Individual Socio-Economic Level Assessment Based on Trajectory Activity Semantics
Assessment of individual Socio-Economic Levels(SEL)is crucial for business decisions,urban planning,and public health.However,current methods highly rely on location data and call detail records to construct travel locations and mobile business features,which is inadequate to represent the semantic context of individual travel,and fail to understand the motivations and demands of travel activities.Consequently,it makes the modeling process lack interpretability.To address aforementioned issue,this paper proposes a novel assessment method of individual socio-economic levels based on the analysis of trajectory activity semantics.It models individual socio-economic levels from the perspectives of consumption ability and willingness by explicitly extracting six consumption patterns including residence,shopping,dining,entertainment,consumption preferences and exploration,thereby enhancing the interpretability of the assessment method.Specifically,①Stay points extracted from trajectories are categorized into four types of activities,including residence,shopping,dining,and entertainment,by tagging semantic context through a grid-based semantic map;②Spatiotemporal and semantic features such as temporal entropy,gyration radius,and economic level of activity areas,are calculated for the four activities respectively.We then employ the structural equation model to select appropriate features for measuring the values of consumption patterns;③ Extreme random forest is utilized to assess individual socio-economic levels using the values of six consumption patterns,which is calculated based on the economic levels of regions where an individual stays in the travel activities,as well as the preferences for visiting these regions.We use GPS trajectories of 635 anonymous private car drivers in Shenzhen city of China from April to November in 2019 as experimental data,and assess individual socio-economic levels for each driver.The effectiveness of the proposed method is validated by selecting representative individuals with high and low socio-economic levels from five typical scenarios i.e.,central business districts,labor-intensive factories,premium residences,and urban villages,which demonstrates alignment between the calculated socio-economic levels of individuals and the depicted value of the scenarios.Besides,we analyze the spatiotemporal distribution and work intensity of different socio-economic level groups,and explore their differences in travel patterns.The findings indicate that individuals with a higher socio-economic level tend to have more flexible morning commutes,and exhibit a smoother travel distribution in the afternoon.It also presents a more concentrated spatial distribution in terms of their activity areas,which is consistent with the urban structures of Shenzhen.In summary,the proposed method can provide a reference for modeling demographic characteristics of individuals from the perspective of human-environment interaction.
NETL
NSTL
万方数据
