Disasters, natural or manmade, disrupt society and can expose people to public health emergencies. In preparation for impending natural disasters, national agencies, such as the United States Federal Emergency Management Agency (FEMA) will situate supplies (emergency food and rations) close enough to locations facing potential impacts but farenough away not to be compromised in a disaster area. However, these are emergency supplies and are not designed to meet long-term needs or the needs of specific individuals. Private businesses within the disaster zone can also experience supply chain disruptions and thus also need to prepare in advance. This book chapter reviews the topic of disasters and their impact on supply chains, then presents a lesson using GIS to anticipate and address a supply chain disruption, using a Florida hurricane as a case study.BRIEF DESCRIPTION OF ACTIVITYThrough a scenario of pre-natural disaster (hurricane) and using the information given related to distribution centers and retail stores, students will use GIS software to identify the distribution centers best fit to deliver the items needed in anticipation of a hurricane.LEARNING OUTCOMESBy the end of this lesson, students will be able to• recognize the importance of supply chain within retail contexts;• articulate the connection between supply chains and natural disaster preparedness;• utilize GIS tools to solve potential problems in real scenarios; and• propose optimal routes for distributing critical items using GIS tools within imminent natural disaster scenarios.This activity is part of Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).

Informed decision-making about spatial data selection and reliability is a fundamental part of spatial literacy. The proliferation of spatial data on the internet and the large quantity of user-generated data increases thechances of integrating unreliable data into research. This chapter focuses on evaluating spatial data using the Currency, Reliability, Authority, and Purpose (CRAAP) test with additional focus on the importance of Margins of Error (MOE) in American Community Survey (ACS) data. Reviews have shown that the ACS MOE is often overlooked during research or unreported in academic papers, resulting in misleading or invalid results that significantly impact policy and planning.The goals of the decision-making process in selecting existing data include: finding information that is most relevant to the research question; gaining a comprehensive understanding of available data and what it represents; comparing similar datasets; planning strategies for processing, integrating, and using data that are not a perfect match for project needs; and, ultimately, determining if a meaningful conclusion can be adequately derived from it.The CRAAP Test is a flexible and general framework for evaluating information resources in many disciplines and venues. To focus on specific concepts relevant to data capture in GIS using the CRAAP Test, we expand the basic concepts of the test and examine how they can be used to evaluate data sources in GIS.This activity is associated with a chapter in Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).

Environmental health (EH) is the study of physical, chemical, and biological factors in the environment that affect human health. EH data include environmental exposures, health outcomes, and socioeconomic status (SES), which are often place-based or have geographic correlations. This chapter aims to develop students’ spatial literacy skills to address two EH themes—environmental disparities and exposure-health associations—with open online mapping tools. Environmental disparity studies address the disproportionate exposures among populations of low SES and of color. Students will learn to use EJScreen to display maps of emission clusters, pollution levels, and SES, and interpret their relationships. Environmental exposures are associated with multiple adverse health outcomes—e.g., respiratory and cardiovascular diseases. Students will explore the associations by comparing spatial patterns of exposure and disease generated with EJScreen and PLACES, respectively. Students will gain an impression of EH topics and online geospatial tools with class activities and examples.This activity is associated with a chapter in Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).

The two learning activities outlined in this PowerPoint will aid in developing students’ understanding of spatial epidemiology and the intersectionality of socio-economic and environmental factors. Task 1 involves a short presentation on the software SaTScan covering cluster analysis and including the data types needed for the analysis. Following this, students receive a scenario-based task using a pre-designed hypothetical dataset of the spread of a contagion in the UK. Students input the appropriate text files, developed from contagion datasets, into SaTScan to produce a cluster analysis of unusually high rates of contagion in the region. This task allows for many different manipulations and outputs from the analysis by changing the parameters, such as cluster size and type of analysis in the software. Guidance on the use of SaTScan for the lesson is provided for teachers to help students understand and interpret output using multiple parameter settings. In brief, SaTScan tests the null hypothesis that cases of disease are randomly distributed. Statistical significance suggests that unusual spatial clustering is unlikely to have occurred by chance. This method has been used previously to identify clusters of contagious diseases, such as malaria,23 HIV,24 tuberculosis,25 as well as chronic diseases.26 The output of this session will be a cluster analysis Keyhole Markup Language (.kml) file which shall be used in task 2.Task 2 utilises the cluster outputs produced in task 1. Students then import the .kml cluster analysis layer produced by SaTScan into ArcGIS (or QGIS if preferred) and overlay this layer over a publicly available dataset that contains multiple spatial indexes. This socio-economic dataset uses real-world data on a region of the UK. This process allows students to visually explore the possible characteristics of a region that may explain where clusters fall. For example, students can choose to layer a measure of deprivation (The Index of Multiple Deprivation) over cluster output to visually examine the socio-economic characteristics of individual clusters. The assessment for this task is a student-led presentation and discussion based on their own critical thinking about which factors may predict cluster membership as well as maps that reflect these ideas. This activity is designed to help students develop their visual presentation and interpretation skills and become familiar with linking spatial factors to epidemiological trends.This activity is associated with a chapter in Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).

Public health concerns are often multi-faceted, complex problems. Any exploratory analysis conducted to support public health concerns should be scalable to this multi-attribute nature. This activity pairs with a chapter focused on spatial clustering techniques for multivariate analysis, which can reveal the locations with unusually high or low occurrences of multiple diseases. We will use obesity and insufficient sleep, which often occur together, as conditions to analyze considering their serious impacts on public health. Once contributing factors are determined, policymakers can be informed so that they can begin to address the negative impacts.In the activity, students will use SaTScan to find spatiotemporal hotspots and coldspots in obesity and insufficient sleep data representing American children. After a brief Q&A session, students will follow the walkthrough to complete the spatial clustering with SaTScan (Version 9.4). (SaTScan is a free software designed to detect clusters of spatial, temporal, or spatiotemporal data using scan statistics. This allows for complex relationships within the data to be revealed and explored. You can download SaTScan at https://www.satscan.org/.) The lecture can be closed with a discussion session where students will evaluate the statistically significant hotspots where they are found.LEARNING OUTCOMESBy the end of this lesson, students will be able to• analyze multivariate spatiotemporal data on obesity and insufficient sleep among children by using SaTScan;• interpret and evaluate hotspots and coldspots regions; and• create spatial cluster maps by handling spatial data files.This activity is part of Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).

The lesson covers using PolicyMap and Social Explorer to visualize socioeconomic variables. These two mapping tools let learners view geographic distribution of variables (e.g., evidence) and begin their exploration of spatial literacy. In addition, the visualizations can lead learners to question assumptions and examine the impact of social determinants of health among other issues. The lesson walks learners through using these tools to examine issues, and combine a narrative and visualizations to write a policy report.

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Tips and Advice on using ChatGPT effectively and ethically (English & Spanish)

This handout on business resources was designed in collaboration with the Ciocca Center for Entrepreneurship & Innovation. Although the quips aren't exactly witty, the aim of each remark is to answer the simple question: Why would I use this resource anyway? Even though the handout is created with a specific audience in mind, the quips could be used to highlight any of these resources, anywhere information is needed.

This exercise scaffolds Google and Library resources in order to help students prepare for "career conversations" with industry professionals. The presentation is designed for a business communication class in which students conduct industry research as prepartion for a strategic professional networking assignment. The assumption exercise is designed explicitly to encourage students to question their assumptions about librarians and other career professionals. Padlet is used to encourage group work and for assessment purposes.