COVID-19

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

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

 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

  1. https://gis.osu.edu/COVID19-Dashboard/
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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/

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.

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. https://mp.weixin.qq.com/s/8x8UYZBZwMGn86Wq7iz4og, in Chinese.
  2. https://vis.ucloud365.com/ncov/china/en.html

 

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