Mapping Informal and Alternative Housing in the United States: A Big Data Approach for Examining Spatial Inequality.

绘制美国的非正式和替代住房：检查空间不平等的大数据方法。

• 批准号: 2048562
• 负责人: Noah Durst
• 金额: $ 35.92万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048562&HistoricalAwards=false
• 关键词: Mapping; Informal; Alternative; Housing; United

Many Americans live in two distinct community forms: (1) informal subdivisions (ISs), where residents use incremental self-building for housing development that does not adhere to formal land planning and housing construction practices and (2) manufactured home communities (MHCs), where the dominant housing model is manufactured, low-cost factory-built housing that provides the single largest source of unsubsidized affordable housing in the United States. Although these communities provide a major source of affordable housing and low-income home ownership, case study research suggests that they are spatially marginalized and exposed to concentrated forms of economic, social, and environmental vulnerability. Due to the difficulty of identifying their location across a broader geography, there is currently no systematic data on their total number or location, nor are there national-level analysis of the spatial inequalities they face. This project uses big data and machine learning to produce more robust and refined measurements of the characteristics of all U.S. neighborhoods (formally planned suburbs, ISs, and MHCs). This allows documentation of the location of ISs and MHCs nationwide and modeling of policy and market factors that explain patterns of uneven development, segregation, and environmental inequalities across neighborhood types. The databases and publications generated by this project have the potential to generate knowledge needed to develop more equitable housing policies as well as to support further research. The dissemination plan allows knowledge sharing with the public, local planners, and other stakeholders and policymakers through local community engagement workshops, a series of regional webinars, and an easy-to-use publicly available data mapping and visualization dashboard. The study builds on geographic theories of socio-spatial peripheralization and uneven development by examining the nature, causes, and consequences of the proliferation of ISs and MHCs and their relationship with the uneven spatial distribution of poverty and vulnerability in the United States. The project uses Python programming language, a national dataset of building footprints, and supervised and unsupervised machine learning methods to identify the distinct dimensions of neighborhood morphology (the size, shape, orientation, and other arrangements of buildings) in ISs, MHCs, and formally planned suburbs across the country. In doing so, it produces more robust and refined measurements of the characteristics of all U.S. neighborhoods, as well as a first-time national level database of ISs and MHCs. This dataset enables the examination of the relationship between segregation by neighborhood types and spatial inequalities, including residential segregation by race, income, and tenure as well as exposure to various types of environmental risk. Project findings contributes to: (1) methodological advancements in the spatial study of neighborhood morphologies, (2) theoretical advancements in scholarship on peripheralization, uneven development, and suburbanization of poverty and (3) empirical advancements in the documentation and analysis of informal housing relative to social vulnerabilities and environmental hazards. The study allows users of the research products to analyze neighborhood morphologies; examine social, economic, and environmental impacts of uneven community development; and identify policies that can ameliorate these impacts.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.

许多美国人生活在两种不同的社区形式中：（1）非正式小区（IS），其中居民使用增量自建住房开发，不遵守正式的土地规划和住房建设实践;（2）制造家庭社区（MHC），其中主要的住房模式是制造的，低成本工厂建造的住房，提供了美国最大的无补贴经济适用房来源。虽然这些社区提供了一个负担得起的住房和低收入家庭所有权的主要来源，个案研究表明，他们在空间上被边缘化，并集中暴露于形式的经济，社会和环境脆弱性。由于很难在更广泛的地理范围内确定其位置，目前没有关于其总数或位置的系统数据，也没有关于其面临的空间不平等的国家一级分析。该项目使用大数据和机器学习来对美国所有社区（正式规划的郊区，IS和MHC）的特征进行更强大和更精确的测量。这使得文件的位置，信息系统和MHC的全国性和建模的政策和市场因素，解释模式的不平衡发展，隔离，和环境不平等的邻里类型。该项目产生的数据库和出版物有可能产生制定更公平的住房政策所需的知识 以及支持进一步的研究。该传播计划允许通过地方社区参与讲习班、一系列区域网络研讨会和易于使用的公开数据映射和可视化仪表板，与公众、地方规划者和其他利益攸关方和决策者分享知识。该研究以社会空间边缘化和发展不平衡的地理理论为基础，研究了独立系统和初级保健扩散的性质、原因和后果，以及它们与美国贫困和脆弱性空间分布不平衡的关系。该项目使用Python编程语言，建筑足迹的国家数据集，以及监督和无监督机器学习方法来识别IS，MHC和正式规划的郊区的邻里形态的不同维度（建筑物的大小，形状，方向和其他安排）。在这样做的过程中，它产生了对所有美国社区特征的更强大和更精确的测量，以及第一次国家级的IS和MHC数据库。该数据集可以检查邻里类型隔离与空间不平等之间的关系，包括种族，收入和任期的居住隔离以及暴露于各种类型的环境风险。项目结果有助于：（1）邻里形态空间研究的方法进步，（2）边缘化，不平衡发展和贫困郊区化学术的理论进步，（3）非正规住房相对于社会脆弱性和环境危害的文献和分析的经验进步。该研究允许研究产品的用户分析邻里形态;检查不平衡社区发展的社会，经济和环境影响;并确定可以改善这些影响的政策。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。