Classification of Beijing Line 10 Subway Living Circle Based on Multi-source Big Data

In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.

Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.

A Multi-Scale Method for PM2.5 Forecasting with Multi-Source Big Data

In the age of big data,the Internet big data can finely reflect public attention to air pollution,which greatly impact ambient PM2.5 concentrations;however,it has not been applied to PM2.5 prediction yet.Therefore,this study introduces such informative Internet big data as an effective predictor for PM2.5,in addition to other big data.To capture the multi-scale relationship between PM2.5 concentrations and multi-source big data,a novel multi-source big data and multi-scale forecasting methodology is proposed for PM2.5.Three major steps are taken:1)Multi-source big data process,to collect big data from different sources(e.g.,devices and Internet)and extract the hidden predictive features;2)Multi-scale analysis,to address the non-uniformity and nonalignment of timescales by withdrawing the scale-aligned modes hidden in multi-source data;3)PM2.5 prediction,entailing individual prediction at each timescale and ensemble prediction for the final results.The empirical study focuses on the top highly-polluted cities and shows that the proposed multi-source big data and multi-scale forecasting method outperforms its original forms(with neither big data nor multi-scale analysis),semi-extended variants(with big data and without multi-scale analysis)and similar counterparts(with big data but from a single source and multi-scale analysis)in accuracy.