Spatio-temporal multinomial autologistic modeling of land-use change: A parcel-level approach

Abstract

Land-use change models that accurately replicate the complex dynamics of land development provide vital information for urban planning and policy. These models require both detailed data and advanced statistical methods. Many factors influence land-use change decisions, such as parcel characteristics, accessibility to activities, and current and historical neighborhood conditions. Therefore, spatial and temporal components must be incorporated in a model at the highest possible disaggregation level in order to achieve robust results. A spatio-temporal multinomial autologistic model, incorporating space and time and their interactions, is introduced to investigate land-use dynamics at the parcel-level, and is applied to Delaware County, Ohio. It is able to capture the impacts of the existing and historical neighborhood conditions of parcels with high accuracy. Advanced computational methods are used to deal with the computational challenges of parameter estimation. The model is validated, estimating 91.4% of all observations correctly for the period 2005–2010, and is applied to land-use forecasting.

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

“…The impacts of recreational areas on land development are always positive . In this study, the effect of the agricultural land share is assessed as negative, consistent with the results in Deng and Srinivasan (2016) and Tepe and Guldmann (2020). However, other studies display negative associations (Carrión-Flores and Irwin, 2004;Irwin et al, 2003;Liao and Wei, 2014).…”

“…The literature review on LUC modeling suggests that: (1) LUC results from the complex interactions between human activities and the built environment (Verburg et al, 2004); (2) Land-use models mimic complex land development dynamics (Noszczyk, 2019); (3) Incorporating spatial dependencies in LUC models is necessary to account for feedback mechanisms among neighbors (Munroe and Müller, 2007); (4) Historical trends are also essential in LUC models because certain historical conditions impact current and future land developments (Kim et al, 2022; Tepe and Guldmann, 2017, 2020; Tepe and Safikhani, 2023). The focus of this review is on recent statistical-based LUC models.…”

“…Bhat et al (2015), Deng and Srinivasan (2016), and Hu and Lo (2007) use spatial lag components to control for spatial dependency. Similarly, temporal lagged components are used by Bhat et al (2015), Huang et al (2009), and Tepe and Guldmann (2017, 2020). Alternatively, Carrión-Flores & Irwin (2004) specify a simultaneous autoregressive process in the error terms of their residential land conversion model.…”

“…Finally, some studies incorporate spatial and temporal dynamics in their LUC models. Tepe and Guldmann (2017, 2020) include spatial-temporal autoregressive structures to account for the parcel dynamics in Delaware County, Ohio. Ferdous and Bhat (2013) account for both spatial and temporal dependency by applying a spatial lag specification on latent variables of decision-makers and including a temporal autoregressive error term in the spatial panel ordered response variable to uncover changes in land development intensity in Austin, Texas.…”

“…Recent studies have also incorporated spatial and temporal dependencies because neighborhood and historical effects play critical roles in land dynamics (Deng and Srinivasan, 2016). Tepe and Guldmann (2017, 2020) introduce an autologistic regression methods explicitly accounting for spatial and temporal dependencies. Including these lags resolves the omitted variable bias problem in OLS regression-based LUC models.…”