城市建筑物人口时空分布模型与实验分析——以北京东华门街道为例

精细尺度上的城市人口分布是解决城市规划与管理、预警与应急等问题的关键。研究精细尺度上城市人口的时空分布需要还原与模拟城市人口与城市建筑物之间的关系。本文利用高分辨率遥感影像提取城市精细尺度的建筑物信息,建立了适用于城市人口分布研究的城市建筑物功能分类体系,并获取了不同使用功能的建筑物人口容纳系数;在实际调查和前人研究的基础上得到不同使用功能的城市建筑物人口吸引率曲线,建立城市建筑物人口分布模型,实现城市精细尺度上的人口分布情况的模拟。以北京东华门街道为例,对16个时间点(0∶00、6∶00、7∶00、8∶00、9∶00、10∶00、11∶00、12∶00、13∶00、14∶00、15∶00、16∶00、17∶00、18∶00、19∶00、22∶00)的城市建筑物人口分布情况进行了模拟,并根据建筑物功能的聚集及道路空间分割情况划分为4个区域进行了人口数量变化分析和原因探讨。最后,讨论了研究中存在的问题,并提出了增加实际调查数据等可能的改进方法。

Simulation of Urban Small-area Population Space-time Distribution Based on Building Extraction: Taking Beijing Donghuamen Subdistrict as an Example

Small-area population space-time distribution is the key point to urban planning, administration and warning for emergency. How to get the data of population distribution becomes the urgent task. While the urban buildings are the main place bearing population, it is necessary and meaningful to find and use the relationship between urban population and urban buildings. In this paper, buildings are extracted with high-resolution remote sensing images, and then a classification system of building functions is established. Based on the classification system, the extracted buildings are classified to 16 classes such as residential, office, financial, leisure, school, catering, accommodation, medical, business, comprehensive and then accommodating capacity coefficients of the buildings are estimated. Based on the survey and existing papers, population-attracting rate curve of buildings of all kinds of function are described. Model of space-time distribution of people is built to simulate the population distribution in Donghuamen Subdistrict of Beijing's Dongcheng District. According to the results of the simulation, the total capacity of all buildings in the target region is 693 thousands. The population distribution of 0:00, 6:00, 7:00, 8:00, 9:00, 10:00, 11:00, 12:00, 13:00, 14:00, 15:00, 16:00, 17:00, 18:00, 19:00 and 22:00 are simulated and exhibited in this paper. In order to make the results clearer, the region is divided into smaller regions: A, B, C and D, and the population quantity changes of the four regions are described as curves. Then reasons for the changes are analyzed. In the end, improvement methods for the research are discussed, i.e., finer classification system of building functions, a finer urban population bearing units classification, and a finer population-attracting rate curve of buildings.