Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of ...Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.展开更多
城乡能源消费格局是应对气候变化的重要议题,也是低碳治理的关键对象。市域尺度城乡能耗空间定量化研究对区域可持续发展及规划决策具有重要支撑作用。基于山东省潍坊市行业部门综合能源消耗量,结合兴趣点(point of interest,POI)大数...城乡能源消费格局是应对气候变化的重要议题,也是低碳治理的关键对象。市域尺度城乡能耗空间定量化研究对区域可持续发展及规划决策具有重要支撑作用。基于山东省潍坊市行业部门综合能源消耗量,结合兴趣点(point of interest,POI)大数据和土地利用等多源空间数据,统筹考虑企业数量、人口密度、车流量及耕地面积等关键因素,量化分析工业、生活、交通及农业部门的能耗格网空间分布。研究发现:工业、交通及生活用能的空间分布具有相似性;能源总消费量在各区域间具有差异性,呈现不同的热点分布特征,以团簇热点式分布为主;核心城区的能耗需求强烈,高强度用能区覆盖率显著高于其他地区。通过分析市域尺度上的能源消费格网格局特征,探索公里格网尺度下融合多源数据的空间可视化比较分析方法,可为区域低碳规划、国土空间规划以及可持续发展提供理论支持。展开更多
利用FY-2G静止卫星数据反演的云宏微观特征参量(简称“云参量”),对2018—2020年青海全省及3个子研究区云参量时空分布特征进行分析。结果表明:云顶高度(cloud top height,CTH)、云顶温度(cloud top temperature,CTT)、过冷层厚度(overc...利用FY-2G静止卫星数据反演的云宏微观特征参量(简称“云参量”),对2018—2020年青海全省及3个子研究区云参量时空分布特征进行分析。结果表明:云顶高度(cloud top height,CTH)、云顶温度(cloud top temperature,CTT)、过冷层厚度(overcooled layer depth,OLD)、云光学厚度(cloud op⁃tical depth,COD)、云粒子有效半径(effective radius,ER)及液水路径(liquid water path,LWP)6个云参量全省区域年平均值分别为3.8 km、-9.7℃、2.0 km、7.1、7.1μm及63.7 g∙m^(-2)。纬度相同的柴达木盆地、青海东北部除CTT外,其余云参量月变化大致呈双峰双谷分布,峰值基本出现在5、11月,谷值基本出现在8、9月及12、1月,三江源各云参量大致呈单峰分布,峰值基本在11月。各云参量年平均值空间分布均呈沿地形和山脉走向分布的特征,除CTT外,其余云参量高值区与高大山脉相对应、低值区与沙漠盆地及低海拔地区相对应,柴达木盆地在四季均存在一低值区,夏季低值区范围最大,三江源地区及青海祁连山区在春、冬季存在明显高值区。三江源地区OLD、COD及LWP在春季及秋季较大,青海东北部地区OLD、LWP在春季最大,而春、秋季则是进行以水源涵养、抗旱减灾等为目的的人工增雨作业的较佳时机。展开更多
基金Under the auspices of Natural Science Foundation of China(No.41971166)。
文摘Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.
文摘城乡能源消费格局是应对气候变化的重要议题,也是低碳治理的关键对象。市域尺度城乡能耗空间定量化研究对区域可持续发展及规划决策具有重要支撑作用。基于山东省潍坊市行业部门综合能源消耗量,结合兴趣点(point of interest,POI)大数据和土地利用等多源空间数据,统筹考虑企业数量、人口密度、车流量及耕地面积等关键因素,量化分析工业、生活、交通及农业部门的能耗格网空间分布。研究发现:工业、交通及生活用能的空间分布具有相似性;能源总消费量在各区域间具有差异性,呈现不同的热点分布特征,以团簇热点式分布为主;核心城区的能耗需求强烈,高强度用能区覆盖率显著高于其他地区。通过分析市域尺度上的能源消费格网格局特征,探索公里格网尺度下融合多源数据的空间可视化比较分析方法,可为区域低碳规划、国土空间规划以及可持续发展提供理论支持。
文摘利用FY-2G静止卫星数据反演的云宏微观特征参量(简称“云参量”),对2018—2020年青海全省及3个子研究区云参量时空分布特征进行分析。结果表明:云顶高度(cloud top height,CTH)、云顶温度(cloud top temperature,CTT)、过冷层厚度(overcooled layer depth,OLD)、云光学厚度(cloud op⁃tical depth,COD)、云粒子有效半径(effective radius,ER)及液水路径(liquid water path,LWP)6个云参量全省区域年平均值分别为3.8 km、-9.7℃、2.0 km、7.1、7.1μm及63.7 g∙m^(-2)。纬度相同的柴达木盆地、青海东北部除CTT外,其余云参量月变化大致呈双峰双谷分布,峰值基本出现在5、11月,谷值基本出现在8、9月及12、1月,三江源各云参量大致呈单峰分布,峰值基本在11月。各云参量年平均值空间分布均呈沿地形和山脉走向分布的特征,除CTT外,其余云参量高值区与高大山脉相对应、低值区与沙漠盆地及低海拔地区相对应,柴达木盆地在四季均存在一低值区,夏季低值区范围最大,三江源地区及青海祁连山区在春、冬季存在明显高值区。三江源地区OLD、COD及LWP在春季及秋季较大,青海东北部地区OLD、LWP在春季最大,而春、秋季则是进行以水源涵养、抗旱减灾等为目的的人工增雨作业的较佳时机。
