北京测绘2024，Vol.38Issue(12)：1652-1658,7.DOI:10.19580/j.cnki.1007-3000.2024.12.002
兴趣点数据的城市功能区识别方法对比
Comparison of identification methods for urban functional areas based on point of interest data
摘要
Abstract
As the carrier of social and economic activities,urban functional areas are of great significance to urban resource allocation and planning management.The traditional identification method for urban functional areas has the defects of strong subjectivity,low efficiency,and poor accuracy.In view of this,this paper introduced the latent Dirichlet allocation(LDA)model in natural language processing(NLP)and the term frequency-inverse document frequency(TF-IDF)model to explore semantic information of urban point of interest(POI)data and reveal regional potential function utilization patterns.Firstly,the urban space was divided into a 500 m×500 m granular grid,and the POI data was mapped to the corresponding geographical grid units.The corpus was built based on the bag of word model.Secondly,the LDA model and TF-IDF model were used to calculate the distribution patterns between grid units and POI data,so as to identify urban functional areas.Finally,the identification results of urban functional areas were compared with the Baidu electronic map and street view images to evaluate the accuracy.The experimental results show that the accuracy of the LDA model is 78%,which is higher than 63%of the TF-IDF model.The LDA algorithm can identify the function utilization patterns of urban functional areas more accurately and can achieve better identification effects in the functional areas that are hard to distinguish by the TF-IDF model.This paper reveals the potential semantic relationship between POI data and urban functional areas,which can be used as a reference and supplement for the research on urban functional areas.It can also assist urban planners in dynamically monitoring urban structure and guide the layout of future urban renewal and development.关键词
城市功能区/兴趣点(POI)/自然语言处理(NLP)/词频-逆文档频率模型(TF-IDF)/潜在狄利克雷分布(LDA)Key words
urban functional area/point of interest(POI)/natural language processing(NLP)/term frequency-inverse document frequency(TF-IDF)model/latent Dirichlet allocation(LDA)分类
天文与地球科学引用本文复制引用
崔方迪,袁璞..兴趣点数据的城市功能区识别方法对比[J].北京测绘,2024,38(12):1652-1658,7.基金项目
北京市自然科学基金(8222011) （8222011）
