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2. Data Analysis and Visualization of Crime Data

   https://doi.org/10.2352/EI.2024.36.1.VDA-364  

   Sharma, Sharad; Dronavalli, Sri Chandra (January 2024, Electronic Imaging)

   The rise in crime rates over the past few years is a major issue and is a huge source of worry for police departments and law enforcement organizations. Crime severely harms the lives of victims and the communities they live in many places throughout the world. It is an issue of public disturbance, and large cities often see criminal activity. Many studies, media, and websites include statistics on crime and it is contributing elements, such as population, unemployment, and poverty rate. This paper compares and visualizes the crime data for four different cities in the USA, namely Chicago, Baltimore, Dallas, and Denton. We assess areas that are significantly affected based on zip codes and variations in crime categories. As the crime rates have significantly changed both upward and downward throughout time, these changes are compared to their external causes such as population, unemployment, and poverty. The results show crime frequency and distribution across four different cities and supply valuable information about the complex relationship between social factors and criminal behavior. These results and outcomes will help the police department and law enforcement organizations better understand crime issues, map crime incidents onto a geographical map, and supply insight into factors affecting crime that will help them deploy resources and help in their decision-making process. 

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4. Data Puzzles: Bridging Research Data and Science Classrooms to Broaden Impact and Build Data Literacy

   Gold, A (December 2025, American Geophysical Union)

   Data literacy is a critical skill for developing the next generation of scientists and informed citizens. Authentic engagement with real-world Earth science datasets in K–12 classrooms is often limited by the complexity of research data and the need for effective pedagogical strategies to support sensemaking. Data Puzzles, a collaborative effort between scientists, curriculum developers, and educators housed at the Cooperative Institute for Research in Environmental Sciences (CIRES), addresses this challenge by transforming authentic Earth and climate science datasets into structured, instructional modules designed for secondary classrooms. Each Data Puzzle is anchored in a relevant Earth system phenomenon and pairs authentic datasets (e.g., from satellite observations, field sensors, and physical samples) with research-based instructional practices from Ambitious Science Teaching (AST, Windschitl et al., 2020 ) to support data-driven reasoning, uncertainty exploration, and evidence-based explanation. Data Puzzle lessons have been adopted by hundreds of educators, empowering thousands of students to analyze and interpret authentic datasets while building critical data literacy skills. In parallel, the emerging Puzzle Piece resources allow students and teachers to extend structured learning into open-ended investigations using large, secondary datasets within a free, web-based analysis platform (CODAP). This combination of structured and open-ended resources creates an accessible pathway from curated datasets toward exploratory science practices, amplifying the’ broader impacts of science by translating complex datasets into classroom-ready learning experiences. This presentation will demonstrate how Data Puzzles serve as a model for scientists to broaden the impact of their research, enabling authentic data use and discovery in classrooms nationwide. We will share examples of how scientists’ data have been integrated into Data Puzzle lessons, highlight outcomes from teacher professional learning experiences, and discuss how this framework helps foster equitable data access and supports the next generation of Earth scientists and informed citizens. 

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5. Data and incentives

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   Liang, Annie; Madsen, Erik (January 2024, Theoretical Economics)

   “Big data” gives markets access to previously unmeasured characteristics of individual agents. Policymakers must decide whether and how to regulate the use of this data. We study how new data affects incentives for agents to exert effort in settings such as the labor market, where an agent's quality is initially unknown but is forecast from an observable outcome. We show that measurement of a new covariate has a systematic effect on the average effort exerted by agents, with the direction of the effect determined by whether the covariate is informative about long‐run quality versus a shock to short‐run outcomes. For a class of covariates satisfying a statistical property that we callstrong homoskedasticity, this effect is uniform across agents. More generally, new measurements can impact agents unequally, and we show that these distributional effects have a first‐order impact on social welfare. 

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