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

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的犯罪率差异提供了重要的线索。一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、身体紊乱和绿地。除了社会和经济因素(如种族紧张、就业机会等)外，街头活动的数量和性质等方面也影响犯罪。这个项目提出了包括因果推理建模和机器学习(ML)在内的新方法，结合来自NSF资助的物联网(AoT)项目的图像和声音等新的数据源，探索与犯罪有关的三个时变的邻里因素：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；现有的研究表明，它们与犯罪有关的重要性及其干预的可能性。这些发现可以通过测量复杂的社会和物理环境变量来改变社会科学研究，这些变量的规模是以前从未研究过的。这也将推动智能分布式传感器网络中嵌入的ML算法的边界。这个项目将开发新的ML算法来量化社交互动的质量，这一点到目前为止还没有得到探索。科学家、居民和组成社区的组织之间的融合将增加社区对互联社区的“智能”技术的理解和接受，以解决社会科学问题。此外，该项目通过与社区组织Colony 5和MAPSCorps合作，对增加STEM领域的多样性产生直接影响，这两个组织为芝加哥各地的少数族裔青年提供动手培训，使用物联网(IoT)和城市数据来了解和改善他们的社区。该项目的结果可能会带来新的智能城市技术，并能够绘制整个城市的重要社会行为变量，如社会凝聚力。然后，这项工作可能会为干预措施提供信息，这些干预措施可能会通过城市绿地来增加社会凝聚力，这可能会减少犯罪，提高城市居民的整体福祉。衡量街道活动和社会凝聚力很困难，通常需要人类观察或调查，这给观察者带来了繁重的负担，并限制了可以研究的社区的数量。该项目建议使用最新的技术进步来衡量人类的行为和社会互动。智能手机可以提供移动测试实验室，促进新形式的调查，衡量一个人随着时间的推移暴露在城市的认知影响，以及匿名跟踪移动性。与ML的进步相结合，这为探索城市中的人类互动开辟了新的潜力。AOT将提供芝加哥约200个地点的图像和声音，使用的技术可以很容易地扩展到更全面地测试选定的实验场地，探索使用ML“在边缘”来量化和表征不同社区的街道活动、无序和绿地使用。该项目将使用芝加哥开放的犯罪数据库，将这些措施与犯罪联系起来，以检查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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.