Wang Pengyang, Liu Kunpeng, Wang Dongjie, Fu Yanjie
Computer Science Department, University of Central Florida, Orlando, FL, United States.
Front Big Data. 2021 Jun 10;4:690970. doi: 10.3389/fdata.2021.690970. eCollection 2021.
The pervasiveness of mobile and sensing technologies today has facilitated the creation of Big Crowdsourced Geotagged Data (BCGD) from individual users in real time and at different locations in the city. Such ubiquitous user-generated data allow us to infer various patterns of human behavior, which helps us understand the interactions between humans and cities. In this article, we aim to analyze BCGD, including mobile consumption check-ins, urban geography data, and human mobility data, to learn a model that can unveil the impact of urban geography and human mobility on the vibrancy of residential communities. Vibrant communities are defined as places that show diverse and frequent consumer activities. To effectively identify such vibrant communities, we propose a supervised data mining system to learn and mimic the unique spatial configuration patterns and social interaction patterns of vibrant communities using urban geography and human mobility data. Specifically, to prepare the benchmark vibrancy scores of communities for training, we first propose a fused scoring method by fusing the frequency and the diversity of consumer activities using mobile check-in data. Besides, we define and extract the features of spatial configuration and social interaction for each community by mining urban geography and human mobility data. In addition, we strategically combine a pairwise ranking objective with a sparsity regularization to learn a predictor of community vibrancy. And we develop an effective solution for the optimization problem. Finally, our experiment is instantiated on BCGD including real estate, point of interests, taxi and bus GPS trajectories, and mobile check-ins in Beijing. The experimental results demonstrate the competitive performances of both the extracted features and the proposed model. Our results suggest that a structurally diverse community usually shows higher social interaction and better business performance, and incompatible land uses may decrease the vibrancy of a community. Our studies demonstrate the potential of how to best make use of BCGD to create local economic matrices and sustain urban vibrancy in a fast, cheap, and meaningful way.
如今,移动和传感技术的普及促进了来自城市中不同地点的个体用户实时创建大规模众包地理标记数据(BCGD)。这种无处不在的用户生成数据使我们能够推断出人类行为的各种模式,这有助于我们理解人类与城市之间的相互作用。在本文中,我们旨在分析BCGD,包括移动消费签到、城市地理数据和人类移动数据,以学习一个能够揭示城市地理和人类移动对居住社区活力影响的模型。充满活力的社区被定义为展现出多样且频繁消费活动的地方。为了有效识别此类充满活力的社区,我们提出了一种监督数据挖掘系统,利用城市地理和人类移动数据来学习和模拟充满活力社区独特的空间配置模式和社会互动模式。具体而言,为了准备用于训练的社区基准活力得分,我们首先提出一种融合评分方法,通过融合使用移动签到数据的消费活动频率和多样性。此外,我们通过挖掘城市地理和人类移动数据来定义和提取每个社区的空间配置和社会互动特征。此外,我们战略性地将成对排序目标与稀疏正则化相结合,以学习社区活力的预测器。并且我们为优化问题开发了一种有效的解决方案。最后,我们在北京的房地产、兴趣点、出租车和公交车GPS轨迹以及移动签到等BCGD上进行了实验。实验结果证明了所提取特征和所提出模型的竞争力。我们的结果表明,结构多样的社区通常表现出更高的社会互动和更好的商业表现,而不相容的土地用途可能会降低社区的活力。我们的研究展示了如何以快速、廉价且有意义的方式充分利用BCGD来创建地方经济矩阵并维持城市活力的潜力。