Analyzing spatial varying effect is pivotal in geographic analysis. Yet, accurately capturing and interpreting this variability is challenging due to the complexity and non-linearity of geospatial data. Herein, we introduce an integrated framework that merges local spatial weighting scheme, Explainable Artificial Intelligence (XAI), and cutting-edge machine learning technologies to bridge the gap between traditional geographic analysis models and general machine learning approaches. Through tests on synthetic datasets, this framework is verified to enhance the interpretability and accuracy of predictions in both geographic regression and classification by elucidating spatial variability. It significantly boosts prediction precision, offering a novel approach to understanding spatial phenomena.

This paper brings a comprehensive systematic review of the application of geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including the subdomains of cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this extensive review, we obtain 14,537 papers from the Web of Science in the relevant fields and select 1516 papers that we identify as human geography studies using GeoAI via human scanning conducted by several research groups around the world. We outline the GeoAI applications in human geography by systematically summarising the number of publications over the years, empirical studies across countries, the categories of data sources used in GeoAI applications, and their modelling tasks across different subdomains. We find out that existing human geography studies have limited capacity to monitor complex human behaviour and examine the non-linear relationship between human behaviour and its potential drivers—such limits can be overcome by GeoAI models with the capacity to handle complexity. We elaborate on the current progress and status of GeoAI applications within each subdomain of human geography, point out the issues and challenges, as well as propose the directions and research opportunities for using GeoAI in future human geography studies in the context of sustainable and open science, generative AI, and quantum revolution.

Quantitative geographical methods have played an important role in COVID-19 research. To complement and extend previous review studies, we conduct a scoping review of COVID-19 studies employing quantitative geographical approaches by focusing on 331 papers published in 45 journals in geography, urban studies, and planning. We identify four major research themes (clusters): (1) how SARS-CoV-2 viruses spread in cities, (2) the COVID-19 mortality (death) rates and their association with socioeconomic variables, (3) how the COVID-19 pandemic changed people’s mobilities, and (4) how the COVID-19 pandemic affects air pollution. We conclude that spatial models play a key role in COVID-19 quantitative geographical approaches, and human mobility is an important and widely studied topic. We also reveal a lack of research focusing on environmental pollution (other than air pollution) that potentially worsened during the pandemic.

The lack of large-scale data revealing the interactions among firms has constrained empirical studies. Utilizing relational web data has remained unexplored as a remedy for this data problem. We constructed a Digital Layer by scraping the inter-firm hyperlinks of 600,000 German firms and linked the Digital Layer with several traditional indicators. We showcase the use of this developed dataset by testing whether the Digital Layer data can replicate several theoretically motivated and empirically supported stylized facts. The results show that the intensity and quality of firms’ hyperlinks are strongly associated with the innovation capabilities of firms and, to a lesser extent, with hyperlink relations to geographically distant and cognitively close firms. Finally, we discuss the implications of the Digital Layer approach for an evidence-based assessment of sectoral and place-based innovation policies.

Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub (https://github.com/wybert/Comparative-Study-of-Methods-for-Drive-Time-Es...) to facilitate further usage and research by the wider academic community. 

The properties of emerging, digital, general-purpose technologies make it hard to observe their adoption by firms and identify the salient determinants of adoption. However, these aspects are critical since the patterns related to early-stage diffusion establish path-dependencies which have implications for the distribution of the technological opportunities and socio-economic returns linked to these technologies. We focus on the case of artificial intelligence (AI) and train a transformer language model to identify firm-level AI adoption using textual data from over 1.1 million websites and constructing a hyperlink network that includes >380,000 firms in Germany, Austria, and Switzerland. We use these data to expand and test epidemic models of inter-firm technology diffusion by integrating the concepts of social capital and network embeddedness. We find that AI adoption is related to three epidemic effect mechanisms: 1) Indirect co-location in industrial and regional hot-spots associated to production of AI knowledge; 2) Direct exposure to sources transmitting deep AI knowledge; 3) Relational embeddedness in the AI knowledge network. The pattern of adoption identified is highly clustered and features a rather closed system of AI adopters which is likely to hinder its broader diffusion. This has implications for policy which should facilitate diffusion beyond localized clusters of expertise. Our findings also point to the need to employ a systemic perspective to investigate the relation between AI adoption and firm performance to identify whether appropriation of the benefits of AI depends on network position and social capital.

Firms may seek non-redundant information through inter-firm relations beyond their geographic and cognitive boundaries (i.e., relations with firms in other regions and active in different fields). Little is known about the conditions under which firms benefit from this high-risk/high-gain strategy. We created a digital layer of 600,000 German firms by using their websites’ textual and relational content. Our results suggest that strong relations (relations with common third partners) between firms from different fields and inter-regional relations are positively associated with a firm’s innovation level. We also found that a specific combination of weak and strong relations confers greater innovation benefits.

The Geospatial Analytics Extension for KNIME (GAEK) is an innovative tool designed to integrate visual programming with geospatial analytics, streamlining GIS education and research in social sciences. GAEK simplifies access for users with an intuitive, visual interface for complex spatial analysis tasks and contributes to the organization of the GIS Knowledge Tree through its geospatial analytics nodes. This paper discusses GAEK's architecture, functionalities, and its transformative impact on GIS applications. While GAEK significantly enhances user experience and research reproducibility, future updates aim to expand its functionality and optimize its bundled environment.

Background

Descriptive epidemiological data on incidence rates (IRs) of asthma with recurrent exacerbations (ARE) are sparse.

Objectives

This study hypothesized that IRs for ARE would vary by time, geography, age, and race and ethnicity, irrespective of parental asthma history.

Methods

The investigators leveraged data from 17,246 children born after 1990 enrolled in 59 US with 1 Puerto Rican cohort in the Environmental Influences on Child Health Outcomes (ECHO) consortium to estimate IRs for ARE.

Results

The overall crude IR for ARE was 6.07 per 1000 person-years (95% CI: 5.63-6.51) and was highest for children aged 2-4 years, for Hispanic Black and non-Hispanic Black children, and for those with a parental history of asthma. ARE IRs were higher for 2- to 4-year-olds in each race and ethnicity category and for both sexes. Multivariable analysis confirmed higher adjusted ARE IRs (aIRRs) for children born 2000-2009 compared with those born 1990-1999 and 2010-2017, 2-4 versus 10-19 years old (aIRR = 15.36; 95% CI: 12.09-19.52), and for males versus females (aIRR = 1.34; 95% CI 1.16-1.55). Black children (non-Hispanic and Hispanic) had higher rates than non-Hispanic White children (aIRR = 2.51; 95% CI 2.10-2.99; and aIRR = 2.04; 95% CI: 1.22-3.39, respectively). Children born in the Midwest, Northeast and South had higher rates than those born in the West (P < .01 for each comparison). Children with a parental history of asthma had rates nearly 3 times higher than those without such history (aIRR = 2.90; 95% CI: 2.43-3.46).

Conclusions

Factors associated with time, geography, age, race and ethnicity, sex, and parental history appear to influence the inception of ARE among children and adolescents.

Promoting well-being is one of the key targets of the Sustainable Development Goals at the United Nations. Many national and city governments worldwide are incorporating Subjective Well-Being (SWB) indicators into their agenda, to complement traditional objective development and economic metrics. In this study, we introduce the Twitter Sentiment Geographical Index (TSGI), a location-specific expressed sentiment database with SWB implications, derived through deep-learning-based natural language processing techniques applied to 4.3 billion geotagged tweets worldwide since 2019. Our open-source TSGI database represents the most extensive Twitter sentiment resource to date, encompassing multilingual sentiment measurements across 164 countries at the admin-2 (county/city) level and daily frequency. Based on the TSGI database, we have created a web platform allowing researchers to access the sentiment indices of selected regions in the given time period.

Background: Stay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies.

Objective: This study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups.

Methods: We constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups.

Results: We combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect.

Conclusions: This study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks.

This textbook integrates GIS, spatial analysis, and computational methods for solving real-world problems in various policy-relevant social science applications. Thoroughly updated, the third edition showcases the best practices of computational spatial social science and includes numerous case studies with step-by-step instructions in ArcGIS Pro and open-source platform KNIME. Readers sharpen their GIS skills by applying GIS techniques in detecting crime hotspots, measuring accessibility of primary care physicians, forecasting the impact of hospital closures on local community, or siting the best locations for business.

FEATURES

• Fully updated using the latest version of ArcGIS Pro and open-source platform KNIME
• Features two brand-new chapters on agent-based modeling and big data analytics
• Provides newly automated tools for regionalization, functional region delineation, accessibility measures, planning for maximum equality in accessibility, and agent-based crime simulation
• Includes many compelling examples and real-world case studies related to social science, urban planning, and public policy
• Provides a website for downloading data and programs for implementing all case studies included in the book and the KNIME lab manual

Intended for students taking upper-level undergraduate and graduate-level courses in quantitative geography, spatial analysis, and GIS applications, as well as researchers and professionals in fields such as geography, city and regional planning, crime analysis, public health, and public administration.

A lightning talk given at the 2023 Esri International Users Conference.

Equity in health care delivery is a longstanding concern of public health policy. Telehealth is considered an important way to level the playing field by broadening health services access and improving quality of care and health outcomes. This study refines the recently developed “2-Step Virtual Catchment Area (2SVCA) method” to assess the telehealth accessibility of primary care in the Baton Rouge Metropolitan Statistical Area, Louisiana. The result is compared to that of spatial accessibility via physical visits to care providers based on the popular 2-Step Floating Catchment Area (2SFCA) method. The study shows that both spatial and telehealth accessibilities decline from urban to low-density and then rural areas. Moreover, disproportionally higher percentages of African Americans are in areas with higher spatial accessibility scores; but such an advantage is not realized in telehealth accessibility. In the study area, absence of broadband availability is mainly a rural problem and leads to a lower average telehealth accessibility than physical accessibility in rural areas. On the other side, lack of broadband affordability is a challenge across the rural-urban continuum and is disproportionally associated with high concentrations of disadvantaged population groups such as households under the poverty level and Blacks.

This paper seeks to unveil how (geospatial) connection strategies associated with business innovation, differ between geolocated social media and hyperlink company networks. Thereby, we provide a first step towards understanding connection strategies of innovative companies on social media platforms. For this purpose, we build a hyperlink and Twitter follower network for 11,892 companies in the information technology (IT) sector and compare them along four dimensions. First, underlying network structures were assessed. Second, we asserted information flow patterns between companies with the help of centrality measures. Third, geographic and cognitive proximities of companies were compared. Fourth, the influence of company characteristics was examined through linear and logistic regression models. This comparison revealed, that on a general level the basic connection patterns of the hyperlink and Twitter network differ. Nevertheless, the geospatial dimension (geographic proximity) and the knowledge base of a company (cognitive proximity) appear to have a similar influence on the decision to connect with other companies on Twitter and via hyperlinks. Further, the results suggest that innovative companies most likely align their connection strategies across hyperlink and Twitter networks. Thus, business innovation might effect connection strategies across online company networks in a comparable manner.

The lack of large-scale data revealing the interactions among firms has constrained empirical studies. Utilizing relational web data has remained unexplored as a remedy for this data problem. We constructed a Digital Layer by scraping the inter-firm hyperlinks of 600,000 German firms and linked the Digital Layer with several traditional indicators. We showcase the use of this developed dataset by testing whether the Digital Layer data can replicate several theoretically motivated and empirically supported stylized facts. The results show that the intensity and quality of firms’ hyperlinks are strongly associated with the innovation capabilities of firms and, to a lesser extent, with hyperlink relations to geographically distant and cognitively close firms. Finally, we discuss the implications of the Digital Layer approach for an evidence-based assessment of sectoral and place-based innovation policies.

Coronavirus disease 2019 (COVID-19) continues to affect the world, and the design of strategies to curb disease outbreaks requires close monitoring of their trajectories. We present machine learning methods that leverage internet-based digital traces to anticipate sharp increases in COVID-19 activity in U.S. counties. In a complementary direction to the efforts led by the Centers for Disease Control and Prevention (CDC), our models are designed to detect the time when an uptrend in COVID-19 activity will occur. Motivated by the need for finer spatial resolution epidemiological insights, we build upon previous efforts conceived at the state level. Our methods—tested in an out-of-sample manner, as events were unfolding, in 97 counties representative of multiple population sizes across the United States—frequently anticipated increases in COVID-19 activity 1 to 6 weeks before local outbreaks, defined when the effective reproduction number Rt becomes larger than 1 for a period of 2 weeks.

Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub (https://github.com/wybert/Comparative-Study-of-Methods-for-Drive-Time-Es...) to facilitate further usage and research by the wider academic community.

This study examined transit-based accessibility to hospitals in Ulaanbaatar, the capital city of Mongolia, which is one of the low- and middle-income countries (LMICs). Promoting transit-based accessibility to hospitals is an important public health policy goal because limited accessibility can lead to adverse health outcomes. Public transit is especially crucial for people living in LMICs because many of them lack private vehicles. However, transit-based hospital accessibility has not been widely studied in LMICs. With the recent development of open-source transit analysis tools and standardization of schedule-based transit network data protocols, we could build Ulaanbaatar's schedule-based transit network dataset in great detail. We computed transit-based accessibility to hospitals from 128,032 residential parcels in Ulaanbaatar. Overall, transit-based accessibility to hospitals was higher in the central area than in the peripheral areas of Ulaanbaatar. Specifically, transit-based accessibility to hospitals was significantly lower in the ger area (settlements without central infrastructure connection to heat, water, and sewage) than in the non-ger area (apartment area). The results revealed that about 10% of people living in the study area have inadequate transit-based accessibility to hospitals. Our research is one of the first studies attempting to create a detailed schedule-based transit network and measure transit-based accessibility to hospitals in a rapidly growing, under-examined city in LMICs.

Cycling mobility contributes to better livability in cites, helps societies to reduce greenhouse gas emissions and their dependency on fossil fuels, and shows positive health effects. However, unattractive conditions, primarily inadequate infrastructure, hinder the further growth of cycling mobility. As interactions of cyclists with the (built) environment are complex, assessing potential impacts of an intervention aimed at improving physical conditions is not trivial. Despite a growing body of literature on various facets of cycling mobility, assessments are widely limited to a single method and thereby either focus on one detailed aspect or on one perspective. While multi-method and mixed methods studies are emerging, they are not embedded into a structured, integrated framework for assessing systemic effects of interventions yet. Therefore, we propose a conceptual integration of several relevant methods such as questionnaires, interviews, GIS analyses and human sensing. In this paper, we present a generic, extensible framework that offers guidance for developing and implementing case-specific mixed methods designs for multifaceted assessments of interventions. The framework supports domain experts and researchers across different stages of conducting a study. Results from this research further indicate the added value of mixed methods studies compared to single-method approaches.

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.

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.

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.

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.

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

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.

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.

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.

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 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.