Department of Geography | Department of Geography

Derby Diaries: A Transformative Experience

March 23 2022

I wasn’t sure what to expect when I entered Derby Hall in 2008, yet I suspected and hoped it would be a transformative experience. I found faculty research grounded in the physical and social sciences both intriguing and daunting, especially since I earned an undergraduate degree in the natural sciences. And I knew that OSU’s Geography program was long-respected and highly ranked in the field. The next two years–plus an additional four years spent earning my PhD–proved to be an excellent fit and provided me with a strong foundation for my academic career and personal life.

My Geography courses were demanding and rigorous, yet flexible enough to accommodate my research interests. My graduate student peers provided much needed levity in the form of office banter, trivia nights and High Street outings, and testing my (inferior) ping pong and badminton skills. And Staff like Diane Carducci and Colin Kelsey were warm and supportive. Friendships and professional networks developed during that time remain strong to this day.

My advisors Becky Mansfield and Kendra McSweeney supported my multidisciplinary pursuits in History, Rural Sociology, and Public Health. They, along with Nancy Ettlinger, Joel Wainwright, and Cathy Rakowski, among others, encouraged me as I developed research projects in Nicaragua and Latin America. As a Teaching Assistant, graduate students Eveily Freeman and Jessica Barnes modeled excellent undergraduate teaching and opened my eyes to online learning, which has proven immensely helpful in the last two years.

SUNY Old Westbury students and I (lower right) visit a coffee farm near Matagalpa, Nicaragua, in March 2018.

Ultimately, my years in Derby Hall prepared me for my current position as an Assistant Professor of Public Health at SUNY Old Westbury. My broad background and pedagogical training suited my current department’s diverse liberal arts courses: Introduction to the Social Determinants of Health, Global Health, and Research Methods, to name a few. The Environmental Justice course that I developed is heavily inspired by the Nature and Society courses that I took in Derby Hall. I am particularly proud of having taken undergraduate students to Nicaragua and Bolivia; if travel restrictions ease in time, I’ll add Cuba to that list this summer!

Community celebration at Sure We Can, a not-for-profit that supports informal recyclers and advocates for their well-being in Brooklyn, New York, where I serve as Co-Chair of the Board of Directors.

Finally, my time at OSU helped me realize the importance of community. Ed Malecki, Ola Ahlqvist, Alvaro Montenegro, and Max Woodworth regularly ate lunch with graduate students in the lounge, and Morton O’Kelly, Kendra McSweeney, and Becky Mansfield hosted get-togethers in their homes. Importantly, these conversations and gatherings gave me an idea of what work-life balance and parenthood as an academic might look like. (My first child was born in December 2021!) Also, they reinforced the importance of building community beyond Derby Hall and academia, and the possibilities that arise from scholar-activist engagement.

In sum, my time in Derby Hall was truly transformative, and I am happy to be part of OSU Geography’s storied history!

Chris Hartmann, MA (2010), PhD (2016)

Learning Beyond the Classroom

February 16 2022February 17, 2022

My name is Laurel Bayless, and I graduated last year with a BS in Physical Geography. I’m incredibly fortunate to have worked with multiple faculty and researchers since I started the geography major as a sophomore!

I took Dr. Ellen Mosley-Thompson‘s course on climate change (Geog 3901H), and I was fascinated to learn about her research in ice core paleoclimatology. After asking her how to get involved at the Byrd Polar and Climate Research Center, I started working in the Goldthwait Polar Library. Soon thereafter, I had the opportunity to conduct research with the Ice Core Paleoclimatology Research Group! Last year I completed my senior honors thesis, Signatures of El Niño-Southern Oscillation in an Ice Core from Huascarán, Peru, 1994-2019, under the guidance of Dr. Mosley-Thompson, Dr. Lonnie Thompson, and other members of the Ice Core Paleoclimatology Research Group. We found that isotopic signals found in cores extracted from Huascarán, Peru provide a robust proxy record for central Pacific sea surface temperatures. Since the Huascarán ice core record goes back around 19,000 years, the isotopic record can be used to interpret some aspects of El Niño-Southern Oscillation (ENSO) history for the entirety of the Holocene and into the late Pleistocene. This record could help us to understand how ENSO has varied over time and how it may now be changing due to climate change. Records of deuterium excess, ice accumulation, and insoluble dust require further research but may yield promising results.

I also worked with Dr. Becky Mansfield after taking her course on nature-society geography (Geog 3800). Our project focused on the portrayal of Neanderthals and prehistoric anatomically modern humans in popular books about human evolution over the past hundred years. The science of human evolution is fascinating, but I also am intrigued by the ways in which our ideas about nature and society are used to shape our ideas of human evolution, and the ways in which these ideas about prehistory are then used to shape our ideas of human nature. With Dr. Mansfield, I explored how the dichotomy between human and Neanderthal has been maintained despite changing ideas of Neanderthals, and how our conceptions of Neanderthals have been developed in conjunction with colonial ideas of race. This paper is currently under review.

Research has been a fantastic opportunity for me to learn beyond the classroom, explore ideas I’m interested in, and to see how the scientific process works! Not only has undergraduate research helped me learn so much about ice core paleoclimatology and science studies, but it has also helped me understand my interests and how pursue them beyond my degree. I am currently pursuing an MPhil in Holocene Climates at the University of Cambridge.

Laurel Bayless, BS Geography 2021,

The Ohio State University

Then and Now

September 15 2021

When I was invited to blog about “then and now”, I thought about historian David McCullough’s statement: “One might also say that history is not about the past. If you think about it, no one ever lived in the past… They lived in the present.” If then is made in the now as McCullough seems to suggest, now is also a piece of then. It is in this spirit that I see then, my time at OSU in the late 1980s and early 1990s, as a source of inspiration to the making of a protean career, and a piece of now as an economic geographer.

The “boundaried” career is centered on the organization, the university. The tenure institution focuses academics on the needs and requirements of the university early on in an individual’s career. In the 1990s, the old social contract gave way to newer forms of social contracts arising from downsizing and the emergence of smaller and innovation-driven firms. Such instability also occurred in academia with adjunctivization. The less predictable work organization resulted in greater instability for the employee but the protean career also sneaked up on me.

When I graduated from The Ohio State University (OSU) in the 1990s, I was confronted with such instability. Fewer academic positions were available and quantitative economic geography was on the decline. Unlike other students of Emilio Casetti’s (my dissertation adviser), many of whom were assuming illustrious careers in major departments, I had graduated from OSU without a publication because I had spent my time in the Economics and Sociology departments expanding my understanding of international trade and Asia. The lure towards interdisciplinarity is a big piece of now. Graduating without a publication was not my biggest challenge. Speaking the language of the expansion method was. I decided early on to write my papers differently, focusing on the research question than the methodology. The first paper was a hit and was subsequently selected as a classic for a regional science volume. I went on to publish more expansion method pieces despite warnings of doom from colleagues. Part of such early adaptability was honed from animated arguments with Edward Taaffe, Nancy Ettlinger and Kevin Cox, and the audacity to pry apart Larry Brown’s hot off the press “Place, Migration and Development in the Third World”. But one of the biggest resources, graduate students in Derby Hall, fomented a training ground that was to last a lifetime. Some rejected objectivism and forced me to reflect on my intellectual biases. Others tempted me with the lure of emerging geospatial technology. Enlarging boundary has allowed me to cross disciplinary and epistemological aisles, publish using a wide range of methodological tools, and enjoy a protean career as an economic geographer.

Then was a time of learning to embrace methodological pluralism, and now is a piece of then as such pluralism has continued to define my scholarly work. This centennial celebration has provided an opportunity for reflections, OSU being the place where I began my journey across boundaries. Congratulations on your centennial anniversary.

Jessie Poon, Professor, University at Buffalo (SUNY),

Co-Editor, Environment & Planning A,

Chair, Regional Studies Association

City’s Municipal Service Requests May Help Identify “Hotspots” for Opioid Use and Overdoses

July 28 2021July 26, 2021

Background

Opioid use disorder and overdose deaths is a public health crisis in the United States. In the year of 2019, over 70% of all drug overdose deaths involved an opioid, including prescription opioids, heroin, and synthetic opioid like fentanyl (National Institute of Drug Abuse, 2021). Ohio is among the states hit hardest by the “opioid epidemic” with the rise in the misuse and abuse of prescription opioid pain relievers like OxyContin and Fentanyl and non-prescription opioids like heroin. Franklin County, which includes City of Columbus and the surrounding suburbs, experienced 547 drug overdose deaths in 2019 the largest number of any region in the state, and representing a 14.9% increase over the previous year (Ohio Department of Health, 2020).

There is increasing recognition that crisis’s etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. This places attention on the health effects of upstream social factors such as economic, education, and demographic that shape downstream factors such as behavior, economic stability, stress levels, support networks, neighborhood and physical environment, and access to healthy food and health care. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. A lack of timely, high-resolution data hampers research into the neighborhood social determinants of opioid use disorder. We explore the use of nontraditional municipal service requests (also known as “311” requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. These are public data that are frequently updated (in many cases, daily) and have high spatial resolution (latitude and longitude).

Results

We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008–2017. We find 10 out of 21 types of 311 requests (abandoned vehicles, animal complaints, code violation, law enforcement, public health, refuse trash litter, street lighting, street maintenance, traffic signs, and water sewers drains) spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively.

Figure (a) shows the actual spatial distribution of OOE hot spots and cold spots in Columbus, 2017. The remaining maps show the three most accurate predictors based on predict accuracy: code violation (b), public health (c) and street lighting (d). Figure also shows the three most inaccurate predictions: traffic signs, street maintenance, and waters sewers drains in Fig. e–g, respectively. (Li et al., 2020)

Implications

The results from this study support the view that opioid crisis is rooted in social and neighborhood distress. We show such spatial characteristics can be used along with 311 data itself to predict the trends of opioid overdose hotspots when OOEs data is not available. Since 311 requests are publicly available and with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy. It is worth mentioning that our research is not a predictive policing tool. An appropriate use is to help think strategically about where to allocate outreach, programs and resources to at-risk individuals and how to alleviate the underlying social and environmental stressors in our city.

Yuchen Li, PhD Candidate

Department of Geography

The Ohio State University

References

Li, Y., Hyder, A., Southerland, L. T., Hammond, G., Porr, A., & Miller, H. J. (2020). 311 Service Requests As Indicators of Neighborhood Distress and Opioid Use Disorder. Scientific Reports, 10(1), 1–11. https://doi.org/10.1038/s41598-020-76685-z

National Institute of Drug Abuse. (2021). Overdose Death Rates. Retrieved May 13, 2021, from National Institute on Drug Abuse website: https://www.drugabuse.gov/drug-topics/trends-statistics/overdose-death-rates

Ohio Department of Health. (2020). 2019 Ohio Drug Overdose Data: General Findings. Retrieved from https://odh.ohio.gov/wps/wcm/connect/gov/0a7bdcd9-b8d5-4193-a1af-e711be4ef541/2019_OhioDrugOverdoseReport_Final_11.06.20.pdf?MOD=AJPERES&CONVERT_TO=url&CACHEID=ROOTWORKSPACE.Z18_M1HGGIK0N0JO00QO9DDDDM3000-0a7bdcd9-b8d5-4193-a1af-e711be4ef541-nmv3qSt

New Avenues for Remote Sensing in Disaster Monitoring and Assessment

June 30 2021June 30, 2021

2021 marks an increasing trend of putting analytics directly into the space. While before remote sensing researchers used to download “raw” satellite images of the Earth from centralized websites to their computer for further analysis (and even catch physical film canisters from a satellite ironically named Corona back in the 1950s¹), now their work gets much easier. Initiatives like from the European Space Agency² use artificial intelligence on-board of satellites to process images into ready usable products and send them to the ground. In the context of my research, disaster monitoring and assessment, that could mean no more hours spent on working with raw images and building my own algorithms to derive extent of disaster damage from space. Instead, the focus is shifted towards utilizing downloaded image products, like flood masks, in more complicated computer models for various applications and integrating with other datasets.

My current research is about urban disaster damage assessment that goes beyond simple from-above physical damage identification with satellite images. I am interested in linking the pixels to people³ and understanding impact of past and on-going disasters on a society. This large-scale analysis is again, only possible thanks to the computational progress described above, availability of usable data and emergence of big data to better understand our changing environment. For disaster assessment, that means gathering and incorporating big volumes of almost real-time information from individuals together with field surveys, remotely sensed images of the area and longer-term census data. For example, Fig. 1 (below) shows this “layer cake” of disparate data that was implemented by the team of Ohio State researchers for hurricane flood monitoring. The computer platform considers many sensor feeds, including individuals data from Twitter. Another interesting example is FloodFactor platform⁵ that rather than assessing extent and damage of on-going disasters, offers predictions of future damage risk for a given residential address. The methodology relies on deriving maps of flood probability and putting those in the context of each building type and historical losses in the area. All in all, while merging datasets in damage assessment is not new, there are still several methodological challenges and key datasets needed to be explored. One of which I am focusing on right now is incorporating economic and social geospatial data as “proxies” for physical damage measures in situations of missing data.

Fig. 1 Disaster monitoring and relief framework based on multiple sensor feeds (left), and the schematics of their visualization as a web GIS (right). Source: [4]

Most importantly, I would like to position my work within the context of smart cities. Disaster damage assessment is an integral part of future smart cities that “use connected technology and data to improve the efficiency of city service delivery, enhance quality of life for all, and increase equity and prosperity for residents and businesses”⁶. That is very important when one realizes the exacerbating climate change realities and social vulnerabilities in cities across the globe that lead to disasters. The risk and damage assessment of the future that we need is the one relying on interconnected sensors (satellites, social media, field data etc.), merging of data, and exercised by local municipalities for better decision-making. Accordingly, I seek to contextualize my future findings from local case studies in a broader narrative of smart city development and disaster risk reduction initiatives.

Polina Berezina

PhD Student, Department of Geography

The Ohio State University

References

Would global trade contribute to food security without overwhelming our planet?

January 27 2021January 26, 2021

Global trade supplies food to countries in conditions of food scarcity by redistributing food commodities among the different regions of the world; at this time, roughly one-fourth of the food supply globally is provided through international trade (D’Odorico et al., 2014). If more productive regions (producing more output per unit input of land) export their produce to countries with lower productivity, we could feed more people than we could when the food supply is only domestically generated. As shown in Figure 1, for example, cereal demand in Africa and East Asia can hardly be met without global trade. The global food trade is also a more efficient way of using natural resources world-wide. 588m³ of water is needed to produce 1 ton of wheat in France, whereas 18,698m³ of water is required for producing the same amount of wheat in Somalia (Mekonnen & Hoekstra, 2011). By importing agricultural commodities, countries with low agricultural productivity and scarce natural resources like Somalia can optimize resource use (i.e., water or land) at both the national and global level. Thus, the global food trade not only plays an important role in global food security, but has also become a crucial part of allocating limited global resources.

Figure 1. Global Cereal Trade in 2017. The colors of the regions represent the net import of cereal products (import-export). Reds are net importers and blues are net exporters. The top 10 flows in terms of the volume traded are shown. The flows shown account for 16.3% of the total cereal products related to global trade. Cereals include wheat, rice, maize, barley, millet, oat, rye, and sorghum. Trade data were taken from the FAOSTAT database.

However, global trade is not always conducive to global food security and resource conservation.

First, decreased food prices due to trade can boost consumption in the importing countries, thus in turn causing overproduction in the exporting countries (Kastner et al., 2014). The so-called rebound effect highlights a possibility that increased production efficiencies through trade may increase overall demand, so that resource use can instead be expanded. For example, deforestation in the Brazilian Amazon has been driven by the increase in soybean production for livestock feed in developing countries, with a rebound of soybean prices in global markets (Morton et al., 2006).

Second, trade dependency can make importing countries vulnerable to external shocks, as these countries increasingly rely on resources that they do not directly control (D’Odorico et al., 2014). It is widely known that droughts in production regions, as well as banned grain exports, triggered the Arab Spring in 2011[i]. An unexpected crisis like the pandemic last year (and continuing into this year) poses greater challenges to import-dependent countries as well[ii]. Furthermore, an influx of cheap subsidized commodities from exporting countries can threaten both the domestic market and local biodiversity, as well as undermine rural livelihoods, which in turn strengthens the trade dependency (Carr et al., 2016). For instance, exports of maize from the U.S. to Mexico under NAFTA are reported to be as detrimental to Mexico’s smallholder farmers and domestic biodiversity of maize varieties (Martinez-Alier, 1993).

Third, the increasing export of value-added crops (i.e., coffee, cocoa, or tropical fruits) from lower-income countries may influence food security at the local level. The increasing rate of traded volume from 1987 to 2017 is higher in stimulants (244%) and fruits (217%) than in cereals (153%), which has been largely driven by developing countries. While some argue that cultivating such crops is beneficial to food security because of increase in rural income (Kuma et al., 2016), others find negative relationships between household food security and value-added crop production (Anderman et al., 2014). For example, the expansion of banana plantations in Northern Laos for Chinese customers raises concerns about food security due to the conversion of paddy rice fields to the plantation and rising rice prices (Friis & Nielsen, 2016).

The three points listed above imply that “a multifaceted and linked global strategy” (Godfray et al., 2010) should complement international food trade in order to feed growing populations without overwhelming our planet. Measures to shift dietary preferences toward less consumption of meat products can be helpful for mitigating the rebound effect. Continuous efforts to increase domestic productivity and to diversify suppliers will buffer external supply shocks in import-dependent countries. Strategies to ensure the food security of cash crop farmers in developing countries are required, and environmental regulations for sustainable resource use need to be implemented as well.

Sohyun Park

PhD Candidate in Department of Geography

The Ohio State University

Anderman, T. L., Remans, R., Wood, S. A., DeRosa, K., & DeFries, R. S. (2014). Synergies and tradeoffs between cash crop production and food security: A case study in rural Ghana. Food Security, 6(4), 541–554.
Carr, J. A., D’Odorico, P., Suweis, S., & Seekell, D. A. (2016). What commodities and countries impact inequality in the global food system? Environmental Research Letters, 11(9), 095013.
D’Odorico, P., Carr, J. A., Laio, F., Ridolfi, L., & Vandoni, S. (2014). Feeding humanity through global food trade. Earth’s Future, 2(9), 458–469.
Friis, C., & Nielsen, J. Ø. (2016). Small-scale land acquisitions, large-scale implications: Exploring the case of Chinese banana investments in Northern Laos. Land Use Policy, 57, 117–129.
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., & Toulmin, C. (2010). Food security: The challenge of Feeding 9 Billion People. Science, 327.
Kastner, T., Erb, K.-H., & Haberl, H. (2014). Rapid growth in agricultural trade: effects on global area efficiency and the role of management. Environmental Research Letters, 9(3), 034015.
Kuma, T., Dereje, M., Hirvonen, K., & Minten, B. (2016). Cash crops and food security: Evidence from Ethiopian smallholder coffee producers. The Journal of Development Studies, 55(6), 1267-1284.
Martinez-Alier, J. (1993). Distributional Obstacles to International Environmental Policy: The Failures at Rio and Prospects after Rio. Environmental Values, 2(2), 97–124.
Mekonnen, M. M., & Hoekstra, A. Y. (2011). The green, blue and grey water footprint of crops and derived crop products. Hydrology and Earth System Sciences, 15(5), 1577–1600.
Morton, D. C., DeFries, R. S., Shimabukuro, Y. E., Anderson, L. O., Arai, E., Del Bon Espirito-Santo, F., Freitas, R., & Morisette, J. (2006). Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon. Proceedings of the National Academy of Sciences of the United States of America, 103(39), 14637–14641.

[i] https://www.pbs.org/newshour/world/world-july-dec11-food_09-07

[ii] https://www.brookings.edu/blog/future-development/2020/07/14/middle-east-food-security-amid-the-covid-19-pandemic/

Map Production to Support 2020 Census Programs and Operations

October 28 2020October 20, 2020

Maps have always played a significant role in conducting a census. This post discusses map production within the Geography Division of the U.S. Census Bureau to support the 2020 Census.

Although most of the field operations for the 2020 Census were conducted on digital instruments rather than paper, there is still a need for paper maps to support many operations. For instance, staff in the area census offices (ACOs) use large-format paper maps to help understand and visualize the boundaries of the collection geographic areas within their ACO or to visualize the workload for a particular operation. Other operations, such as Remote Alaska, Update Enumerate, and the Island Areas Census, are conducted entirely on paper. In these cases, staff use small-format maps to locate their workloads and annotate updates in the field.

In order to provide the number of maps required for geographic programs and field operations under strict production timelines, the Census Bureau utilizes the Census Automated Map Production System (CAMPS). CAMPS is automated mapping software that runs in a batch environment. CAMPS was developed in the lead up to the 2010 Census and continues to be a major part of production mapping. Cartographers design maps by setting up project parameter tables that describe everything from the title that will appear on a map to the appearance of labels and features for each feature class that will appear on a map. During peak operations, CAMPS creates thousands of map sheets per hour. For example, for the Local Update of Census Addresses Operation (LUCA), CAMPS created more than 800,000 unique map sheets over an 11 day period!

Largely due to the digital nature of many 2020 Census operations, most operations requested that we create large-format maps on as few sheets as possible, preferably a single sheet for each mapping entity. This presented some design challenges as CAMPS traditionally scales maps to as many sheets as needed based on the density of a particular geographic type within the subject area. Due to the large variety of the sizes of the subject areas for some map types, we designed multiple scale-dependent parameter configurations for each map type. Figure 1 shows a map of the Dayton ACO. This map was created in CAMPS and identifies the boundaries of the ACO. This map type included maps with scales as small as 1:1,000,000 and as large as 1:11,000 for the ACOs in the contiguous U.S.

Although we created the vast majority of our 2020 Census field maps in CAMPS, we also used commercial mapping software to produce maps, using manual interactive design and scripting. We designed map document templates in the commercial mapping software and developed scripts to automate the creation of the maps. For the Island Areas Census, we created approximately 5,600 basic collection unit (BCU)-based maps with imagery. Figure 2 shows an example of one of these maps in Guam, which was used in the field along with additional small-format maps to conduct the Island Areas Census.

Web maps and web map applications have also become a large part of our work for the 2020 Census. Web maps are particularly valuable for the communication of 2020 Census concepts to the general public and our partners. We developed web map applications to aid in outreach efforts in hard-to-count communities with the Response Outreach Area Mapper and to indicate how the Census Bureau planned to conduct the census with the Type of Enumeration Area Viewer, the In-Field Address Canvassing Viewer, and the Mail Contact Strategies Viewer. We were able to communicate with the public about the participation of local partners in programs such as LUCA, the Participant Statistical Areas Program, and the New Construction Program. In addition, we found the web map applications to be useful for the internal tracking of the status and progress of programs.

We are currently planning and developing maps and cartographic products for the next phase of the 2020 Census: data dissemination.

Kevin Hawley is an OSU alumnus and chief of the Cartographic Products and Services Branch in the Geography Branch at the U.S. Census Bureau. Any views expressed are those of author and not necessarily those of the U.S. Census Bureau.

You can see more of Kevin Hawley’s important work and its basis in Geography below.

The 2020 U.S. Census and Indigenous peoples

September 2 2020September 10, 2020

The 2020 Census is in full swing in the United States. By the end of the Census in December, virtually every American citizen will have been asked to provide information to the Census—whether it is via the traditional forms we receive in the mail, via telephone, or online. Americans are consistently reminded of the importance of the Census—besides providing a more accurate count of the population of the country, its states, and the many counties/parishes/boroughs, cities, towns, villages that comprise each state, it has economic and political ramifications. According to the U.S. Census Bureau, Federal funding is guided by population shifts, states can gain or lose Congressional representation based on their new populations, and local governmental units draw boundaries or allocate resources based on information gathered in the Census. If those reasons aren’t enough, we also are faced with the ‘threat’ of a Census enumerator coming to our front doors to collect the information, or in some cases, a $100 fine. Clearly, it is important for Americans to do the Census.

However, for one group of Americans, the Census both carries deeper historical and contemporary significance. For the over 570+ Federally recognized tribes (including Alaskan Native tribes) in the United States, information from the Census helps influence Federal funding for tribally-focused programs, as well as allowing tribes to make local planning decisions for tribal programs and services. However, Federal counting of Indigenous peoples in the United States has a very fraught history that is linked with colonialism and dispossession of tribal lands and political power. For example, tribal ‘rolls’ were routinely used from the late 19^th century to count members of tribes and determine their blood quantum, deeming them worthy or unworthy of allotments and/or tribal citizenship. Federal policies such as relocation/termination and sending Indigenous children to boarding schools have created a legacy where many tribal members do not trust the Federal government or its initiatives, making it very difficult to secure Indigenous buy-in to the Census, as Kirsten Carlson writes.

Even when Indigenous people have participated in the Census, there has been notorious undercounting of Indigenous tribes and individuals; according to this report by the National Congress of American Indians, the percentage of Indigenous people that have been undercounting has ranged anywhere from 4.9% (in the 2010 Census) to 12.2% (in the 1990 Census). The same report echoes the relative mistrust in Indigenous communities as to the purposes and benefits of doing the Census. The rise of the COVID-19 pandemic in the United State also represents a barrier to participation, as does technological barriers that are often present on reservations (lack of Internet access, etc.) and the fact that the Census Form is not offered in tribal languages. Of course, there is also the fact that the Federal definition of who is an ‘American Indian’ or ‘Alaska Native’ does not always matchup with tribal or individual definitions of Indigeneity (see Liebler, 2018 for a more detailed explanation).

With clear political and economic ramifications at stake, and in an effort to counteract the problematic history of counting Indigenous people, the U.S. Census Bureau has undertaken a massive effort to attempt to solicit as much participation in the 2020 Census by Indigenous peoples in the United States as possible. One way that this has occurred is through an increase in the dissemination of information related to what the Census is, and what it means for Indigenous communities in the United States. A wide variety of press releases, handouts and multimedia have been made available for tribal governments and tribal citizens to learn more about the Census, including articles, brochures, podcasts, sample invitation letters, and even videos that explain more about the Census enumeration process. A press kit provides additional information, including a really great blog post from the Director of the U.S. Census Bureau, that talks specifically about the importance of the Census for Indigenous people, with a particular focus on Alaskan Natives.

However, the Census Bureau is not the only ones that are working hard to ensure an accurate count of Indigenous people in 2020. Indigenous people themselves have taken many steps in order to get the word out in their own communities to explain the importance of participating in the 2020 Census. Much of this work occurs at a national level, such as via the U.S. Indigenous Data Sovereignty Network, founded by Northern Cheyenne tribal member and all-around Indigenous badass Dr. Desi Rodriguez-Lonebear. But, there is also a lot of work that happens at a local level. Tribes such as the Pullayup Tribe in Washington, the Navajo Nation, and many others have not only built relationships with the U.S. Census Bureau, but they are also taking their own actions to help drive up participation. Tribal nations have sought to overcome mistrust regarding the Census is by involving Indigenous people in the collection process. My own tribe, the Leech Lake Band of Ojibwe sent out a call earlier this year for Census takers, for example:

Leech Lake Band of Ojibwe Posting

It is clear that tribal nations understand the importance of the Census to their communities, and to holding the Federal government accountable to its obligations to tribes as part of the nation-to-nation framework that characterizes the relationship between Indigenous nations and the United States. Through cooperation between tribes and the Census Bureau, as well as steps that tribes are taking themselves, the process of including Indigenous people in the Census will hopefully improve, allowing for the conditions necessary for Indigenous nations to receive the support and services they need.

Deondre Smiles (Ph.D., 2020, OSU Geography)

President’s Postdoctoral Scholar

Department of History

The Ohio State University

Census Data: A Personal Note on Some Challenges and Successes

August 26 2020September 2, 2020

The census data we use today is a symbol of American democracy. The U.S. Constitution states that “the actual enumeration shall be made … within every subsequent term of ten years, in such manner as they shall by Law direct” (Article 1, Section 2). After this historical point, the census has a brand new meaning beyond the mere means for the royalty or state to make their economic or political gains. Today, U.S. census data are commonly used for mapping and many other purposes. It is literally the textbook example of spatial data and applications in GIS and cartography education. Indeed, the U.S. census has empowered individuals and organizations around the world. A few simple clicks on an interactive map shown below, for example, will reveal some stunning pattern of spatial dynamics across America, even at a county level.

An interactive map to explore census data of U.S. counties.

However, as useful and powerful as the census data are, shortcomings and challenges are also noticeable. Let’s start from a spatial perspective and ask ourselves this question: are census data safe? The following map comes from the dark side of using census data. It was made by Nazi Germany circa 1940, before the U.S. formally entered the Second World War. This map details the first and second generation of middle and western Europe immigrants in the United States, based on the publicly available data from the 1930 U.S. Census. It also has a label at the top left corner that reads “For official use only!” Its cartographic achievement aside, this map was used by the Nazi propaganda machine to strategically spend their war money to persuade the public opinion in the U.S. to avoid being involved in the war raging in Europe. Many believed such a campaign was successful to some extent. It is safe to say that, ever since then, the use of the census data and maps in today’s affairs, from political campaigns to social media disinformation to foreign meddling of our elections, is everything but the lack of imagination.

“20th Century Through Maps” Courtesy of British Library (permission pending)

The arguably darkest moment of the U.S. census also came in the Second World War, when the census information used by the U.S. government directly led to the internment of Japanese Americans after the Pearl Harbor attack. So is it really provocative to ask will the census data, mapped or not, put us in danger? Will history repeat itself in the 21st century? Will another ethnic group become the victims? While the 2020 census eventually did not include citizenship questions, it should not be the time to celebrate. Instead, we should ask will such questions ever come back, and in what form? These issues may be beyond the scope of the census, but the census has been the vehicle that carries these issues.

Also from the spatial perspective, it is well known that census geographies are designed in a hierarchical fashion where the blocks are the smallest spatial units and from there we can aggregate to units such as block groups, tracts, and counties. Census tracts have been considered to be the relatively stable units for statistical analysis because by design they aim to have an ideal population of 4,000. But why should space be delineated in such a fixed and perhaps artificial way? What if we can re-arrange the blocks and come up with different kind of units that are compatible with the official tracts? This is a notoriously difficult task because there are an astronomical amount of ways to recombine the blocks. But if we test some algorithms on a manageable number of units, we can see how the world can be different. The two figures below show the result of such an exercise. It is clear that we can actually achieve a better set of spatial units where the population is more evenly distributed and more centered around the ideal size. Also, the new aggregated units show no significant spatial auto-correlation, which makes them more suitable for statistical analysis.

Population of the 284 census tracts in Franklin County, Ohio.

Population of the 284 new units that are aggregated using the 887 census block groups in Franklin County, Ohio.

Issues related to spatial units are not new and have been around in statistics and geography for at least more than a half century. Computational advances have made it possible to explore new and different approaches to spatial organization. The question is: how can we embrace such a new way of thinking about these statistical units? Should we even go down this rabbit hole where things will become constantly changing.

We can certainly read the history of the use of census data through different lenses. But, however we read it, we will find both bright and dark sides that are full of conflicts, betrayal, conspiracy, struggle, and promises. The world envies the richness of the census data available in the United States that dates back to the beginning days. From this perspective, I personally do see more promises than anything else, as the new century should be the time for us, the research community as well as the general public, to re-imagine what the census data could be.

Ningchuan Xiao, Professor

Department of Geography

The Ohio State University

Exploring the Spatio-Temporal Dynamics of Socio-Economic Dimensions of the COVID-19 Pandemic: An Interactive Dashboard Approach

July 23 2020July 23, 2020

The COVID-19 pandemic has presented a myriad of challenges to the world. While many of the challenges are related to the medical aspects of the disease and how it spreads, for communities to survive and thrive in this public health crisis, it is also extremely important to understand the socio-economic dimensions of the pandemic. Specifically, the spatio-temporal dynamics of the implications and consequences of COVID-19 are related to a multitude of social, demographic, and economic factors. Exploring these factors, especially their spatio-temporal trends and how they are related to the infection cases, will help reveal the key determinants that can be used to understand the spread of the disease. As a response to this need, a COVID-19 dashboard^[1] herein presents a highly-interactive, map-oriented visualization platform to explore the coronavirus outbreak from its underlying socio-economic contexts. The dashboard enables its users through visual exploration and comparisons to recognize the extent of coronavirus spread and its association with socio-economic characteristics of the communities at various geographic scales.

A glance at the plots of the dashboard, one can identify that top-ranked states exhibit two different trends. States like New York, New Jersey, Illinois, and Massachusetts are showing a flattening curve, leading the overall trendline of coronavirus confirmed cases in the United States to a flatter direction (Figure 1 top row). However, coronavirus is still spreading at an alarming rate in many states, including California, Florida, Texas, Arizona (Figure 1 bottom row). It will be interesting to see how modified stay-at-home orders and early reopening of business activities (California – May 8^[3], Florida – May 18^[4], Texas – May 1^[4], Arizona– May 8^[3]) will affect the continuing upward trend of COVID-19 in these states.

Figure 1: Top-ranked states for coronavirus cases, as of June 28, 2020.

Along with the spatio-temporal dimensions of COVID-19 spread, the dashboard can also be used to reveal that the nature of COVID-19 outbreak is associated with the socio-demographic and economic profile of each state. The following findings can be summarized by further exploring the dashboard.

Population. While states or counties with large populations tend to have more cases, the dashboard indicates that the rate of coronavirus spread, however, is indifferent to the population size. As shown in Figure 2, many counties in California, Texas, and Florida (marked in red) have a greater population size with a lower confirmed case and death rates than the counties in New York and New Jersey (marked in yellow).
Figure 2: Plot showing the relationship between the rate of coronavirus cases (per 1000 people) and other socio-economic indicators in the counties of New Jersey, New York, California, Florida, and Texas. Note that multiple colors are made available by modifying the source code of the dashboard.
Age. Although the virus is dangerous for any age group, counties with high percentages of adult population reflect a high coronavirus confirmed rate. The geographic spread of the disease does not show a noticeable correlation with the geography of other age groups. As hinted by this observation, researchers can further explore whether the chances of being affected by
Figure 3: Plot showing correlation between coronavirus confirmed rate and percentage of non-white population in the counties of Georgia, New Jersey, New York, and Maryland

coronavirus depends more on peoples’ daily activity pattern and level of exposure to the outside environment than their physical age.
Race and Poverty Rate. It is evident that counties with both racial and economic disadvantages are more affected by COVID-19 than other well-off counties. For example, states with high percentages of non-white people (such as New York – 33.9, California – 35.9, New Jersey – 30.1, Maryland – 41.1, Mississippi – 40.2, Georgia – 39, and Louisiana – 36.1, numbers in percent) also have high numbers of confirmed cases. Besides, counties within these states indicate a positive relationship between the percentages of non-white people, and the coronavirus confirmed case rate (examples are illustrated in Figure 3).

The findings from racial profiling also complement the results related to the economic statuses of each state. The coronavirus confirmed case rate tends to be high in the states where a high percentage of households are living below the national poverty threshold (e.g., Mississippi – 15.9, Louisiana – 14.6, and Alabama – 13, numbers in percent, marked in shades of red in Figure 4). On the contrary, New Mexico -15.3, Kentucky – 13.5, and West Virginia – 13 (marked in shades of green in Figure 4), that also have a high poverty rate, show insignificant/negative relationship with confirmed case rate. The difference between the former states and later states lies in the racial distribution. The later states have a lower percentage of non-white populations (New Mexico -22.9, Kentucky – 10.8, and West Virginia – 5.2) than the former states (Mississippi – 40.2, Louisiana – 36.1, and Alabama – 30.1). This observation can further be exemplified by the stark contrast between Mississippi and West Virginia (marked respectively in dark red and dark green in Figure 4) in terms of confirmed case rate, white and non-white population, and poverty level. This finding pronounces the long-prevailed racial and economic disparity of the country, which have been overlooked by the government leaders and policymakers for years^[6] and have exacerbated the COVID-19 situation for non-white communities than others.

Figure 4: Plot showing the relationship between the rate of coronavirus spread and other socio-economic indicators in the counties of Louisiana, Mississippi, Alabama, New Mexico, Kentucky, and West Virginia

Occupation. The relationship between coronavirus confirmed case rate and job categories mostly depends on their possibility for remote working. The spatial distribution of jobs that can be supported with work-from-home opportunities such as education, public administration, and other services present no significant relationship with the spatial distribution of coronavirus spread (Figure 5).
Figure 5: Plot showing correlation between rate of confirmed coronavirus cases and percentages of jobs in education (left), public administration (center) and other services (right) in the counties of the United States
The work-from-home opportunity for employees working in information, finance, and professional sectors depends on the subcategory of businesses and the type of services provided by them. However, the percentages of population working in these job sectors indeed show a positive correlation with the coronavirus confirmed case rate. This finding can be attributed to the fact that states with high confirmed case rate contain a high percentage of the population working in information (New York, California, Colorado, New Jersey), finance (New Jersey, New York, Connecticut), and professional sectors (California, Virginia, Colorado, New Jersey, Florida) (Figure 6).
Figure 6: Plot showing correlation between coronavirus confirmed rate and percentages of jobs in finance (left), information (center), and professional services (right) in the counties of the United States
The remaining business sectors, such as agriculture, construction, manufacturing, recreation, wholesale, and retail, indicate no impact on the spatial distribution of coronavirus spread. These business sectors certainly need direct physical presence of workers and consumers, but their business activities were either shut down or operating at a limited scale during the lockdown period of the COVID-19 crisis. These sectors are mentioned in the initial reopening phase for most of the states starting between early-May to late-May ^[2,3,4,5]. The impact of these job sectors on the rate of coronavirus spread can better be explained when these sectors will be fully operational.

Along with the outlined observations, the dashboard facilitates exploring the spatial relationship between coronavirus cases and their associated socio-economic indicators for any county or state of the nation. By contextualizing the public health crisis, the dashboard can be used as an exploratory tool for the decision-makers, practitioners, and the general public to monitor their local COVID-19 situation. The dashboard can also help researchers to examine patterns of COVID-19 cases, which will prompt interesting research questions and hypotheses for further investigation.

Armita Kar (PhD Student, Geography), Luyu Liu (PhD Student, Geography), Yue Lin (PhD Student, Geography), Ningchuan Xiao (Professor, Geography)

Department of Geography

The Ohio State University

References

https://gis.osu.edu/COVID19-Dashboard/
Treisman, R. (2020, May 28). Midwest: Coronavirus-Related Restrictions By State. NPR. Retrieved from: https://www.npr.org/2020/05/01/847413697/midwest-coronavirus-related-restrictions-by-state
Treisman, R. (2020, May 28). West: Coronavirus-Related Restrictions By State. NPR. Retrieved from: https://www.npr.org/2020/05/01/847416108/west-coronavirus-related-restrictions-by-state
Treisman, R. (2020, May 28). South: Coronavirus-Related Restrictions By State. NPR. Retrieved from: https://www.npr.org/2020/05/01/847415273/south-coronavirus-related-restrictions-by-state
Treisman, R. (2020, May 29). Northeast: Coronavirus-Related Restrictions By State. NPR. Retrieved from: https://www.npr.org/2020/05/01/847331283/northeast-coronavirus-related-restrictions-by-state
Long, H. & Dam, A. V. (2020, June 4). The black-white economic divide is as wide as it was in 1968. Retrieved from: https://www.washingtonpost.com/business/2020/06/04/economic-divide-black-households/