GIS-based facility location analysis for the public and private sectors
- When:
- Friday 23 October 2020
- Time:
- 11:00 - 12:00 (BST)
- Where:
- Online (via Zoom) GET DIRECTIONS
The recording of this webinar and related resources are available on our Interactive Data Dives page.
Siting key facilities, such as hospitals and fire stations, in the right locations is vital for providing the services we need. Cities are continually reviewing the location of these facilities as populations grow or services are reorganised. New technologies, such as electric or hydrogen power for vehicles, can create demand for new networks of service centres within existing urban areas.
In this webinar, led by UBDC's Dr Jing Yao, we will introduce some classic facility location models, with examples of location-allocation analyses in ArcGIS using open data.
In this session, we will use typical facility location models such as Location Set Covering Problem and Maximal Covering Location Problem.
Session format
The session will begin with a lecture-style overview, which will be followed by an explanation of the tutorial activity that participants will then be able to work through in their own time.
What you will learn
- Understand the basic elements involved in location analysis
- How to implement location analysis in ArcGIS
- How the UBDC data service can help with your research
Data and software requirements
Before the session, you should download the following data and documentation that will be used for the tutorial:
Who should attend
Postgraduate students and practitioners who are interested in facility location analysis, with basic knowledge of GIS and experience of using ArcGIS software, comfortable with mathematical modelling should attend.
About the Data Dives series
This series of free online interactive tutorials taking place throughout October 2020, will enable you to dive confidently into urban data science with the help of UBDC researchers and our data collections.
These online courses are delivered by members of our research staff, who want to share their knowledge and advice on working with novel forms of data with you.
