Geographical Analysis Research Articles

The granular landscape of health disparities: a socio-spatial examination of chronic disease prevalence within New York City's community districts

This study investigates the relationship between neighborhood Socioeconomic Position (SEP) and the prevalence of hypertension and coronary heart disease (CHD) across New York City's community districts. By using a composite SEP index derived from multiple socioeconomic indicators, the research seeks to uncover health disparities that may be obscured in broader geographic analyses. A primary objective is to identify community districts where hypertension and CHD prevalence is significantly higher than expected, even after adjusting for SEP. This focus helps pinpoint areas where targeted public health interventions may be urgently required, especially when the anticipated inverse relationship between SEP and health outcomes does not hold. A linear mixed-effects model (LMM) was used to analyze the association between SEP and these health outcomes, incorporating both fixed and random effects to account for variation across the city's 59 community districts. The analysis revealed a significant negative association between SEP and both hypertension and CHD, with higher SEP levels generally correlating with lower prevalence rates. However, some districts exhibited high random effects, indicating a higher-than-expected prevalence of these conditions, even after controlling for SEP. The authors suspect that additional factors-such as environmental exposures, healthcare accessibility, or community infrastructure-may contribute to these unexplained disparities. The findings emphasize the need for public health strategies that are sensitive to local contexts and the multifactorial nature of health inequities. Understanding the complex drivers behind these disparities can inform policies aimed at reducing health inequities in diverse urban environments.

The charcoal footprint of greater Accra on the Afram Plains: Urban energy consumption and forest degradation in Ghana

Rapid urbanisation in sub-Saharan Africa is leading to increased energy consumption and a growing reliance on charcoal as a fuel source, with significant environmental implications. This study examines the impact of charcoal consumption in Greater Accra, Ghana, on the Afram Plains ecosystem. Employing a mixed-methods approach, including geographical analysis, quantitative data collection, and sustainability assessment, we evaluate the charcoal footprint of Greater Accra. Our findings reveal that charcoal remains the dominant cooking fuel in Greater Accra despite increased access to electricity, resulting in an estimated loss of 354,479 trees annually in the Afram Plains. This demand creates an ecological deficit of 18,850 hectares/yr, significantly exceeding the ecosystem's regenerative capacity. We observe complex dynamics between urban energy needs and rural resource sustainability, highlighting socio-economic factors driving persistent charcoal use. The study proposes a conceptual framework integrating urban demand, rural supply, ecological impact, and natural regeneration. Our research contributes to urban sustainability literature, offering insights for policymakers to develop more sustainable energy strategies, including quota systems for harvesting, promoting alternative cooking fuels, community-based forest management, and improved charcoal production technologies. This case study provides valuable lessons for other rapidly urbanizing regions facing similar sustainability challenges in sub-Saharan Africa.

Utilizing HPLC-DAD, multivariate analysis, molecular docking, and in vitro validation methods to investigate phenolic compounds with potential antidiabetic properties in Fraxinus mandshurica

This comprehensive study utilized response surface methodology (RSM) to optimize extraction conditions for total flavonoids and phenols from F. mandshurica. Optimal conditions for flavonoids were 75 % ethanol, 63 °C, and 84 min, yielding 14.98 ± 0.13 mg/g. For phenols, it was 70 % ethanol, 66 °C, and 87 min, resulting in 26.04 ± 0.22 mg/g. UPLC-Q-TOF-MS/MS identified 68 constituents in F. mandshurica, categorized into groups. HPLC-DAD established a method for quantitative analysis of 12 target compounds. Geographical distribution analysis highlighted the northeast region as the main production area. Fingerprinting of 30 samples and multivariate analysis identified five active compounds. Hierarchical cluster analysis (HCA) showed differences in active compounds based on origin. In vitro antidiabetic evaluation demonstrated α-glucosidase inhibitory effects. Molecular docking studies were conducted between 12 active compounds and the antidiabetic-related protein 8A27. Esculetin had the highest binding affinity. The study examined extraction methods, analyzed samples, and explored antidiabetic effects, providing data support and insights for F. mandshurica as a natural industrial product and for extracts and active compounds as antidiabetic sources.

The Development of a Geographic Information System for Mapping Creative Economy Actors in Balikpapan Using the Prototype Method

Geographic Information Systems (GIS) play a crucial role in collecting, managing, analyzing, and presenting geographically referenced data. This research focuses on the development of a GIS-based application designed to map and analyze the distribution of creative economy participants in Balikpapan. Using the System Development Life Cycle (SDLC) Prototype method, the study integrates spatial data with geographic analysis, utilizing technologies such as Node.js, Leaflet, Laravel, and QGIS in the development process. Data related to creative economy actors is gathered locally, while spatial data is sourced from the Ina Geoportal website. This web-based application provides comprehensive mapping capabilities, enabling users to identify strategic business locations, optimize business potential, and improve service accessibility. Additionally, the tool aims to offer insights for local government policymaking, contributing to regional economic growth and enhancing community welfare. The integration of GIS into the application also supports the preservation and promotion of regional cultural identity, offering valuable information for sustainable development. Ultimately, this research highlights the significant role of GIS in improving economic planning, fostering informed decision-making, and driving sustainable development in Balikpapan.

Feasibility of upcycling red mud for low-CO2 cement via calcination: A comparative study with FA and GGBFS based in China

This study examines the feasibility of developing low-CO2 cement using calcined red mud (RM) and explores positive scenarios that simultaneously attain technical, environmental, and economic benefits in reference to conventional fly ash (FA) and ground granulated blast furnace slag (GGBFS) cements. Based on the variables of RM's content and calcination temperature, we investigated the fresh properties (paste rheology and mortar flowability), strength development, microstructure, embodied CO2 emissions, and cost of the RM-blended cement. The results show that there was an optimal range of RM's calcination temperature (400–600 °C) that improved the cement paste viscosity while attaining a higher strength than that of FA cement. Considering the cradle-to-gate CO2 emissions and cost of cement production, the optimal cement composition is comprised of up to 30% RM calcined at 600 °C, where the cement cost is lowered by 19.3% compared to the FA reference having a comparable CO2 footprint. According to the geographical analysis, the calcined RM shows minimal advantages over GGBFS but could substitute FA as a cleaner, cheaper, and more reactive supplementary cementitious material in regions where electricity grid is dominated by clean energy. Therefore, RM offers a plausible solution to future low-CO2 cement in light of the increasing shortage of FA led by the declining use of coal-powered electricity.

Environmental Fragility in Commercial Forest Plantations Aiming at Sustainable Management

Objective: The objective of this study is to investigate the environmental fragility of two landscapes in which there is a predominance of commercial forest plantations, with the aim of analyzing this fragility through multicriterial evaluation, focusing on the planning of forest roads. Theoretical Framework: The theory of environmental fragility, multi-criteria evaluation methods, and principles of landscape management stand out, providing a solid basis for understanding the context of the investigation. These frameworks are crucial for analyzing the impact of forest roads on environmental variables such as water dynamics, sediment production, and soil erodibility. The integration of these theories allows for a comprehensive assessment of environmental fragility, essential for effective forest planning and road maintenance in forested areas Method: The methodology adopted involves a multi-criteria evaluation approach to assess environmental fragility in forest landscapes. Key criteria, including slope, distance from water bodies, soil erodibility, and topographic humidity, were integrated to create fragility maps. Data were collected through geographical and environmental analysis, processed with spatial analysis tools, and supplemented by principal component analysis to identify influential criteria. This approach enabled a detailed assessment of forest roads' environmental impact, supporting informed decisions in road planning and landscape management. Results and Discussion: The results revealed notable variations in environmental fragility. The analysis of forest roads highlighted their varying impact on environmental fragility. These results are framed within the context of environmental fragility and multi-criteria evaluation, emphasizing the role of criteria such as slope, proximity to water bodies, soil erodibility, and topographic humidity in assessing landscape degradation. The study underscores the need for careful forest road planning to mitigate environmental impacts and promotes the use of comprehensive evaluation methods in sustainable landscape management. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of forest management and environmental conservation. These implications could encompass forest road planning, sustainable landscape management, and the development of environmental policies aimed at mitigating the impact of infrastructure on natural ecosystems.The study's findings emphasize the need for incorporating multi-criteria evaluation methods into forest planning processes to identify and mitigate areas of high environmental fragility. This approach can guide decision-makers in prioritizing areas for road maintenance, rehabilitation, or even rerouting to minimize ecological damage. Furthermore, the research underscores the importance of integrating environmental fragility assessments into broader forest management strategies, enhancing the sustainability of commercial forestry operations while preserving essential ecosystem services. Originality/Value: This study contributes to the literature by introducing an innovative application of multi-criteria evaluation in assessing environmental fragility within forest landscapes, specifically in the context of forest road planning. The research combines various environmental criteria into a comprehensive fragility map, offering a nuanced understanding of landscape vulnerability that is less commonly addressed in existing studies. Notably, this methodology focused on forest roads has not been previously explored in the literature, highlighting the unique contribution of this research to the field.The relevance and value of this research are evidenced by its potential to influence forest management practices, particularly in the sustainable planning and maintenance of forest roads.

Vector Borne Disease Ecology and Environment: Remote Sensing and GIS for Control and Management: A Systematic Review

The prevalence of vector borne disease epidemics are influenced by the thickness of the vectors persistent to climate, landscape, and environment. Historically evidenced of triggering epidemics in both vertical magnitude and horizontal spread (spatial diffusion) of increasing vector borne diseases, such as; malaria, dengue, chikungunya, Japanese encephalitis, filariasis, leishmaniases, scrub typhus, Kyasanur forest disease (KFD), West Nile virus, Lyme disease, Zika virus, and Rift valley fever in the tropical and subtropical regions, particularly, in the third world countries for the past several decades. The geographical analysis of each one VBD epidemics has a unique vector ecology and environment. The prevalence of epidemic distribution patterns and seasonal variations, and associated vector ecology were analyzed throughout the world for the past periods, in order to assess the probability of potential risk and delineate the hotspot regions susceptible to the VBD outbreaks in the country, using Remote Sensing and GIS mapping analysis. It offers a quick and efficient approach to mapping of land uses/land cover patterns and its changes over space and time, and the information on these changes linking with vector profusion and disease ecology. Vector-borne disease epidemics across the country have been evidently associated with climate factors, landscape changes, potential breeding sources; such as, water bodies, rivers, streams, and mega water resource irrigation projects, dams / reservoirs, water pools, lakes, urban agglomerations, Urban sprawl, urban dynamics, industrial developments and growths, floating population, urban migration, land use / land cover changes, wet irrigation agriculture practices, seasonal migrations on occupation, cargo shipping movements, trade and commerce, etc., and have been acted upon changing the aboriginal territories by extent of epidemic scenarios into the non-endemic regions. The integrated Earth Observation Resource Satellite data integrated with GIS has effectiveness upon proved to disease mapping, ecological niche modeling, risk assessment, spatial prediction, and offering answers to both the public health programmers and research scientists concerned on vector fecundity, and vector borne disease ecology, and thus, the geographical, ecological and environmental determinants of its range is considered to be essential for sustainable public health planning. Accordingly, prevention measures and appropriate control strategy may possibly be applied during the sporadic outbreaks situations in the new epidemic regions as well in the hot spot endemic regions regularly, so as to control the erratic transmission and prevent acceleration of epidemics early in advance successfully.

The Most Influential Publications Regarding Hair Transplantation: A Bibliometric Review.

This bibliometric review aims to assess the impact of significant publications within the field of hair transplantation. Citation counts will serve as a primary influence indicator. An exhaustive search was conducted using Clarivate's Web of Science database, yielding 260 publications related to hair transplantation. These were evaluated and sorted based on citations, narrowing down to the 50 most highly cited works for analysis. Parameters including citation density, authorship, institutional affiliations, country of origin, year of publication, article topic, and the level of evidence for each publication were obtained. Analyzed publications were cited a total of 1341 times. Authorship analysis revealed that the most significant contributors regarding hair transplantation were Bernstein and Rassman. We also identified the leading institutions affiliated with these works, highlighting the primary academic and research centers driving the field. Geographical analysis exhibited the US' dominance in producing impactful publications. Most publications were also classified within Level IV and Level V according to the Oxford Levels of Evidence system. This review provides a comprehensive snapshot of the pivotal publications shaping hair transplantation. Our findings underscore significant contributions within this field and may assist researchers and clinicians in understanding the evolution and the current state of the hair transplantation literature. This bibliometric analysis can serve as a roadmap for those seeking to delve into this rapidly evolving field, facilitating the identification of research gaps and formulating future research directions. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Geographic matching analysis between green finance development and carbon emissions in China’s new era of environmental transition

Green finance plays a pivotal role in driving sustainable development. While its effectiveness in promoting green development is well-established, the impact of regional heterogeneity on its outcomes warrants further investigation. By introducing the Spatial Durbin Model (SDM) model, we analyze the causal relationship between provincial carbon emissions and green finance development in China, accounting for spatial spillovers across provinces. A geographic mismatch between regions of green finance development and carbon emission concentration is revealed. Central and Western areas exhibit higher carbon emissions, indicating a greater need for targeted green finance initiatives to promote sustainable development. However, green finance growth in these areas remains sluggish. Moreover, carbon emissions hinder local green finance development and negatively impact neighboring regions. We present new insights into the influence of carbon emissions on green finance, highlighting the importance of inter-regional collaboration and the potential applicability to other countries facing similar challenges.

Wastewater-based evaluation of the efficacy of oxycodone regulations in Australia.

Between 2018 and 2020, Australia implemented major policy changes to improve the quality and safety of opioid prescribing, with a specific focus on oxycodone. This study used wastewater-based epidemiology to assess the efficacy of Australia's regulatory reforms by measuring change in consumption of oxycodone via exploratory analysis. Wastewater analysis data on oxycodone consumption was from the National Wastewater Drug Monitoring Program. The program captures data from more than 50 wastewater treatment plant catchments across Australia, equivalent to more than 50% of the national population. Geographic trend analyses were conducted for both major cities and regional areas within all states and territories of Australia over a 6-year period between 2017 and 2023. Oxycodone consumption showed a statistically significant increase nationally from 78 mg/day/1000 people (95% confidence interval [CI] = 71, 84) in 2017 to 120 mg/day/1000 people in August 2019 (95% CI = 110, 120), an increase of 52% (95% CI = 42, 62, P < 0.0001). From August 2019 to December 2020, there was a statistically significant decrease from 120 to 65 mg/day/1000 people (95% CI = 60, 71), a decrease of 45% (95% CI = 40, 51), followed by a modest 2.4% increase to the end of the study period in April 2023 (95% CI [2.0,2.7]). A 45% reduction in oxycodone consumption in Australia from 2019 to 2020 coincided with national policy changes that aimed to reduce consumption of prescription opioids. The overall declining trend in consumption was suggestive of the effectiveness of national interventions in reducing pharmaceutical opioid use. Wastewater-based epidemiology provides an effective approach for assessing the effectiveness of controlled substances policy changes.

Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020

In the context of global warming, comprehending the dynamics of terrestrial water storage (TWS) and its responses to natural and anthropogenic factors is paramount for hydrological research and the management of water resources in China. This study utilized GRACE (Gravity Recovery and Climate Experiment)/GRACE-Follow On (GRACE-FO) satellite data to analyze terrestrial water storage across nine basins in China from 2005 to 2020 at multiple temporal and spatial scales. Subsequently, employing a Geographic detector model, potential influencing factors were identified, and an enhanced Geographically Weighted Regression (GWR) method was proposed for attributing changes in TWS in China. The findings reveal a consistent declining trend in TWS based on GRACE/GRACE-FO data across different temporal scales, with the most pronounced decreases observed in August and September. Geographic Detector analysis unveils significant interactions among various environmental factors, with climate variables playing a pivotal role in modulating hydrological characteristics of major river basins, where rising temperatures can exacerbate the severity of precipitation events, thus increasing the risk of floods and droughts. Moreover, analysis of the primary influencing factors indicates significant impacts of population density and topography on water resources in the southeastern and southwestern regions, particularly amidst increasing human activities and urbanization expansion. The results of this study are crucial for comprehending the dynamic changes and mechanisms of TWS in China, as well as for formulating water resource management strategies.

How Many Autistic Children are there in Colombia? A Nationwide Examination of Autism Through Health System Data.

Accurate healthcare data is indispensable for monitoring the epidemiology of autism spectrum disorders (ASD) and improving the quality of care for individuals on the spectrum. In Colombia, the Ministry of Health has developed the social protection information system (SISPRO) as a comprehensive registry, drawing data from the healthcare system with close to universal coverage (approximately 95%). This study utilizes data gathered by SISPRO to estimate the prevalence and specific characteristics of autistic children registered between January 2020 and December 2022. A descriptive epidemiological approach was employed, using the International Statistical Classification of Diseases as search terms for ASD within the SISPRO dataset. The study revealed a prevalence of 13.788 cases per 10,000 children in 2022 among aged 4 to 14. Regarding healthcare coverage types in 2022, the majority of autistic children served were under the contributory regime (68.28%), followed by the subsidized regime (25.36%). Geographic analysis indicated a non-uniform distribution of ASD prevalence in Colombia. The regions with the highest GDP, such as Antioquia, Atlántico, Bogotá, Cundinamarca, and Valle del Cauca, exhibited the highest prevalence (M = 17.90; SD = 14.3). In contrast, areas with the lowest GDP, including Amazonas, Guainía, Vaupés, Vichada, and Guaviare, showed the lowest prevalence among children (M = 2.6; SD = 2.5). The estimation of ASD prevalence in Colombia represents an ongoing initiative to inform public policy actions. During the COVID-19 pandemic, there was a decrease in the number of autistic children served by the healthcare sector; however, the prevalence of ASD changed to higher levels in 2022. These findings contribute to strategies aimed at improving the quality of life for autistic individuals and mitigating the economic burden on their families.

The accessibility of public electric vehicle (EV) charging infrastructure: Evidence from the cities of Nottingham and Frankfurt

AbstractThe distribution of public electric vehicle (EV) charging infrastructure is a widespread approach for promoting EV adoption and decarbonising transportation. A significant amount of literature explores the distribution of EV charging points at a country scale, but there is a lack of studies focusing on a district scale. This study aims to contribute to this gap by gaining insights into the distribution of EV charging points per district within cities, such as Nottingham and Frankfurt. The study investigates the current distribution of EV charging points across 38 postcode districts in Frankfurt and 9 postcode districts in Nottingham, using geographical data analysis and a linear regression approach. The following factors in response to the number of EV charging points per postcode district (ZIP code) are examined: the percentage of apartment buildings/floor area ratio, the availability of amenities, population, charging capacity (kW), area size, strategic approaches, including policy goals and principles. The results reveal disparities in access to EV charging infrastructure across districts and underscore the importance of expanding EV charging networks not only in districts located near urban centres or those with high availability of amenities but also ensuring that users without home charging options are not left behind.

Investigation of Fasciola gigantica in freshwater snail Radix (Lymnaea) spp. In the highly parasite-prevalent area of Nakhon Ratchasima Province, Thailand

ABSTRACT This study investigates the distribution of the Lymnaea (Radix) spp. in Pak Chong district, Nakhon Ratchasima province, northeast Thailand, where a vast cattle farming area is located and has a high prevalence of Fasciola spp. in the cattle. By random selection, 1,414 snails were collected from the natural and man-made ponds. The snails were recorded for morphology and processed for DNA isolation. The snail species were investigated by conventional PCR using a 16S rDNA-specific primer. The result demonstrated that all collected snails were R. (L.) rubiginosa. Moreover, the infection of Fasciola gigantica in the snails was investigated by PCR using a cytochrome c oxidase I (COX1)-specific primer. The results illustrated that the overall prevalence was 22.5% (318/1414), with the highest prevalence in the Nong Sa Rai subdistrict at 73.6% (81/110), which is the highest prevalence of Fasciola gigantica in the snail host that has ever been reported. The lowest prevalence existed in the Pong Ta Long subdistrict at 3.7% (4/109). Our results corresponded to the previous report on the Fasciola spp. infection in the cattle from this area, and the geographical analysis revealed that the most suspected factor would be the earth dam located in these subdistricts, where many animals live freely during the day. Our findings could be helpful for further parasite control and could trigger the study of the biology and associated factors in the future.

A GPT-enhanced framework on knowledge extraction and reuse for geographic analysis models in Google Earth Engine

ABSTRACT Geographic analysis models are becoming increasingly important in the era of geospatial big data. As a cloud platform that combines massive geospatial data resources with powerful computational capabilities, the Google Earth Engine (GEE) gathers a large number of workflow scripts for different geographic analysis tasks and gradually evolves into a comprehensive knowledge repository. However, due to the complexity nature of GEE workflow scripts, extracting modeling knowledge from these GEE workflow scripts remains a challenge. Besides, these GEE workflow scripts are often tightly coupled with the GEE environment, which limits their reuse in the broader geospatial community. To address these issues, this paper proposes a GEE knowledge extraction and reuse framework, which uncovers factual meta-information and modeling processes embedded within the GEE workflow script by leveraging ChatGPT and the abstract syntax tree (AST). A GEE knowledge encapsulation template is developed to systematically describe this modeling knowledge. Through case studies, we demonstrate that this framework can be integrated with existing geospatial service technologies, simplify the extraction of modeling knowledge from GEE workflow scripts, and enhance their application in different modeling environments. Moreover, by incorporating ChatGPT, this framework exhibits the potential for intelligent generation of geographic analysis models.

The selection of hospital sites and adaptive medical resource planning in the post-COVID era: navigating public health crises in China?

Urban management and planning during public health emergencies, such as the COVID-19 pandemic, are critical. This article examines hospital site selection in Yinzhou, Ningbo, China, using geographical data analysis. It evaluates travel costs and referral patterns between hospitals and community health facilities to establish a sustainable hierarchical diagnosis and treatment system. The results reveal that existing medical facilities, particularly in rural areas, are inadequate during public health emergencies. The article proposes considering total transportation costs in allocating the sites of new hospitals and suggests adopting a ‘permanent + temporary’ facility planning approach to ease financial burdens. Addressing these issues can mitigate the inequitable distribution of urban and rural medical resources. This article was published open access under a CC BY licence: https://creativecommons.org/licenses/by/4.0/ .

Longitudinal and Geographic Trends in Perceived Racial Discrimination Among Adolescents in the U.S.: The Adolescent Brain Cognitive Development (ABCD) Study.

This study examines longitudinal and geographic trends in perceived racial discrimination among U.S. adolescents using data from the Adolescent Brain Cognitive Development (ABCD) Study. A diverse sample of 11,868 children aged 9-10 at baseline from 22 sites across the U.S. was analyzed, assessing perceived discrimination at ages 10-11, 11-12, and 13-14 using items adapted from the Perceived Discrimination Scale. Binomial logistic regression models were used to evaluate longitudinal trends and geographic variation, adjusting for demographic factors such as race/ethnicity, parental education, and income. Results show that perceived racial discrimination increased significantly from ages 10-11 to 13-14, particularly among Black and Asian adolescents. By age 13-14, nearly half of Black adolescents and over a quarter of Asian adolescents reported discrimination. Geographic analysis revealed that Black adolescents in the Western U.S. and predominantly White affluent neighborhoods had the highest odds of perceived discrimination. Higher state-level anti-Black bias was associated with lower discrimination rates among Black adolescents but higher rates for Asian adolescents. These findings highlight the evolving nature of racial discrimination during adolescence and underscore the need for targeted interventions that address racism's mental health impacts on adolescents, particularly in high-risk geographic and socio-economic contexts.

Spatiotemporal Evolution and Influencing Factors of Heat Island Intensity in the Yangtze River Delta Urban Agglomeration Based on GEE

Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration.

THE ROLE OF BIG DATA ANALYSIS IN FASHION DESIGN

Today, as the contemporary, economic and cultural landscape becomes increasingly volatile, the dynamics of the fashion industry are becoming less predictable. The fast-changing fashion industry is constantly generating data. Big data is a powerful tool that offers new and exciting opportunities for fashion designers. It is used for different purposes such as understanding consumer behaviour, predicting trends and creating more personalized products. Design processes that were previously driven by intuition and instinct have become more fluid, data and goal-oriented with the opportunities offered by big data. This article aims to reveal the purposes of using big data by investigating fashion design models based on big data in the fashion industry in recent years. For this purpose, the current models of big data in fashion design, its limitations and development direction are discussed. While many of the areas of use of big data in the fashion industry (trend analysis, demographic, geographical analysis, age and gender, etc.) have been intensively researched, the number of purely design-oriented research is only recently increasing. This study is unique in that it focuses on the design phenomenon and applications of big data in fashion. In this context, there is no academic study in the national literature. It is expected to contribute to the discussion on the effects of big data and big data-based digital technologies in fashion design.

Geographical Distribution of Osteopathic Neurosurgery Residents.

The neurosurgery residency match has grown increasingly competitive, especially for osteopathic (DO) medical students, amidst the transition to a single accreditation system in 2020. This shift required former American Osteopathic Association (AOA) programs to apply for Accreditation Council for Graduate Medical Education (ACGME) accreditation, leading to a notable reduction in programs with a history of accepting DO applicants. This study aims to explore both potential geographical trends in residency match among recent DO neurosurgical residents and in the number of DO neurosurgical residents pre- and post-ACGME merger. Neurosurgery residency programs during the 2023-2024 academic year were identified, and each program's residents, resident degrees, and resident post-graduate years were collected from residency programs' websites. Descriptive statistics were used to analyze the ratios of DO and allopathic (MD) residents, while regression analyses were used to determine the trends in DO residents between 2017 and 2024. DO residents were also collated by state to observe their geographical distribution. A comprehensive cross-sectional analysis of 115 neurosurgery residency programs across the United States from 2016 to 2024 reveals a significant decrease in DO residents, from 14 in 2016 to four in 2024, with an average of six DO residents per year post-merger. A geographical heatmap analysis pinpointed New Jersey, Michigan, and California as states with the highest proportions and numbers of DO neurosurgery residents. These findings show the geographical distribution of DO neurosurgery residents in the US. Recognizing and understanding these geographical trends could be essential in the strategic application planning for DO candidates and the need for residency programs to reassess selection criteria to be more inclusive of DO applicants.