Coupling data science with community crowdsourcing for urban renewal policy analysis: An evaluation of Atlanta’s Anti-Displacement Tax Fund
Coupling data science with community crowdsourcing for urban renewal policy analysis: An evaluation of Atlanta’s Anti-Displacement Tax Fund
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DOI：
10.1177/2399808318819847
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发表时间：
2018-12
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通讯作者：
Jeremy Auerbach;Christopher Blackburn;Hayley Barton;Amanda Meng;E. Zegura
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作者：
Jeremy Auerbach;Christopher Blackburn;Hayley Barton;Amanda Meng;E. Zegura
我们利用数据科学和机器学习技术估算了亚特兰大西区（佐治亚州）拟议的反流离失所计划的成本和影响。该计划旨在为那些正在进行两个主要城市更新项目（一个体育场和一条多用途步道）的社区的符合资格的居民提供全额财产税增加补贴。我们首先利用应用于公开家庭数据的数据科学和机器学习方法来估计该计划的家庭收入资格。然后，我们使用随机森林和历史税收评估数据来预测由于城市更新项目而导致的未来房地产升值。将这些预测与家庭级别的资格相结合，我们估算了不同资格情景下的计划成本。我们发现，我们的家庭级数据和机器学习技术导致符合条件的房主减少，但由于房产升值率高于基于人口普查和城市级数据的原始分析，因此计划成本显着增加。我们的方法存在局限性，即数据集不完整、代表性收入样本的准确性、财产税升值模型的特征训练集数据的可用性以及验证模型结果的挑战。我们生成的资格估计和房产升值预测也被纳入一个交互式工具中，供居民确定计划资格并查看他们的预期房屋价值增长。社区居民参与了这项工作，并提高了拟议计划的透明度、问责制和影响力。从居民那里收集的数据还可以纠正和更新信息，这将提高计划估计的准确性并验证模型，从而带来社区驱动的数据科学的新颖应用。
We estimate the cost and impact of a proposed anti-displacement program in the Westside of Atlanta (GA) with data science and machine learning techniques. This program intends to fully subsidize property tax increases for eligible residents of neighborhoods where there are two major urban renewal projects underway, a stadium and a multi-use trail. We first estimate household-level income eligibility for the program with data science and machine learning approaches applied to publicly available household-level data. We then forecast future property appreciation due to urban renewal projects using random forests with historic tax assessment data. Combining these projections with household-level eligibility, we estimate the costs of the program for different eligibility scenarios. We find that our household-level data and machine learning techniques result in fewer eligible homeowners but significantly larger program costs, due to higher property appreciation rates than the original analysis, which was based on census and city-level data. Our methods have limitations, namely incomplete data sets, the accuracy of representative income samples, the availability of characteristic training set data for the property tax appreciation model, and challenges in validating the model results. The eligibility estimates and property appreciation forecasts we generated were also incorporated into an interactive tool for residents to determine program eligibility and view their expected increases in home values. Community residents have been involved with this work and provided greater transparency, accountability, and impact of the proposed program. Data collected from residents can also correct and update the information, which would increase the accuracy of the program estimates and validate the modeling, leading to a novel application of community-driven data science.