 Hi again, this presentation sets up your studies in Lesson 5 of Geography 581. Ever notice how small organizations tend to have long names? For example, consider the University Consortium for Geographic Information Science. UCGIS is a club of 60 or so mostly U.S. universities that host research and teaching in geospatial technology and methods. UCGIS is part of the legacy of the 1988 National Science Foundation grant that established the National Center for Geographic Information and Analysis at another, more exclusive consortium of leading universities. The center had a tremendous impact on the emerging discipline that came to be known as Geographic Information Science. It's also part of the legacy of Professor Michael Goodchild, the celebrated scholar who led the National Center and who coined the term G.I. Science in a 1992 speech. In 2016, Luke Anselin, UCGIS fellow and the scholar who invented spatial econometrics, wrote an article for the UCGIS blog entitled Space Skepticism. He expressed concern that even after 25 years of basic research in G.I. Science, mainstream scientists remained skeptical about the value of spatial thinking. However, Luke observed hopefully that G.I. Science is morphing into spatial data science. I myself found that many self-identified recent graduates of higher education programs related to GIS noticed changes in the contemporary geospatial technology field. Our survey of Esri's Young Professionals Network in 2017 revealed a significant difference in respondents' confidence about their software and application development competencies in comparison to the more traditional position and data acquisition and analysis and modeling competencies identified by the U.S. Department of Labor. Here are some of the advice young professionals offered to educators like me. Make GIS harder. One wrote, Include more data science, more machine learning, more computer science. Programming abilities should be heavily taught from day one. Every class should use programming. Another advised coding is such an important aspect of advanced geospatial analysis that it should be addressed even in basic classes. The most important thing I wish I had learned as part of my undergrad GIS curriculum, a respondent said, is basic programming, scripting, particularly with Python and SQL. Said another, define a curriculum that maintains the importance of geography yet incorporates GIS, scripting languages, and web development early and often in coursework. So maybe Luke Anselin was on to something. Maybe one of the emerging specializations in data science is spatial data science. Penn states two-year long effort to propose and gain university approval for a master of science degree in spatial data science is just one example of educators paying attention to our field's evolution. Quite a few other universities have responded in their own ways. A key reading in this lesson is geographic data science by Alex Singleton and Daniel Arribas Bell of the University of Liverpool. The article came out in 2021 in a special issue on the next 50 years of geographical analysis. Their audience was academic peers in the discipline of geography and in particular specialist communities like geographic information science. Their argument is essentially that geographers and GIS people have a lot to learn from data science and that data scientists have a lot to learn from them. They envision geographic data science as a disciplinary interface at which data scientists benefit from the critically reflective perspective that geography takes on new computational approaches to location problems as well as methodological contributions that better account for some of the key challenges in building models with spatial data. GI scientists benefit by exposure to methodological and technical methodological and technical aspects of working with big data. And geography benefits too, Alex and Danny argue, by maintaining and intensifying its relevance in greater scientific and industrial arenas. As you read this article, notice the central importance of ethics in their vision. Geographical data science would, they write, closely align with the core critical and ethical principles that have been advanced within geography and in particular the subdisciplinary field of GI science. Alex and Danny cite Agnieszka Lasinski as one of the geographers who's contributing to the nascent field of critical data studies. Her article Spatial Big Data and Anxieties of Control is worth reading for a couple of reasons. One reason is her section on spatial big data and state surveillance in which she explains in chilling detail the kinds of personal information that are generated, intercepted, and appropriated by the securities services. The most sought after among which, she says, is personal location data. Significantly for our purposes, she stresses that big data is first and foremost a practice of data generation through the establishment of correlations across data flows. That's such a thick and important observation, I think I should repeat it. Big data is first and foremost a practice of data generation through the establishment of correlations across data flows. Data scientists are expert in that practice. Lasinski's main interest in this research is to characterize the anxiety people feel about the collection and use of their personal location data. Rather than what others have termed surveillance anxieties, she argues that the lived reality of spatial big data is better understood as controlled anxiety, the desire to discern and direct flows of personal location information about oneself, yet feeling that any attempt to do so is essentially futile. Until this point, we've studied professional ethics and moral reasoning as things that individual supply to challenges they confront in the workplace. In this lesson, we'll shift perspectives from the individual to the organizations within which individuals practice spatial data science. Organizations typically have ethics codes of their own. One example is Esri's code of business conduct in ethics. Its primary concerns are compliance with government regulations, safeguarding proprietary information, avoiding or disclosing conflicts of interest, and honest dealing in financial matters. Ethics in compliance are not the same thing, however. According to Charlotte McDaniel, author of organizational ethics, ethics is not primarily concerned with the regulatory and financial aspects of organizations, whereas compliance always has these concerns. Ethics concerns itself with behaviors exhibiting justice, respect, or beneficence on the job. While compliance like ethics sets a standard for behavior McDaniel continues, compliance sets the minimum standard. Thus organizational ethics is fundamentally concerned with ethical behavior among collectives of individuals who share a common purpose. Organizational ethics is said to be good business, because organizations with regard for good relationships with employees, justice, and good conduct in the workplace are desirable places for employees, and desirable workplaces attract and retain good employees. Conversely, organizations whose reputations suffer for questionable ethical practices may face bad publicity or worse. The first data analytics organization I recall seeing on the news was Cambridge Analytica. According to whistleblower Christopher Wiley and The Guardian, Cambridge Analytica acquired profile data for some 50 million Facebook users and used the data to train models to target voters and influence Great Britain's Brexit referendum as well as the 2016 US presidential election. Cambridge Analytica ceased operations in 2018 in the wake of the scandal, though spin-offs have since taken its place. Palantir is another big data analytics firm that's attracted controversy in recent years. Federal government agencies are big customers. As you may know, one of its software products, Palantir Gotham, is widely used by counter-terrorism analysts across the US intelligence community and Department of Defense. Palantir Gotham is also used by big city police departments. Its use in New Orleans drew media attention and concerns about predictive policing. Critics argue that predictive policing algorithms and training data could re-inform harmful biases, and that stakeholders should have much greater insight into Gotham's design and use in cities and elsewhere. Palantir donated products and services to the city of New Orleans Police Department, and Gotham usage coincided with a decrease in the city's crime rate. Even so, the NOPD soon distanced itself from Palantir technologies. One of our signed readings is a Palantir origin story published in the Washingtonian magazine in 2012. Ethical concerns notwithstanding, the general trend is that cities are using more data analytics against crime, not less. Spatial data science included. If we can take any lesson from these thumbnail cases, it's easy to get into trouble, ethical trouble, in this arena, even if you're starting with the best of intentions. So what can spatial data science organizations do to navigate this ethical minefield? Through my work at Penn State in Esri, I was fortunate to meet and befriend Robert Cheatham. In 2001, Robert founded the company now known as Azavia. It's a mission-driven company that creates advanced geospatial technology for civic and environmental impact. For me, Azavia exemplifies organizational ethics in spatial data analytics. Visit its website and follow the link to what we do, and you'll find the essence of Azavia's mission statement, geospatial technology for good. An example project concerns the Dane County, Wisconsin, 2021 decennial redistricting project, specifically a sub-project that provided geospatial software to facilitate public engagement in the redistricting process. In 2016, Dane County established a non-partisan Independence Citizen Redistricting Commission. The ordinance specified that mapping process shall include opportunities for the public to submit map proposals. The Dane County Land Information Office, which has a long history of progressive applications of GIS, was one of the county departments involved in supporting the commission's work. An FAQ published by the county addressed the question of, what kind of tools do we need to involve the public? The staff team, the FAQ explained, is in the process of identifying options for online mapping software. The team subsequently identified Azavia's free and open source district builder redistricting tool. The 11 member commission developed eight district maps using district builder. A total of 70 members and volunteers created accounts to view the proposed maps and to propose their own. On October 4, 2021, the commission presented three map options to the county's executive board. On October 6, the executive committee recommended one option to the board of supervisors, which subsequently approved the proposal. The success of this implementation project, among others, contributed to an award for Azavia in the Census Bureau's Open Data for Good Challenge. In a later personal conversation, Robert confided that district builder will never make us money, but I'm proud of the work and that it's proven useful in many communities. Azavia is a for profit business. The company's core values are reflected in its voluntary certification as a B corporation. B corporation certification is designed to help companies around the world improve their social and environmental impact. I've assigned several of Robert's blog posts in this lesson. In the first, he reflects on Azavia becoming a B corporation. The second and third discuss the rationale and effects of Azavia's project selection guidelines. I've also shared a copy of the guidelines with Robert's permission. Following the readings, I'll ask you to reapply your moral reasoning skills to your choice of several scenarios that involve organizational ethics. I look forward to your presentations and discussion.