Estimating potential illegal land development in conservation areas based on a presence-only model.

Conservation areas are facing increasing threats from anthropogenic land use activities. It is important to reasonably recognize and predict suspected illegal land development in advance. However, traditional methods easily suffer from selection bias due to the lack of accurate and reliable absence data. To tackle this problem, we have presented a novel method for estimating potential illegal land development based on the presence-only maximum entropy (MAXENT) model. The principle of MAXENT can guarantee that no additional unknown information (e.g., inaccurate pseudo-absence samples) will be introduced into the estimation procedure. This method was applied to the conservation areas in a fast-growing city, and the robustness of the MAXENT models was confirmed by the high AUC scores (over 0.80). The results indicated that the proposed method performs more effectively than the presence-absence random forest model. In addition, topographic conditions and proximity to transportation networks played dominant roles in the emergence of suspected illegal land development. Moreover, the probability map generated by MAXENT suggests that a considerable amount of forest, farmland, grassland, and water bodies will face a high degree of danger. Therefore, both superior and local governments should pay much more attention to regions with a higher potential for illegal land development. In summary, our findings are expected to support decision-making in the management and assessment of conservation areas in fast-growing regions. More importantly, the proposed method can be further applied to illegal land development estimation in many other regions.

Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model.

Urban vitality is a major indicator used for evaluating the sustainability and attractiveness of an urban environment. Global experience indicates that urban vitality can be stimulated through a reasonable urban design. However, it remains incompletely understood in the literature which building-related indicators can substantially affect urban vitality in Asian countries. To give an insight into this question, our study took a step forward by focusing specifically on the influence of the three-dimensional built environment on urban vitality, based on which decision makers could enhance urban vitality from the perspective of vertical building design. A machine-learning-based framework was developed in this study. First, we utilized several building-related indicators to thoroughly measure the spatial characteristics of buildings at the township level. Second, the relationship between a three-dimensional built environment and urban vitality was revealed based on a combined use of the correlation method, scatter charts, and a random forest. In the random forest, both a benchmark and a new model were constructed to evaluate the importance of those building-related indicators. The results suggested that urban vitality was closely related to the three-dimensional built environment, which played an even more important role than common benchmark factors in stimulating urban vitality. The building coverage ratio, density of tall buildings, and floor area ratio were essential spatial drivers behind urban vitality. Therefore, urban designers and decision makers should not only take traditional factors into account but also carefully consider the potential influence of high-rise buildings and the outdoor thermal environment so that urban vitality can be enhanced. Our study's results can offer practical recommendations for improving urban vitality from the perspective of vertical building design. The proposed framework can also be used for measuring the potential influence of the three-dimensional built environment in other areas.

Investigating the influence of three-dimensional building configuration on urban pluvial flooding using random forest algorithm.

Urban pluvial flooding has emerged as a serious threat to environmental conditions and human lives. Identifying its key drivers is crucial for the mitigation of flood risks. Although previous studies have demonstrated that pluvial flooding is caused by both natural (e.g., topography) and anthropogenic factors (e.g., land cover condition), much less effort has been devoted to investigating the potential influence of three-dimensional building configuration on pluvial flooding. To shed some light on this topic, we first analyzed the linear relationship between the density of flooding hotspots and different potential drivers in a highly-urbanized city using Pearson correlation analysis. Next, we designed two random forest-based models to quantify the importance of various building metrics. The first model considers only common drivers, while the second one also includes different types of building metrics. Results indicate that the density of buildings, building congestion degree, and building coverage ratio have exerted considerable influence on the occurrence of pluvial flooding. For example, the root relative squared error of our enhanced model (28.36%) is lower than that of the baseline model (32.58%). Our findings are expected to provide practical guidance for the mitigation of pluvial flood risks from the perspective of three-dimensional urban planning. Moreover, this methodological framework can be further applied to the analysis of flooding in many other regions.

Conflict resolution in the zoning of eco-protected areas in fast-growing regions based on game theory.

Zoning eco-protected areas is important for ecological conservation and environmental management. Rapid and continuous urban expansion, however, may exert negative effects on the performance of practical zoning designs. Various methods have been developed for protected area zoning, but most of them failed to consider the conflicts between urban development (for the benefit of land developers) and ecological protection (local government). Some real-world zoning schemes even have to be modified occasionally after the lengthy negotiations between the government and land developers. Therefore, our study has presented a game theory-based method to deal with this problem. Future urban expansion in the study area will be predicted by a logistic regression cellular automaton, while eco-protected areas will be delimitated using multi-objective optimization algorithm. Then, two types of conflicts between them can be resolved based on game theory, a theory of decision-making. We established a two-person dynamic game for each conflict zone. The ecological compensation mechanism was taken into account by simulating the negotiation processes between the government and land developers. A final zoning scheme can be obtained when the two sides reach agreements. The proposed method is applied to the eco-protected area zoning in Guangzhou, a fast-growing city in China. The experiments indicate that the conflicts between eco-protection and urban development will inevitably arise when using only traditional zoning methods. Based on game theory, our method can effectively resolve those conflicts, and can provide a relatively reasonable zoning scheme. This method is expected to support policy-making in environmental management and urban planning.