Research On Method Of Urban Job-housing Space Identification Based On Open Travel And POI Data

As the urbanization process continues to advance and the population scale continues to expand,the construction of a large number of suburban new cities and industrial parks has emerged,housing market reforms have been completed,and the urban transportation infrastructure has been gradually improved and the transportation methods have become increasingly rich.Substantial changes have taken place,and the characteristics of urban job-housing space in China have undergone profound changes.The phenomenon of separation and dislocation of urban job-housing space has gradually become more prominent.The imbalance in urban job-housing space has continued to increase,resulting in traffic congestion,residential isolation and environmental pollution.A series of "urban diseases" has become an important issue facing sustainable urban development.Commuting,as a travel behavior caused by the separation of urban residents’ employment and residence,how to effectively grasp the distribution characteristics and relationships of urban residents’ job-housing space has become the foundation and key to effectively alleviate the separation of occupational and residential and the series of urban diseases it causes.Research on traditional job-housing space and commuting characteristics are mostly based on questionnaires and census statistics,which not only requires a lot of manpower and material resources,but also has the disadvantages of long data acquisition time period,small sample size,unrepresented sample group,and single data source,affecting the accuracy of the research results.In recent years,with the continuous development and wide application of perceptual positioning technology and information and communication technology(ICT),the data including the spatiotemporal trajectory of residents’ travel behavior has gradually enriched,providing new opportunities and conditions for urban space research and mining of travel patterns for residents.In this paper,open travel data and POI data are used as research data,and Haikou City is used as the experimental area.According to the characteristics of travel data and POI data,job-housing space recognition method based on travel flow model and POI quantitative identification method were constructed respectively.Then,the recognition results of the two methods were fused to construct a job-housing space recognition method which fused the two kinds of data,and the recognition results were verified and analyzed based on Aude map.Finally,the paper analyzes the commuting time of urban residents based on the working and living space recognition results,Commute distance,commute flow characteristics,construct outbound/outbound commuting rate,internal and external commuting ratio and other indicators to analyze the urban job-housing balance situation.The main contributions of this article are as follows:(1)The open travel data set is introduced into the study of urban job-housing space,and a method for identifying job-housing space based on a travel flow model is proposed.Open Travel Data,as a new type of trajectory data that includes urban residents’ travel information in the Internet era,provides a new perspective for the study of urban professional living space.This study introduces this data,analyzes the time distribution characteristics of travel data,selects time periods with living and working characteristics,proposes a travel flow model,and constructs residence and work factors for job-housing space identification.(2)Construct a quantitative identification method for POI.The urban POI data reflects the static spatial distribution of various geographical entities in the city.In this paper,the POI frequency density and the POI category ratio index are used to identify the occupational and residential space by counting the number of various POIs in the grid,and 50% is selected to judge the functional nature of the unit.When the proportion of a certain type of POI in the grid cell is 50% or more,the grid cell is determined to be a single functional area of POI nature;When the proportion of POI of all types in the grid cell does not reach 50%,the functional area is defined as a mixed functional area.(3)An identification method based on a combination of travel flow model and quantitative POI identification method is proposed.Open travel data contains the spatio-temporal information of passengers’ boarding and disembarking points,reflecting passenger travel characteristics;POI data contains rich semantic information,which is a true reflection of the spatial distribution of urban geographic entities.The job-housing space identification method based on the travel flow model analyzes the dynamic changes of urban populations and the characteristics of movement patterns,calculates the difference in the flow volume in each study area,and identifies the distribution of occupational and residential locations.To the static spatial distribution of various service facilities in the study area,there may be over-identification.The quantitative identification method of POI is based on the current static distribution characteristics of urban service facilities to identify the distribution of various functional areas in the city.However,the POI data of the city often has the disadvantages of not being updated in time and being weak,which affects the results of urban job-housing space identification.Therefore,this study combines the advantages and disadvantages of two types of data,and integrates the static recognition results of POI on the basis of dynamic recognition,and accurately identifies the spatial distribution of job-housing space in cities.(4)Analysis and verification of identification results.In this paper,Haikou City is selected as the experimental area to compare and analyze the recognition results of the three recognition methods.Finally,12 streets including Bailong Street,Baisha Street,Fucheng Town,Guoxing Street,and Haidian Street are dominated by residential types;Xixiu Streets and Xiuying Streets are dominated by service types;Binhai Street,Jinmao Street and Pingnan Street are dominated by mixed service and residence types;Bo’ai Street and Zhongshan Street are dominated by mixed service and service types;Chengxi Street and state-owned Guilinyang The four streets,such as the farm,are dominated by work types;Haixiu Street is a mixed residential and service area.Finally,based on the Gaode map,with the help of road assistance,select a unified grid unit on the Gaode map and the experimental area to verify the recognition results of the fusion of open travel data and POI.The results show that the recognition results are basically consistent with the actual situation.The experimental results are relatively accurate.(5)This paper proposes a job-resident balance analysis method based on job-resident space recognition results.Based on the result of job-housing space identification,the paper analyzes the characteristics of commuting time,commuting distance and commuting flow of urban residents,and constructs indicators such as outgoing/outgoing commuting rate and internal/external commuting ratio to analyze the urban job-housing balance.The verification results of Haikou City as an example show that the commuting mode in Haikou City is based on single-center commuting,with a high commuting rate inside and outside the central group,a high degree of mixed work-living,and a high level of job-housing balance.The internal and external commuting rate of the Jiangdong Group is relatively low,the external commuting rate is greater than the external commuting rate,the region is more attractive,and the overall presentation of the working characteristics;the internal and external commuting rate of the Changliu Group is the lowest,the external and external commuting rates are high,and the level of regional job-housing balance is low.