Qingsong He

Simultaneously simulate vertical and horizontal expansions of a future urban landscape: a case study in Wuhan, Central China

ABSTRACT While there are extensive studies of urban 2D forms, research on the varying geometric features and spatial distribution patterns of urban 3D spaces is comparatively rare. In this paper, we propose a coupled model, known as BPANN-CBRSortCA, which is based on a back propagation artificial neural network (BPANN) and case-based reasoning technology with sort cellular automaton (CBRSortCA) to simulate future urban building heights and their spatial distribution. BPANN–CBRSortCA uses BPANN to predict the vertical extrusion of building heights and uses CBRSortCA to simulate horizontal urban expansion. The BPANN–CBRSortCA model is innovative because of its capabilities to simultaneously project urban growth in the vertical and horizontal dimensions. The proposed model also overcomes the limitations of the traditional cellular automata models that cannot simulate ‘diffused’ urban expansion. This research used Wuhan City as a case study to simulate vertical and horizontal urban expansion from 2015 to 2025. The results showed the following: (1) in the next 10 years, new build-up will mainly appear along the edge of Hongshan and Hanyang Districts or will occupy bare land in the form of ‘filling’ and (2) the tallest buildings will be mainly located to the south of East Lake in Hongshan District and on undeveloped land within the city. These simulation results can provide a reference for future urban planning.

Urban Ecological Security Simulation and Prediction Using an Improved Cellular Automata (CA) Approach—A Case Study for the City of Wuhan in China

Ecological security is an important research topic, especially urban ecological security. As highly populated eco-systems, cities always have more fragile ecological environments. However, most of the research on urban ecological security in literature has focused on evaluating current or past status of the ecological environment. Very little literature has carried out simulation or prediction of future ecological security. In addition, there is even less literature exploring the urban ecological environment at a fine scale. To fill-in the literature gap, in this study we simulated and predicted urban ecological security at a fine scale (district level) using an improved Cellular Automata (CA) approach. First we used the pressure-state-response (PSR) method based on grid-scale data to evaluate urban ecological security. Then, based on the evaluation results, we imported the geographically weighted regression (GWR) concept into the CA model to simulate and predict urban ecological security. We applied the improved CA approach in a case study—simulating and predicting urban ecological security for the city of Wuhan in Central China. By comparing the simulated ecological security values from 2010 using the improved CA model to the actual ecological security values of 2010, we got a relatively high value of the kappa coefficient, which indicates that this CA model can simulate or predict well future development of ecological security in Wuhan. Based on the prediction results for 2020, we made some policy recommendations for each district in Wuhan.

An Efficient Vector-Raster Overlay Algorithm for High-Accuracy and High-Efficiency Surface Area Calculations of Irregularly Shaped Land Use Patches

The Earth’s surface is uneven, and conventional area calculation methods are based on the assumption that the projection plane area can be obtained without considering the actual undulation of the Earth’s surface and by simplifying the Earth’s shape to be a standard ellipsoid. However, the true surface area is important for investigating and evaluating land resources. In this study, the authors propose a new method based on an efficient vector-raster overlay algorithm (VROA-based method) to calculate the surface areas of irregularly shaped land use patches. In this method, a surface area raster file is first generated based on the raster-based digital elevation model (raster-based DEM). Then, a vector-raster overlay algorithm (VROA) is used that considers the precise clipping of raster cells using the vector polygon boundary. Xiantao City, Luotian County, and the Shennongjia Forestry District, which are representative of a plain landform, a hilly topography, and a mountain landscape, respectively, are selected to calculate the surface area. Compared with a traditional method based on triangulated irregular networks (TIN-based method), our method significantly reduces the processing time. In addition, our method effectively improves the accuracy compared with another traditional method based on raster-based DEM (raster-based method). Therefore, the method satisfies the requirements of large-scale engineering applications.

Urban Growth Modeling Based on a Game between Farmers and Governments: Case Study of Urban Fringe in Wuhan, Hubei Province in China

AbstractUrban systems are complicated systems where land-use changes may significantly affect the environment and the ecosystem. Therefore, modeling urban growth is crucial for urban planners and administrators to support sustainable development. This paper provides a spatially disaggregated model for urban growth simulation that is characterized by the innovative idea that considers the behavior of residents and couples the explorations of the game between farmers and governments in the land development process. Three kinds of agents, namely, residents, farmers, and governments, make their decisions according to their land use–conversion preferences. Through the use of different strategies that are abstracted from actual land transactions in China, the payoffs to farmers and governments in the game of land expropriation are quantified and then the Nash equilibrium solution of the game is worked out. Those cells with mixed strategy Nash equilibrium solutions that include a probability of greater than 0.5 ...

A new method based on association rules mining and geo-filter for mining spatial association knowledge

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining (GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.