论文标题
评估从自动柜员机（ATM）得出的交易和基于位置的见解作为接近实时的经济冲击系统
Assessing the use of transaction and location based insights derived from Automatic Teller Machines (ATMs) as near real time sensing systems of economic shocks
论文作者
论文摘要
大数据源为政府和发展利益相关者提供了一个重要的机会，可以接近实时感知和确定冲击对高空间和时间暂时决议的经济影响。在这项研究中，我们评估了属于印度尼西亚主要私营部门银行的自动柜员机（ATM）终端获得的交易和基于位置的措施的潜力，以便在接近实时接触到收入群体的冲击影响。对于每个客户和2014年和2015年的单独客户，我们对每年的现金提取的总量度，客户在特定年份进行的总末端距离和报告的客户收入组进行了建模。结果表明，该模型能够以80％的精度预测相应的收入组，并且在这两年中，与基线模型相比，具有高精度和回忆值。沙普利的价值表明，客户收入群体之间，每年客户每年遍历的总末端距离都有很大差异。 Kruskal-Wallis测试进一步表明，中低中等阶级收入组的客户与高（2.55 kms and 2014和2015年的2.55 kms和0.66 kms）相比，末端距离的中位数值显着高（2014年和2015年为7.21 kms），以及2014年和2014年和2014年和2015年和2015年和2014年的6.47 kms。尽管在2014年和2015年没有注意到重大冲击，但我们的结果表明，与低收入和高收入群体的客户相比，中低水平的阶级收入群体客户的移动性相对较高。需要额外的工作来利用这些数据的感应能力来提供有关震惊影响的人口影响，何处，何处，何处以及多少人口，以促进有针对性的响应。
Big data sources provide a significant opportunity for governments and development stakeholders to sense and identify in near real time, economic impacts of shocks on populations at high spatial and temporal resolutions. In this study, we assess the potential of transaction and location based measures obtained from automatic teller machine (ATM) terminals, belonging to a major private sector bank in Indonesia, to sense in near real time, the impacts of shocks across income groups. For each customer and separately for years 2014 and 2015, we model the relationship between aggregate measures of cash withdrawals for each year, total inter-terminal distance traversed by the customer for the specific year and reported customer income group. Results reveal that the model was able to predict the corresponding income groups with 80% accuracy, with high precision and recall values in comparison to the baseline model, across both the years. Shapley values suggest that the total inter-terminal distance traversed by a customer in each year differed significantly between customer income groups. Kruskal-Wallis test further showed that customers in the lower-middle class income group, have significantly high median values of inter-terminal distances traversed (7.21 Kms for 2014 and 2015) in comparison to high (2.55 Kms and 0.66 Kms for years 2014 and 2015), and low (6.47 Kms for 2014 and 2015) income groups. Although no major shocks were noted in 2014 and 2015, our results show that lower-middle class income group customers, exhibit relatively high mobility in comparison to customers in low and high income groups. Additional work is needed to leverage the sensing capabilities of this data to provide insights on, who, where and by how much is the population impacted by a shock to facilitate targeted responses.
