As housing costs continue to increase across many cities in North America and Europe, local governments face pressure to understand how housing’s rising cost is changing neighbourhoods and to ensure that everyone can access a home they can afford. To confront displacement concerns, cities are adapting models developed within academia to identify neighbourhoods that may be susceptible to gentrification and displacement. We compare four gentrification and displacement risk models developed by and for the US cities of Seattle, Washington; Los Angeles, California; Portland, Oregon; and Philadelphia, Pennsylvania, and apply all four methodologies to one city, Boston. We identify the geographic areas of agreement and disagreement among the methods. The comparison reveals striking differences between the models, both in inputs and outputs. Of the 18 variables considered among the four models, only two variables appear in all four models. In the resulting maps, the four methods identified between 25 and 119 of the 180 Boston census tracts as at risk of gentrification and displacement, or as currently gentrifying. There are only seven tracts that all four models agreed were either gentrifying or at risk of gentrification and displacement. The findings indicate a need for cities to consider critically the assumptions of the models that are included in urban policy documents, as indicators and thresholds have major impacts on how neighbourhoods in the liminal space of gentrification and displacement are characterised. This novel comparison of United States local government analyses of gentrification provides insight as modelling moves from theory to practice.

As governments have digitized their operations, they have opened themselves to cyberattacks, resulting in harmful disruptions to government services. The scholarly world has been slow to pick up on this growing risk. Professional associations have conducted studies of their own, and produced recommendations, but few scholars have looked closely at cybersecurity practices at the municipal level. The interconnectedness of local infrastructure—across and among agencies and levels of government—makes it hard to figure out what is happening. In this paper, we urge scholars from multiple disciplines to examine the dangers created by the cross-linkages that characterize local cybersecurity. We examine the existing academic research, and demonstrate the significant growth in cybersecurity practice that has cropped up in spite of the relative sparsity of academic work. Theory and practice need to catch up with each other.

We develop an expectations-based measure of gentrification. Property values today incorporate market participants’ expectations of the neighbourhood’s future. We contrast this with present-oriented variables like demographics. To operationalise the signal implicit in property values, we contrast the percentile rank of a neighbourhood’s average house price to that of its average income, relative to its metropolitan area. We take as our signal of gentrification the rise of a neighbourhood’s house value percentile above its income percentile. We show that a gap between the house value and income percentiles predicts future income growth. We further validate our metric against existing approaches to identify gentrification, finding that it aligns meaningfully with qualitative analyses built on local insight. Compared to existing quantitative approaches, we obtain similar results but usually observe them in earlier years and with more parsimonious data. Our approach has several advantages: conceptual simplicity, communicative flexibility with graphical and map forms and availability for small geographies on an annual basis with minimal lag.

No abstract