Understanding Citizens’ emotion States under the Urban Livability Environment through Social Media Data: a Case Study of Wuhan

It is recognized that a city with a livable environment can bring happiness to residents.In this study,we explored the social media users’emotional states in their current living spaces and found out the relationship between the social media users’emotions and urban livability.We adopt six urban livability indicators(including education,medical services,public facilities,leisure places,employment,and transportation)to construct city livable indices.Also,the Analytic Hierarchy Process(AHP)spatial statistic method is applied to identify and analyze the different habitable regions of Wuhan City.In terms of citizen’s emotion analysis,we use Long Short-Term Memory(LSTM)neural network to analyze the Weibo text and obtain the Weibo users’sentiment scores.The correlation analysis of residents’emotions and city livability results shows a positive correlation between the livable city areas(i.e.,the area with higher livable ranking indices)and Weibo users’sentiment scores(with a Pearson correlation coefficient of 0.881 and P-Value of 0.004).In other words,people who post Weibo in high livability areas of Wuhan express more positive emotional states.Still,emotion distribution varies in different regions,which is mainly caused by people’s distribution and the diversity of the city’s functional areas.

A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data

Mixed use has been extensively applied as an urban planning principle and hinders the study of single urban functions.To address this problem,it is worth decomposing the mixed use.Inspired by the concept of spectral unmixing in remote sensing applications,this paper proposes a framework for mixed-use decomposition based on big geo-data.Mixeduse decomposition in terms of human activities differs from traditional land use research,and it is more reasonable to infer the actual urban function of land.The framework consists of four steps,namely temporal activity signature extraction,urban function base curve extraction,mixeduse decomposition,and result validation.First,the temporal activity signatures(TASs)of each zone are extracted as the proxy of human activity patterns.Second,the diurnal TASs of routine activities are extracted as urban function base curves(i.e.endmembers).Third,a linear decomposition model is used to decompose the mixed use and obtain multiple results(urban function composition,dynamic activity proportions,and the mixing index).Finally,result validation strategies are concluded.This framework offers method extensibility and has few requirements for the input data.It is validated by means of a case study of Beijing,based on a social media check-in dataset.