相关论文: Assessing the quality of home detection from mobil…

Large-scale location based traces, such as mobile phone data, have been identified as a promising data source to complement or even enrich official statistics. In many cases, a prerequisite step to deploy the massively gathered data is the…

Non-continuous location traces inferred from Call Detail Records (CDR) at population scale are increasingly becoming available for research and show great potential for automated detection of meaningful places. Yet, a majority of Home…

Estimation of people's home locations using location-based services data from smartphones is a common task in human mobility assessment. However, commonly used home detection algorithms (HDAs) are often arbitrary and unexamined. In this…

Home detection, assigning a phone device to its home antenna, is a ubiquitous part of most studies in the literature on mobile phone data. Despite its widespread use, home detection relies on a few assumptions that are difficult to check…

In this contribution we summarize insights on the geographical veracity of using mobile phone data to create (statistical) indicators. We focus on problems that persist with spatial allocation, spatial delineation and spatial aggregation of…

Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations, including costs and infrequent sampling. When censuses are used, there is typically a time gap - up to a few years - between the data…

Smartphone location data have become a key resource for understanding urban mobility, yet extracting actionable insights requires robust and reproducible preprocessing pipelines. A central step is the identification of individuals' home and…

Traditional sources of population data, such as censuses and surveys, are costly, infrequent, and often unavailable in crisis-affected regions. Mobile phone application data offer near real-time, high-resolution insights into population…

Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications. The ability to accurately and extensively monitor and analyze these data is necessary. Much concern in mobile data analysis is…

Today, 95% of the global population has 2G mobile phone coverage and the number of individuals who own a mobile phone is at an all time high. Mobile phones generate rich data on billions of people across different societal contexts and have…

High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a trending proxy to evaluate population density, activity and social characteristics. They offer additional advantages for…

In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a real-world environment, collect a large dataset of 3110 datapoints,…

For the last few years, the amount of data has significantly increased in the companies. It is the reason why data analysis methods have to evolve to meet new demands. In this article, we introduce a practical analysis of a large database…

The aim of this study is to demostrate that mobile phone usage data can be used to make predictions and find the best classification method for credit scoring even if the dataset is small (2,503 customers). We use different classification…

We can extract useful information from social media data by adding the user's home location. However, since the user's home location is generally not publicly available, many researchers have been attempting to develop a more accurate home…

Accurately detecting home locations from GPS data generated by mobile devices is a foundational step in human mobility research, with significant implications for transportation planning, public health, and emergency response. However,…

Human mobility analysis is an important issue in social sciences, and mobility data are among the most sought-after sources of information in ur- Data ban studies, geography, transportation and territory management. In network sciences…

Thanks to the use of geolocated big data in computational social science research, the spatial and temporal heterogeneity of human activities are increasingly being revealed. Paired with smaller and more traditional data, this opens new…

Land use classification is essential for urban planning. Urban land use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been…

Socio-economic indicators provide context for assessing a country's overall condition. These indicators contain information about education, gender, poverty, employment, and other factors. Therefore, reliable and accurate information is…