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SCC-CIVIC-PG Track A: Data-driven and Community Engaged Planning Tools For Addressing Spatial Mismatch

SCC-CIVIC-PG 轨道 A：用于解决空间不匹配问题的数据驱动和社区参与的规划工具

基本信息

批准号：
2043858
负责人：
Megan Ryerson
金额：
$ 4.98万
依托单位：
University of Pennsylvania
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：
2021-01-15 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2043858&HistoricalAwards=false
关键词：
SCC CIVIC PG Track Data

项目摘要

To address spatial mismatch, housing agencies seek to locate affordable housing proximate or well-connected to jobs and opportunities. At the same time, transit agencies strive to expand access while minimizing operational costs and energy consumption. Both of these efforts are often done at scales that limit the ability to do deep, community-engaged planning around housing and transit needs. The goal of this planning grant is to identify gaps in current methods of estimating spatial mismatch from both a stakeholder and community perspective; to explore and document models based on data from the community; and design internal and community-facing frameworks for cross-sector planning tools. Ultimately, this will enable the creation of an analytical framework for addressing spatial mismatch using quantitative and qualitative approaches and develop transparent scenario planning tools. The project brings together community-engaged planning, housing policy and finance, transportation demand and network modeling, transportation energy-efficiency, and data-driven public policy analysis.This project aims to build community-informed frameworks representing spatial mismatch, which lay the foundation for planning tools that directly integrate with housing and transportation planning processes. Accessibility is a function of the transit quality experienced by individuals (i.e., travel cost, time, reliability) and the proximity and connectivity to jobs that match a worker’s skills and other opportunities. Existing methods to study spatial mismatch force an inferential structure on low-income traveler choice; additionally, they focus on adjustments to transit while treating housing as fixed, and vice versa. Spatial mismatch is neither a binary nor static metric, nor can it be measured by empirics alone: its measurement requires community input in the form of travel patterns, constraints, and needs, as well as the quality of transit connections. This project aims to develop a framework for probabilistic, non-linear path models of spatial mismatch interventions, that allow for qualitative data to inform quantitative metric construction. The community engagement process will allow for identification of gaps in current planning processes and new variables that belong in the calculation of accessibility. This project is in response to the Civic Innovation Challenge program, Track A— Communities and Mobility: Offering Better Mobility Options to Solve the Spatial Mismatch Between Housing Affordability and Jobs—and is a collaboration between NSF and the Department of Energy.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.

为了解决空间不匹配的问题，住房机构寻求将负担得起的住房安置在工作和机会附近或与工作和机会密切相关的地方。与此同时，过境机构努力扩大准入，同时尽量减少运营成本和能源消耗。这两项工作的规模往往限制了围绕住房和交通需求进行深入的、社区参与的规划的能力。这项规划补助金的目标是从利益攸关方和社区的角度确定目前估计空间不匹配的方法中的差距;根据社区的数据探索和记录模型;为跨部门规划工具设计内部和面向社区的框架。最终，这将有助于建立一个分析框架，利用定量和定性方法解决空间不匹配问题，并开发透明的情景规划工具。该项目汇集了社区参与规划、住房政策和金融、交通需求和网络建模、交通能源效率和数据驱动的公共政策分析，旨在构建代表空间不匹配的社区知情框架，为直接与住房和交通规划流程相结合的规划工具奠定基础。可达性是个人所经历的交通质量的函数（即，旅行成本、时间、可靠性），以及与工人技能和其他机会相匹配的工作的邻近性和连通性。现有的方法来研究空间不匹配力的推理结构上的低收入的旅行者的选择，此外，他们专注于调整过境，而将住房作为固定的，反之亦然。空间不匹配既不是一个二元的也不是静态的指标，它也不能单独用交通学来衡量：它的测量需要社区以出行模式、限制和需求的形式投入，以及交通连接的质量。该项目旨在为空间不匹配干预的概率非线性路径模型开发一个框架，该框架允许定性数据为定量指标构建提供信息。社区参与进程将有助于确定当前规划进程中的差距和计算无障碍环境时应考虑的新变量。该项目是响应公民创新挑战计划，轨道A-社区和流动性：提供更好的流动性选择，以解决住房负担能力和工作之间的空间不匹配，是NSF和能源部之间的合作。该奖项反映了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。