| 顾及人群集聚和情绪强度的城市综合活力评价及影响因素 |
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| 引用本文： | 梁立锋,曾文霞,宋悦祥,邵振峰,刘秀娟.顾及人群集聚和情绪强度的城市综合活力评价及影响因素[J].地球信息科学,2022,24(10):1854-1866. |
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| 作者姓名： | 梁立锋 曾文霞 宋悦祥 邵振峰 刘秀娟 |
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| 作者单位： | 1.岭南师范学院 地理科学学院,湛江 5240572.武汉大学 测绘遥感信息工程国家重点实验室,武汉 430079 |
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| 基金项目： | 国家自然科学基金项目(42090012);广东省哲学社会科学规划项目(GD20XYJ04);广东省科技创新战略专项资金(pdjh2021b0315);广东省科技创新战略专项资金(pdjh2022b0326);广东省教育厅基金项目(2019KTSCX089);岭南师范学院人才专项(ZL1936);广东省大学生创新创业训练计划(S202110579021);灾害天气国家重点实验室开放课题(2021LASW-A17) |
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| 摘 要： | 城市活力的科学定量评估,能够为城市规划和协调发展提供重要依据,针对城市活力容易忽略居民情感的现状,本文选用百度热力图数据与微博情感分析结果,分别衡量人群集聚强度和情绪强度,并结合TOPSIS方法,提出一种顾及人群集聚和情绪强度的综合活力评估框架。从城市物理环境、经济环境和生态环境3个维度,选择8个关键影响因子,结合地理探测器空间分析方法,探讨影响因子对城市活力空间异质性的影响。结果表明：① 融合人群集聚强度和情绪强度的综合活力评估方法,能够较好反映城市活力空间分异格局;通过对典型样本区域分析,验证了本文提出的城市综合活力评价框架的有效性;② 城市POI密度对城市综合活力的解释力最显著,而植被覆盖度因子对城市综合活力的解释力最弱;但是植被覆盖度因子与其他因子的交互作用,对于城市活力空间异质性的影响力提升最为显著,表明植被覆盖度因子并不是直接作用于城市活力的空间异质性,而是通过耦合空间可达性、POI密度以及建筑密度等影响因子,间接影响城市综合活力的空间分异。
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| 关 键 词： | 多源大数据 城市活力 综合感知 地理探测器 优劣解距离法 百度热力图 文本情感分析 |
| 收稿时间： | 2022-01-16 |
Urban Comprehensive Vitality Evaluation and Influencing Factors Analysis Considering Population Agglomeration and Emotional Intensity |
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| LIANG Lifeng,ZENG Wenxia,SONG Yuexiang,SHAO Zhenfeng,LIU Xiujuan.Urban Comprehensive Vitality Evaluation and Influencing Factors Analysis Considering Population Agglomeration and Emotional Intensity[J].Geo-information Science,2022,24(10):1854-1866. |
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| Authors: | LIANG Lifeng ZENG Wenxia SONG Yuexiang SHAO Zhenfeng LIU Xiujuan |
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| Institution: | 1. School of Geographical Sciences, Lingnan Normal University, Zhanjiang 524057, China2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China |
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| Abstract: | The scientific and quantitative evaluation of urban vitality can provide an important basis for urban planning and urban coordinated development. Existing studies on urban vitality and urban planning both focus on the characteristics of people's activities, without considering the psychological feelings of urban residents. In view of the current situation that residents' emotions are easily ignored in research, this study selects Baidu heat map to measure the intensity of population agglomeration intensity and uses the emotional analysis results of micro-blog text data to measure emotional intensity. Using the TOPSIS model to calculate the comprehensive vitality of the city, a comprehensive vitality evaluation framework considering population agglomeration and emotional intensity is proposed. This study selects eight influencing factors from three dimensions: urban physical environment, economic environment, and ecological environment, including road accessibility, land use mix, the density of POI, building density, nighttime light intensity, salary level, housing price level, and vegetation coverage. The influence of the eight influencing factors on the spatial heterogeneity of urban vitality is further explored by the GeoDetector model. This study shows that: (1) The comprehensive vitality evaluation method integrating population agglomeration intensity and emotional intensity proposed in this study can better reflect the spatial differentiation pattern of urban vitality. The effectiveness of this proposed framework for evaluating urban comprehensive vitality is verified by the analysis results of the typical sampling regions; (2) Among the eight influencing factors, the density of POI has the greatest influence on urban comprehensive vitality, while the influence of vegetation coverage on urban comprehensive vitality is the weakest. However, the interaction between vegetation coverage and other factors has the most significant impact on the spatial heterogeneity of urban vitality. It shows that the vegetation coverage factor does not directly act on the spatial heterogeneity of urban vitality but indirectly affects the spatial differentiation of urban comprehensive vitality by coupling the road accessibility, density of POI, and building density. |
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| Keywords: | multi-source big data urban vitality comprehensive vitality perception GeoDetector TOPSIS Baidu heat map text sentiment analysis |
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