Modeling urban sprinkling with cellular automata

Abstract

This paper presents a spatiotemporal analysis to simulate and project urban sprinkling with coupled cellular automata (CA) and multinomial logistic regression (MLR) model. Our case study, the Basilicata region, south of Italy, is characterized by urban sprinkling-literally "a small amount of urban territory distributed in scattered particles". The region is witnessing a decoupled growth between demographic trend and urban expansion. We applied a coupled approach based on CA and MLR for urban sprinkling modeling and simulation. From three regional building datasets (1989, 1998 and 2013) building density maps were created and used to calibrate and validate the model and to project future urban expansion. Built-up causative factors were identified through an analysis of 19 articles that were compared and discussed according to their main features (methods, case studies, drivers, urbanization dynamics and demographic growth). The transition probability for the first period (1989-1998) was calibrated with MLR for built-up causative factors and with the multiobjective genetic algorithm (MOGA) for CA neighborhood effects. The calibrated model was used to simulate the 2013 urban pattern which was compared with the actual map of 2013 (validation). We then used our calibrated model to simulate urban expansion in 2030. The results of the 2030 forecast show the largest variations in class 1 (low density built-up patches) that correspond to urban sprinkling.

Ecosystem Services Multifunctionality: An Analytical Framework to Support Sustainable Spatial Planning in Italy

“…Looking at Figure 2, which shows the increase in artificial surfaces in 2018 compared to those already existing in 2000, it is possible to recognize different dynamics. Considering that urban growth processes in Italy occurred as described by Romano et al [46,48,49], we highlight that they are still ongoing, and we also point out the lack of both an adequate monitoring system and an effective interpretative framework of the effects that these transformations exert on environmental and territorial components.…”

“…Considering that urban growth processes in Italy occurred as described by Romano et al [46,48,49], we highlight that they are still ongoing, and we also point out the lack of both an adequate monitoring system and an effective interpretative framework of the effects that these transformations exert on environmental and territorial components.…”

“…In addition to these, there are further settlement dynamics typical of low-density contexts, which happen as part of the "sprinkling" process [45], characterized by spontaneous urban development and featured by a building density of about 0.1 building/ha and concerning also inner areas where processes of depopulation, marginalization and land abandonment have been occurring for several years [22,46]. Due to its geometric/spatial characteristics, which cannot be captured by the CLC's minimum mapping units, this phenomenon is currently underestimated at the European level [47].…”

Frameworks to envision equitable urban futures in a changing climate: A multi-level, multidisciplinary case study of New York City

“…The future visions are coded into spatially and temporally explicit land use transition rules to be simulated through a land use/cover change (LUCC) model (as shown in Table 1). Land use change models are widely used to simulate future land use patterns that assume no major changes in socioeconomic settings or policies so that the future changes are just an extrapolation of observed historical changes (even though many land use change models that have examined past changes and/or simulated possible future land use patterns have found that many demographic variables are important drivers of land use changes (Puertas et al, 2014;Rienow and Goetzke 2015;Ku, 2016;Vermeiren et al, 2016;Saganeiti et al, 2021).…”

Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area

“…This phenomenon is contrary to the findings of many observational studies that suggest that a region can drive the development of surrounding areas once it has reached a developed state. If neighborhood interaction is to be considered, the simulation framework must be able to discretize the whole into individuals, and the model to do this is cellular automata (CA) [48,59], at present the most widely used bottom-up model for simulating urban pattern evolution [49].…”