Research On Commuting Space In Urban Center Based On Big Data Of Mobile Signaling

With the accelerating urbanization,the rapid growth of urban population,the expansion of urban residents’ travel scale,and the increasingly serious urban problems characterized by traffic congestion,the commuting space of cities has become an important element of urban development research.With the popularity of cell phone communication devices,cell phone signaling data has become a new data source for commuting space research.At present,domestic and foreign scholars have studied commuting space from different data sources and methods,but there are few analyses of urban multi-scale commuting space as well as few studies combining multi-source data such as mobile phone signaling and geographic detectors on the impact factors of commuting volume.Based on the 2017 cell phone signaling data,this paper takes the central urban area within the outer ring highway of Nanning City as the research area,identifies the resident population in the central urban area,analyzes the spatial and temporal distribution characteristics of the population,extracts the population in the workplace,constructs the population commuting space,analyzes the commuting space distribution characteristics,combines the population data identified by cell phone signaling,POI data,OSM data,land use type data,housing prices and other data multi-source data and geographic probes to study the factors affecting commuting volume.The main work of the study is reflected in the following aspects:(1)Construct a multi-scale population spatiotemporal distribution model based on mobile signaling data.Based on mobile phone signaling data,construct a population spatiotemporal distribution model at the grid block street scale,analyze the spatiotemporal distribution characteristics of the population,and perform a fit analysis with the seventh population census data.The results indicate that the population data identified based on mobile phone signaling data is highly consistent with the population data of the "Seven Prevalences".At the grid scale,the urban center gathers a large number of high-density and medium to high-density population distribution grids,and presents a spatial distribution pattern of population density "multi core outward distance attenuation".At the block scale,the population density shows a spatial distribution pattern of "decreasing circle structure",with a high concentration of population in the core circle,and the population density gradually decreasing towards the outer circle.On the street scale,the population density distribution shows a spatial distribution pattern of "dense distribution in the central urban area,and reduced density in the periphery of the urban area".Among them,Hengyang Street,Beihu Street,Xinzhu Street,and Huaqiang Street in the central urban area are always densely populated areas in an average day,and the population density outside decreases in turn.(2)Construct a multi-scale urban commuting space model.Based on the occupancy and residence algorithm to extract the population of occupancy and residence,we construct the occupancy and residence OD commuting space and analyze the distribution characteristics of commuting space in the central city of Nanning.The results show that at the grid scale,the commuting scale is unevenly distributed,and the east-west commuting scale is larger than the north-south commuting scale.At the neighborhood scale,the large-scale commuting population is strongly aggregated,and the commuting distance is shorter in areas with large commuting population.At the street scale,the internal commuting population is larger than the cross-street commuting population,and the cross-street commuting flow is more and the flow scale is unevenly distributed,with most of the commuting population concentrated in 2-3 streets for commuting.(3)Combining multi-source data and geographic detectors to study the influencing factors of commuting volume in central urban areas.Combining population data,POI data,OSM data,land use type data,and housing price data identified by mobile signaling,analyze the factors affecting commuting volume based on geographic detectors.The results indicate that from the perspective of single factor detection,the size of single factor impact values is as follows: permanent population factor>residential distribution factor>medical facilities factor>entertainment facilities factor>catering service factor>workplace distribution factor>commuting distance factor>bus stop factor>road network density factor>land use type factor>housing price factor,indicating that the permanent population,residential distribution,medical facilities,entertainment facilities Catering services and workplace distribution are important control factors affecting commuting scale,and the interaction between permanent population factors,residential distribution factors,and housing price factors can reach the strongest impact value.The research results are beneficial for understanding the commuting laws of cities and providing scientific basis for urban spatial planning.