GIScience and neighborhood change: Toward an understanding of processes of change

Abstract

Processes of neighborhood change are the result of the unfolding of events and decisions by multiple actors operating at varying spatial and temporal scales, enabled and constrained upon an unequal urban landscape. The contributions of GIScience toward understanding these processes have evolved from the simple mapping of static, cross‐sectional maps toward an embrace of novel data and methods that enable longitudinal trajectories to be extracted and neighborhood futures to be predicted. In this article, I review these advancements and chart a course forward that considers a future research agenda that is critically cognizant of the potentials and perils of new data sources and method, is representative of the full spectrum of processes operating both visibly and invisibly that give rise to observed neighborhood outcomes, and considers their varying spatial and temporal scales.

An evaluation framework for predictive models of neighbourhood change with applications to predicting residential sales in Buffalo, NY

“…Data used in the prediction of neighbourhood change vary widely, and include census data (Delmelle, 2022; Zuk et al, 2018), survey data (Carlson, 2021), data on foreclosures (Williams et al, 2013) and large-scale data on building transactions (Ding et al, 2016; Yonto and Schuch, 2020). Scholars have also recently turned to ‘big data’ sources such as images from Google Street View (Hwang and Sampson, 2014) and social media data (Chapple et al, 2022; Gibbons et al, 2018; Glaeser et al, 2018), as well as more difficult-to-gather administrative data on credit scores (Hwang and Ding, 2020).…”

“…Much of the literature on predictive modelling of neighbourhood change draws from theory and/or domain expert guidance on both feature and model construction (Bayer et al, 2016; Zuk et al, 2018). However, a growing literature explores the use of machine learning to make more accurate forecasts (Delmelle, 2022). Machine learning methods are based on the idea that modelling can be done independently of feature selection.…”

“…Here, we situate our work within the literature on predictive modelling of neighbourhood change through a discussion of these four stages of development. For a more comprehensive overview, we point to Delmelle (2022: 567), who provides a thorough overview of ‘the potentials and perils of new data sources and methods’.…”

Quantitative methods II: Big theory

“…Employing a spatial optimization approach, Robinson et al (2022) evaluate equity of coverage in smart city infrastructures. Looking at neighbourhood change, Delmelle (2022) emphasizes the importance of underlying processes and the need for methods that can adequately measure the nature and scope of these processes. Starting from a blank slate (but not an isotropic plain!…”

Self‐organizing maps as a dimension reduction approach for spatial global sensitivity analysis visualization

“…Such a comparison can be found in Skupin and Fabrikant (2003) and Koua et al (2006). Among the various visualization methods, the self‐organizing map is found to be effectively applicable to datasets containing a very large number of observations or dimensions for pattern discovery, exploring relationships, and detection of irregularities in the data (Andrienko et al, 2010; Delmelle, 2021; Kolovos et al, 2010; Koua & Kraak, 2004; Luo & Yuan, 2021; Skupin & Esperbé, 2011).…”