A Machine Learning Approach to Demographic Prediction using Geohashes
A Machine Learning Approach to Demographic Prediction using Geohashes
复制标题
使用 Geohashes 进行人口统计预测的机器学习方法
DOI：
10.1145/3055601.3055603
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
E. Pebesma
中科院分区：
文献类型：
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作者：
Avipsa Roy;E. Pebesma
随着智能手机的迅速普及，人类通过携带可共享位置数据的 GPS 设备充当社交传感器。这导致了长时间收集的大量传感器数据。对于组织来说，从多个不同来源积累的大量时空数据中获得有意义的见解通常是一个挑战。电信提供商识别移动电话用户的人口统计数据就是这样的例子。通过深入了解用户的移动模式，人口统计信息在将在线广告定位到重点用户群体方面发挥着非常重要的作用。然而，在实践中，出于隐私考虑，应用程序开发人员大多无法开放年龄和性别等人口统计信息。在本文中，我们试图解决如何利用人口统计数据丰富位置数据的差距，这对于应用程序开发人员来说可能很有价值。在我们的研究中，我们使用机器学习方法，使用基于 Geohashes 概念的预测模型，从 60,865 个独特设备的 3,252,950 条匿名 GPS 轨迹中预测手机用户的性别和年龄。我们研究了用户的人口统计数据可以在多大程度上通过编码从他们经常访问的位置推断出来，通过制定多级分类算法来找到最常访问的 Geohashes 并将它们与最近的兴趣点相关联，这将能够预测喜欢按顺序访问特定位置的用户的年龄组和性别。实验是在电信提供商收集和共享的真实手机用户数据集上进行的。实验结果表明，该算法对用户性别和年龄组的预测平均预测准确率分别为71.62%和96.75%。
With the rapid proliferation of smartphones, human beings act as social sensors by means of carrying GPS-enabled devices that share location data. This has resulted in an abundance of sensor data gathered over long periods of time. Gaining meaningful insights from such massive amounts of spatio-temporal data accumulated by several disparate sources is often a challenge for organizations. Identifying demographics of mobile phone users by telecommunication providers is one such example. Demographic information plays a very significant role in targeting online advertisements to focused user groups by gaining insights about userfis mobility patterns. However, in practice, demographic information such as age and gender are mostly unavailable to app developers for open access due to privacy concerns. In this paper, we try to address the gap of how to enrich location data with demographics, which could be valuable for app developers. In our study, we use a machine learning approach to predict the gender and age of mobile phone users from a set of 3,252,950 anonymised GPS trajectories with 60,865 unique devices using a predictive model which is based upon the concept of Geohashes. We study to what extent usersfi demographics could be inferred from their frequently visited locations by encoding by formulating a multi-level classification algorithm to find the most frequently visited Geohashes and associating them with nearest points of interests which would enable predicting age-group and gender of the users who prefer to visit a specific location in a sequential manner. Experiments are conducted on a real dataset of mobile phone users collected and shared by a telecommunication provider. Th The experimental results show that the proposed algorithm can achieve mean prediction accuracy scores of 71.62% and 96.75% for predicting gender and age groups of the users respectively.