A cellular-automata model for assessing the sensitivity of the street network to natural terrain

Abstract

Natural and human-made features are not exclusive in settlements but interact across time and space, placing the context in constant evolution. The purpose of this paper is to search for the influence of terrain, a natural feature, on the configuration of the street network, a human-made feature, by analysing the results of two transition states of cellular automata used to model street networks. This work uses data from open-source projects and open-source applications.The first transition state models the street network considering the neighbourhood rules and randomness, assuming the natural terrain and street are exclusive. The second transition state models the street network as the product of characteristics of the terrain, neighbourhood rules, and randomness, thus assuming the natural terrain and street network interacting with one another. The model is run thirteen times for four different cities by varying the terrain characteristics and calibrated by comparing the simulated street maps with recent street maps.The results are compared and found that the CA model with the second transition state yields better simulation results than the first transition state. In one of the four cities studied, the first transition state results are similar to a specific state of the second transition state, indicating a weak inter-connectedness between the terrain and the street network in the mega-city. Further research can reveal whether the amount of inter-connectedness is specific to the city's terrain or size. The recognition of the inter-connectedness of the road to terrain can help plan for resilient human settlements.

Past rainfall-driven erosion on the Chinese loess plateau inferred from archaeological evidence from Wucheng City, Shanxi

“…The intelligence method is a spatiotemporal dynamic simulation system (SDSS) that consists of four elements: cells, states, neighbours and rules 55,56 . Cells are basic units of cellular automata, while states are the attribute values of cells at a certain time.…”

Built-Up Development Prediction Based on Cellular Automata Modelling Around New Yogyakarta International Airport

“…This form of modeling refers to the use of a dynamic model that simulates the local interactions between cells within a grid. In this context, each cell represented a specific land use, and the changes were highly influenced by rules that take into account the land use of neighboring cells [George et al, 2021;Grattarola et al, 2021]. As rightly established by previous examinations, the CA model relies on its main components, which include state cells, rules or change functions (transition rules or transition functions), and the consideration of neighboring cells [Bobkov et al, 2021].…”

Assessing the Dynamics of Plant Species Invasion in Eastern-Mediterranean Coastal Dunes Using Cellular Automata Modeling and Satellite Time-Series Analyses

“…Unlike other species distribution models that are based on current and modeled future environmental predictions. More specifically, in ecological applications CA is built using environmental suitability data, transition probabilities of the spatial patterns between different time periods, and predictions of future changes based on past trends [36][37][38][39].…”