Geographical Information Science Research UK (GISRUK) is a community of academics and practitioners whose mission is the advancement of spatial sciences and the nurturing of the next-generation spatial experts. The 33rd annual conference took place from 23rd to 27th April 2025 at the University of Bristol, England.

Nick presented the following paper:

Malleson, N., Quang, T. B., Nguyen Thi Thuy, H., Hoang Huu, P., Kieu, M., Asher, M., Wang, Y., & Comber, A. (2025, April 16). Using Large Language Models to Predict Neighbourhood Change. 33rd Annual GIS Research UK Conference (GISRUK), University of Bristol, UK. https://doi.org/10.5281/zenodo.15231204

Abstract: This paper uses a large-language model (LLM) to read user-contributed text data – in this case property listings on Airbnb – and assign a score to indicate the degree to which the property’s neighbourhood is potentially undergoing gentrification. Preliminary results for a case study in Bristol are evaluated. These scores will ultimately be used as an input in a model of dynamic spatio-temporal neighbourhood change with the aims of (i) quantifying the impact that neighbourhood reputation has on historical house prices and (ii) predicting the emergence of new house price bubbles.

The full conference paper is available here. The full conference paper is available here

Lex presented the following papers:

##1

Comber, A., Asher, M., Wang, Y., Le Kieu, M., Quang, T., Nguyen Thi, H., Hoang Huu, P., & Malleson, N. (2025, April 15). Using house sales transactions data to identify potentially gentrifying neighbourhoods. 33rd Annual GIS Research UK Conference (GISRUK), University of Bristol, UK. https://doi.org/10.5281/zenodo.15225193

Abstract: This paper describes initial work seeking to identify locales of neighbourhood gentrification. It summarises house sales transactions over MSOAs, compares average annual house price with neighbouring areas over a 10 year period, and uses a time series analysis to identify neighbourhoods with high rates of change of these as potential gentrifying neighbourhoods. The next steps are to link to analyses of social media data to characterise the nature of the observed neighbourhood change. A number of areas of further work are described and a number of critical considerations are discussed

The full conference paper is available here.

##2

Comber, A., Harris, R., & Brunsdon, C. (2025). Using GAMs to understand whether and how processes vary over space and time. 33rd Annual GIS Research UK Conference (GISRUK), University of Bristol, UK. https://doi.org/10.5281/zenodo.15225162

Abstract: This paper an informed approach for constructing varying coefficient regression models using Generalized Additive Models (GAMs) with Gaussian Process (GP) smooths. Using a house price data over 13 years for a local case study, it investigates different model forms in order to determine the most probable model given the data. It determines the presence of any space-time dependencies between the target and each predictor variable is present, and if so their nature (ie whether they are independent or interact). It does this to avoid assumptions about the nature of spatial and / or temporal dependencies, in contrast to many existing approaches for space-time regressions which implicitly include baked in assumptions assumptions about the presence of these. The analysis uses tools in the stgam R package to undertake the analysis and to extract the varying coefficients from the model and a number of areas of further work are identified.

The full conference paper is available here.