Geospatial cellular automata (Geo-CA) models have encountered challenges in computing efficiency and scalability when conducting large-scale land use change simulation applications. Parallel computing has proven to be effective to address these challenges. However, parallelization strategies for existing parallel Geo-CA models are always optimized for specific models and lack applicability to others. Besides, most parallel Geo-CA models focus on parallelizing land use change simulations, ignoring land use transition rule mining. Moreover, there is a lack of effective parallel strategies for demand-constrained land use change simulation on distributed heterogeneous architectures. This study proposes a parallel framework on hybrid parallel computing architectures applicable for raster-based Geo-CA models to enhance their computing efficiency and scalability while maintaining simulation accuracy. The framework provides parallelization strategies for both the land use transition rule mining for multiple land use types and the demand-constrained land use change simulation on distributed heterogeneous architectures. The framework was employed to parallelize two contemporary Geo-CA models, i.e., PLUS and MCCA. Experiments showed that the parallelized models achieved significant improvements in computing efficiency and scalability, confirming the effectiveness of the proposed framework for large-scale land use change simulation studies.

Land use and land cover change (LUCC) simulation aids the interpretation of the causes and consequences of future landscape dynamics under various scenarios, which in turn supports policy decisions. The essence of LUCC simulation lies in representing complex spatiotemporal associations among land types, including competitions and interactions. Currently, analyses of complex spatiotemporal LUCC associations mainly focus on the spatial configuration of land use while ignoring the intricate spatiotemporal co-evolution patterns of land types. Therefore, by integrating spatiotemporal co-evolution pattern mining (STC) in a future land use simulation (FLUS) model, a land use change simulation model named STC-FLUS was developed in this study. The proposed model is innovative because it can accurately quantify the spatiotemporal co-evolution patterns of land types, which can be effectively incorporated into LUCC simulations. A set of simulations indicate that the STC-FLUS model is more accurate than the classical FLUS model, with a figure of merit score of 0.135 compared with 0.114. Simulation results under five localized shared socioeconomic pathway scenarios from 2020 to 2040 demonstrate that the proposed model is effective for future LUCC simulation under a set of development scenarios. We conclude that spatiotemporal co-evolution patterns of land types can enhance the reliability of land use projections. Moreover, the STC-FLUS model can serve as a useful tool to understand future land use dynamics.

The reverse k nearest neighbors (RkNN) query is a prominent yet time-consuming spatial query used in facility siting, influential domain analysis, potential customer analysis, etc. Its aim is to identify all points that consider the query point as one of their k closest points. However, when k is relatively large (e.g. k = 1000), existing RkNN techniques often struggle to provide acceptable response times (within a few seconds). To address this issue, we propose a verification approach called conic section discriminance (CSD). This method serves to determine whether points belong to the RkNN set. With CSD, only a small fraction of candidates require costly k nearest neighbors (kNN) queries for verification, while the rest can be rapidly verified with O(1) complexity. Furthermore, we propose a Voronoi-based candidate generation approach to curtail the candidate set size. By leveraging the VoR-tree structure, we integrate these two approaches to form a novel RkNN algorithm named CSD-RkNN. A comprehensive set of experiments is conducted to compare CSD-RkNN with Slice as the state-of-the-art RkNN algorithm, and VR-RkNN as the original RkNN algorithm on VoR-tree. The results indicate that CSD-RkNN consistently outperforms the other two algorithms, especially when k is relatively large.

Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (CA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified CA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, China from 2013 to 2019. The proposed model is applied to future simulation in 2019–2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs.

Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the exploration of the transition suitability of cells, leading to lower simulation accuracy. This study proposes a convolutional neural network (CNN)-VCA model that adopts the CNN to extract the high-level features of the driving factors within a neighborhood of an irregularly shaped cell and discover the relationships between multiple land use changes and driving factors at the neighborhood level. The proposed model was applied to simulate urban land use changes in Shenzhen, China. Compared with several VCA models using other machine learning methods, the proposed CNN-VCA model obtained the highest simulation accuracy (figure-of-merit = 0.361). The results indicated that the CNN-VCA model can effectively uncover the neighborhood effects of multiple driving factors on the developmental potential of land parcels and obtain more details on the morphological characteristics of land parcels. Moreover, the land use patterns of 2020 and 2025 under an ecological control strategy were simulated to provide decision support for urban planning.

Urban land-use change is affected by urban planning and government decision-making. Previous urban simulation methods focused only on planning constraints that prevent urban growth from developing in specific regions. However, regional planning produces planning policies that drive urban development, such as traffic planning and development zones, which have rarely been considered in previous studies. This study aims to design two mechanisms based on a cellular automata-based future land-use simulation model to integrate different planning drivers into simulations. The first update mechanism considers the influence of traffic planning, while the second mechanism can model the guiding effect of planning development zones. The proposed mechanisms are applied to the Pearl River Delta region, which is one of the fastest growing areas in China. The first mechanism is validated using simulations from 2000–2013 and demonstrates that simulation accuracy is improved by the consideration of traffic planning. In the simulation from 2013–2052, the two mechanisms are implemented and yield more realistic urban spatial patterns. The simulation outcomes can be employed to identify potential urban expansion inside the master plan. The proposed methods can serve as a useful tool that assists planners in their evaluation of urban evolvement under the impact of different planning policies.