关键词: 开放大数据, 人口聚集, 职住平衡, 交通响应, 广州市

Abstract:

This paper comprehensively analyzes the spatial distribution relationship between employment and residence in central Guangzhou on a meso level with the help of the Baidu Thermal Map, Baidu Real-time Road Conditions, and Baidu POI data. The results show that: 1) high-value areas where the population gathers during working hours are decently marked with plaques, but most of these are concentrated in the core area. The population is primarily concentrated around the commercial areas and transportation lines of various districts because of the close connection between employment activities and transportation. Examining the main functions of specific areas revealed that they are dominated by employment centers, business centers, and transportation hubs with a strong orientation towards work. High-value leisurely areas are relatively concentrated, have higher land use efficiency, and show a polycentric circle structure. The population is principally concentrated in the traditional local and newly developed commercial residential areas, which are relatively staggered from the core business district. There is a distinct difference between the working areas and places of gathering during non-work hours. The high-value places of gathering during non-work hours are mostly residential land; 2) from the correlation between the degree of population aggregation and the density of POI facilities, the density of POI facilities is more significant in areas with higher population aggregation, during both the work and non-work hours. Therefore, population aggregation has certain selectivity and is primarily concentrated in the area where urban infrastructure development is relatively complete; 3) according to Jobs-housing balance index measurements at the meso level, the ratio of occupation and housing (indicates the relative balance of occupation and housing) of each block in central Guangzhou is between 0.73 and 1.54 with little difference between regions. From a distribution perspective, the blocks with higher scores are mostly distributed in the core area (primarily concentrated in the Yuexiu District, northern Liwan District, and southern Tianhe District) and the blocks with lower scores are mostly distributed in the peripheral or marginal zones of the core area (primarily scattered in the Haizhu, Liwan, and Baiyun Districts); 4) from the perspective of the city’s response to traffic, peak morning congestion in central Guangzhou is greater than peak evening traffic. However, the overall variation in road conditions is minimal, and there is no particularly serious commuting phenomenon in the area.

Key words: open big data, population aggregation, jobs-housing balance, traffic response, Guangzhou