关键词: 迎风面积密度（FAD）, 建筑, 城市形态, 城市微气候, 晋江市

Abstract:

Frontal Area Density (FAD) of urban buildings is one of the important parameters of urban morphology. Therefore, the quantitative analysis and its mapping play a significant role in the field of urban microclimate research. It helps climatologists and urban planners mark out the detected ventilation paths, which could improve the thermal conditions in the inner city. In order to determine an effective and reliable method of analyzing the distribution of urban FAD, we took Jinjiang city of Fujian province as an example. We selected the vector and the raster calculation model to simulate FAD. Considering computational efficiency, we analyzed the obtained results from various scales and land use types. Two computer models were developed based on GIS and geodatabase. Each calculation model has an advantage of its specific data type. Mean, maximum, minimum and standard deviation of FAD for some chosen sample areas in Qingyang subdistrict were calculated and there were significant differences between selected areas. The research shows that the vector model is more efficient than the raster model. At the urban scale, the simulations of the vector and raster models are both consistent to the distribution characteristics of buildings at the macro level. At the neighborhood scale, the results of the raster model are more in line with building distribution than the vector model. When the scale of the area is reduced, the differences between two models increase. The raster model is more stable than the vector model, and is less affected by the resolution. In different land use types, the mean FAD values in business districts and urban residential areas are higher than the others. In the raster model, the average difference between the two resolutions of urban residential area is the lowest. In the vector model, the average difference of the green space is lower than the other three land use types. Thus, the two models are applicable for sparse distribution areas, but for building dense regions, such as business districts and city residential areas, the raster model performs better.

Key words: Frontal Area Density, buildings, urban morphology, urban microclimate, Jinjiang