Compact development and preferences for social mixing in location choices: Results from revealed preferences in Santiago, Chile

Abstract

Even though densification and social mixing are declared objectives of many nowadays urban planning paradigms, their simultaneous implementation is usually questioned by different actors and is not frequent in practice. In a market economy, understanding potential demand for this class of development, from different types of households, is essential to define public policies oriented to achieve both compact development (CD) and social mixture. To understand the preferences of households and potential demand, we implement a location choice model based on a bid–rent framework and spatial latent classes (LC), using census data and location attributes. By using spatial LC, we do not impose exogenous definitions of which zones are perceived as CD or suburban, rendering a robust method to identify variation in preferences. We apply the model to Santiago de Chile, where social mixing in dense and well‐located areas is being intensely discussed. We find strong differences in households' valuation of attributes between spatial classes. Results show that social mixing is more difficult in dense, well‐connected areas than in suburban areas because higher‐income households are more sensitive to the socioeconomic context of the location in compact areas. Besides showing evidence on household preferences and their implications for social‐mixing policies, this paper also provides a proof of concept for the use of spatial LC (proposed in previous work by the authors), showing this is a robust methodology allowing to generate behavior‐based classifications for urban areas. The paper also contributes methodologically, by deriving the elasticity formulation for bid‐auction location choice models, which allows quantifying the importance of location attributes in location probability.

Exploring the Spatially Heterogeneous Effects of the Built Environment on Bike-Sharing Usage during the COVID-19 Pandemic

“…ey found that the influence of the independent variables like household income on ridership at most stations was spatially different [25]. To further investigate the spatialimpacts, Cox and Hurtubia used spatial regression models to count for the spatial autocorrelation [10,[32][33][34]. And one of the studies concluded that the usage of dockless bike stations was spatially autocorrelated in commercial areas and road intersections [31].…”

“…Despite their wide use, they do not capture the spatial variation in the relationship between predictor and response variables, especially in the case of large study areas [8,9]. erefore, the spatial latent class model [10] and the geographically weighted regression (GWR) models [7] are some of the methods adopted to capture this spatial variation. In this study, the GWR model is used.…”