Silent Disco: Geocoding Historical Data Using QGIS
Online
Our 'Silent Disco' workshops are based on tutorials from the Programming Historian website. This training event will follow content from the tutorial, Geocoding Historical Data using QGIS.
In this tutorial, you will learn how to ‘geocode’ historical data containing placenames (towns, counties, countries, etc), thus making them mappable using QGIS, a digital mapping software suite. This will allow you to:
- Display your data as a map (whether it originated as a list, table, or prose)
- Analyse distances between locations in your data
- View and analyse geographical distribution within your data
The workshop will take place via Microsoft Teams in a ‘Silent Disco’ format. Participants will work on the tutorial at their own pace. The facilitator will be available via Teams Chat to reply to any questions that arise during the workshop, and to help with installation, troubleshooting or other issues.
This is an intermediate-level workshop. You will need a basic understanding of geographical information systems. If you want an introduction to geographical information systems you can attend our course, Introduction to Geospatial Data and QGIS, that will take place on the 22nd and 29th of October.
To attend this course, you will have to join the associated Microsoft Teams group. The link to join the group will be sent to attendees prior to the course start date, so please make sure to do so in advance.
This Silent Disco will be facilitated by Ki Tong.
After taking part in this event, you may decide that you need some further help in applying what you have learnt to your research. If so, you can book a Data Surgery meeting with one of our training fellows.
More details about Data Surgeries.
If you’re new to this training event format, or to CDCS training events in general, read more on what to expect from CDCS training. Here you will also find details of our cancellation and no-show policy, which applies to this event.
If you're interested in other training on geographical data analysis, have a look at the following:
