Compulsory education is an important aspect of the societal development. Meanwhile, education equality safeguards the effectiveness of education systems and is an important part of social equality. This study analyzes the inequality of compulsory education from the perspective of imbalanced spatial distribution. Unlike previous studies that have measured the spatial distribution of education simply based on the spatial position of primary and secondary schools, we explore spatial accessibility based on the shortest travel distance from residents to schools, and then analyze the inequality of compulsory education through the distribution of spatial accessibility. We use 2873 Chinese counties as statistical units, and perform a statistical and graphical analysis of their spatial accessibility using the Theil index and spatial autocorrelation analyses. To analyze the differences in the spatial accessibility distribution on the national and regional levels, we use three partitioned modes: the terrain partitioned mode, the economic development partitioned mode, and the province-level partitioned mode. We then analyze the spatial agglomeration characteristics and distribution patterns of compulsory education accessibility through global autocorrelation, local autocorrelation, and hot-spot and cold-spot analysis. The results demonstrate an obvious imbalance in the distribution of spatial accessibility to compulsory education at the national level. Accessibility and equality in eastern and central regions are significantly better than those in the western region; both are significantly better in coastal regions than in inland regions; and equality alone is better in the municipalities, such as Shanghai, Tianjin, and Chongqing, than in other provinces and autonomous regions. The spatial pattern analysis shows significant global autocorrelation and obvious clusters. Counties in cold-spot areas (clusters of good spatial accessibility) are large in number but small in size. Cold-spot areas present a ring-shaped structure in space with Henan Province as the core. Counties in hot-spot areas (clusters of weak spatial accessibility) are not as numerous, but most are large in size; hot-spot areas are mainly in the northwest regions, characterized by complex terrain and severe economic difficulty. This study can provide significant information to aid policy making related to compulsory education sustainability in China and can facilitate research on the equality and sustainable development of compulsory education.

Rural China has experienced rapid urbanization and industrialization, accompanied with rural–urban migration since 1978. This tremendous transition has caused a series of negative consequences, necessitating a spatial-territorial reorganization of rural settlements. Previous studies on the restructuring of rural settlements are insufficient for inter-settlement connection consideration and practical and dynamic decision-making techniques. To overcome these concerns, a dynamic spatial-territorial reorganization model (SRM) of rural settlement is proposed herein based on graph theory and genetic algorithm (GA). The model involves two parts. In Part 1, consolidated settlements are identified according to the socio-economic network performance under four types of attack. In Part 2, GA model is repeatedly executed to scientifically resettle consolidated settlements into nearby townships or central settlements with objectives of suitability, compactness, and local connectivity under the control of the constraints. This paper presents an application of SRM to Chengui Town, Hubei Province. Empirical results suggest that: (1) removing settlements in order of node degree is the least efficient way to destroy the entire functional system; and (2) the proposed model can yield satisfactory solutions in terms of spatial reorganization of settlements. The SRM may also serve as a valuable reference for planners in devising plans and making decisions.

Although many publications have noted the impact of urban planning on urban development and land-use change, the incorporation of planning constraints into urban growth simulation has not been adequately addressed so far. This study aims to develop a planning-constrained cellular automata (CA) model by combining cell-based trade-off between urban growth and natural conservation with a zoning-based planning implementation mechanism. By adjusting the preference parameters of different planning zones, multiple planning-constrained scenarios can be generated. Taking the Wuhan Urban Development Area (WUDA), China as a case study, the planning-constrained CA model was applied to simulate current and future urban scenarios. The results show a higher simulation accuracy compared to the model without planning constraints. With the weakening of planning constraints, urban growth tends to occupy more ecological and agricultural land with high conservation priority. With the increase in preference on urban growth or natural conservation, the future urban land pattern will become more fragmented. Furthermore, new urban land beyond the planned urban development area can be captured in future urban scenarios, which will provide certain early warning. The simulation of the current urban spatial pattern should help planners and decisionmakers to evaluate the past implementation of urban planning, and scenarios simulation can provide effective support for future urban planning by evaluating the consequences.

Cellular Automaton (CA) is widely used because of its ability to simulate complex spatiotemporal dynamic processes through applying simple rules. The basis of the CA model is the definition of transformation rules. During a simulation process, the rules determine the change of the cell state. However, existing processing methods calculate the driving factors based on single-point time (start time or end time), making it difficult to reflect the fact that numerous driving factors affecting the cell conversion dynamically change with time. Based on the time dynamics perspective and the data set of multiple time series, this paper designs a method of dynamic adjustment of driving factors of urban expansion on the local cell-scale. It uses linear, exponential, logarithmic, and polynomial fitting to develop a CA model of dynamic adjustment that conforms to the characteristics of local spatial evolution. The main conclusions of the paper are as follows: (1) The polynomial fitting has the highest average R², indicating that the driving factors experiences large fluctuations over time; (2) Secondly, the simulation result kappa obtained by the four fitting methods is between 0.781–0.810, which is higher than the simulation accuracy obtained by using only a single time point. In other words, the factor does not dynamically fit with time and (3) The fitting accuracy of road density is a key indicator of correct and incorrect simulation parts of construction land. Our results demonstrate that the precision of the CA model may be significantly improved by capturing the time development law of environmental variables affecting urban development at the micro-scale.

The implementation of an urban growth boundary (UGB) can effectively control urban sprawl and promote efficient land use, which is crucial for future urban development. However, most of existing studies overlook the reuse of existing idle and inefficient land within the city in the delineation of UGBs. With China’s urban construction shifting from incremental development to stock development, this study focuses on Wuhan and presents a set of technical approaches for delineating UGBs with a stock development orientation. First, a built-up area composite index (POI&ISA) is constructed based on point of interest (POI) kernel density analysis and impervious surface index extraction to evaluate constructive levels in 2010 and 2020 and identify the urban vitality zone. Then, we combine the current land use status and control policies to divide the urban spatial development potential into five categories: urban vitality land, urban non-vitality land, other vitality land, other non-vitality land, and restricted development land. Finally, the PLUS model is applied in the analysis of the driving forces of land use change in Wuhan, simulating the UGBs in three stages of incremental development (2020–2030), incremental and stock development (2030–2040), and stock development (2040–2050). Finally, the PLUS model simulation projects the UGB areas to be 436.436 km², 474.617 km², and 520.396 km² for the years 2030, 2040, and 2050, respectively. The predicted timespan of urban development extends up to 30 years, serving as a reliable reference for Wuhan’s long-term and near-term planning.