There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteri...There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.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
为更好地调度出租车运力,缓解热点载客区域出租车供需不平衡现象,需探究出租车需求的时空分布特征及其影响因素。鉴于此,基于出租车GPS数据、计价器数据、公共交通刷卡数据和兴趣点(Point of Interesting,POI)数据等多源异构数据,结合...为更好地调度出租车运力,缓解热点载客区域出租车供需不平衡现象,需探究出租车需求的时空分布特征及其影响因素。鉴于此,基于出租车GPS数据、计价器数据、公共交通刷卡数据和兴趣点(Point of Interesting,POI)数据等多源异构数据,结合相关性分析法对区域出租车出行需求影响因素进行筛选,建立多维度的影响因素集,构建基于地理加权回归的区域出租车出行需求影响模型。以北京市1 398个交通小区的数据为例,分析不同时空条件下各影响因素对出租车出行需求的影响程度。结果表明:出租车出行需求空间分布具有空间集聚效应,影响因素对出租车需求的影响程度具有空间非稳态特征;各中心区域住宅密度、周边且公司密集区域办公密度及城市外围区域的休闲娱乐服务密度对出租车出行需求有很强的正影响;城市外围区域住宅密度、各中心区域办公密度与出租车出行需求呈负相关;非工作日休闲娱乐服务密度对出租车出行需求促进作用明显大于工作日;区域公共交通产生量对出租车出行需求的影响早、晚高峰差异显著。通过模型对比分析可知,所建模型具有较高的精度,适用于解释各影响因素对出租车出行需求影响的时空差异性。展开更多
为捕捉由轨道交通站点周边建成环境与客流时变特征的互动关系而反映的站点类型差异,基于地铁刷卡数据与站点周边兴趣点(Point of Interest,POI)数据,分别通过客流时间序列分析和地理加权回归模型进行时空维度聚类变量提取.应用K-means+...为捕捉由轨道交通站点周边建成环境与客流时变特征的互动关系而反映的站点类型差异,基于地铁刷卡数据与站点周边兴趣点(Point of Interest,POI)数据,分别通过客流时间序列分析和地理加权回归模型进行时空维度聚类变量提取.应用K-means++聚类算法将杭州地铁1、2、4号线站点划分为工作导向型、居住导向型、商业型以及工作-居住混合型4种类型.研究结果表明:该方法相对于传统K-means算法具有更优的性能表现,其中轮廓系数、Davies-Bouldin指数与Calinski-Harabaz指数等3项聚类评价指标的改善幅度分别为30.43%、10.51%、9.02%,因而能够准确识别时空视角下的轨道交通站点类型并反映其客流出行模式,进而为站点客流预测、站城一体化建设等后续研究提供分析依据.展开更多
Urban landscape forms can be effective in reducing increasing PM_(2.5) concentrations due to urbanization in China,making it crucially important to accurately quantify the spatiotemporal impact of urban landscape form...Urban landscape forms can be effective in reducing increasing PM_(2.5) concentrations due to urbanization in China,making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM_(2.5) variations.Three landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020,using a spatiotemporal geographically weighted regression model,random forest models and partial dependence plots.The results show that there are spatiotemporal differences in the impacts of landscape indices on PM_(2.5).the proportion of urban green infrastructure(PLAND-UGI)and the fractal dimension of urban green infrastructure(FRACT-UGI)exacerbate PM_(2.5) concentrations in the northwest,the proportion of impervious surfaces(PLAND-Impervious)mitigates air pollution in northwest and southwest China,and shannon’s diversity index(SHDI)has seasonal differences in the northwest.PLAND-UGI is the landscape index with the largest contribution(30%)and interpretable range.The relationship between FRACT and PM_(2.5) was more complex than for other landscape indices.The results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM_(2.5),contributing to clean urban development and sustainable development.展开更多
基金Under the auspices of National Social Science Foundation of China (No.21BJY202)。
文摘There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.
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。
文摘为更好地调度出租车运力,缓解热点载客区域出租车供需不平衡现象,需探究出租车需求的时空分布特征及其影响因素。鉴于此,基于出租车GPS数据、计价器数据、公共交通刷卡数据和兴趣点(Point of Interesting,POI)数据等多源异构数据,结合相关性分析法对区域出租车出行需求影响因素进行筛选,建立多维度的影响因素集,构建基于地理加权回归的区域出租车出行需求影响模型。以北京市1 398个交通小区的数据为例,分析不同时空条件下各影响因素对出租车出行需求的影响程度。结果表明:出租车出行需求空间分布具有空间集聚效应,影响因素对出租车需求的影响程度具有空间非稳态特征;各中心区域住宅密度、周边且公司密集区域办公密度及城市外围区域的休闲娱乐服务密度对出租车出行需求有很强的正影响;城市外围区域住宅密度、各中心区域办公密度与出租车出行需求呈负相关;非工作日休闲娱乐服务密度对出租车出行需求促进作用明显大于工作日;区域公共交通产生量对出租车出行需求的影响早、晚高峰差异显著。通过模型对比分析可知,所建模型具有较高的精度,适用于解释各影响因素对出租车出行需求影响的时空差异性。
文摘为捕捉由轨道交通站点周边建成环境与客流时变特征的互动关系而反映的站点类型差异,基于地铁刷卡数据与站点周边兴趣点(Point of Interest,POI)数据,分别通过客流时间序列分析和地理加权回归模型进行时空维度聚类变量提取.应用K-means++聚类算法将杭州地铁1、2、4号线站点划分为工作导向型、居住导向型、商业型以及工作-居住混合型4种类型.研究结果表明:该方法相对于传统K-means算法具有更优的性能表现,其中轮廓系数、Davies-Bouldin指数与Calinski-Harabaz指数等3项聚类评价指标的改善幅度分别为30.43%、10.51%、9.02%,因而能够准确识别时空视角下的轨道交通站点类型并反映其客流出行模式,进而为站点客流预测、站城一体化建设等后续研究提供分析依据.
基金funded by the Natural Science Foundation of Hunan Province,China(2023JJ40443)the Outstanding Youth Project of Hunan Provincial Education Department(22B0088 and 22B0055)+1 种基金the Joint Fund for Regional Innovation and Development of the National Natural Science Foundation(U22A20570)the Science and Technology Innovation Program of Hunan Province(2022RC4027),China.
文摘Urban landscape forms can be effective in reducing increasing PM_(2.5) concentrations due to urbanization in China,making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM_(2.5) variations.Three landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020,using a spatiotemporal geographically weighted regression model,random forest models and partial dependence plots.The results show that there are spatiotemporal differences in the impacts of landscape indices on PM_(2.5).the proportion of urban green infrastructure(PLAND-UGI)and the fractal dimension of urban green infrastructure(FRACT-UGI)exacerbate PM_(2.5) concentrations in the northwest,the proportion of impervious surfaces(PLAND-Impervious)mitigates air pollution in northwest and southwest China,and shannon’s diversity index(SHDI)has seasonal differences in the northwest.PLAND-UGI is the landscape index with the largest contribution(30%)and interpretable range.The relationship between FRACT and PM_(2.5) was more complex than for other landscape indices.The results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM_(2.5),contributing to clean urban development and sustainable development.
