Measuring and mapping displacement: the problem of quantification in the battle against gentrification

Debates concerning residential population displacement in the context of gentrification remain vociferous, but are hampered by a lack of empirical evidence of the extent of the displacement occurring. The lack of quantitative evidence on gentrification-induced displacement and the difficulties in collecting it has long hampered the fight against it. Based on a systematic review of quantitative studies of the displacement associated with gentrification, this paper considers how researchers have attempted to measure displacement using a range of statistical and mapping techniques reflecting the multidimensional character of gentrification. We note that these techniques often struggle to provide meaningful estimates of the number of individuals and households displaced by gentrification, something compounded by the lack of data available on a sufficiently granular temporal and spatial scale. Noting the limitations of extant methods, we conclude by considering the potential of more novel data sources and emergent methods involving the processing of larger amounts of (micro)data, as well as participatory GIS methods that involve affected communities themselves. This implies that whilst the quantitative study of displacement remains difficult, patterns and processes of displacement can be inferred through existing data sources, as well as data generated from those who themselves have experienced displacement

Mobile learning in a human geography field course

This paper reports on reusable mobile digital learning resources designed to assist human geography undergraduate students in exploring the geographies of life in Dublin. Developing active learning that goes beyond data collection to encourage observation and thinking in the field is important. Achieving this in the context of large class sizes presents several challenges. Combining in-situ learning with spatially-accurate historical and contemporary multimedia, we developed a set of location-aware digital mobile tools or ‘mediascapes’. We explore how scaffolding can be achieved in such a context, focusing on the development of students’ observational, enquiry and thinking skills in the field

Learning urban form through unsupervised graph-convolutional neural networks

Graph theory has long provided the basis for the computa-tional  modelling  of  urban  flows  and  networks  and,  thus,  for  the  studyof urban form. The development of graph-convolutional neural networksoffers the opportunity to explore new applications of deep learning ap-proaches  in  urban  studies.  In  this  paper,  we  propose  an  unsupervisedgraph representation learning framework for analysing urban street net-works. Our results illustrate how a model trained on a 1% random sampleof street junctions in the UK can be used to explore the urban form of thecity of Leicester, generating embeddings which are similar but distinctfrom  classic  metrics  and  able  to  capture  key  aspects  such  as  the  shiftfrom urban to suburban structures. We conclude by outlining the cur-rent limitations and potential of the proposed framework for the studyof urban form and function.</p

Defining Natural Points of Interest

This paper contributes to the working definition of natural points of interest(NPOI). We combine a theory-driven approach exploring existing definitions of points of interest and natural features and a data-driven approach in which we systematically assess datapoints from three separate data sources, proposing a set of criteria for the classification of natural points of interest

Lockdown lessons: an international conversation on resilient GI science teaching

We report the findings from two global panel “conversations” that, stimulated by the exceptional coronavirus pandemic of 2020/21, explored the concept of resilience in geographic science teaching and learning. Characteristics of resilient teaching, both in general and with reference to GISc, are listed and shown to be essentially what might in the past have been called good teaching. Similarly, barriers to resilient teaching are explored and strategies for overcoming them listed. Perhaps the most important conclusion is a widespread desire not to “bounce back” to pre-COVID ways, but to use the opportunity to “bounce forward” towards better teaching and learning practices.</p