| 摘要: |
| 户外骑行活动作为一种休闲活动和
零碳交通方式，深受骑行爱好者和通勤人士
的喜爱。城市化的快速发展加剧了城市的热
岛效应，使得越来越多的人口暴露于城市高
温环境中，也必然影响居民开展户外骑行的
意愿。基于景观格局指数、线密度分析、双
变量空间自相关，分析北京市高温暴露景观
格局、刻画骑行路线偏好选择的密度特征、
探究高温暴露与户外休闲骑行之间的空间关
联，提出优化策略，进而为城市的友好骑行
建设提供参考依据。结果表明：一、北京市
的地表温度整体上从东城区和西城区等中心
城区向外呈递减趋势，城市的建成区面积高
于山地和丘陵区域，不同区的温度景观格局
不同。二、受骑行者欢迎路线的空间分布较
为集中，这些区域距离市区适中，可以满足
骑行者亲近自然的需求。三、地表温度与骑
行路线的选择偏好存在显著的空间负相关，
地表温度会显著的影响骑行者的路线选择，
并且空间格局呈“离散”状态。高温暴露—
低偏好和低温暴露—高偏好的骑行路空间分
布最多。 |
| 关键词: 户外骑行 高温暴露 景观格局 空间关联 |
| DOI：10.13791/j.cnki.hsfwest.20240519001 |
| 分类号: |
| 基金项目:国家社科基金年度项目（22BTY020） |
|
| Spatial correlation of high temperature landscape pattern and outdoor leisure cycling in Beijing |
|
XIE Yuanjiang,XU Haibin,HUANG Jiantuan
|
| Abstract: |
| Outdoor cycling activities, as both a leisure activity and a zero-carbon mode of transportation,
are popular among cycling enthusiasts and commuters. However, rapid urbanization has exacerbated the
urban heat island effect, exposing more people to high-temperature environments, which, in turn, affects
residents’ willingness to engage in outdoor cycling. This study aims to explore the spatial correlation
between the landscape pattern of surface high-temperature exposure and outdoor cycling activities in
Beijing, evaluate the impact of high-temperature exposure on route selection, and propose optimization
strategies to inform the creation of a cycling-friendly urban environment. This study analyzes the
landscape pattern of high-temperature exposure in Beijing, characterizes the preference density features
of cycling routes, and investigates the spatial correlation between high-temperature exposure and
outdoor leisure cycling using landscape pattern indices, line density analysis, and bivariate spatial
autocorrelation. Landsat 8 Level 2 remote sensing data, specifically the ST_B10 band, were used to
obtain the surface temperature of Beijing. The landscape pattern indices, including the number of
patches, mean patch size, Shannon’s diversity index, and aggregation index, were applied to describe
the spatial distribution of temperature. Cycling route data were obtained from the Xingzhe platform, and
a spatial density analysis of cycling preferences was conducted. A total of 465 cycling routes were
collected, including starting and ending coordinates, average slope, elevation, distance, and download
volume. Using ArcGIS software, a 100-meter buffer was constructed around each route, and the spatial
characteristics were extracted. Line density analysis was also performed, with density values weighted
by route downloads. Bivariate global and local spatial autocorrelation analyses were conducted to
explore the spatial relationship between high-temperature exposure and cycling preferences. Global
spatial autocorrelation results show a significant negative correlation between surface temperature and
cycling route preference, indicating that higher surface temperatures lead to lower cycling preferences.
Local spatial autocorrelation further reveals four types of spatial relationships: high-temperature
exposure—high preference (H-H), high-temperature exposure—low preference (H-L), low-temperature
exposure—high preference (L-H), and low-temperature exposure—low preference (L-L). H-H areas are
primarily cycling routes with beautiful natural scenery but lacking shading facilities, attracting cyclists
despite high temperatures. H-L areas are mostly in central urban areas with dense buil湤杩?摧敳洠潡湮獤琊物慮瑳極潦湦?穣潩湥敮獴?楧湲?????慰牡散慥猬?瑲潥?極湬瑴敩杮牧愠瑩敮?湴慨瑥甠牬慯汷?汳慴渠摣獹捣慬灩敮獧?慰湲摥?捥畲汥瑮畣牥慳氮?牌攭獈漠畡牲捥敡獳??灲牥漠浬潯瑣楡湴来?挠祩据氊業湯杵?捴畡汩瑮畯牵敳??呥桧敩?普楳渠摬楩湫来猠?潥普?瑯桵楧獯?猠瑡畮摤礠?桨楡杮桧汰楩杮桧琬?瑷桨敥?湥攠杬慯瑷椠癴敥?楰浥灲慡捴瑵?潥晳?桡楮杤栠?瑡敶浯灲敡牢慬瑥甠牣敯?敤硩灴潩獯畮牳攊?潮湣?捥祡捳汥椠湣杹?慬捩瑳楴癳椙琠楩敮獴?慲湥摳?攮洠灌栭慌猠楡穲敥?瑳栠敡?楥洠灩潮爠瑲慥湭捯整?漠晡?来牡敳攠湦?獲瀠慦捲敯??獴桨慥搠楣湩杴?映慣捥楮汴楥瑲椬攠獷??慣湨搬?潤灥瑳楰浩楴穥攠摨?楶湩普牧愊獬瑯牷略捲琠畴牥敭?楥湲?畴牵扲慥湳?瀠污慲湥渠楬湥杳??灰牲潥癦楥摲楲湥杤?慤?獥挠楴敯渠瑡椠晬楡捣?戠慯獦椠獮?晴潵牲?捬爠敳慣瑥楮湥杲?愠?据祤挠汳極湰杰?晲牴楩敮湧搠汦祡?畩牬扩慴湩?敳渮瘠楔牨潥渊浲敥湳瑵???甠瑳畨牯敷?牴敨獡整愺爠挱栩?捔潨略氠摯?晥畲牡瑬桬攠牳?敲硦灡汣潥爠整?瑭桰敥?牡整汵慲瑥椠潩湮猠桂楥灩?扩敮瑧眠敤敥湣?桥楡杳桥?琠敦浲灯敭爠慴瑨略爠散?敮硴灲潡獬甠牵敲?慡湮搠?潲瑥桡敳爬?晳慵捣瑨漠牡獳?慄景普敧捣瑨楥湮杧?捄祩捳汴楲湩杣??慡湮摤?牘敩癣敨慥汮?琠桄敩?捴潲浩灣汴攬砠?極湴瑷敡牲慤挮琠楔潨湥猠?扵敩瑬睴攭敵湰?畡牲扥慡渠?瑦栠整牨浥愠汣?整湹瘠楩牳漠湬浡敲湧瑥獲?慴湨摡?挊祴捨污楴渠杯?瀠牭敯晵敮牴敡湩据敯獵?琠桡牮潤甠杨桩?浬畹氠瑡楲?獡潳甬爠捡敮?搠慴瑨慥?慴湥慭汰祥獲楡獴??潥映晬敡牮楤湳杣?捰潥洠灰牡整桴敥湲獮椠癶敡?瑩桥敳漠物敮琠楤捩慦汦?獲略灮灴漠牡瑲?晡潳爮′椩洊灔牨潥瘠楳湰条?畩牡扬愠湤?敳湴癲楩牢潵湴浩敯湮琠獯? popular cycling routes is relatively concentrated, and these areas are located
at moderate distances from the city center, which can meet cyclists’ desire to get closer to nature. 3)
There is a significant spatial negative correlation between surface temperature and the preference for
cycling routes. Surface temperature significantly affects route selection, and the spatial pattern is in a
“discrete” state. High-temperature exposure—low preference and low-temperature exposure—highpreference are most prominent on cycling roads. Based on this analysis, this study proposes three optimization strategies to improve the cycling environment in
Beijing: First, increasing greening and shading facilities, particularly in H-H and H-L areas, by planting trees and shrubs, building rooftop gardens, and
implementing vertical greening to reduce urban heat accumulation and improve the cycling experience. Second, optimizing cycling infrastructure by improving the
quality of cycling paths, using permeable and heat-dissipating paving materials, and enhancing road lighting and signage to ensure nighttime safety. Third, the
government should invest in cycling infrastructure, particularly in H-L areas, by adding greening and shading facilities, promoting green travel through media, and
establishing cycli |
| Key words: outdoor cycling high temperature exposure landscape pattern spatial correlation |
