Editorial of the Special Issue on Spatiotemporal Analysis of the Impact of COVID-19

This analysis of U.S.-based survey data reports regional differences (Northeast, Midwest, South, and West) in sweetener knowledge, consumption, and body mass index (BMI) among 877 people with HIV (PLWH; median age 54 years). BMI was lowest in the West and highest in the Midwest. Respondents in the West reported greater sweetener knowledge than in the Northeast, Midwest, and South. Respondents from the West reported lower sweetener consumption than the Midwest and South. Regional differences in BMI, sweetener knowledge, and consumption were demonstrated. Findings support consideration of regional differences when providing nutrition education.

The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controlling the spreading of COVID-19, there were voices against vaccines. Therefore, this research utilizes geo-tweets and Bayesian-based method to investigate public opinions towards vaccines based on (1) the spatiotemporal changes in public engagement and public sentiment; (2) how the public engagement and sentiment react to different vaccine-related topics; (3) how various races behave differently. We connected the phenomenon observed to real-time and historical events. We found that in general the public is positive towards COVID-19 vaccines. Public sentiment positivity went up as more people were vaccinated. Public sentiment on specific topics varied in different periods. African Americans’ sentiment toward vaccines was relatively lower than other races.

The United Nations Sustainable Development Goals (SDGs) include 17 interlinked goals designed to be a blueprint for the world’s nations to achieve a better and more sustainable future, and the specific SDG 3 is a public health–related goal to ensure healthy living and promote well-being for all population groups. To facilitate SDG planning, implementation, and progress monitoring, many SDG indicators have been developed. Based on the United Nations General Assembly resolutions, SDG indicators need to be disaggregated by geographic locations and thematic environmental and socioeconomic characteristics for achieving the most accurate planning and progress assessment. High-resolution data such as those captured at the village level can provide comparatively more precise insights into the different socioeconomic and environmental factors relevant to SDGs, therefore enabling more effective sustainable development decision-making. Using India as our study area and the child malnutrition indicators stunting, underweight, and wasting as examples of public health–related SDG indicators, we have demonstrated a process to effectively derive environmental variables at the village level from satellite big datasets on a cloud platform for SDG research and applications. Spatial analysis of environmental variables regarding vegetation, climate, and terrain have shown spatial grouping patterns across the entire study area, with each village group having different statistics. Correlation analysis between these environmental variables and stunting, underweight, and wasting indicators show a meaningful relationship between these indicators and vegetation index, land surface temperature, rainfall, elevation, and slope. Identifying the spatial variation patterns of environmental variables at the village level and their correlations with child malnutrition indicators can be an invaluable tool to facilitate a clearer understanding of the causes of child malnutrition and to improve area-specific SDG 3 implementation planning. This analysis can also provide meaningful support in assessing and monitoring SDG implementation progress at the village level by spatially predicting SDG indicators using available socioeconomic and environmental independent variables. The methodology used in this study has the potential to be applied to other similar regions, especially low-to-middle income countries where a high number of children are severely affected by malnutrition, as well as to other environmentally related SDGs, such as Goal 1 (No Poverty) and Goal 2 (Zero Hunger).

Processing Earth observation data modelled in a time-series of raster format is critical to solving some of the most complex problems in geospatial science ranging from climate change to public health. Researchers are increasingly working with these large raster datasets that are often terabytes in size. At this scale, traditional GIS methods may fail to handle the processing, and new approaches are needed to analyse these datasets. The objective of this work is to develop methods to interactively analyse big raster datasets with the goal of most efficiently extracting vector data over specific time periods from any set of raster data. In this paper, we describe RINX (Raster INformation eXtraction) which is an end-to-end solution for automatic extraction of information from large raster datasets. RINX heavily utilises open source geospatial techniques for information extraction. It also complements traditional approaches with state-of-the- art high-performance computing techniques. This paper discusses details of achieving big temporal data extraction with RINX, implemented on the use case of air quality and climate data extraction for long term health studies, which includes methods used, code developed, processing time statistics, project conclusions, and next steps.

Additional: Please find a slightly updated version of this paper here that contains updated co-authorship and references.

There are many definitions for geography, most contain the word space or place. In order to foresee the future of geography, let us first examine the presence of the discipline, in particular, its variation in space. This chapter illustrates the distribution of global leading higher education institutions and compare that with the distribution of those leading the study of geography. Are they mostly overlapping? Or in some countries, do they deviate from each other? Among the leading institutions for the study of geography, are they focusing on physical geography, human geography, geographic information science, or all sub-disciplines? Among the leading institutions that are not strong in the study of geography, what are the related disciplines they choose to focus on? Is there a geographic variation in the composition of geographic education? If yes, how to describe it, and how to explain it? Do these patterns reveal any insight to the future of the discipline?

Urban morphology and human mobility are two sides of the complex mixture of elements that implicitly define urban functionality. By leveraging the emerging availability of crowdsourced data, we aim for novel insights on how they relate to each other, which remains a substantial scientific challenge. Specifically, our study focuses on extracting spatial-temporal information from taxi trips in an attempt on grouping urban space based on human mobility, and subsequently assess its potential relationship with urban functional characteristics in terms of local points-of-interest (POI) distribution. Proposing a vector representation of urban areas, constructed via unsupervised machine learning on trip data’s temporal and geographic factors, the underlying idea is to define areas as “related” if they often act as destinations of similar departing regions at similar points in time, regardless of any other explicit information. Hidden relations are mapped within the generated vector space, whereby areas are represented as points and stronger/weaker relatedness is conveyed through relative distances. The mobility-related outcome is then compared with the POI-type distribution across the urban environment, to assess the functional consistency of mobility-based clusters of urban areas. Results indicate a meaningful relationship between spatial-temporal motion patterns and urban distributions of a diverse selection of POI-type categorizations, paving the way to ideally identify homogenous urban functional zones only based on the movement of people. Our data-driven approach is intended to complement traditional urban development studies on providing a novel perspective to urban activity modeling, standing out as a reference for mining information out of mobility and POI data types in the context of urban management and planning.

This research examines water, sanitation, and hygiene (WASH) accessibility and opportunity in Kibera and Mathare during the COVID-19 pandemic in 2021. Kibera and Mathare are two of the largest urban informal settlements in Nairobi (the capital city of Kenya) as well as Sub-Saharan Africa. Accessibility indicates how easily a person can reach WASH facilities from their home by walking. Opportunity represents how many WASH options a person has near their home. We utilize the data on water and toilet facilities collected by GroundTruth Initiative in partnership with Map Kibera Trust (local community partners) between February and April 2021 – amid the COVID-19 pandemic. By conducting quantitative geospatial analysis, we illustrate WASH accessibility and related issues that were not evident in previous studies: (1) 77.4% of people living in Kibera have limited WASH facility accessibility or opportunity; (2) 60.6% of people living in Mathare have limited WASH facility accessibility or opportunity; (3) there is a clear geographic pattern in WASH facility accessibility and opportunity; and (4) overall accessibility and opportunity is better in Mathare than in Kibera. This study is one of the first studies to examine WASH accessibility and opportunity in urban informal settlements during the COVID-19 pandemic by utilizing the current data and quantitative geospatial methods. Based on the results, we discuss important public health policy implications for people living in urban informal settlements to improve their WASH facility accessibility and opportunity during the COVID-19 pandemic.

Importance  In the United States, Black and Hispanic children have higher rates of asthma and asthma-related morbidity compared with White children and disproportionately reside in communities with economic deprivation.

Objective  To determine the extent to which neighborhood-level socioeconomic indicators explain racial and ethnic disparities in childhood wheezing and asthma.

Design, Setting, and Participants  The study population comprised children in birth cohorts located throughout the United States that are part of the Children’s Respiratory and Environmental Workgroup consortium. Cox proportional hazard models were used to estimate hazard ratios (HRs) of asthma incidence, and logistic regression was used to estimate odds ratios of early and persistent wheeze prevalence accounting for mother’s education, parental asthma, smoking during pregnancy, child’s race and ethnicity, sex, and region and decade of birth.

Exposures  Neighborhood-level socioeconomic indicators defined by US census tracts calculated as z scores for multiple tract-level variables relative to the US average linked to participants’ birth record address and decade of birth. The parent or caregiver reported the child’s race and ethnicity.

Main Outcomes and Measures  Prevalence of early and persistent childhood wheeze and asthma incidence.

Results  Of 5809 children, 46% reported wheezing before age 2 years, and 26% reported persistent wheeze through age 11 years. Asthma prevalence by age 11 years varied by cohort, with an overall median prevalence of 25%. Black children (HR, 1.47; 95% CI, 1.26-1.73) and Hispanic children (HR, 1.29; 95% CI, 1.09-1.53) were at significantly increased risk for asthma incidence compared with White children, with onset occurring earlier in childhood. Children born in tracts with a greater proportion of low-income households, population density, and poverty had increased asthma incidence. Results for early and persistent wheeze were similar. In effect modification analysis, census variables did not significantly modify the association between race and ethnicity and risk for asthma incidence; Black and Hispanic children remained at higher risk for asthma compared with White children across census tracts socioeconomic levels.

Conclusions and Relevance  Adjusting for individual-level characteristics, we observed neighborhood socioeconomic disparities in childhood wheeze and asthma. Black and Hispanic children had more asthma in neighborhoods of all income levels. Neighborhood- and individual-level characteristics and their root causes should be considered as sources of respiratory health inequities.

Researchers are increasingly working with large geospatial datasets that contain hundreds of millions of records. At this scale, desktop GIS systems typically fall short and so new approaches and methods are needed. The objective of this work is to develop new approaches to interactively analyze large datasets and then to demonstrate the usefulness of those approaches using a case study looking at voter, or partisan segregation. Historically, the measurement of partisan segregation has been limited to comparing large geographic areas such as counties or states because researchers only had access to aggregated data. In this case study, however, we measure partisan segregation down to the individual for 180 million U.S. voters using advanced geospatial data science and high-performance computing. This article discusses interactive method development for big geospatial data analysis including techniques used, solutions developed, and processing time statistics.

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.

In India, Parliamentary Constituencies (PCs) could serve as a regional unit of COVID-19 monitoring that facilitates evidence-based policy decisions. In this study, we presented the first estimates of COVID-19 cumulative cases and deaths per 100,000 population and the case fatality rate (CFR) between 7 January 2020 and 31 January 2021 across PCs and districts of India. We adopted a novel geographic information science-based methodology called crosswalk to estimate COVID-19 outcomes at the PC-level from district-level information. We found a substantial variation of COVID-19 burden within each state and across the country. Access to PC-level and district-level COVID-19 information can enhance both central and regional governmental accountability of safe reopening policies.

Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, mathematical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from the Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration; 2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic; 3) to improve mathematical models used in analysing, simulating, and predicting the transmission of the disease; and 4) to enrich the source of mobility data to ensure data accuracy and suability.

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies.

This study employs a spatial autoregressive probit-log linear (SAP-Log) hurdle model to investigate the influencing factors on the probability of death and case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) at the city level in China. The results demonstrate that the probability of death from COVID-19 and the CFR level are 2 different processes with different determinants. The number of confirmed cases and the number of doctors are closely associated with the death probability and CFR, and there exist differences in the CFR and its determinants between cities within Hubei Province and outside Hubei Province. The spatial probit model also presents positive spatial autocorrelation in death probabilities. It is worth noting that the medical resource sharing among cities and enjoyment of free medical treatment services of citizens makes China different from other countries. This study contributes to the growing literature on determinants of CFR with COVID-19 and has significant practical implications.

This volume of case studies focuses on the geographies of COVID-19 around the world. These geographies are located in both time and space concentrating on both first- and second-order impacts of the COVID-19 pandemic. First-order impacts are those associated with the immediate response to the pandemic that include tracking number of deaths and cases, testing, access to hospitals, impacts on essential workers, searching for the origins of the virus and preventive treatments such as vaccines and contact tracing. Second-order impacts are the result of actions, practices, and policies in response to the spread of the virus, with longer-term effects on food security, access to health services, loss of livelihoods, evictions, and migration. Further, the COVID-19 pandemic will be prolonged due to the onset of variants as well as setting the stage for similar future events. This volume provides a synopsis of how geography and geospatial approaches are used to understand this event and the emerging “new normal.” The volume's approach is necessarily selective due to the global reach of the pandemic and the broad sweep of second-order impacts where important issues may be left out. However, the book is envisioned as the prelude to an extended conversation about adaptation to complex circumstances using geospatial tools.

Using case studies and examples of geospatial analyses, this volume adopts a geographic lens to highlight the differences and commonalities across space and time where fundamental inequities are exposed, the governmental response is varied, and outcomes remain uncertain. This moment of global collective experience starkly reveals how inequality is ubiquitous and vulnerable populations – those unable to access basic needs – are increasing. This place-based approach identifies how geospatial analyses and resulting maps depict the pandemic as it ebbs and flows across the globe. Data-driven decision making is needed as we navigate the pandemic and determine ways to address future such events to enable local and regional governments in prioritizing limited resources to mitigate the long-term consequences of COVID-19.

The Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) are an important city planning resource in the USA. However, curating these statistics is resource-intensive, and their accuracy deteriorates when changes in population and urban structures lead to shifts in commuter patterns. Our study area is the San Francisco Bay area, and it has seen rapid population growth over the past years, which makes frequent updates to LODES or the availability of an appropriate substitute desirable. In this paper, we derive mobility flows from a set of over 40 million georeferenced tweets of the study area and compare them with LODES data. These tweets are publicly available and offer fine spatial and temporal resolution. Based on an exploratory analysis of the Twitter data, we pose research questions addressing different aspects of the integration of LODES and Twitter data. Furthermore, we develop methods for their comparative analysis on different spatial scales: at the county, census tract, census block, and individual street segment level. We thereby show that Twitter data can be used to approximate LODES on the county level and on the street segment level, but it also contains information about non-commuting-related regular travel. Leveraging Twitter’s high temporal resolution, we also show how factors like rush hour times and weekends impact mobility. We discuss the merits and shortcomings of the different methods for use in urban planning and close with directions for future research avenues.

Accurate descriptions of current ecosystem composition are essential for improving terrestrial biosphere model predictions of how ecosystems are responding to climate variability and change. This study investigates how imaging spectrometry-derived ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional-scale carbon, water and energy fluxes. Incorporating imaging spectrometry-derived composition of five plant functional types (Grasses/Shrubs, Oaks/Western Hardwoods, Western Pines, Fir/Cedar and High-elevation Pines) into the Ecosystem Demography (ED2) terrestrial biosphere model improves predictions of net ecosystem productivity (NEP) and gross primary productivity (GPP) across four flux towers of the Southern Sierra Critical Zone Observatory (SSCZO) spanning a 2250 m elevational gradient in the western Sierra Nevada. NEP and GPP root-mean-square-errors were reduced by 23%–82% and 19%–89%, respectively, and water flux predictions improved at the mid-elevation pine (Soaproot), fir/cedar (P301) and high-elevation pine (Shorthair) flux tower sites, but not at the oak savanna (San Joaquin Experimental Range [SJER]) site. These improvements in carbon and water predictions are similar to those achieved with model initializations using ground-based inventory composition. The imaging spectrometry-constrained ED2 model was then used to predict carbon, water and energy fluxes and above-ground biomass (AGB) dynamics over a 737 km² region to gain insight into the regional ecosystem impacts of the 2012–2015 Californian drought. The analysis indicates that the drought reduced regional NEP, GPP and transpiration by 83%, 40% and 33%, respectively, with the largest reductions occurring in the functionally diverse, high basal area mid-elevation forests. This was accompanied by a 54% decline in AGB growth in 2012, followed by a marked increase (823%) in AGB mortality in 2014, reflecting an approximately 10-fold increase in per capita tree mortality from ~55 trees km⁻² year⁻¹ in 2010–2011, to ~535 trees km⁻² year⁻¹ in 2014. These findings illustrate how imaging spectrometry estimates of ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional carbon, water, and energy fluxes, and biomass dynamics.

Importance  Geographic targeting of public health interventions is needed in resource-constrained developing countries.

Objective  To develop methods for estimating health and development indicators across micropolicy units, using assembly constituencies (ACs) in India as an example.

Design, Setting, and Participants  This cross-sectional study included children younger than 5 years who participated in the fourth National Family and Health Survey (NFHS-4), conducted between January 2015 and December 2016. Participants lived in 36 states and union territories and 640 districts in India. Children who had valid weight and height measures were selected for stunting, underweight, and wasting analysis, and children between age 6 and 59 months with valid blood hemoglobin concentration levels were included in the anemia analysis sample. The analysis was performed between February 1 and August 15, 2020.

Exposures  A total of 3940 ACs were identified from the geographic location of primary sampling units in which the children’s households were surveyed in NFHS-4.

Main Outcomes and Measures  Stunting, underweight, and wasting were defined according to the World Health Organization Child Growth Standards. Anemia was defined as blood hemoglobin concentration less than 11.0 g/dL.

Results  The main analytic sample included 222 172 children (mean [SD] age, 30.03 [17.01] months; 114 902 [51.72%] boys) from 3940 ACs in the stunting, underweight, and wasting analysis and 215 593 children (mean [SD] age, 32.63 [15.47] months; 112 259 [52.07%] boys) from 3941 ACs in the anemia analysis. The burden of child undernutrition varied substantially across ACs: from 18.02% to 60.94% for stunting, with a median (IQR) of 35.56% (29.82%-42.42%); from 10.40% to 63.24% for underweight, with a median (IQR) of 32.82% (25.50%-40.96%); from 5.56% to 39.91% for wasting, with a median (IQR) of 19.91% (15.70%-24.27%); and from 18.63% to 83.05% for anemia, with a median (IQR) of 55.74% (48.41%-63.01%). The degree of inequality within states varied across states; those with high stunting, underweight, and wasting prevalence tended to have high levels of inequality. For example, Uttar Pradesh, Jharkhand, and Karnataka had high mean AC-level prevalence of child stunting (Uttar Pradesh, 45.29%; Jharkhand, 43.76%; Karnataka, 39.77%) and also large SDs (Uttar Pradesh, 6.90; Jharkhand, 6.02; Karnataka, 6.72). The Moran I indices ranged from 0.25 to 0.80, indicating varying levels of spatial autocorrelation in child undernutrition across the states in India. No substantial difference in AC-level child undernutrition prevalence was found after adjusting for possible random displacement of geographic location data.

Conclusions and Relevance  In this cross-sectional study, substantial inequality in child undernutrition was found across ACs in India, suggesting the importance of considering local electoral units in designing targeted interventions. The methods presented in this paper can be further applied to measuring health and development indicators in small electoral units for enhanced geographic precision of public health data in developing countries.

Secondary cities are rapidly growing areas in low- and middle-income countries that lack data, planning, and essential services for sustainable development. Their rapid, informal growth patterns mean secondary cities are often data-poor and under-resourced, impacting the ability of governments to target development efforts, respond to emergencies, and design sustainable futures. The United Nations’ Sustainable Development Goal (SDG) 11 focuses on inclusive, safe, resilient, and sustainable cities and human settlements. SDG Indicator (SDGI) 11.3.1 calculates the ratio of land consumption rate to population growth rate to enhance inclusive and sustainable urbanization. Our paper compares three cities—Denpasar, Indonesia; Kharkiv, Ukraine; and Mekelle, Ethiopia—that were part of the Secondary Cities (2C) Initiative of the U.S. Department of State, Office of the Geographer and Global Issues to assess SDGI 11.3.1. The 2C Initiative focused on field-based participatory mapping for data generation to assist city planning. Urban form and population data are critical for calculating and visually representing this ratio. We examine the spatial extent of each city to assess land use efficiency (LUE) and track changes in urban form over time. With limited demographic and spatial data for secondary cities, we speculate whether SDGI 11.3.1 is useful for small- and medium-sized cities.