Understanding small areas: spatial analysis of population and neighbourhood data (fully booked)
07/02/2019 - 08/02/2019
The University of Manchester
Dr Alan Smith (University of Plymouth) & Dr Andy Newing (University of Leeds)
Intermediate (some prior knowledge)
Claire Spencer, 0161 275 4579, email@example.com
View in Google Maps (M139PL)
Cathie Marsh Institute, HBS Building, Oxford Road Campus
This two day workshop equips participants with conceptual understanding and technical skills to obtain, analyse and visualise spatial data related to populations and neighbourhoods. Using freely available data related to the UK, a series of guided workshops enable participants to:
- obtain census and survey data related to geographic areas
- visualise, map and explore spatial patterns within individual and neighbourhood data
- apply a suite of powerful spatial analysis tools and techniques to address real-world policy relevant questions at the small area level
- uncover spatial characteristics and properties of small area data
- understand how to convert data between different geographic boundaries used for data dissemination and the limitations of doing so
- classify small areas and individuals based on their characteristics and understand their widespread application in neighbourhood analysis
- explore novel near-real time user-generated data from social media
- appreciate the technical, ethical and legal challenges and opportunities for working with individual level population data.
Hands on training introduces participants to powerful spatial analysis software (ArcGIS) as well as the programming language R. Practical sessions are interspersed with lecture and discussions to contextualise and consolidate learning. Examples are drawn from the UK and are applicable in an international context and in a variety of application areas at different spatial scales.
Participants also have the chance to discuss their own related research or applications with other participants and the course tutors.
Population and neighbourhood data are widely used by the academic, policy-making and commercial sectors.
They drive resource allocation, decision making and policy evaluation. On completion of this course participants will be able to:
- identify the population and neighbourhood data required to develop or enhance their application interests.
- apply and critique relevant spatial analysis techniques to geolocate, explore and visualise these data.
- communicate insights gained to support policy-evaluation, commercial decision making or address research questions.
- No prior knowledge of statistical or spatial analysis is required. ArcGIS and R will be taught from scratch with a focus on learning the essential skills required to handle, visualise and analyse data related to populations and small areas. This course is not designed to provide comprehensive training in programming using R. However, it will equip participants with tools grounded in R to execute advanced analytical tasks relating to the acquisition, processing and representation of social media data using a live, real-time data feed from Twitter as an example. Participants will leave with detailed training materials allowing them to confidently apply these tools independently.
- Participants must be willing to sign up for a Twitter account but are not required to populate it with any personal information. They will need to use their account to pre-register to use the Twitter API. Full instructions are provided during the course.
- Participants may find it helpful to familiarise themselves with the excellent NCRM resources introducing geographical referencing found online at: http://www.restore.ac.uk/geo-refer/resources.php
Additional reading material and web-based resources will be recommended during the course
For UK registered postgraduate students £30 per day
For staff at UK academic institutions, ESRC funded researchers and registered charity organisations £60 per day
For all other participants £220 per day
Quantitative Data Handling and Data Analysis
Related publications and presentations: