To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China,this study uses the coupling coordination degree model and ge...To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China,this study uses the coupling coordination degree model and geographically and temporally weighted regression(GTWR)model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990.The results are as follows.First,the urban-rural coupling coordination degree in Northeast China was very low and improved slowly,but its stages of evolution is a good interpretation of the strategic arrangements of China's urbanization.Second,the urban-rural coupling coordination degree in Northeast China had spatial differences and was characterized by central polarization,converging on urban agglomeration,which was high in the south and low in the north.Moreover,the gap between the north and south weakened.Third,the spatial-temporal evolution of the urban-rural coordination relationship in Northeast China was influenced by pulling from the central cities,pushing from rural transformation,and government regulations.The influence intensity of the three mechanisms was weak,but the pulling from the central cities was stronger than that of the other two mechanisms.Furthermore,the spatial difference between the three mechanisms determines the spatial pattern and its evolution of the urban-rural coordination relationship in Northeast China.Fourth,to promote the development of urban-rural coordination in Northeast China,it is essential to advance urban-rural economic correlation,enhance the government^role in regulating and guiding,and adopt different policies for each region in Northeast China.展开更多
轨道交通客流量影响因素是轨道交通方面研究的一个关注点,不同站点客流量的时空非平稳性被认为与站域建成环境有关。通过构建时空地理加权(geographically and temporally weighted regression,GTWR)模型,揭示了土地多样性、密度、站点...轨道交通客流量影响因素是轨道交通方面研究的一个关注点,不同站点客流量的时空非平稳性被认为与站域建成环境有关。通过构建时空地理加权(geographically and temporally weighted regression,GTWR)模型,揭示了土地多样性、密度、站点属性3个方面因素在时间和空间维度上对天津市轨道交通客流量的影响。结果表明:相较于传统的地理加权(geographically weighted regression,GWR)模型和最小二乘法(ordinary least squares,OLS)模型,GTWR具有更好的拟合优度;公交站点密度对轨道交通客流产生促进作用,尤其在工作日的早晚高峰时段和中心城区位置;市中心的商业设施在工作日晚高峰吸引更多的地铁乘客,而在近郊区它们在早高峰吸引更多的地铁乘客;人口密度促进轨道交通的客流量;充足的停车场设施数量可以吸引更多的轨道交通乘客。展开更多
文章选取成都市内一个65 km^(2)范围为研究区域,运用表征城市建成环境的6种兴趣点(point of interest,POI)和土地利用混合度数据,结合网约车订单数据,构建影响网约车客流的建成环境因素集,建立基于时空地理加权回归(geographically and ...文章选取成都市内一个65 km^(2)范围为研究区域,运用表征城市建成环境的6种兴趣点(point of interest,POI)和土地利用混合度数据,结合网约车订单数据,构建影响网约车客流的建成环境因素集,建立基于时空地理加权回归(geographically and temporally weighted regression,GTWR)模型的网约车客流影响模型,探究各因素与网约车客流之间的关系。相比于普通最小二乘(ordinary least squares,OLS)法和地理加权回归(geographically weighted regression,GWR)模型,采用GTWR模型能更好地解释城市建成环境因素对网约车客流的影响,并定量分析解释城市建成环境因素的时空异质性影响。研究结果表明:网约车客流主要受购物服务、公司企业、餐饮服务影响,且影响程度时空分布不均衡;土地利用混合度始终会抑制网约车的客流出行,但抑制程度较弱。研究结果可为网约车的运营管理提供参考。展开更多
为掌握河北省服务区驶入量的时空分布规律,构建了时空地理加权回归(geographically and temporally weighted regression,GTWR)模型,揭示了服务区规模、服务区地理区位、关联地区土地利用、高速公路类型等因素在时间和空间上对服务区不...为掌握河北省服务区驶入量的时空分布规律,构建了时空地理加权回归(geographically and temporally weighted regression,GTWR)模型,揭示了服务区规模、服务区地理区位、关联地区土地利用、高速公路类型等因素在时间和空间上对服务区不同车型驶入量的影响。结果表明:时空地理加权回归模型的拟合结果显著优于最小二乘回归模型与地理加权回归模型;断面交通量对3种车型均具有促进作用,特别是在夏季高温地区服务区对于小型车驶入量促进作用显著;2~4 h车程范围内,风景名胜密度对小型车驶入量具有促进作用,且在旅游旺季及位于旅游业发达城市的服务区影响最显著;2~4 h车程范围内工商业型信息点(point of information,POI)密度对大中型车驶入量具有促进作用,特别是在货运高峰期及位于商贸发达城市的服务区促进作用显著;所属高速公路沿途资源型城市数量对服务区大型车驶入量具有显著促进作用,特别是在供暖季节。展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41401182,41501173)Youth Fund for Humanities and Social Sciences of the Ministry of Education of China(No.19YJC630177)+2 种基金Natural Science Foundation of Heilongjiang Province(No.LH2019D008)University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(No.UNPYSCT-2018194)Talent Introduction Project of Southwest University(No.SWU019020)。
文摘To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China,this study uses the coupling coordination degree model and geographically and temporally weighted regression(GTWR)model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990.The results are as follows.First,the urban-rural coupling coordination degree in Northeast China was very low and improved slowly,but its stages of evolution is a good interpretation of the strategic arrangements of China's urbanization.Second,the urban-rural coupling coordination degree in Northeast China had spatial differences and was characterized by central polarization,converging on urban agglomeration,which was high in the south and low in the north.Moreover,the gap between the north and south weakened.Third,the spatial-temporal evolution of the urban-rural coordination relationship in Northeast China was influenced by pulling from the central cities,pushing from rural transformation,and government regulations.The influence intensity of the three mechanisms was weak,but the pulling from the central cities was stronger than that of the other two mechanisms.Furthermore,the spatial difference between the three mechanisms determines the spatial pattern and its evolution of the urban-rural coordination relationship in Northeast China.Fourth,to promote the development of urban-rural coordination in Northeast China,it is essential to advance urban-rural economic correlation,enhance the government^role in regulating and guiding,and adopt different policies for each region in Northeast China.
文摘轨道交通客流量影响因素是轨道交通方面研究的一个关注点,不同站点客流量的时空非平稳性被认为与站域建成环境有关。通过构建时空地理加权(geographically and temporally weighted regression,GTWR)模型,揭示了土地多样性、密度、站点属性3个方面因素在时间和空间维度上对天津市轨道交通客流量的影响。结果表明:相较于传统的地理加权(geographically weighted regression,GWR)模型和最小二乘法(ordinary least squares,OLS)模型,GTWR具有更好的拟合优度;公交站点密度对轨道交通客流产生促进作用,尤其在工作日的早晚高峰时段和中心城区位置;市中心的商业设施在工作日晚高峰吸引更多的地铁乘客,而在近郊区它们在早高峰吸引更多的地铁乘客;人口密度促进轨道交通的客流量;充足的停车场设施数量可以吸引更多的轨道交通乘客。
文摘文章选取成都市内一个65 km^(2)范围为研究区域,运用表征城市建成环境的6种兴趣点(point of interest,POI)和土地利用混合度数据,结合网约车订单数据,构建影响网约车客流的建成环境因素集,建立基于时空地理加权回归(geographically and temporally weighted regression,GTWR)模型的网约车客流影响模型,探究各因素与网约车客流之间的关系。相比于普通最小二乘(ordinary least squares,OLS)法和地理加权回归(geographically weighted regression,GWR)模型,采用GTWR模型能更好地解释城市建成环境因素对网约车客流的影响,并定量分析解释城市建成环境因素的时空异质性影响。研究结果表明:网约车客流主要受购物服务、公司企业、餐饮服务影响,且影响程度时空分布不均衡;土地利用混合度始终会抑制网约车的客流出行,但抑制程度较弱。研究结果可为网约车的运营管理提供参考。
文摘为掌握河北省服务区驶入量的时空分布规律,构建了时空地理加权回归(geographically and temporally weighted regression,GTWR)模型,揭示了服务区规模、服务区地理区位、关联地区土地利用、高速公路类型等因素在时间和空间上对服务区不同车型驶入量的影响。结果表明:时空地理加权回归模型的拟合结果显著优于最小二乘回归模型与地理加权回归模型;断面交通量对3种车型均具有促进作用,特别是在夏季高温地区服务区对于小型车驶入量促进作用显著;2~4 h车程范围内,风景名胜密度对小型车驶入量具有促进作用,且在旅游旺季及位于旅游业发达城市的服务区影响最显著;2~4 h车程范围内工商业型信息点(point of information,POI)密度对大中型车驶入量具有促进作用,特别是在货运高峰期及位于商贸发达城市的服务区促进作用显著;所属高速公路沿途资源型城市数量对服务区大型车驶入量具有显著促进作用,特别是在供暖季节。
