[论文解读] Leveraging Data Driven Approaches to Quantify the Impact of Construction Projects on Urban Quality of Life

Zhengbo Zou, Semiha Ergan|arXiv (Cornell University)|Jan 25, 2019

Occupational Health and Safety Research参考文献 62被引用 4

一句话总结

本文提出一种数据驱动框架，利用纽约市的开放数据量化建设项目对城市生活质量的影响。通过分析311服务请求和27个道路项目，识别出随时间推移投诉量上升的趋势——尤其是噪音、空气质量与卫生问题，并利用机器学习模型在预测投诉趋势方面实现了0.67的R平方值。

ABSTRACT

According to the World Bank, more than half of the world's population now lives in cities, creating burdens on the degraded city infrastructures and driving up the demand for new ones. Construction sites are abundant in already dense cities and have unavoidable impacts on surrounding environments and residents. However, such impacts were rarely quantified and made available to construction teams and local agencies to inform their planning decisions. A challenge in achieving this was the lack of availability of data that can provide insights about how urban residents respond to changes in their environment due to construction projects. Wider availability of data from city agencies nowadays provides opportunities for having such analysis possible. This paper provides the details of a generic data-driven approach that enables the analysis of impact of construction projects on quality of life in urban settings through the quantification of change on widely accepted quality of life indicators in cities. This paper also evaluated the approach using data from publicly construction projects' information and open city data portals from New York City. Historical 311 Service Requests along with 27 road reconstruction projects were used as testbeds. The results showed that 61% of the projects analyzed in this testbed experienced higher 311 requests after the commencement of construction, with main complaints of 'noise', 'air quality', and 'sewer' at the beginning of construction, and 'sanitation' and 'waste' towards the end. Prediction models, built using regression machine learning algorithms, achieved an R-Squared value of 0.67. The approach is capable of providing insights for government agencies and construction companies to take proactive actions based on expected complaint types through different phases of construction.

研究动机与目标

• 为解决缺乏量化数据来衡量建设项目对城市居民生活质量影响的问题。
• 通过公开的城市数据识别并分析与建设项目相关的居民投诉。
• 开发一种可扩展的、数据驱动的方法，用于评估不同项目阶段的建设影响。
• 通过预测性洞察支持建设项目团队和城市机构的主动规划。
• 使用纽约市311服务请求和道路重建项目的实际数据验证该方法。

提出的方法

• 将历史311服务请求用作居民对建设项目活动投诉的代理指标。
• 整合来自城市门户网站的开放数据，包括27个道路重建项目的开工日期、位置和持续时间。
• 应用时间序列分析，追踪建设各阶段（施工前、施工中、施工后）投诉量的变化。
• 采用基于回归的机器学习模型，根据项目时间表和特征预测投诉类型和数量。
• 通过自然语言处理对311请求文本进行分类，将投诉划分为噪音、空气质量、卫生、垃圾和污水等问题类别。
• 使用R平方指标验证模型性能，评估投诉趋势预测的准确性。

实验结果

研究问题

• RQ1 建设项目如何随时间影响居民对城市生活质量的投诉？
• RQ2 在建设的不同阶段，哪些类型的投诉（如噪音、空气质量、卫生）最为普遍？
• RQ3 机器学习模型在多大程度上能基于项目数据预测投诉数量和类型？
• RQ4 像311服务请求这样的开放数据源能否可靠反映建设项目对城市居民的影响？
• RQ5 在建设项目不同阶段，哪些是城市生活质量下降的关键指标？

主要发现

• 在分析的27个道路重建项目中，61%的项目在施工开始后产生了更高数量的311服务请求。
• 噪音、空气质量与污水投诉在建设项目初期最为频繁。
• 卫生与垃圾相关投诉在建设项目接近尾声时显著增加。
• 机器学习预测模型在预测项目各阶段投诉趋势方面实现了0.67的R平方值。
• 该数据驱动方法成功识别出居民投诉的时间模式，实现了分阶段的影响评估。
• 将311数据与项目元数据结合，为城市规划和建设管理提供了一种可扩展且可操作的方法。