Ximeng Cheng
Dados Biográficos
| ID | 3635874 |
|---|---|
| NOME | Ximeng Cheng |
| PRENOMES | Ximeng |
| SOBRENOME | Cheng |
| ASSINATURA | CHENG X |
| AFILIAÇÕES | Peking University |
| ORCID | 0000-0001-9923-7240 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 3 |
| TOTAL DE CITAÇÕES | 9 |
| TOTAL COMO AUTOR | 3 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2020 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2024 |
| ÍNDICE H | 1 |
Inferring “high-frequent” mixed urban functions from telecom traffic
Precise distinction of mixed functions on urban land is essential for urban studies and planning, while existing methods are limited by high sampling bias, low observation frequency, and lack of semantic information in common data sources. In this paper, we introduce a new proxy for human behavior, the telecom traffic data as a remedy to the above limitations, and present an analytical framework which utilizes anonymized and aggregated telecom tr…
Multi-scale detection and interpretation of spatio-temporal anomalies of human activities represented by time-series
Understanding Place Characteristics in Geographic Contexts through Graph Convolutional Neural Networks
Inferring the unknown properties of a place relies on both its observed attributes and the characteristics of the places to which it is connected. Because place characteristics are unstructured and the metrics for place connections can be diverse, it is challenging to incorporate them in a spatial prediction task where the results could be affected by how the neighborhoods are delineated and where the true relevance among places is hard to identi…
Understanding Place Characteristics in Geographic Contexts through Graph Convolutional Neural Networks
Inferring the unknown properties of a place relies on both its observed attributes and the characteristics of the places to which it is connected. Because place characteristics are unstructured and the metrics for place connections can be diverse, it is challenging to incorporate them in a spatial prediction task where the results could be affected by how the neighborhoods are delineated and where the true relevance among places is hard to identi…
Understanding Place Characteristics in Geographic Contexts through Graph Convolutional Neural Networks
Inferring the unknown properties of a place relies on both its observed attributes and the characteristics of the places to which it is connected. Because place characteristics are unstructured and the metrics for place connections can be diverse, it is challenging to incorporate them in a spatial prediction task where the results could be affected by how the neighborhoods are delineated and where the true relevance among places is hard to identi…
Multi-scale detection and interpretation of spatio-temporal anomalies of human activities represented by time-series
Inferring “high-frequent” mixed urban functions from telecom traffic
Precise distinction of mixed functions on urban land is essential for urban studies and planning, while existing methods are limited by high sampling bias, low observation frequency, and lack of semantic information in common data sources. In this paper, we introduce a new proxy for human behavior, the telecom traffic data as a remedy to the above limitations, and present an analytical framework which utilizes anonymized and aggregated telecom tr…
Computer Science (3 obras) · Geography (3 obras) · Human Mobility and Location-Based Analysis (3 obras) · Artificial Intelligence (2 obras) · Data mining (2 obras) · Data science (2 obras) · Machine learning (2 obras) · Mathematics (2 obras) · Anomaly detection (1 obras) · Cartography (1 obras)