SCC-IRG Track 1: Understanding the Impact of Social and Physical Environment Factors on Crime Using Urban Sensing and Machine-Learning

SCC-IRG 第 1 轨道：利用城市感知和机器学习了解社会和物理环境因素对犯罪的影响

• 批准号: 1952050
• 负责人: Marc Berman
• 金额: $ 250万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952050&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Understanding; Impact

Understanding the root causes of crime in urban areas is critical to effectively addressing this serious problem. Variation in crime levels across and within cities offers important clues. Some heterogeneity may be related to physical and social characteristics of urban environments, such as social cohesion, physical disorder, and greenspace. In addition to sociological and economic factors (e.g., racial tension, job opportunities, etc.), aspects such as the amount and character of street activity also impact crime. This project proposes new methods including causal inference modeling and machine learning (ML), combined with new data sources such as imagery and sound from the NSF-funded Array of Things (AoT) project, to explore three time-varying neighborhood factors on crime: 1) the amount and usage of greenspace, 2) the amount and character of street activity and related social cohesion; 3) the level of visual and auditory disorder; as extant research suggests their importance in relation to crime and their potential for intervention. These discoveries could transform social science research by measuring complex social and physical environment variables at scales never before investigated. This will also push the boundaries of ML algorithms embedded in intelligent distributed sensor networks. This project will develop novel ML algorithms that quantify the quality of social interactions, which thus far have not been explored. Convergence among scientists, residents, and organizations that comprise neighborhoods will increase community understanding and acceptance of "smart" technologies for connected communities to address social science questions. Further, the project has direct impacts on increasing diversity in STEM fields through partnerships with community organizations Colony 5 and MAPSCorps, which provide hands-on training of minority youth across Chicago to use Internet of Things (IoT) and urban data to understand and improve their neighborhoods. Results from this project could lead to new smart city technologies and the ability to map important social behavioral variables such as social cohesion across entire cities. This work may then inform interventions that could be used to increase social cohesion, potentially through urban greenspaces, which could lead to reductions in crime and overall increased well-being for urban residents.Measuring street activity and social cohesion is difficult, typically requiring human observation or surveys, which are taxing on observers and limit the number of neighborhoods that can be studied. This project proposes to use recent technological advances to measure human behavior and social interactions en masse. Smartphones can provide mobile testing labs, facilitating new forms of surveys measuring the cognitive impact of a person's urban exposure over time as well as tracking mobility anonymously. Combined with advances in ML, this opens new potential for exploring human interactions in cities. AoT will provide images and sound from some 200 Chicago locations, using technology that can be readily extended to more fully instrument selected experimental venues, exploring the use of ML "at the edge" to quantify and characterize street activity, disorder, and greenspace use across diverse neighborhoods. This project will relate these measures to crime, using Chicago's open crime database, to examine the physical and social environmental factors that may influence crime and its mitigation.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.

了解城市地区犯罪的根源对于有效解决这一严重问题至关重要。城市之间和城市内部犯罪水平的变化提供了重要的线索。某些异质性可能与城市环境的物理和社会特征有关，如社会凝聚力、物理无序和绿地。除了社会和经济因素（例如，种族紧张局势、就业机会等），街头活动的数量和性质等方面也对犯罪产生影响。该项目提出了新的方法，包括因果推理模型和机器学习（ML），结合新的数据源，如图像和声音从NSF资助的数组的东西（AoT）项目，探索三个随时间变化的邻里因素对犯罪：1）绿地的数量和使用，2）街道活动的数量和特征以及相关的社会凝聚力; 3）视觉和听觉障碍的程度;现有研究表明，这两种障碍与犯罪有关，具有重要的干预潜力。这些发现可以通过以前所未有的规模测量复杂的社会和物理环境变量来改变社会科学研究。这也将推动嵌入智能分布式传感器网络的ML算法的边界。该项目将开发新的机器学习算法，量化社交互动的质量，到目前为止还没有探索过。科学家、居民和社区组织之间的融合将增加社区对互联社区解决社会科学问题的“智能”技术的理解和接受。此外，该项目通过与社区组织Colony 5和MAPSCorps的合作关系，对STEM领域日益增加的多样性产生了直接影响，这些组织为芝加哥的少数民族青年提供实践培训，以使用物联网（IoT）和城市数据来了解和改善他们的社区。该项目的成果可能会带来新的智慧城市技术，以及绘制重要社会行为变量（如整个城市的社会凝聚力）的能力。这项工作可能会为可用于增加社会凝聚力的干预措施提供信息，可能通过城市绿地，这可能会导致犯罪减少和城市居民整体幸福感的提高。衡量街道活动和社会凝聚力很困难，通常需要人工观察或调查，这对观察者来说是一种负担，并且限制了可以研究的社区数量。该项目建议使用最新的技术进步来衡量人类行为和社会互动。智能手机可以提供移动的测试实验室，促进新形式的调查，测量一个人的城市暴露随着时间的推移以及匿名跟踪流动性的认知影响。结合ML的进步，这为探索城市中的人类互动开辟了新的潜力。AoT将提供来自大约200个芝加哥地点的图像和声音，使用的技术可以很容易地扩展到更全面的仪器选定的实验场地，探索使用ML“在边缘”量化和表征不同社区的街道活动，混乱和绿地使用。该项目将使用芝加哥的开放式犯罪数据库，将这些措施与犯罪联系起来，研究可能影响犯罪及其减轻的物理和社会环境因素。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。

项目成果

Nature's path to thinking about others and the surrounding environment

思考他人和周围环境的自然之路

• DOI: 10.1016/j.jenvp.2023.102046
• 发表时间: 2023
• 期刊: Journal of Environmental Psychology
• 影响因子: 6.9
• 作者: Schertz, Kathryn E.;Kotabe, Hiroki P.;Meidenbauer, Kimberly L.;Layden, Elliot A.;Zhen, Jenny;Bowman, Jillian E.;Lakhtakia, Tanvi;Lyu, Muxuan;Paraschos, Olivia A.;Janey, Elizabeth A.
• 通讯作者: Janey, Elizabeth A.

City Population, Majority Group Size, and Residential Segregation Drive Implicit Racial Biases in U.S. Cities

城市人口、多数群体规模和居住隔离导致美国城市隐性种族偏见

• DOI: 10.2139/ssrn.4342718
• 发表时间: 2023
• 期刊: SSRN Electronic Journal
• 作者: Stier, Andrew;Sajjadi, Sina;Karimi, Fariba;Bettencourt, Luis;Berman, Marc G.
• 通讯作者: Berman, Marc G.

Early pandemic COVID-19 case growth rates increase with city size

• DOI: 10.1038/s42949-021-00030-0
• 发表时间: 2021-06-28
• 期刊: NPJ URBAN SUSTAINABILITY
• 作者: Stier, Andrew J.;Berman, Marc G.;Bettencourt, Luis M. A.
• 通讯作者: Bettencourt, Luis M. A.

Neighborhood street activity and greenspace usage uniquely contribute to predicting crime

邻里街道活动和绿地使用对预测犯罪有独特贡献

• DOI: 10.1038/s42949-020-00005-7
• 发表时间: 2021
• 期刊: npj Urban Sustainability
• 作者: Schertz, Kathryn E.;Saxon, James;Cardenas-Iniguez, Carlos;Bettencourt, Luís M.;Ding, Yi;Hoffmann, Henry;Berman, Marc G.
• 通讯作者: Berman, Marc G.

Effects of the physical and social environment on youth cognitive performance.

物理和社会环境对青少年认知表现的影响。

• DOI: 10.1002/dev.22258
• 发表时间: 2022
• 期刊: Developmental psychobiology
• 影响因子: 2.2
• 作者: Meredith,WesleyJ;Cardenas-Iniguez,Carlos;Berman,MarcG;Rosenberg,MonicaD
• 通讯作者: Rosenberg,MonicaD