Interactive Data Dives
Dive confidently into urban data science with the help of UBDC researchers and our data collections.
This series of free online interactive tutorials took place throughout October 2020 and were delivered by members of our research staff who shared their knowledge and advice on working with novel forms of data.
Videos of these webinars and other resources from the sessions will be available on this page soon.
Summaries of the content covered in each seminar are shown below.
GIS-based facility location analysis for the public and private sectors
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 introduced some classic facility location models, with examples of location-allocation analyses in ArcGIS using open data.
In this session, we used typical facility location models such as Location Set Covering Problem and Maximal Covering Location Problem.
Many homeowners and tenants are at risk of fuel poverty in the UK. Those on low incomes are also experiencing “double deprivation” due to the low energy efficiency of their houses or rental properties. These poor thermal conditions may cause chronic health problems and threaten lives. Properties in the private rented sector are known to have particularly significant problems with energy efficiency while the proportion of low-income households in private renting is rising rapidly.
In this webinar, led by UBDC's Dr Qunshan Zhao, we explored whether low-income neighbourhoods experience worse energy efficiency in the private rental market. We examined if a lack of energy efficiency also increases the fuel poverty level and how we can help those on low incomes. Results from research like this can serve as guidance for the Scottish Government to identify low energy efficiency areas in the private rental market.
In this session, we used Glasgow as an example and attempt to accurately identify the fuel poverty area within the city. To achieve this goal, we used Zoopla data hosted by UBDC, Scottish Index of Multiple Deprivation (SIMD data), Scottish Census data, and the Scottish EPC data.
The United Nations Educational, Scientific and Cultural Organisation (UNESCO)’s Learning Cities agenda promotes education across all sectors and environments, but the success of its initiatives have not been rigorously evaluated.
In this webinar, led by UBDC's Professor Catherine Lido with Rachel Cassar (postgraduate researcher for the VisNET project), we presented the work and data behind the Oxford Review paper ‘Lifewide learning in the city: novel big data approaches to exploring learning with large-scale surveys, GPS, and social media’. In this session, we examined the background to UNESCO Learning Cities and how it informed the integrated Multimedia City Data (iMCD) project, with a discussion around the educational aspects of the survey, education-relevant twitter data harvesting and the role of GPS and lifelogging images for less formal types of learning practice.
The benefits of cycling have been well examined for several decades. It could reduce car-dependency, air pollution and improve public health. Based on these factors, many local authorities in the UK have implemented several means to encourage people to cycle. However, there are only a few empirical studies that examine cycling activities at the fine temporal and geographical scales due to data limitation. Since only a small number of people cycle, most travel surveys do not include enough cyclists for sophisticated quantitative analyses.
In this webinar, led by UBDC's Dr Jinhyun Hong, we introduced crowdsourced cycling data and discussed its potential benefits and shortcomings. In addition, we introduced two examples to show how UBDC researchers have used Strava data for cycling research. This will help transport researchers and planners to understand new forms of cycling data and how to use it for their own purposes. Since data formats are the same for other cities, methods can be transferable to other cities.