Food Access: A Time Issue

In the US, the rising obesity rate and obesity-related comorbidities, such as cardiovascular diseases and Type-II diabetes, have drawn health geographers’ attention. It is generally understood that the lack of access to healthy food provisioning, such as grocery stores selling fresh fruits and vegetables, is driving this obesity crisis. Under this context, the US Department of Agriculture (USDA) Economic Research Service (ERS) develops an inquiry tool, the Food Access Research Atlas (1), generally known as the “food desert locator,” to highlight areas with both low-income and limited access to grocery stores. The tool also incorporates other variables, such as car-ownership, to identify communities at risk of food insecurity.

“Food deserts” identified by the USDA Food Access Research Atlas (1)


This spatial approach, however, has raised questions about the etiology of obesity. It has been found that the correlation between healthy food access and healthy diets is not statistically consistent and is somewhat insignificant (2). In order to articulate the health effects of the community food environment, health geographers argue that other non-spatial variables need to be considered. One such variable is time.

Time shapes food access in two dimensions. On the one hand, the time component, or “temporality,” manifests in the urban food system (3) — grocery stores have different opening hours, farmers’ markets operate in different seasons. For example, it is found that grocery stores in downtown Columbus, Ohio, although there are many of them, close relatively early than stores located in the suburb. This disparity in space-time access to food is visualized by a 3D Geographical Information System (GIS) (4). The plentiful spatial access but limited temporal access could be explained by the store type (e.g., mostly privately owned) and the relatively high crime rate in the downtown neighborhood. Since downtown stores have limited operating hours, local residents may restrict their food choices and could be subject to diet-related health consequences. On the other hand, time shapes individuals’ mobility to procure food. People burdened with multiple social roles, such as childcare while raising an income, may find themselves less available to procure healthy food (5). A study using a travel diary survey identifies that the difference in time use exists between genders and among different races. Full-time employed women and African Americans are at the disadvantage of having less discretionary time (6). The lack of time may victimize these vulnerable social groups and expose them to food insecurity.

A 3D visualization of space-time food access in Columbus (4)


Thus, food access is not only a spatial issue but also a temporal issue. Employing a spatial approach alone to evaluate food access is insufficient. Other tiers of non-spatial variables, such as time, should be factored in to produce knowledge about food access equity and justify the health effects of community food environments.

Xiang Chen

Department of Geography

University of Connecticut

Xiang Chen is an Assistant Professor in the Department of Geography, University of Connecticut, USA. He received a Ph.D. in Geography at The Ohio State University (2014). His research is focused on GIScience, community health, and food accessibility.


  2. Caspi, C. E., Sorensen, G., Subramanian, S. V., & Kawachi, I. (2012). The local food environment and diet: A systematic review. Health & Place, 18(5), 1172-1187.
  3. Widener, M. J., & Shannon, J. (2014). When are food deserts? Integrating time into research on food accessibility. Health & Place, 30, 1-3.
  4. Chen, X., & Clark, J. (2016). Measuring space-time access to food retailers: a case of temporal access disparity in Franklin County, Ohio. The Professional Geographer, 68(2), 175-188.
  5. Rose, D., & Richards, R. (2004). Food store access and household fruit and vegetable use among participants in the US Food Stamp Program. Public Health Nutrition, 7(8), 1081-1088.
  6. Kwan, M. P. (2002). Feminist visualization: Re-envisioning GIS as a method in feminist geographic research. Annals of the Association of American Geographers, 92(4), 645-661.



Agricultural Risks to Changing Snowmelt

Snowpacks store cold water in winter, which is later melted in warmer spring months to produce streamflow. Historically, irrigated agriculture has relied on snowmelt runoff as an important seasonal water supply in many regions across the world, such as the western United States.

However, Climate change has already begun to change the spatial and temporal patterns of snowmelt runoff — causing a decreasing magnitude of snowfall and earlier melting of snowpacks. Consequently, irrigated agriculture, which has been depending on snowmelt runoff happening with a certain magnitude, at a specific time, and in a given location, are exposed to potentially important risks under a warming climate.

Although such changes in snowmelt-derived water resources are often cited as a key threat to irrigated agriculture and global food security, previous studies have focused on annual changes in runoff, without resolving the sub-annual changes in water supply and crop-specific water demand.

To characterize such risks, we differentiate surface water supply from three sources: snowmelt runoff, rainfall runoff, and alternative water supply such as inter-basin water transfer. Comparing monthly surface water supply with surface water demand under both the historical climate (1985-2015) and predicted warming scenarios (2°C and 4°C above pre-industrial conditions), we identify where irrigated agriculture has been mostly depending on snowmelt runoff in the past 30 years. Also, we find basins in high-mountain Asia (the Tibetan Plateau), central Asia, central Russia, the western U.S., and the southern Andes are particularly vulnerable to decreasing snowmelt availability in crops’ growing seasons due to a future warming climate.

Therefore, these most risky basins will require increasing additional water supplies by increasing inter-basin transfer, pumping additional groundwater, or consuming water required for other uses. Notably, providing those additional water supplies may sometimes cause additional environmental and social problems, thus improved irrigation practices or crop switching may be needed to ensure food security under changing snowmelt.

Yue Qin

Assistant Professor, Department of Geography

The Ohio State University

Uncertainty Problems and Census Data: The 2020 Census & Exurbanization Example

Last year, I had opportunities to learn about the 2020 Census from a research seminar and a professional meeting to promote Complete Count of the 2020 Census. As you may have heard already, there are some new characteristics in the 2020 Census as below (U.S. Census Bureau, n.d.-a, n.d.-c, n.d.-b):

  • The 2020 Census will be the first to offer options for internet and phone responses.
  • There will be a greater reliance on technology to prepare for and execute the count.
  • The 2020 Census will update its Master Address File (MAF) and ensure that every living quarter in the U.S. is included in the census universe by collaborating with state and local governments and using aerial imaging software.
  • For enhanced enumeration, Census takers will be equipped with smart devices, and data will be collected digitally in real-time.
  • There are no questions about citizenship on the 2020 Census.
  • Responses for the Census will never be shared with agencies of immigration or law enforcement.
  • The country is experiencing a period of heightened fear and deliberate misinformation.

Potential Uncertainty in the 2020 Census

Most of the characteristics above seem to be helpful to produce more accurate Census data. On the other hand, there might be some potential uncertainty in the 2020 Census data. First, there are some challenges to being counted on the Census data, including language barriers, mistrust in government, privacy/cybersecurity concerns, physical barriers such as inaccessible multifamily units, untraditional living arrangements, and lack of reliable broadband or internet access. Second, there may exist hard-to-count (HTC) groups for the Census, including children under five years old, racial and ethnic minorities, limited English proficiency households, immigrants, renters and residents who often move, alternative or overcrowded housing units, gated communities and publicly inaccessible multifamily units, persons displaced by natural disasters, persons experiencing homelessness, young mobile adults, and single-parent headed households (The City of Stillwater, Payne County, OK, n.d.). Thus, the 2020 Census may provide enhanced accuracy of the data and also uncertain data for some criteria of the Census.

Example of Uncertainty in Visualizing Exurbanization

Due to the potential uncertainty in the Census data, some geographic inquiries that utilize the Census data may reveal the uncertainty problems, such as visualizing the location of exurban areas. Simply speaking, the exurban areas have characteristics between urban and suburban areas. There are multiple different definitions of exurbanization in literature, and the location of certain exurban areas on maps may vary depending on the definition (Ban & Ahlqvist, 2009). In specific, you can visualize the exurbanization of certain areas by using the Census data, geospatial data, and fuzzy-set approach (Ban & Ahlqvist, 2009; Fisher, 2000; Wechsler et al., 2019; Zadeh, 1965), and could create a map that represents different degrees of exurbanization (Figure 1). In Figure 1, the degree of exurbanization of Los Angeles County, CA is visualized based on the definition of exurbanization in (Daniels, 1999). According to Daniels (1999), the exurban areas are defined using value ranges of some attributes, including population, distance from a major urban center, commuting distance, and population density. The definitions of Daniels (1999) themselves include semantic uncertainty due to the vagueness and the ambiguity (see Ban & Ahlqvist (2009) for details). However, in this blog, we will focus on the population attribute of the exurbanization definition. As mentioned above, the potential uncertainty in 2020 Census data may introduce another type of uncertainty, the error (Fisher, 2000). Most of the definitions of exurbanization use the population attribute (Berube et al., 2006). It is likely that the results of the visualization of exurbanization may present uncertainty in the locations of exurban areas.

Figure 1. Visualization of the degree of exurbanization of Los Angeles County, CA based on the exurban definition from Daniels (1999). (A) shows boundaries of exurban areas in crisp, non-fuzzy membership and (b) in the fuzzy-set membership (reproduced from Figure 16.5 of Wechsler et al. (2019)).

There would exist other geographical inquiries that might introduce uncertainty when dealt with the 2020 Census data. What would be the examples? Then how could the uncertainty problems be resolved? Things to ponder remains, and indeed, the initial process of thinking could be definitely uncertain.



  1. Ban, H., & Ahlqvist, O. (2009). Representing and negotiating uncertain geospatial concepts – Where are the exurban areas? Computers, Environment and Urban Systems, 33(4), 233–246.
  2. Berube, A., Singer, A., Wilson, J. H., & Frey, W. H. (2006). Finding Exurbia: America’s Fast-Growing Communities at the Metropolitan Fringe. 48.
  3. Daniels, T. (1999). When City and Country Collide: Managing Growth In The Metropolitan Fringe. Island Press.
  4. Fisher, P. (2000). Sorites paradox and vague geographies. Fuzzy Sets and Systems, 113(1), 7–18.
  5. The City of Stillwater, Payne County, OK. (n.d.). Historically Hard to Count Populations. Retrieved November 30, 2020, from
  6. US Census Bureau. (n.d.-a). About the 2020 Census. The United States Census Bureau. Retrieved November 30, 2020, from
  7. US Census Bureau. (n.d.-b). Census.Gov. Retrieved November 30, 2020, from
  8. US Census Bureau. (n.d.-c). What Is the 2020 Census? 2020Census.Gov. Retrieved November 30, 2020, from
  9. Wechsler, S., Ban, H., & Li, L. (2019). The Pervasive Challenge of Error and Uncertainty in Geospatial Data: Volume Eight (pp. 315–332).
  10. Zadeh, L. A. (1965). Information and control. 8(3), 338–353.

Hyowon Ban

Class of 2009, Associate Professor

Department of Geography

California State University, Long Beach


A First Step

I’m not a geographer by training or by discipline. I do have two Bachelor’s degrees in the following majors; Criminology, International Studies, Russian, and Political Science. I also have a Master’s degree in Public Policy and Administration. This doesn’t mean a whole lot, but what it does mean is that, there are certain issues that I’ve looked at through varying lenses depending on what discipline you’re talking about. As an example, policing and law enforcement in Criminology, Political Science, and Public Policy vary on methodology, cause and effect, history, and how to move forward depending on which one or which class you’re engaged in. The Cold War is different when you investigate the perspectives from an international relations lens versus a purely Russian cultural lens. None of them are wrong but none of them are 100% correct either. We, as human beings, engage in our world in different ways and the impacts of those actions snowball to coalesce into a much larger picture. If nothing else, what these varying disciplines have taught me is to think critically, think outside the box, and never to accept the given status quo.

Now, you would think, that something as simple as counting people would be – just that – simple. However, that is far from the case.

Yes, from a political science perspective, the census is written into the Constitution of the United States and accounts for distribution of representation across the country. The short answer about what the census is, can be wrapped up in a nice little bow by the following video.

However, the reality of the Census, is something much more complicated, long-lasting, and carries with it the ability to change our nation on a fundamental level. As the representation shifts across the country with each census count, the outcomes determine not only votes in the House of Representatives but resources distributed, prevailing ideologies, future political, social, and economic policies, and electoral college votes. Many people look at the census as a benign exercise and means very little the them personally, after all, it has little impact on their individual day to day lives.

The exact opposite is true. Maybe it’s the political science or public policy speaking, but for me, the fundamental importance of the census is the basis for everything else. The census is the first step in our nation deciding what type of country we want to be, what we value, and what is important to us. The census allows the voters to decide what type of leaders we send to Washington, and what the priorities are for the future.

The census is not the last step or the only step in changing our country and making it better, but it is the first. The first in a long line of standing up to be counted, and of encouraging the same of others – even against their fear and the prejudice they may face. The census is a call to action for anyone who is or isn’t happy with the policies being enacted on their behalf. If you ignore the call and are not counted, you are doing a disservice to yourself, your neighbors, and your community.

What does any of this have to do with Geography?

Great question! If you’re not a student of Geography-like me-, you’re not the only person asking it.

Geography is a varied discipline that encompasses not only cartography and maps, but the study of what makes those maps what they are, why people are where they are. It encompasses sustainability, mobility, climatology, social justice issues, immigration and migration, as well as land use. What does all this mean?

Well, the population isn’t spread evenly across the country. We’ve all seen the red and blue maps on election nights and how the distribution of those populations have an impact on viewpoints, ideologies, and politics. We know that the country is divided in their way of life: urban and rural. These divides correlate to race, economic status, education, and a whole host of other demographic markers that can be identified. We’ve also seen how the policies of our government impact land use, our national parks, and the regulations enacted to protect citizens from harmful pollutants. Becky Mansfield’s post regarding EPA deregulation is a great example. In Geography, not only are these issues discussed but form the foundation for critical thinking along lines that focus on the world around us and our impacts on it. The census, either directly or indirectly, has an impact on all of us. And in the end, what this all amounts to is power. Who has it? Who should get it?

We are at a critical stage. The U.S. Census bureau will end counting efforts on September 30th (a full month early), according to NPR. This means that if you hadn’t already filled out the questionnaire online, then your time is running out. You don’t have to go anywhere or talk to anyone to complete your census entry. It is the easiest first step to being a part of the process of representation you can ask for. Now is your chance to step up and be counted, just click the link below.



Suzanne M.S. Mikos

Department Manager

Department of Geography & Center for Urban and Regional Analysis


References and Links:

Locating the Rural Forested Community; Aka When Bugs Bunny Saved the Day

In 2012, about a dozen people began meeting at the Socio-environmental Synthesis Center (or SEYSNC) in Annapolis, MD, as part of a new project: Rural Forest Communities at a Tipping Point? Trends & Actionable Research Opportunities. We were a group of researchers spanning the social and environmental sciences from Virginia, Maine, Oregon, Ohio, Wisconsin, Quebec, and elsewhere, and we were interested in a seemingly straightforward, yet in practice elusive, concept of the rural forested community. We knew from the work we conducted in forested regions of the US and Canada that these areas had experienced dramatic employment shifts, out of extractive industries like timber or mining, manufacturing jobs in textile mills, and other activities that had previously made more intensive use of the land.

We were particularly interested in forested ecosystems that had experienced some regrowth and restoration of a variety of plant and animal species, and understanding how loss of traditional industries, burgeoning new industries like tourism, “green” manufacturing, or call centers seeking cheaper land and labor costs, were intersecting with new demographic trends, like exurban or second-home migrants.

We wanted to know which rural forested communities were able to offset job losses as traditional industries waned, while managing new threats to forests in the form of parcelization and forest fragmentation, climate change, coordination of forest management, invasive species and pests, among other factors. We had big questions we wanted to answer: if rural community and forestry futures were intertwined, in what ways were communities working to maintain or enhance these joint prospects? Were certain types of economic strategies (pursuing tourism over manufacturing) better or worse for the community or the forest? Our collective experience in studying small field sites across North America, and our transdisciplinary orientation, was to provide a rich foundation for answering these questions.

What follows is a story about data. Well, it’s really about teamwork, interdisciplinarity, measurement, accuracy versus precision, and how cartoons once came to our proverbial rescue.

Lesson 1: Collaboratively define a research object

We had assembled an intrepid disciplinary team of around a dozen ecologists, remote sensing specialists, environmental economists, rural sociologists, and geographers, among others. We all agreed in principle about the threats to rural forested communities in North America, and eagerly discussed similarities and differences among our regions. However, when it came down to specifying exactly what the defining characteristics of rural forested communities might be, and what data we would use to identify them spatially (what is rural? What is a forest? What is a community??), we were in trouble.

Figure 1: Locating the rural forested counties of the U.S. (Bell et al. 2019).

We argued that the word “community” means something specific in ecology, yet remains vague or contradictory across the social sciences. We argued that the Census counts record someone’s residence, not their place of work, and commuting relationships vary regionally and across the urban-rural continuum. How do we precisely map which forest is used by which people? How much vegetation cover should there be to define a forest, in such an ecologically diverse place as the continental U.S., even? There is no clear geographic definition of what constitutes a rural forested community. But we needed to operationalize data – census data on population, economic census data on employment and occupation, land-cover datasets for information on forest extent, etc.

People are mobile and drive for miles to harvest or hike in wooded areas. Trees are stationary, yet their benefits to people and animals extend beyond the boundaries of the forest. There will always be a spatial mismatch (Bell 2005) in assimilating data from ecological and social systems, and we were likely to underbound (leave out areas that fit our conceptual understanding of these places) or overbound (include areas that didn’t really fit with what we want to study) our identification of actual rural forested communities.

The more we discussed how we were going to measure this idea we had, this concept that we knew existed in the “real world,” the more we began to struggle. We began to brainstorm terms that might capture the idea. Of course, being scientists, we wanted clever acronyms for those terms.

The suggestion came up to identify a placeholder, such as a name or object that would be easy to remember, to communicate effectively until we figured out exactly what we wanted to call this phenomenon. Voices got louder. The tone got more heated. Scribbles were madly filling up the white board. We were at an impasse. I sat back, observing some of the chaos.

“George!”, I said, “Why don’t we just call it George!”

A dozen heads snapped around to look quizzically at me. I had just thought about the Abominable Snowman in Bugs Bunny. When Bugs misses that left turn at Albuquerque and ends up in the Himalayas (neocolonial problems with this representation aside), the Snowman picks him up as a pet, and says, “My own little bunny rabbit, I will hug him and squeeze him, and name him George.”

So we agreed to use the placeholder George, to stand in for this concept that we could all imagine but could not agree how to measure, so that we could move on to develop our larger conceptual framework, and all the factors we wanted to consider (Morzillo et al. 2015, van Berkel et al. 2018): roads, governing institutions, ecological conditions, political economy, etc. We got a lot of mileage out of simply invoking the name George, over and over and over.

Lesson 2: Know when to “lump” and when to “split”

Ultimately, we realized that our big group had substantive tensions in focus. The ecologists, broadly, were most interested in forest resources in situ, focusing more directly on the people, and the houses, that were in the midst of or on the edge of those forests. The social scientists on the other hand, were most interested in the function of the community rather than individual plots of land: how were local institutions navigating big structural economic changes, and how were community strategies for development differentially experienced by residents?

Eventually, we broke the puzzle into two pieces – understanding the extent, spatial patterns and regional variations across these forested regions (van Berkel et al. 2018), and hypothesizing how differing local characteristics correspond with trajectories of change (decline, tourism, new types of production) (Morzillo et al. 2015).

There is a literature about “lumpers vs. splitters” (Endersby 2009); i.e., whether scientists tend to be more comfortable with broad generalization, or whether more specificity, and more categories or exemplars, are necessary to capture finer-scale variation. I have been in several interdisciplinary teams in my 20-year career, and have experienced this phenomenon multiple times. What’s most interesting to me as a researcher and teacher is that no perfect theory and no perfect dataset can ever solve this conundrum. It is fundamentally a social one, that requires interpersonal communication and understanding. And, like all good inquiry, it depends on the research question.

Lesson 3: Specialize as needed

Ultimately, our SESYNC group, which was assembled from several existing projects, tag-teamed the two foci: alternatively, looking at settlements within forested pixels (van Berkel et al. 2018), versus forests within 100 miles of communities (Munroe 2019).

A somewhat smaller plucky band: the rural sociologist, the human dimensions expert, the two most open-minded economists I have ever known, and myself, have continued to forge ahead. This subset, when we received funding from the USDA National Institute of Food and Agriculture, continued to refine our definition of rural forested communities, a region we now refer to as Eunice.

Figure 2: The (smaller) interdisciplinary team in Logan, Ohio. Photo Credit: Kymber Anderson

What were the lessons of this experience? Well, anyone can claim to be a quarterback on Monday morning, so take these as intended. What I personally learned is the following:

  • Don’t shy away from tough conversations. It’s not impolite to disagree about concepts or data.
  • Focus on what you can agree on, and what you can’t.
  • Be willing to change, follow the inspiration of the moment.
  • Be generous ex post (you’ll note we were generous with coauthorship).

In the endnotes of our first published paper, we have an acknowledgement to “George.”



I’d like to thank my close group of collaborators, Kathleen Bell, Chris Colocousis, Mindy Crandall, Anita Morzillo and the rest of the SEYSNC team, past and present. All errors herein are my own.

Darla Munroe

Professor & Chair

Department of Geography

The Ohio State University




  • Bell, K. P. (2005). Spatial analysis and applied land use research. Land Use Problems and Conflicts, 118.
  • Bell, Kathleen P., Mindy Crandall, Darla K. Munroe, Anita Morzillo, & Chris Colocousis (2019). Rural forested landscapes, economic change, and rural development. 2019 North American Meetings of RSAI, Pittsburgh, PA. 15 November.
  • Endersby, J. (2009). Lumpers and splitters: Darwin, Hooker, and the search for order. science, 326(5959), 1496-1499. DOI: 10.1126/science.1165915
  • Fry, J. A., Xian, G., Jin, S. M., Dewitz, J. A., Homer, C. G., Yang, L. M., … & Wickham, J. D. (2011). Completion of the 2006 national land cover database for the conterminous United States. PE&RS, Photogrammetric Engineering & Remote Sensing, 77(9), 858-864.
  • Munroe, D. K., Crandall, M. S., Colocousis, C., Bell, K. P., & Morzillo, A. T. (2019). Reciprocal relationships between forest management and regional landscape structures: applying concepts from land system science to private forest management. Journal of Land Use Science, 14(2), 155-172.
  • Morzillo, A. T., Colocousis, C. R., Munroe, D. K., Bell, K. P., Martinuzzi, S., Van Berkel, D. B., … & McGill, B. (2015). “Communities in the middle”: Interactions between drivers of change and place-based characteristics in rural forest-based communities. Journal of Rural Studies, 42, 79-90.
  • SEDAC, & US census Bureau (2000). Census block data on poverty, housing stock, education and key demographic. Retrieved from:
  • Van Berkel, D. B., Rayfield, B., Martinuzzi, S., Lechowicz, M. J., White, E., Bell, K. P., … & Parmentier, B. (2018). Recognizing the ‘sparsely settled forest’: Multi-decade socioecological change dynamics and community exemplars. Landscape and Urban Planning, 170, 177-186.

Fieldwork in a 250 sq ft Studio

One of the sampling methods commonly used in fieldwork is the snowballing technique. The method is to recruit the next research participants by asking current participants to help researchers in identifying potential participants, which is a kind of referral system. I took advantage of the method in my preliminary fieldwork last year. My generous interviewees gladly introduced me to someone they knew and sometimes took me directly if it was not far away from their places. Yet, COVID-19 changed everything.

The goal of my fieldwork this summer was to compare the two largest strawberry production regions in South Korea. I was supposed to participate in farm conferences and local education programs to recruit farmers for interviews and then apply the snowballing technique. However, most of those gatherings were canceled due to the virus. Even if there were meetings, they were held behind closed doors. I had no choice but to conduct “un-immersive fieldwork,” relying on cold calling from my rented studio in Seoul.

Three strategies that got me through this challenge were calling organizations’ representatives, virtual snowballing, and random calls.

Calling organizations’ representatives: There are several farmers’ organizations in the Korean strawberry sector (i.e., cooperatives, agricultural corporations, and strawberry research groups). The contact information for the organizations’ representatives is often posted on the government’s website, making it easy to make contact. Also, the representatives are usually more likely to be extroverted. If things went well, an interviewee was interviewed by phone while they were driving long distances, enabling in-depth interviews to take place over one to two hours. In the worst-case scenario, however, the interviewee was not able to answer questions properly, because they were on their way to visit a hospital or bank. One-third of the potential interviewees declined to be interviewed, refusing in the same manner as they would with spam calls. They said, “I don’t do such a thing. Sorry.”

Virtual snowballing: At the end of every telephone interview, I asked interviewees to introduce me to another close farmer. They were a little reluctant to introduce another farmer than I expected, and said, “Other farmers will say the same anyway.” This was in contrast to the welcome introduction of the surrounding farmers during the in-person visits last year. Meanwhile, experts (researchers, technicians, etc.) and government officials whom I met in person last year gladly introduced farmers they knew this year. I guess people become willing to introduce me to other people when they are sure that an interviewer is reliable and the introduction will not harm their reputations.

Random calls: I tried random calls to various farmers through a Google search. Usually, farmers who left their contact information on the Internet were selling agricultural machinery and fertilizers or running pick-your-own strawberry farms in addition to farming, and most of them willingly accepted the interview request. They answered my questions as comfortably as they treated their customers. I also posted a promotional post on the web communities for strawberry farmers, hoping to coax the farmers to call me. Despite the offer of compensation, only a small number of farmers contacted me. Interestingly, they had something in common in that they felt sorry for Ph.D. students because they or their children had graduate school experience.

In the COVID-19 era, while many people are getting used to new technologies such as Zoom, there remain difficulties in applying these technologies to fieldwork. The use of video calls or online messaging platforms was almost impossible, especially since farmers are of high average age and are usually conservative. Nevertheless, there must be huge room for improvement: a more friendly way of phone calls, writing an attractive promotional post, and many more creative methods. We keep learning how to adapt to uncertainties by doing everything in the virtual field.

As mentioned, there is always room for improvement, and I welcome new ideas. Please feel free to contact me if you have any suggestion for my virtual fieldwork!


Sohyun Park, PhD Candidate

Department of Geography

The Ohio State University

The Jottings in the Margins of Fieldwork Diary—What Do They Tell Us?

Jotting 1: “Waiting! Waiting…still waiting. Am waiting…patience, fieldwork means patience with a capital ‘P’!

Comic illustration by artist Madhushree Basu

Sitting on a red plastic chair on the verandah of Periamma’s house facing the street in Sriperumbadur town, Kancheepuram District, Tamil Nadu, India. Field dairy, 7/30/2013. (Comic illustration of author’s notes by artist Madhushree Basu for author’s forthcoming book)

Jotting 2: “In bus route no. 549, going home after an interview. It’s so hot and sweaty. The driver is still drinking his tea.  A young man is sitting across me. A smartly dressed young woman joins him in a few minutes. I think they are from Nagaland, lot of people from northeastern states in this town. Five school boys get into the bus, maybe 13-14 years of age. They start teasing the young Naga couple. They are laughing at them, saying something in Tamil, asking for their tickets. How annoying!  I want to scream at those kids. I can’t stop myself. “Leave them alone” I finally tell them in Tamil. They laugh at me. They know from my accent that I am not a ‘native’ Tamil speaker. Suddenly I become subject of their derision, they start laughing at me.  The bus is starting, the boys are getting off. The Naga couple look different, a bit vulnerable in this crowd, easy targets for the bigots. It starts so young.”

Sriperumbadur bus stand, Field diary, 10/8/2013


These are jottings from the margins of my field diary that did not make it to the pages of my PhD dissertation manuscript. We often do not pay much attention to these and cast them as asides in the corners of our diaries. Reading through the margins of my field diary, I came across many such short hurriedly scribbled notes that I wondered why I had written those in the first place? Were they in any way reflecting the process or phase of my field research at that moment? What is the link, if any, between those jottings and my research? As Cindi Katz had noted, these bits in the margins keeps a researcher “afloat” in the field – “I secreted my crankiness, recorded my amusements and amazements, and kept myself afloat…it was private, reflective, and therapeutic” (2013:1). Katz used to keep a comic book journal while doing her fieldwork in rural Sudan.

I have chosen the above two ‘jottings’ randomly to illustrate the nature of these observations. Reflecting upon them, I think there is a methodological link. The writings on the margins often tend to capture situations at a very gut level. Take for instance, the first jotting, where I have written about waiting. This was a phase of my fieldwork when I was meeting young migrant women who worked in factories outside the city of Chennai in southern Indian state of Tamil Nadu. My anxiety levels were high, I was searching for co-researchers. The women worked in three shifts, traveled long distances, had only one day off per week – their daily rhythms were very different from mine. I was trying to negotiate many practicalities of doing the fieldwork, including time, interests, gate-keepers, places to meet and my own childcare responsibilities. The only thing I could do at that moment was to wait – often by the phone, at a tea stall or a bus stop or a market place or someone’s house – keeping my chin up was not always easy. It was all part of the research process.

Thinking back on the second jotting, it reflects the general politics of  othering in urban places in India – towards migrant workers or people who look or dress differently (Reena, 2020). Sriperumbadur town, where I witnessed this incident, is burgeoning with migrant workers from eastern, north-eastern Indian states, and different districts of Tamil Nadu. Young women and men stay in rented rooms in the small towns or surrounding villages and work in the factories, beauty parlors and local restaurants.  The ‘encounter’ that I witnessed in the bus that day is part of the everyday othering process experienced by workers, especially young women who come for work in the urban centers or industrial towns. Their very visible presence in public places – streets, buses, market places, creates a sense of moral anxiety in the public psyche of a socially hierarchical patriarchal society like India.

Therefore ‘notes from the margins’ are not insignificant, they reflect the researcher’s sub-conscious observations and ‘feelings’ of a place or how she/he sees a place or people, which can help in situating the research.

One of the things that I also did during waiting or travelling (often over 100 kilometers a day, changing buses, trains and walking) was to take photographs, some of them randomly, some purposefully, capturing the mundane everyday human activities in different places. Since my research revolved around work and working lives, the field seemed vast to me, “geography [of work] was everywhere” (Cook, 2005:169). It didn’t begin or end at the factory gate, but extended to the tea stalls, verandas or roadsides where I observed people doing all sorts of work for livelihood. In a blog post later, I wrote a photo-essay based on these images connecting lives and places.[1]  As Karin Becker Ohrn had noted photo essays “illustrate relationships among diverse people, places, or events”. These images, jottings, random conversations helped me in situating the larger contexts in which the lives and labor of women are located—a key inquiry of my research.

Madhumita Dutta

Assistant Professor

Department of Geography, The Ohio State University



[1]  ‘The everyday’: A photo-essay from the ‘field’ (accessed 6/23/2020)

Race, Place, and COVID-19: Mapping and Modeling a Spatial Relationship

This post was independently organized by graduate students enrolled in Geography 5103, instructed by Professor Elisabeth Root, during spring semester of 2020. 

By now, most of us are familiar with the risk factors for severe COVID-19 disease: being over 65, male, and suffering from heart problems, diabetes, or hypertension, all seem to contribute to mortality (Harrison, 2020; Rogers, 2020; Wadman, 2020). What hasn’t been discussed as frequently is how the social determinants of health – the neighborhood conditions in which people are born, grow, live, work, and age that affect health (Florida, 2020) – may also impact the risk of contracting and dying from COVID-19. While the health outcomes of a particular individual cannot be predicted solely by the social determinants of health, these measures allow researchers and the interested public to engage with social inequality in public health resources and to devise location-specific improvements.

For COVID-19 this means that these risk factors vary across space. For example, there are some places where the percent of the population that is obese has a strong positive correlation with mortality, while in other places there is no association whatsoever between the two variables. Understanding these place-specific nuances is key in organizing an effective response to COVID-19 outbreaks in a region or community.

As students, we decided to examine whether the relationship between COVID-19 mortality and area-level factors such as race, age, and poverty, varied across counties in the United States. This is a spatial property called “spatial nonstationarity,” meaning some determinant of health (e.g., race) does not have the same effect on an outcome (e.g., COVID-19 mortality) across space. We used a modeling technique called Geographically Weighted Regression (GWR) which we learned about in GEOG 5103, Dr. Elisabeth Root’s class. It is particularly well suited to capturing different effects of potential determinants on mortality across space. We chose the number of deaths per one thousand people as our outcome of interest, and then picked potential explanatory variables based on the COVID-19 research to date (05/14/2020). Among a large set of candidates, seven variables were selected. The variables are rurality, percent of adults reporting to be obese, percent of adults reporting to have asthma, percent of Black or African American, percent of seniors (65 or older), percent of people living in poverty, and the number of confirmed cases. These variables were found to be significant predictors and explain 43% of the total variation of COVID-19 mortality.

The maps below show how the relationship between each variable and COVID mortality varies across the country. So, for example, in places where the map is blue, there is actually a negative relationship between poverty and fatality (Figure 1). However, as the color becomes tan and then red, we begin to see both a stronger relationship and a positive one. These maps also account for the p-value. This means that the shaded areas show us where our results are statistically significant- that is, that the result displays a pattern or strength of the relationship that cannot just be attributed to random noise.

Figure 1. Relationship between the percentage of poverty and COVID-19 deaths: The impact of poverty on mortality is negative in many parts of the country including Ohio, Mississippi, and South Carolina. Exceptionally, positive relationships are clustered in Minnesota and Oklahoma, indicating higher risks in poor neighborhoods.

What you can see in these maps is that while all the variables have geographic variation, the counties where the African American population is a higher percentage of the population show a strong correlation with our outcome variable of fatalities. It’s also important to note the direction of these relationships. For the percentage of seniors, for example, much of the West Coast displays a negative relationship between the variable and the outcome: there, an increase in the percent of seniors was associated with a decrease in COVID-19 mortality (Figure 2). In contrast, large portions of the country show a positive relationship between the percentage of African Americans in a county and COVID-19 mortality (Figure 3). Indeed, of all our variables, this is only the one that displays such a consistently positive relationship across many different locations.

Figure 2. Relationship between the percentage of seniors and COVID-19 deaths: Fewer seniors are related to higher fatality in several parts including the West, Louisiana, and Illinois. On the contrary, the positive relationships are mostly clustered in Florida and parts of Ohio, indicating higher percentages of seniors tend to associate with higher fatality in those regions.

These results do not indicate that African Americans are somehow inherently more susceptible to this disease but instead reflect the structural racism of our country. In the United States, African Americans are more employed in service occupations (U.S. Bureau of Labor Statistics, 2018) and are more likely to live in a neighborhood with higher poverty (Greene et al., 2017). These social determinants of health make them particularly vulnerable to this disease because these risk factors lead to higher rates of exposure and poorer access to health care. Inequalities in health care access are particularly problematic for diseases like COVID-19. The longer a person waits to get necessary care – often because of a lack of insurance or health care resources in their community – the more likely they are to have severe COVID-19 disease. Recognizing the relationship between the social determinants of health and race, and then identifying how those relationships manifest in local contexts are the first steps in addressing the profound inequality presented in these maps.

Figure 3. Relationship between the percentage of African Americans and COVID-19 deaths: While higher percentages of African Americans tend to associate with higher fatality the Southern and Eastern states, Minnesota in particular show the highest correlation. Given the fact that Minnesota has a lower percentage of African Americans compared to Southern states, Black communities in the state seem to be exposed to relatively higher risks.


Author Contribution: Dr. Elisabeth Root (Professor, Geography & College of Public Health, Division of Epidemiology) conceived the research and supervised writing; Sohyun Park (Ph.D. candidate, Geography) collected data and supervised analysis; Yun Ye (Ph.D. student, Public Health), Kaiting Lang (Ph.D. student, Public Health), and Junmei Cheng (Ph.D. student, City and Regional Planning) analyzed the data; Anisa Kline (Ph.D. student, Geography) wrote the post; Blake Acton (MA student, Geography) created maps. All the authors contributed to interpreting the results.

Sustainability in a World of Cities

The 21st century witnessed an epochal event in human civilization: in 2008, the world became majority urban for the first time in history. Urbanization is accelerating: two-thirds of the global population will live in cities by 2030. Some scholars are projecting an essentially urban planet by the end of the century, with 90% of the world population crowded into urban areas.  A world of 10 billion people living predominantly in cities— of which 60% globally have yet to be built —underscores the critical need and immense opportunity for new scientific and policy approaches that can achieve sustainable urban systems.

graphic to demonstrate CMAX Bus service after 6pm

Figure 1: Locations reachable at 6pm on a typical weekday from the Linden neighborhood of Columbus by public transit and walking after new CMax bus rapid transit service. Graphic courtesy of Harvey Miller

Mobility is central to urbanity: transportation is how we organize our cities. While the personal automobile has generated stunning levels of travel and activity over the past century, it has also led to urban transportation systems being inefficient, costly, inequitable, unsafe and unhealthy, and damage environments at local to global scales.  This is leading to a mobility crisis that will get worse as the world continues to urbanize.

In many cities, we are seeing the deployment of new technology-enabled mobility services such as vehicle sharing, hailing services, and micromobility such as scooters and bikeshare systems. These innovations are disrupting the mobility landscape of cities, with even larger disruptions inevitable with the coming of connected autonomous vehicles.  While these hold promise, they also may make an unsustainable situation even worse.

Are New Mobility Technologies Sustainable?

Introducing disruptive mobility technologies to cities is a large-scale, real-world experiment that will impact cities for decades. The outcomes of these mobility disruptions have profound implications for urban air quality, social equity, energy consumption, greenhouse gas emissions, safety and health.  So far, the evidence is mixed. For example, some evidence suggest that Lyft and Uber are reducing drunk and impaired driving (although possibly at the expense of heavier drinking).  However, these services are also increasing traffic congestion, undermining public transit and leading to higher energy consumption and emissions.

graphic example of data dashboards from available data

Figure 2: The Columbus Urban and Regional Information Observatory (CURIO) – a geospatial data dashboard for Columbus, Ohio. Graphic courtesy of Harvey Miller

Whether new mobility services will make cities more sustainable is an open question, one that will be difficult to answer using 20th century urban scientific and management approaches.  In the past we have relied on simple data and measures that could be easily collected.  For example, automobile traffic counts have been easy to collect: consequently our main transportation performance measure is how many vehicles we can shove through a network.  Our simple, 20th century models also treat mobility as undifferentiated flow, like water – consequently, we made traffic congestion worse by trying to build bigger “pipes” because of a phenomenon known as induced demand.

In the 21st century, we need transportation measures and analytics that:

  • i) focus on people and their activities, not vehicles and their movements;
  • ii) recognize the heterogeneity of peoples’ needs and capabilities with respect to mobility and accessibility, and;
  • iii) capture the full cost of transportation, especially externalities such as emissions, noise, risk and other social and environmental impacts.

New Geospatial Technologies and Sustainable Mobility Science

New sources of data are emerging that could enable some of this system science. Location-aware technologies such as mobile phones and global position system (GPS) receivers, environmental sensors, social media and smart technologies are generating data at unprecedented volumes and spatio-temporal resolution, facilitating new insights into mobility patterns and urban dynamics.  The cost of data storage has plummeted, allowing these data to be saved and archived over time.  Advances in machine learning, geospatial data mining, geovisualization and other knowledge discovery techniques are helping specialized and siloed practitioners work together to make sense of this data avalanche.  Cloud computing, geospatial data portals, application-programming interfaces and data dashboards allow scientists to share these data and information widely with the public. These new technologies are creating a new kind of data-enabled and computation-rich mobility science that can lead to more nuanced, appropriate and sustainable solutions to our growing urban mobility crisis.

Harvey Miller

Bob and Mary Reusche Chair in Geographic Information Science

Professor of Geography

Director, Center for Urban and Regional Analysis (CURA)


Some of the geospatial data-enabled sustainable mobility research projects conducted in the OSU Department of Geography and the Center for Urban and Regional Analysis (CURA) include:


A Geospatial Perspective of the Novel Coronavirus Outbreak

The Department of Geography welcomes Yaoli  Wang – a post doctoral researcher – and Yu Liu – a full professor – from Peking University, providing a guest post during the COVID-19 outbreak.

The Spring Festival of 2020 was determined to be historic. One week before the Chinese New Year Eve (Jan 24), Beijing public transport tubes were still in hustle and bustle like usual. Within four days, the outbound flow back home from Beijing made the city quiet, when the news came clearly to everybody that a SARS-like virus has struck Wuhan. At 10 am, Jan 23, 2020, Wuhan was forced into lock down. Things evolved rapidly from there. Until the time of quarantine, 5 million people had left Wuhan. Along with the flow of migration was the spreading of a very contagious and novel coronavirus – COVID-19. All Chinese provinces and many worldwide countries reported infections. People, however, always have normalcy bias, inclining to believe that nothing bad will happen to them and thus not careful enough. The outbreak worldwide is already good proof.

Lockdown of Wuhan in January

Lockdown of Wuhan in January (Courtesy of Professors Qingyun Du and Zhixiang Fang, Wuhan University)

Lockdown of Wuhan in January

Lockdown of Wuhan in January (Courtesy of Professors Qingyun Du and Zhixiang Fang, Wuhan University)

Underneath the accident is essentially a spatiotemporal problem. Within China, the problem can be divided into two scales: inter-city and intra-city. Now in late February, geospatial scientists are trying to reverse the course of COVID-19 spread over China using inter-city movement flow and city-level reported confirmed cases in time series. We would imagine a spread dynamic like wavefront and wish to construct a model of migration interaction based on which the arrival time or amount of illnesses can be inferred. The lock down of Wuhan apparently put a brake on the spreading process, but could not eliminate it. Already infected people outside Wuhan continued to transmit the virus to other cities or within their own cities. The hierarchy of the interaction network potentially captures the spreading path, which indicates an effective interruption. Here we see the huge potential of space-time big data. There is a study at the beginning of the outbreak which, by analyzing the outbound movement flow from Wuhan to areas around, drew a conclusion that the up-till-then reported illness count was underestimated 1; the conclusion, unfortunately, was proven to be true when the statistics were complete.

graph displaying Novel Coronavirus Pneumonia in China

Figure 1: Spatial distribution of the confirmed COVID-19 cases in China on February 17, 2020, when the total number is 72,528 [2]

Inside a city, the general public is nervous and cautious with 2nd or 3rd degree transmission. Spatially clustered illnesses are the majority of cases, for example, in departments of a hospital, in a family, and in a canteen of a company. Close contact between two individuals is a typical space-time relationship, which in GIScience we usually use “spatio-temporal co-occurrence” to model it. That is why the government asks the public to stay at home and shuts down all public recreation places, especially indoor venues. Random encounters are much more difficult to trace back than regular social networks. Back in 1630, the Milan plague was re-triggered by a big carnival even though the beginning of the plague was well controlled. For the ongoing 2019 coronavirus, people had spent so much time and energy retrieving the individual trajectories of confirmed illnesses. There are even some online gadgets to examine if a person has trajectory overlapping with the confirmed cases. What comes up next is how spatial and information technology can facilitate the process in a more positive way while not intruding on privacy too much? For instance, spatiotemporal trajectories from mobile phone records are ubiquitous, but there is a trade-off: the demand for higher space-time accuracy down to a meter level for automatic screening of virus encounter versus the rejection of privacy exposure and targeted data breach.

Foreseeing the future of urban life and technology, can we imagine a world where every person is implanted with a chip – something we call “human black box” recording all the information throughout life, including his (or her) health, spatiotemporal trajectories, habits, etc? What’s missing is a mature mechanism to protect privacy and data safety. Blockchain might be a potential solution. All the information is not controlled by a central organization. The owner of data has the initiative of data-sharing in an urgent case like the outbreak of coronavirus. As an incentive of sharing data, the user gets bonuses, which is guaranteed by a blockchain system. We believe that most people are not ready for accepting such a technique at present. But could this become reality if we can manage the negative issues, say, 100 years later? Let’s wait and see.

The virus is still happening and boosting technology innovation. City governance needs a more robust system to be responsive to public events; e-commerce is evolving to smooth the channel between suppliers and customers; education is developing new patterns such as online education; crowd-sourcing and public participation is driving for social well-being. Up until the time of writing, many countries all over the world have reported infections, but many of them cannot trace back to the origin of infection. Potentially the geography of virus genomics may map out the trajectories of generations of virus so that we can disclose the mystery of its origin and spreading. All the aspects can be regarded as evolvement of spatiotemporal relationships. We are going for an opening-up of geospatial technologies.

Yaoli Wang (Post Doctoral Researcher), Yu Liu (Professor)

Institute of Remote Sensing and Geographical Information Systems,

Peking University

Yu Liu is the 2019-2020 Robinson Colloquium speaker for the Department of Geography.

  1., in Chinese.


Post has been updated (4-2-2020) with photos from contributors of the author