This study explores the effects that the weather has on people’s everyday activity patterns. Temperature, rainfall, and wind speed were used as weather parameters. People’s daily activity patterns were inferred, such as place visited, the time this took place, the duration of the visit, based on the GPS location traces of their mobile phones overlaid upon Yellow Pages information. Our analysis of 31,855 mobile phone users allowed us to infer that people were more likely to stay longer at eateries or food outlets, and (to a lesser degree) at retail or shopping areas when the weather is very cold or when conditions are calm (non-windy). When compared to people’s regular activity patterns, certain weather conditions affected people’s movements and activities noticeably at different times of the day. On cold days, people’s activities were found to be more diverse especially after 10AM, showing greatest variations between 2PM and 6PM. A similar trend is observed between 10AM and midnight on rainy days, with people’s activities found to be most diverse on days with heaviest rainfalls or on days when the wind speed was stronger than 4 km/h, especially between 10AM–1AM. Finally, we observed that different geographical areas of a large metropolis were impacted differently by the weather. Using data of urban infrastructure to characterize areas, we found strong correlations between weather conditions upon people’s accessibility to trains. This study sheds new light on the influence of weather conditions on human behavior, in particular the choice of daily activities and how mobile phone data can be used to investigate the influence of environmental factors on urban dynamics.
People habitually carry their mobile phones with them much of the time as this pervasive technology offers its users a means for constant and available communication as well as personal entertainment. However, the accompanying mobile phone can also provide researchers with an efficient tool for capturing human mobility pattern. Through this, researchers have a unique opportunity to get a better understanding of the individual as well as social behaviors that collectively shape our society. Along with the logs of incoming and outgoing calls, telecom operators can also capture people’s phones movement, as the phone move through the ubiquitous network of towers. This transforms the phone into individual life loggers, giving longitudinal records of personal mobility while offering unprecedented fine-grained data at the aggregate level. This can give researchers a glimpse of various dimensions of human life. For example using mobile phones to study social structure , how an individual’s diversity of social network can lead to greater personal economic development , and how weather affects people’s use of phone calls to connect with others .
Various researchers have used location traces of connected cellular towers of mobile phones to study human mobility, which is important for urban planning and traffic engineering (e.g.,        ). Several aspects of human mobility have been explored and described. For example, human trajectories show a high degree of temporal and spatial regularity with a significant likelihood of returning to a few highly visited locations . Trajectories of human mobility follow the principle of exploration and preferential return, which governs the way people explore new places while often returning to the previously visited locations . Others try to predict individual mobility by examining phone location traces data (i.e., phone movement) in conjunction with datasets containing geographical features such as point of interest (POI) and land-use information . Despite the differences in people’s travel patterns, there is a strong regularity in their mobility on a regular basis, which makes 93% of people’s whereabouts predictable . Developing an understanding of mobility patterns (through phone location trace data) has helped with detecting the outbreak of mobile phone viruses , comparing people flow between cities , identifying commuting patterns , and understand the geography of social networks .
These emergent studies of mobility have mainly focused upon modeling, predicting, and analyzing human mobility data between cities. However, these approaches often miss out on the richer context of mobility, such as the type of activities that people might be engaged with at the locations they travel to. After all, people move between places in the city for different purposes. Besides travelling between home and work, they are also engaged in activities related to the place they visit, for example, eating in a restaurant, shopping or browsing in a mall, and jogging in a park. Thus, developing methods to help us infer the types of activities associated with different public places can offer a richer characterization of people’s daily activity patterns, which has many potential benefits, such as facilitating urban design and management. In this paper, we describe an approach to characterize human daily activity patterns using detailed location traces of mobile phones and spatial profiles. To build upon, and extend a previous investigation that demonstrates how weather conditions could impact people’s mobile social interactions, this investigation shows how we can glean further insights into people’s behavior with regards to their daily activities by looking for correlations with detailed information about weather conditions. After all, research has shown that weather can affect people’s behaviors such as their mood   thermal comfort level  , and social interaction . The weather can also influence traffic demands, and how we travel   , public health , crime rates , and even stock prices  . Thus, this paper describes our investigation into how weather shapes people’s patterns of mobility and the associated activities in the Tokyo metropolis of Japan. We will refer to this area as Tokyo for the rest of the paper. Make sure you check these blaux portable ac reviews if you need to do physical activities during Summer.
This section will describe the datasets used and the analysis carried out in this study. Given that the cities we live in are increasingly associated with unprecedented amount of data that is being produced and capture, we will demonstrate how we analyze some of these datasets to reveal correlations and hidden patterns of inhabitants in a large metropolis such as Tokyo. Through this, we hope to produce knowledge that can inform better urban planning in ways that are responsive to the needs of its inhabitants.
We used three datasets in this study. The first dataset is the GPS location traces of mobile phone users in Tokyo. The data was collected for a full calendar year from 1st August 2010 to 31st July 2011 during which the location of each mobile phone user was recorded continuously. To reduce battery consumption, the accelerometer was used to detect periods of relative stasis during which power-consuming GPS acquisition functions can be suspended. The sampling rate thus varied with the user’s mobility. However, the rate of sampling did not exceed once every five minutes.
A leading mobile phone operator in Japan provided this mobile phone GPS dataset. In particular, the dataset was derived from mobile phone users who registered for location-based services. The location information was sent through the network and used to perform specific analysis from which certain services were then provided for the registered users, as shown in Fig. 1. As part of this service, the mobile phone users were aware that their locations were being recorded. Furthermore, to preserve user’s privacy, the dataset was completely anonymized by the company. Each entry in the dataset included: unique user ID, position (latitude, longitude), timestamp, altitude, and approximated error (i.e., <100 m, <200 m, or <300 m). This dataset provided finer grained location traces than regular mobile phone call detail records (CDRs) in which the user’s location is recorded only when the connection to cellular network is established e.g., making/receiving a call and sending/receiving text message. As an example, Fig. 2 shows location traces of a mobile phone user.