Hasil untuk "Geography"

Jose Alberto Baeza Guerra

This paper proposes a novel hypothesis about the foundation of Tenochtitlan by combining digital elevation modeling with historical and symbolic analysis. Using geospatial data from EarthExplorer, we simulate various historical water levels in the Valley of Mexico. The resulting lake configurations reveal possible locations for ancient settlements near now-vanished shorelines, suggesting a dynamic transformation of sacred geography that aligns with key Mexica myths. We identify Santa María Aztahuacan as a strong candidate for the historical Aztlan and propose a reinterpretation of foundational codices in light of geomythical correlations.

David Aaron Evans, Kara J. Sulia, Nick P. Bassill et al.

Long Short-Term Memory (LSTM) models are trained to predict forecast error for the High-Resolution Rapid Refresh (HRRR) model using the New York State Mesonet and Oklahoma State Mesonet near-surface weather observations as ground truth. Physical and dynamical mechanisms tied to LSTM performance are evaluated by comparing the New York domain to the Oklahoma domain. The contrasting geography and atmospheric dynamics of the two domains provide a compelling scientific foil. Evaluating them side by side highlights variations in LSTM prediction of forecast error that are closely linked to region-specific phenomena driven by both dynamics and geography. Using mean-absolute-error and percent improvement relative to HRRR, LSTMs predict precipitation error most accurately, followed by wind error and then temperature error. Precipitation errors exhibit an asymmetry, with overforecast precipitation detected more accurately than underforecast, while wind error predictions are consistent across over- and underforecast predictions. Temperature error predictions are relatively accurate but smoother, with respect to variance, than true observations. This paper describes an overview of LSTM performance with the expressed intent of providing forecasters with real-time predictions of forecast error at the point of use within the New York State and Oklahoma State Mesonets. This research demonstrates the potential of LSTM-based machine learning models to provide actionable, location-specific predictions of forecast error for high-resolution operational numerical weather prediction (NWP) systems.

Gianandrea Lanzara, Matteo Santacesaria

Are there multiple equilibria in the spatial economy? This paper develops a unified framework that integrates systems of cities and regional models to address this question within a general geographic space. A key feature is the endogenous formation of commuting areas linking a continuum of residential locations to a finite set of potential business districts. Using tools from computational geometry and shape optimization, we derive sufficient conditions for the existence and uniqueness of spatial equilibria. For plausible parameter values, urban location is indeterminate, but, conditional on an urban system, city sizes are uniquely determined. The framework reconciles seemingly conflicting empirical findings on the role of geography and scale economies in shaping the spatial economy.

Kunlin Wu, Abicumaran Uthamacumaran, Hector Zenil

Blood reference intervals (RIs) are central to diagnosis and therapeutic monitoring, yet most were derived from Western populations and assumed universal. This risks misclassification in regions with diverse demographic, physiological, or genetic profiles. We examined one of the most used panels, the Complete Blood Count (CBC), by compiling RI data from 28 countries and applying a multi-stage analytical framework. Structural similarity was assessed using multiple clustering strategies combining different linkage rules with Euclidean, correlation, and information-theoretic distances. To benchmark sensitivity, we introduced a Two-Level Cohesion Score quantifying continent-level grouping. UMAP embeddings with feature importance scores identified analytes potentially driving geography-related separation. Using BMI as a cross-country positive control, CBC reference intervals showed no reproducible clustering by geography or population genetics; weak, unstable signals were limited to MCV and HGB, unlike BMI. These findings indicate that CBC physiology is not geographically coordinated but instead reflects laboratory equipment, calibration, or logistical practices. Our results support moving from one-size-fits-all global RIs toward adaptive and personalized reference frameworks that link precision and predictive medicine with diagnostic equity in patient care.

Yoonjung Ahn, Christopher K. Uejio

Abstract This dataset presents the most comprehensive estimate of residential air conditioning (AC) prevalence across the continental United States. Using property-level data for over 103 million housing units from the Dewey database, we imputed and classified four AC types: central, other, evaporative cooler, and none, using XGBoost models optimized for performance. Housing characteristics, socioeconomic indicators, and environmental conditions, such as Cooling Degree Days and elevation, informed predictions. The final product offers national coverage with spatial resolution at the census tract, ZIP code, and metropolitan levels. Model validation was conducted using American Housing Survey data, with strong alignment observed for the central and no air conditioning (AC) categories. This dataset addresses longstanding gaps in understanding the geographic and demographic disparities in AC access, critical for public health, climate adaptation, and energy equity research. Users may integrate these data into epidemiological modeling, resilience planning, and policy analysis to support heat vulnerability assessments and infrastructure interventions.

Zhe Zhao, Yi Zhang, You Xu et al.

ABSTRACT: In this study, we constructed an integrated framework of a farming–breeding–bioenergy system to estimate the greenhouse gas (GHG) emission inventories of various farming and breeding processes in the northeast region of China from 2000 to 2021 based on life cycle assessment. Then, we compared the emission differences between the farming–breeding–bioenergy integrated system and the traditional farming–breeding system in different production segments. Finally, we assessed the environmental impact of the integrated system on agricultural GHG emissions. Results showed that the main sources of GHG emissions in Northeast China include enteric fermentation, fertilizer application, crop energy reduction, crop cultivation, and manure management. Emission hotspots also showed a trend of shifting from south to north and from east to west. In terms of environmental impact intensity, the largest increase in environmental impact intensity values among the farming and breeding systems was recorded in Heilongjiang Province (0.36) and Inner Mongolia (0.13), respectively. In terms of mitigation effects, the farming and breeding systems showed a considerable amount of residual straw and manure that can be fed into bioenergy systems, at 1 801.47 and 394.12 Mt, respectively. The farming–breeding–bioenergy integrated system demonstrated mitigating effects on agricultural GHG emissions.

Eva Rumpler, Marc Lipsitch

We conducted a literature review of COVID-19 vaccine allocation modelling papers, specifically looking for publications that considered equity. We found that most models did not take equity into account, with the vast majority of publications presenting aggregated results and no results by any subgroup (e.g. age, race, geography, etc). We then give examples of how modelling can be useful to answer equity questions, and highlight some of the findings from the publications that did. Lastly, we describe seven considerations that seem important to consider when including equity in future vaccine allocation models.

Amin Mekacher, Max Falkenberg, Andrea Baronchelli

Koo is a microblogging platform based in India launched in 2020 with the explicit aim of catering to non-Western communities in their vernacular languages. With a near-complete dataset totalling over 71M posts and 399M user interactions, we show how Koo has attracted users from several countries including India, Nigeria and Brazil, but with variable levels of sustained user engagement. We highlight how Koo's interaction network has been shaped by multiple country-specific migrations and displays strong divides between linguistic and cultural communities, for instance, with English-speaking communities from India and Nigeria largely isolated from one another. Finally, we analyse the content shared by each linguistic community and identify cultural patterns that promote similar discourses across language groups. Our study raises the prospect that a multilingual and politically diverse platform like Koo may be able to cultivate vernacular communities that have, historically, not been prioritised by US-based social media platforms.

Benaissa Chaimae, Rossi Abdelhamid, Bouhmadi Belkacem et al.

This study aims to investigate the physical, chemical, and bacteriological quality of water derived from both a well and a spring across three distinct periods (2008, 2012, and 2021) in both summer and winter. These sampling points are situated within the urbanized area of Al Hoceima and serve as crucial sources of drinking water for a substantial portion of the city's population due to their proximity to the city center. The water hardness values observed at these natural points ranged from 5.9 to 82 (°F), categorizing the water from these sources as very hard. Furthermore, the Piper diagram revealed chemical facies characterized by chlorinated sodium and calcium magnesium sulfate. The elevated concentrations of sodium and chloride were attributed to the proximity of the Mediterranean Sea shoreline. Analysis of bacteriological parameters in these waters uncovered notable contamination by fecal germs. Principal Component Analysis (PCA) of the water samples identified two primary groups, elucidated by two factors that collectively account for 79.37% of the variance. The first factor (50.11%) is linked to gypsum dissolution and marine intrusion, while the second factor (29.26%) is associated with external contributions such as anthropogenic pollution.

D. Chaffey, S. Pinch

Zilberova Inna, Novoselova Irina, Petrov Konstantin et al.

Housing and public utility services constitute one of the most important sectors of the national economy. Reforming and renewal of the housing and public utility services sector is unthinkable without technological modernization of the utility pipeline networks. At the same time, public utility infrastructure facilities in many cities and towns of Russia can be characterized by significant deterioration. Frequent accidents negatively affect the life support of populated areas, which urges the development of organizational and technological approaches to the reconstruction of municipal infrastructure facilities.

Thomas W. Sanchez

The urban planning process is complex, involving social, economic, environmental, and political systems. Knowledge of how these systems interact is the domain of professional planners. Advances in artificial intelligence (AI) present planners with a ripe opportunity to critically assess their approaches and explore how new data collection, analysis, and methods can augment the understanding of places as they seek to anticipate futures with improved quality of life. AI can offer access to more and better information about travel patterns, energy consumption, land utilization, and environmental impacts, while also helping to better integrate these systems, which is what planners do. The adoption process will likely be gradual and involve significant time and resources. This article highlights several topics and issues that should be considered during this process. It is argued that planners will be well-served by approaching AI tools in a strategic manner that involves the topics discussed here.

K. B. Dickson, G. Benneh

L. H. Throckmorton

Anna Haensch, Natasa Dragovic, Christoph Börgers et al.

We introduce a geospatial bounded confidence model with mega-influencers, inspired by Hegselmann and Krause. The inclusion of geography gives rise to large-scale geospatial patterns evolving out of random initial data; that is, spatial clusters of like-minded agents emerge regardless of initialization. Mega-influencers and stochasticity amplify this effect, and soften local consensus. As an application, we consider national views on Covid-19 vaccines. For a certain set of parameters, our model yields results comparable to real survey results on vaccine hesitancy from late 2020.

Furkan Gürsoy, Bertan Badur

Human migration patterns influence the redistribution of population characteristics over the geography and since such distributions are closely related to social and economic outcomes, investigating the structure and dynamics of internal migration plays a crucial role in understanding and designing policies for such systems. We provide an in-depth investigation into the structure and dynamics of the internal migration in Turkey from 2008 to 2020. We identify a set of classical migration laws and examine them via various methods for signed network analysis, ego network analysis, representation learning, temporal stability analysis, community detection, and network visualization. The findings show that, in line with the classical migration laws, most migration links are geographically bounded with several exceptions involving cities with large economic activity, major migration flows are countered with migration flows in the opposite direction, there are well-defined migration routes, and the migration system is generally stable over the investigated period. Apart from these general results, we also provide unique and specific insights into Turkey. Overall, the novel toolset we employ for the first time in the literature allows the investigation of selected migration laws from a complex networks perspective and sheds light on future migration research on different geographies.

E. Samadpour Shahrak, H. Sattari Sarbangholi, M. S. Moosavi

One of the crucial factors for the presence of more people outdoors is to create comfortable conditions. This issue is significant for the elderly due to the different physical conditions. The purpose of this study is to improve the micro-climatic condition around residential complexes considering the elderly in a linear type. For this purpose, two physical indicators, the ratio of the height of buildings to their distance from each other (H/D) and the orientation of them towards the street, were investigated. Regarding H/D, ratios of 0.5, 1, 1.5, and 2, and about the orientation factor, angles of 135° to 200° were examined. This study was conducted outdoors around residential complexes in Iran, Tabriz, with a cold semi-arid climate. Envi-met software model 4.4.5 was used for the simulation. The days June 22 and December 22, 2020 were selected as one of the hottest and coldest day of the year. Two indexes of the Predicted Mean Vote (PMV) and the Universal Thermal Climate Index (UTCI) were examined as essential thermal comfort indexes. Also, for validation, local and field data in six days (21, 22, 23 June in summer and 21, 22, 23 December in winter) were extracted and compared with the data of the software. The results display, the ratio of H/D=1.5 and the angles of 135° and 145° were the most suitable comfort conditions.

C. Willmott

Colin Flint, Peter J. Taylor

A. Ingerson, Michael Williams