DTEx: A dynamic urban thermal exposure index based on human mobility patterns

Spatiotemporal Patterns of Urban Human Mobility

The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others....

Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data

The rapid development of information and communication technologies (ICTs) has provided rich resources for spatio-temporal data mining and knowledge discovery in modern societies. Previous research has focused on understanding aggregated urban mobility patterns based on mobile phone datasets, such as extracting activity hotspots and clusters. In this paper, we aim to go one step further from id...

Exploring Spatial-Temporal Patterns of Urban Human Mobility Hotspots

Understanding human mobility patterns provides us with knowledge about human mobility in an urban context, which plays a critical role in urban planning, traffic management and the spread of disease. Recently, the availability of large-scale human-sensing datasets enables us to analyze human mobility patterns and the relationships between humans and their living environments on an unprecedented...

Exploring Human Mobility Patterns Based on Geotagged Flickr Photos

Proposing a Human Resource Balanced Scorecard based on Dynamic Systems Simulation s-based

Objective: The purpose of this study is to provide a dynamic system model for modeling the growth and learning perspective of a manufacturing company. The main focus of the model is on the company's human resources management process.   Methods: In this study, an approach based on dynamic simulation has been proposed to evaluate the human resource condition. In order to do that, the researchers...