High-resolution prediction and uncertainty estimation of land value and conservation costs

土地价值和保护成本的高分辨率预测和不确定性估计

• 批准号: 2149243
• 负责人: Christoph Nolte
• 金额: $ 28.79万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149243&HistoricalAwards=false
• 关键词: resolution; prediction; uncertainty; estimation; land

Accurate estimates of the fair market value of private land rights are key inputs for many public policy choices, including property taxation and conservation planning. However, this estimation task is challenging, because land value is affected by many environmental and social factors, their importance varies geographically, and available training data of property sales tends to be scarce, clustered, and rarely representative of the landscape. This project investigates the performance of different statistical techniques to estimate land value, conservation cost, and associated uncertainties across large and heterogeneous geographical areas. The data produced in this effort are designed to support government agencies, conservation organizations, and academic research groups in the identification of conservation priorities for current and future species habitat, carbon storage, flood risk reduction, farmland protection, and equitable access to open space. The project also contributes to the education and training of a postdoctoral scholar.With societal demand for land-based benefits shifting as a result of economic crises and climate change, an accurate predictive understanding of the trade-offs between private and public benefits from land is needed for the identification of effective and equitable policy solutions. Using a novel geospatial database of approximately 150 million properties and 4 million sales for the conterminous United States, this project compares the performance of traditional statistical estimators and modern spatial machine learning methods in predicting land values and public acquisition costs across space and time, alongside associated uncertainties. It also examines the performance of new machine learning techniques for the estimation of the value of partial land rights and conservation easements, a rapidly expanding protection instrument in the United States. In doing so, the project contributes to generalizable best practice in predictive modeling and validation of land value and conservation costs over large, nonstationary study domains.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

准确估计私人土地权利的公平市场价值是许多公共政策选择的关键投入，包括财产税和保护规划。然而，这一估计任务具有挑战性，因为土地价值受到许多环境和社会因素的影响，其重要性在地理上各不相同，并且可用的房地产销售培训数据往往是稀缺的，聚集的，很少代表景观。本项目研究不同的统计技术的性能，以估计土地价值，保护成本，以及相关的不确定性，在大型和异构的地理区域。在这项工作中产生的数据旨在支持政府机构，保护组织和学术研究小组确定当前和未来物种栖息地，碳储存，减少洪水风险，农田保护和公平获得开放空间的保护优先事项。随着经济危机和气候变化导致社会对土地惠益的需求发生变化，需要对土地的私人和公共惠益之间的权衡进行准确的预测性理解，以确定有效和公平的政策解决方案。该项目使用一个新的地理空间数据库，其中包含美国境内约1.5亿处房产和400万处销售额，比较了传统统计估计器和现代空间机器学习方法在预测土地价值和公共收购成本方面的表现，以及相关的不确定性。它还研究了新的机器学习技术在估计部分土地权利和保护地役权价值方面的性能，这是美国迅速扩大的保护工具。在此过程中，该项目有助于在预测建模和验证土地价值和保护成本的大型，非固定的研究domains.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。