评估使用基于交易和位置的见解来自自动柜员机（ATM）作为经济冲击的近实时传感系统
Assessing the use of transaction and location based insights derived from Automatic Teller Machines (ATMs) as near real time sensing systems of economic shocks
摘要：
大数据源为政府和发展利益攸关方提供了一个重要机会，使其能够以高空间和时间分辨率近实时感知和确定冲击对人口的经济影响。在这项研究中，我们评估了潜在的交易和基于位置的措施，从自动柜员机（ATM）终端，属于一个主要的私营部门的银行在印度尼西亚，近实时感，冲击的影响，收入群体。对于2014年和2015年的每个客户，我们分别对每年的现金提取总量、客户在特定年份的终端间总距离和报告的客户收入组之间的关系进行建模。结果显示，该模型能够预测相应的收入群体，准确率为80%，与基线模型相比，在这两年中具有高精度和召回值。Shapley值表明，总终端间的距离穿越客户在每年的客户收入群体之间的显着差异。Kruskal-Wallis检验进一步显示，与高收入群体（2014年和2015年分别为2.55公里和0.66公里）和低收入群体（2014年和2015年分别为6.47公里）相比，中低收入群体的客户具有显著较高的终端间距离中值（2014年和2015年为7.21公里）。虽然2014年和2015年没有出现重大冲击，但我们的研究结果显示，与低收入和高收入群体的客户相比，中低收入群体的客户表现出相对较高的流动性。还需要开展更多工作，利用这些数据的感知能力，提供关于谁、在哪里以及受冲击影响的人口的见解，以促进有针对性的应对。
英文摘要：
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.
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