Financial information, physical proximity and COVID: The experience of Asian sell-side equity research analysts

Bratton, William W.; Wójcik, Dariusz

doi:10.1016/j.geoforum.2022.11.001

Cited by 7 publications

(2 citation statements)

References 60 publications

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“…The COVID-19 pandemic has also shaken confidence in stylised facts; for example, the indispensability of proximity in some markets and organisations (Bratton and Wójcik, 2022) just as it has changed the nature of close dialogue. The transaction costs related to arranging interviews have declined.…”

Section: What Happened To Stylised Facts and Close Dialogue?mentioning

confidence: 99%

Stylised facts and close dialogue redux

Clark,

Wójcik

2024

Environment and Planning F

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We reprise Clark’s influential 1998 article on ‘stylized facts and close dialogue’ to reflect on the state of both and their status in economic geography 25 years later. While close dialogue has been embraced by economic geographers, the notion of stylised facts has been neglected. In making the case for more explicit consideration of stylised facts, we highlight their importance in the research process as a catalyst for interaction between theory and empirical evidence and as a facilitator of academic communication across disciplines, methods and the public. The importance of stylised facts is illustrated by focusing on global financial networks and the efficient market hypothesis. In a time of uncertainty, locally and globally, there is increasing demand for close dialogue as well as new stylised facts – new ways of conceptualising the economic geography of the 21st century.

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Section: What Happened To Stylised Facts and Close Dialogue?mentioning

confidence: 99%

Stylised facts and close dialogue redux

Clark,

Wójcik

2024

Environment and Planning F

Self Cite

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“…Recent developments emphasize that spatial analysis, facilitated by the production of maps, is an essential and valid tool in processing, analyzing, and visualizing spatial information in domains such as health and environmental protection, disease ecology, and community health. Spatial analyses facilitate the determination of disease locations, assessment of healthcare facilities and services, and delineation of societal boundaries; therefore, they constitute fundamental elements of epidemiological and health studies (Arvin et al, 2023;Bratton & Wójcik, 2022;Krauss et al, 2023;Yao et al, 2023). The identification of these geographical areas and high-risk groups enables the selection of appropriate healthcare, medical, and social measures to mitigate the impact of these risk factors (Borges et al, 2022;Juneau et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Territorial patterns of COVID-19 in Iran

Aryan Kya

2024

ASPAL

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Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

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Financial centres and information hierarchies: Insights from the geographies of sell-side equity research

Bratton,

Wójcik

2023

Progress in Economic Geography

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Financial information, physical proximity and COVID: The experience of Asian sell-side equity research analysts

Cited by 7 publications

References 60 publications

Stylised facts and close dialogue redux

Stylised facts and close dialogue redux

Territorial patterns of COVID-19 in Iran

Financial centres and information hierarchies: Insights from the geographies of sell-side equity research

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