing framework with which KDL has extensive experience of and has found to be stable, powerful and scalable. The Django based web platform provided functionality to upload the project dataset. With respect to the Proof of Concept, data was exported from the Django database in GeoJSON format so as to generate maps visualisations in the tools mentioned below. The Django based web platform and related tools provides the functionality to record metadata about the archival sources under examination via an administrative interface to the resource. 
+To support advanced queries on the project Django database (e.g. in checking, processing, analysing and visualising the data), KDL set up https://metabase.com/ (a free, stand-alone java web app which can connect to various types of databases, build queries with a user interface, browse, filter and export the results, make simple charts and visualisation and share them as cards into collections) which can be used without advanced coding knowledge (apart from SQL, if needed) and includes a rest API to grab json/csv results from the cards. However, this additional application has not been used as yet to analyse the data systematically.
 
 .. figure:: _images/docker-local.png
     :align: center
@@ -120,6 +123,7 @@ The graphs were generated by the docker-compose-viz_ tool::
     $ docker run --rm -it --name dcv -v $(pwd):/input pmsipilot/docker-compose-viz render -m image local.yml
 
 .. _docker-compose-viz: https://github.com/ahmadawais/Emoji-Log
+.. _Django: https://www.djangoproject.com/
 
 Design process
 --------------