利用公交灵巧卡数据和北京市兴趣点发现功能区
Discovering functional zones using bus smart card data and points of interest in Beijing
摘要：
城市由各种功能区组成，包括居住区、教育区、商业区等。城市规划者必须识别不同的功能区，并了解其在城市中的空间结构，以便更好地进行城市规划。本研究以北京市2008年4月一周内的77976010条公交灵巧卡记录为研究对象，将其转换为各公交站台的二维时间序列数据，通过大数据库系统中的数据采掘，结合前人对市民出行行为的研究，建立了基于灵巧卡数据和兴趣点数据的不同功能区发现模型，并在交通分析区层次上对结果进行了汇总。结果表明，基于降维和EM（Expectation-Maximization）算法的DzoF模型和聚类分析能够识别出与北京市土地利用实际情况相匹配的功能区。本文的研究方法有助于城市规划者和公众对复杂的城市空间结构的认识，对城市地理学和城市规划学的研究也有一定的贡献。
英文摘要：
Cities comprise various functional zones, including residential, educational, commercial zones, etc. It is important for urban planners to identify different functional zones and understand their spatial structure within the city in order to make better urban plans. In this research, we used 77976010 bus smart card records of Beijing City in one week in April 2008 and converted them into two-dimensional time series data of each bus platform, Then, through data mining in the big database system and previous studies on citizens' trip behavior, we established the DZoF (discovering zones of different functions) model based on SCD (smart card Data) and POIs (points of interest), and pooled the results at the TAZ (traffic analysis zone) level. The results suggested that DzoF model and cluster analysis based on dimensionality reduction and EM (expectation-maximization) algorithm can identify functional zones that well match the actual land uses in Beijing. The methodology in the present research can help urban planners and the public understand the complex urban spatial structure and contribute to the academia of urban geography and urban planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
