Spatial And Temporal Variation Of Urban Vitality In Zhengzhou City Based On Multi-Source Big Date And Influencing Factors

Urban vitality is an important prerequisite for achieving sustainable urban development.Vibrant urban spaces are considered to improve people’s subjective perception of urban life,attract investment,enhance competitiveness,and achieve sustainable development.Thus,urban vitality has become an important indicator for city performance evaluation in the era of stock planning.The development of multisource big data provides opportunities for urban vitality research.How to assess the spatial and temporal variation characteristics of urban vitality at fine scale and how to propose targeted vitality enhancement strategies become important issues to be solved.The article uses Baidu population heat,Points of Interest(POI)and built environment data,and uses kernel density analysis,spatial autocorrelation,and multi-scale geographically weighted regression to study the time-by-time neighborhood population heat changes of 4484 neighborhoods in the main urban area of Zhengzhou in 2022 on typical weekdays and rest days to reveal the spatial and temporal heterogeneity of urban vitality,and then explore the multi-scale mechanism of urban vitality,and on this basis,propose recommendations related to the creation and optimization of neighborhood vitality.The results found that:(1)Urban population vitality shows different spatial distribution and temporal rhythm on weekdays and rest days.In general,urban population vitality has a polycentric spatial distribution,but the level of vitality is higher on weekdays,and there are more high-value centers.On weekdays,the urban population converges from the periphery to the center during the day(8:00-20:00)and spreads from the center to the periphery at night(18:00-24:00),showing the characteristic of "separation of employment and residence".Compared with the rest days,the peak of population vitality occurs on weekdays from 0:00 to 6:00,especially in the peripheral areas between the 3rd and 4th rings around 6:00.Population vitality decreases during the daytime(8:00-20:00)on rest days,with fewer high-vitality centers.In the evening(20:00-24:00)there is a tendency for the high value areas of population vitality to cluster towards the periphery.(2)Neighborhood population vitality shows a strong spatial autocorrelation.Neighborhoods with high high(HH)population vitality clustering are mainly distributed within the third ring.High(low)neighborhood vitality tends to positively influence the vitality of its neighboring neighborhoods,with a significant "Matthew effect".The highest number of neighborhoods with high(HH)vibrancy is found between 22:00 and 24:00 on weekdays and rest days,accounting for 10% of all neighborhoods.However,the time differences between the second and third rings of HH clustering neighborhoods are large.Commercial centers and transportation hubs,such as Erqi business district,Wanda Plaza,and high-speed railway stations disappear from 0:00-6:00 on weekdays in the high-high(HH)clustering spatial pattern,and6:00-24:00 again becomes the center of high population vitality.On the rest days,0:00-24:00 has been showing a higher agglomeration pattern.The low-low(LL)clustering neighborhoods are mainly distributed between the third and fourth rings,accounting for 22.3% of all neighborhoods.(3)The Multi-scale geographically weighted regression(MGWR)model takes into account spatial heterogeneity,scale effects,and noise and bias of regression coefficients to better capture the global and local drivers of urban vitality.The four factors of volume ratio,building density,road network density and density of living service facilities show global driving characteristics in general,and are characterized by strong driving effects and low spatial heterogeneity.The higher the volume ratio,the higher the building and road network density,the higher the urban population vitality.The higher the density of living facilities,the lower the urban population vitality.(4)Functional mix,density of commercial facilities and accessibility of public transportation are local drivers with weak driving effects and high spatial heterogeneity.Functional mix has a negative effect on the increase of urban population vitality on weekdays,but has a positive effect on the increase of population vitality in urban centers on rest days.The functional density of commercial facilities has a particularly significant effect on the enhancement of vitality in areas within the second ring and between the third and fourth rings.The accessibility of public transportation has a strong positive effect on the increase of population vitality in general,but a weak effect on the increase of population vitality in some areas between the third and fourth rings.(5)The impact of urban economic level on population vitality is global on rest days,but shows local structural changes on weekdays.On weekdays,population vitality tends to be higher in neighborhoods with higher income levels within the second ring.On rest days,the population vitality of neighborhoods with higher income levels,however,decreases.This study reveals the spatio-temporal heterogeneity of urban vitality through the time-by-time neighborhood population thermal changes on weekdays and rest days based on multi-source big data such as population thermal and POI,and explores the global and local influence mechanisms of built environment on urban population vitality,which not only provides a new method for dynamic monitoring of urban vitality at fine scale,but also provides a practical basis for neighborhood vitality creation and optimization.