BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE
BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE
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DOI：
10.1111/ecin.12364
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发表时间：
2018-01-01
期刊：
影响因子：
1.8
通讯作者：
Naik, Nikhil
中科院分区：
文献类型：
--
作者：
Glaeser, Edward L.;Kominers, Scott Duke;Naik, Nikhil
新的“大数据”来源允许以比以往更高的收集频率和更细粒度的地理尺度来测量城市特征和结果变量。然而，大数据本身并不能解决大的城市社会科学问题。当大城市数据允许对以前不透明的情况进行测量时，或者当它可以与对人或地方的外源冲击相结合时，大城市数据对城市研究最有价值。我们描述了许多新的城市数据源，并说明了如何使用它们来改善城市的研究和功能。我们首先展示了如何使用谷歌街景图像来预测纽约市的收入，这表明类似的图像数据可以用来绘制发展中国家以前未测量地区的财富和贫困。然后，我们讨论如何调查技术可以得到改善，以更好地衡量愿意支付城市设施。最后，我们将解释如何使用互联网数据来提高城市服务的质量。
New, "big data" sources allow measurement of city characteristics and outcome variables at higher collection frequencies and more granular geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big urban data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar imagery data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can be improved to better measure willingness to pay for urban amenities. Finally, we explain how Internet data is being used to improve the quality of city services.