Combining a land parcel cellular automata (LP-CA) model with participatory approaches in the simulation of disruptive future scenarios of urban land use change

Molinero-Parejo, Ramón; Aguilera-Benavente, Francisco; Delgado, Montserrat Gómez; Shurupov, Nikolai

doi:10.1016/j.compenvurbsys.2022.101895

Cited by 20 publications

(13 citation statements)

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“…As a result, both the creation of a diverse urban environment (Rueda Palenzuela et al, 2022) and the control of urban sprawl (Díaz-Pacheco & García-Palomares, 2014) have become important topics on the spatial planning agenda in Spain. However, most existing studies have focused on the analysis and description of past urban growth processes, rather than analyzing possible future scenarios such as those derived from vector urban land simulation maps (Molinero-Parejo et al, 2023), which can assist in the design of proactive policy packages adapted to these scenarios (Ariza-Álvarez et al, 2023). The appropriate use of vector movingwindow metrics as proposed in this article could lead to significant improvements in this area.…”

Section: Backg Rou N Dmentioning

confidence: 99%

“…Most studies of spatial metrics employ raster data because raster has traditionally been the most commonly used output data format in urban simulation models (Aguilera et al, 2011;Barredo et al, 2004;Herold et al, 2005) and it is easier and faster to compute than vector data (Wade et al, 2003;Yao et al, 2022). However, the recent proliferation of significant vector-based cellular automata (CA) models (Abolhasani et al, 2016;Barreira-González et al, 2019;Molinero-Parejo et al, 2023;Moreno et al, 2008;Yao et al, 2017Yao et al, , 2021 has increased the need for new tools for calculating vector spatial metrics.…”

Section: Introductionmentioning

confidence: 99%

“…These scenarios were quantified (quantity of change) and spatialized (allocated) in a workshop(Molinero- Parejo et al, 2021), and then converted into spatially explicit urban land-use maps by simulating with the land parcel cellular automata (LP-CA) model(Molinero-Parejo et al, 2023). The LP-CA model simulates five active land uses: Commerce and utilities, industrial, single-family residential, multifamily residential, and mixed.…”

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confidence: 99%

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Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

Molinero‐Parejo,

Aguilera‐Benavente,

Gómez‐Delgado

2024

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Descriptive scenarios about the possible evolution of land use in our cities are essential instruments in urban planning. Although the simulation of these scenarios has enormous potential, further characterization is needed in order to be able to evaluate and compare them so as to provide more effective support for public policy. One of the most commonly used tools for assessing these scenarios is spatial moving‐window metrics, a useful mechanism for extracting accurate information from simulated land‐use maps on urban diversity and urban growth patterns. This article seeks to explore this question further and has two main aims. First, to develop and implement vSHEI and vLEI, two multiscale composition and configuration vector moving‐window metrics for calculating urban diversity and urban growth patterns. Second, to test these metrics using the spatially explicit simulation of three prospective scenarios in the Henares Corridor (Spain), comparing the results and analyzing how well the scenario narratives match their spatial configuration, as measured using vSHEI and vLEI. Via the implementation of vSHEI and vLEI, we obtained urban diversity and urban expansion values at a local level, offering more precise and more realistic, mappable information on the composition and configuration of urban land use than that provided by raster metrics or by vector Patch‐Matrix model metrics. We also used these metrics to test whether the simulated scenarios matched their description in the narrative storylines. Our results demonstrate the potential of vector moving‐window metrics for characterizing the urban patterns that might develop under different scenarios at the parcel level.

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Section: Backg Rou N Dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

Molinero‐Parejo,

Aguilera‐Benavente,

Gómez‐Delgado

2024

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“…A CA is a lattice dynamics model with discrete time, space, and state, in which a complex system output is generated from simple interactions at the level of individual cells [10]. The characteristics of a CA model are similar to those of the complex urban system in terms of self-organization, self-replication, and the storage and transmission of information, so numerous studies have used such models to simulate urban growth [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Chendai,4 Qingyang,5 Xintang,6 Neikeng,7 Xiyuan,8 Chidian,9 Luoshan,10 Cizao,11 Lingyuan,12 Anhai,13 Yonghe,14 Dongshi,15 Longhu,16 Yinglin,17 Jinjing,18 Shenhu, and19 Meiling.…”

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confidence: 99%

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

2023

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Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use.

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The vehicle routing programming of municipal solid waste collection under cognitive uncertainty

Wang,

Huo,

Lam

2024

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The rapid development of online shopping has led to an increase in express parcels, thus the proliferation of solid waste from parcel packaging. There are many other phenomena that have arisen with rapid economic development that also led to the surge in municipal solid waste. It is urgent to pay attention to municipal solid waste collection. Unexpected disruptive events lead to cognitive uncertainty and cause municipal waste collection to fail to function properly. This article studies how to properly carry out vehicle planning to achieve the shortest driving distance under cognitive uncertainty. The community's time window and the amount of garbage in each community are taken as uncertain variables. Based on the uncertainty theory, an uncertain vehicle routing problem (VRP) model and an uncertain VRP with the loading constraint model are constructed, and the solution algorithms of these two models are given. Several applications are carried out to evaluate the effectiveness of the proposed models and algorithms. This article proposes for the first time the models and algorithms to study the VRP under cognitive uncertainty. The findings draw important practical implications for adjusting municipal solid waste collection planning and logistics during disruptive events.

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Combining a land parcel cellular automata (LP-CA) model with participatory approaches in the simulation of disruptive future scenarios of urban land use change

Cited by 20 publications

References 43 publications

Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

The vehicle routing programming of municipal solid waste collection under cognitive uncertainty

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