Geographies

Web Geographic Information Systems (WebGISs) were widely used to monitor COVID-19 cases and deaths during the pandemic. Furthermore, geotechnologies were also very useful in education, public management, tourism, and other areas. Although there are WebGISs with a high level of sophistication, most are simple, consisting of geovisualizers of cases, deaths, and vaccinations. This study develops a WebGIS that offers information about age, comorbidities, and tests, which can be analyzed from specific points such as hospitals, main access roads, regions, or neighborhoods. Although it is not a highly sophisticated solution, the WebGIS developed in this study is especially useful for municipal governments in developing countries like Brazil that do not have patient health data in geographic databases. The WebGIS developed in this study offers public managers essential information for developing effective public policies to combat the COVID-19 pandemic and other epidemiological phenomena such as dengue and malaria. Full article

The Moesian Platform represents a major tectonic unit of the foreland of the Carpathians and Balkans, spanning across the southern part of Romania and the northern part of Bulgaria. Although the Moesian Platform is considered to be a stable tectonic unit, it has played a significant role in the geological history of the region, influencing the development of the surrounding Carpathian and Balkan mountain ranges, making it an area of interest for studying tectonic history, geological structures, and landscape evolution. In the southern part of the Moesian Platform in Romania, delineated to the north and to the east by the steep slopes of the Argeş River valley and to the south by the steep slopes of the Danube River valley, an elevated and W–E promontory-looking geomorphological feature identified by the local inhabitants as “hill” is distinct from the neighbouring flat relief of the Romanian plain. This study is the result of a comprehensive investigation into the geomorphological features and neotectonic structures within this region. An intriguing outcrop displaying a filled fault, cutting and displacing the Quaternary sedimentary formations of the recently named Argeş Promontory, shed light on recent tectonic activities that have influenced the landscape. By integrating field observations, geological, and tectonic data, as well as satellite geodetic data, our results contribute to a better understanding of the study area’s regional geodynamics, emphasizing the significant role of tectonic activity in shaping the present-day landscape. Full article

Township tourism has become more varied, offering a wider range of products, experiences and services. In this paper, we examine residents and stakeholders’ opinions on township tourism in Langa, Cape Town, South Africa; an area characterised by crime, unemployment, housing backlogs and poverty. Using a qualitative approach, this paper reports on empirical evidence conducted with key tourism stakeholders to understand some of their perceptions regarding township tourism development in Langa. Concerns about safety, poor infrastructure and a lack of interaction between tourists and the local community are raised by Langa residents and community leaders. Their dissatisfaction with tour guides and tour routes serves as a reminder of the need for more inclusive practices. While business owners are aware of the potential of tourism in promoting cross-cultural dialogue, deepening understanding and creating priceless experiences, they are constrained by perceptions of crime, a lack of tourist exposure to local establishments and the exclusion from decision-making processes. Despite these difficulties that township tourism faces, it is crucial to promote ethical tourism practices that put emphasis on genuine encounters and local community empowerment. Full article

The detection and monitoring of changes in land use and land cover play a crucial role in understanding land degradation and are fundamental to preserving agroecosystems. Their association with hydrological information allows essential responses to changes in hydrological patterns to be identified, contributing to water security in watersheds. Therefore, this study aimed to assess spatio-temporal dynamics and physico-hydrological trends in rainfall, runoff and land use in the Paraíba watershed. The study was conducted in the Paraíba watershed, using land use data and information from pluviometric and fluviometric stations with temporal series of more than 30 years. The Mann-Kendall statistical test was adopted to verify trends. Results indicate annual reduction trends for both native forest area and water bodies in the Paraíba watershed. On the other hand, the area designated for agriculture showed a significant increase. The correlation analysis between water bodies and forests (R² = 0.63) highlights a strong association between the decrease in forest area and the reduction in water availability, influencing the decrease in annual flow. These results serve as a warning to expand water resource management for the region, aiming to preserve and to enhance sustainable use. Therefore, the implementation of conservation measures, monitoring procedures, and adequate management is required to face the challenges imposed by climate change and land use and occupation, ensuring the water availability for the future. Full article

This paper aims to outline a framework for reviewing the issues faced by tourism destination planning in the 21st century. This paper documents the use of tourism destination typologies as a framework for policy analysis and as a basis for decision making. The main research hypothesis of this study is that typologies based on, or primarily focused on, geographical dimensions have historically been the appropriate framework for strategic planning. This study proposes the use of a basic geographical typology, according to which destinations are categorized into urban, island, coastal, and mountainous. This paper refers to the evolution, key features, and challenges faced by each type of destination. Through a review of international best practices, this study maps out the fundamental objectives, developmental patterns, and strategies for each geographical type of destination, offering valuable insights for future research. Emphasis is given to contemporary trends in tourism planning in the first few decades of the 21st century. Full article

Dengue is a serious infectious disease worldwide and a climate-sensitive disease. Thus, our goals were to (i) evaluate the relationship between dengue incidence and meteorological variables (rainfall and air temperature); (ii) identify the spatiotemporal pattern of dengue incidence in the municipalities of Mato Grosso from 2001 to 2020; and (iii) verify the spatial dependence of dengue incidence in the dry and wet seasons. We used dengue data from 2001 to 2020, monthly rainfall estimates from GPM, and daily air temperature estimates from ERA-5. The municipalities of the Mato Grosso state are included in 16 healthcare territories. The seasonal rainfall pattern indicates that the peak of the dengue endemic occurred in the wet season. However, drier and/or warmer places had a lower incidence of dengue in the dry season. Furthermore, a lagged effect of meteorological variables on dengue incidence has been identified, ranging from 0 to 7 months. Hotspot areas were identified which might have the potential for an intense spreading of dengue in Mato Grosso. They were mainly concentrated in the healthcare territory of Teles Pires (ID 14) in the dry season, while they were concentrated in the healthcare territories of Garças Araguaia (ID 5), Oeste (ID 11), and Teles Pires (ID 14) in the wet season. In addition, they are located in the Am climate and in the Amazon Forest and Brazilian savanna biomes, which have higher dengue incidence values. These results help to highlight which municipalities decision-makers must intervene in the public health system to prevent and control future epidemics. Full article

During the COVID-19 pandemic, regions were affected by a combination of economic crises: weak demand and constrained supply. Several studies have sought to analyse the heterogeneous effects of supply and demand shocks on the labour market, economic growth, and the environment. This study has a different focus, estimating both direct and indirect effects of demand and supply shocks adopted during the pandemic in Brazil and Chile. Afterwards, the paper compares the degree of regional absorption (leakage) of income resulting from each of these shocks, applying an interregional input–output model for each country. The results of this study show that income absorption by the poorest regions is relatively greater in the case of a supply shock. It can be said, therefore, that this type of shock improves the retention of income generated in the poorest regions, favouring the development of these localities and the reduction in regional inequalities. The main reason for this result is that supply policies have restricted essential sectors to a lesser extent, and these sectors are generally less concentrated in large urban centres in both Brazil and Chile. In other words, much of the interregional leakage is driven by the demand for non-essential products, mainly in the richest urban economy centres. Finally, the geographical dimension of regional inequalities leads to the economic benefit of prosperous areas in the country when shocks occur in vulnerable regions, highlighting the centre–periphery pattern in both countries. Full article

More than half of the global population lives in urban areas, which can cause the phenomenon known as Urban Heat Island (UHI). UHI is a phenomenon where urban areas experience higher temperatures compared to their rural surroundings. The occurrence of UHI in large cities is primarily due to urbanization and increased vehicular emissions. Factors such as wind speed and direction, solar flux, and the thermodynamic properties of surface materials determine the intensity of UHI. It can cause thermal air circulation, leading to high concentrations of urban air pollutants such as fine particulate matter (PM2.5). These pollutants can remain suspended in the air and cause asthma and allergies. It is essential to understand the characteristics of UHI intensity and its effect on air quality. This study aims to analyze the spatiotemporal variations of UHI and their correlation with PM2.5 concentration in three Alabama cities, namely Birmingham, Montgomery, and Mobile, during the summer seasons of 2002, 2012, and 2022. The study also compares UHI in these cities with nearby rural areas to determine the effect of urbanization by calculating the Normalized Difference Building Index (NDBI). To achieve these objectives, the Land Surface Temperature (LST), UHI intensity, and NDBI Datasets were analyzed. The results showed that PM2.5 concentrations in the cities have been decreasing annually since 2002, leading to an improvement in air quality. There was a negative linear correlation between UHI intensity and PM2.5 concentration. However, LST remained consistently high throughout the study period. The correlation between UHI intensity and NDBI was positive. The findings of this study can help us better understand the dynamics and driving mechanisms of the urban heat environment. Furthermore, they can assist urban metropolitan planners in developing more efficient mitigation strategies that reduce the negative impacts of UHI and PM2.5 concentrations on the environment. Full article

This study conducts a bibliometric analysis of 735 research papers on geomorphological mapping published in English between 2000 and 2021 using the Web of Science database. The analysis focuses on key metrics such as annual publication rates, journal distribution, common keywords, and frequently cited papers. The results demonstrate sustained investment in geomorphological mapping research over the past two decades, driven by advancements in data analysis, GIS technologies, and cross-institutional and cross-country collaboration. While European universities and research centers lead the field, researchers from Latin America and Asia are also making noteworthy contributions. However, research concentration remains largely in Europe, particularly at low altitudes. The study highlights the vital importance of investment in geomorphological mapping research and the benefits of collaboration to advance understanding and knowledge production. It also emphasizes the need for greater geographic and cultural diversity among researchers to ensure a more comprehensive and inclusive approach to research in this field. Full article

Social media platforms are valuable data sources in the study of public reactions to events such as natural disasters and epidemics. This research assesses for selected countries around the globe the time lag between daily reports of COVID-19 cases and GDELT (Global Database of Events, Language, and Tone) and Twitter (X) COVID-19 mentions between February 2020 and April 2021 using time series analysis. Results show that GDELT articles and tweets preceded COVID-19 infections in Australia, Brazil, France, Greece, India, Italy, the U.S., Canada, Germany, and the U.K., while for Poland and the Philippines, tweets preceded and GDELT articles lagged behind COVID-19 disease incidences, respectively. This shows that the application of social media and news data for surveillance and management of pandemics needs to be assessed on a case-by-case basis for different countries. It also points towards the applicability of time series data analysis for only a limited number of countries due to strict data requirements (e.g., stationarity). A deviation from generally observed lag patterns in a country, i.e., periods with low COVID-19 infections but unusually high numbers of COVID-19-related GDELT articles or tweets, signals an anomaly. We use the seasonal hybrid extreme Studentized deviate test to detect such anomalies. This is followed by text analysis of news headlines from NewsBank and Google on the date of these anomalies to determine the probable event causing an anomaly, which includes elections, holidays, and protests. Full article

Foraminifera are widely used in paleoclimatic and paleoceanographic studies, providing information about past ocean conditions. However, in order to use these tracers, it is essential to obtain an accurate chronology. Radiocarbon has proven to be a powerful tool in developing robust chronologies. Sample sizes of a few milligrams of carbonate material are needed for precise radiocarbon determination using accelerator mass spectrometry (AMS). In the specific case of paleoceanographic and paleoenvironmental studies, Foraminifera microfossils are the most important indicator of oceanic conditions. However, for establishing the chronology of deposition, sample availability is often limited. In AMS facilities using solid ion sources, such as the Radiocarbon Laboratory of the Universidade Federal Fluminense (LAC-UFF), in Brazil, CO₂ samples need to be converted to graphite after physical and chemical pre-treatment to remove contamination. Reducing the sample sizes increases the relative contribution of contamination and can favor increased background levels. In this work, we tested different amounts of ¹⁴C-free carbonate samples as a means to evaluate the pattern of contamination. For the sealed tube Zn/TiH₂ graphitization method, we tested prebaking the graphitization tubes and compared storage procedures. As a result, the background for regular-sized samples was decreased, and accurate measurement of carbonate samples containing ca. 0.5 mg C could be performed. Prebaked graphitization tubes can safely be stored in desiccator cabinets for up to 4 weeks. Foraminifera samples with mass as low as 1 mg (ca. 0.1 mg C) can now be measured at the LAC-UFF AMS facility, provided that C contamination can be estimated and corrected. The developments presented in this work allowed for the study of species-specific Foraminifera and other small-sized carbonate samples. Full article

The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators. Full article

An enumeration of spatial autocorrelation’s (SA’s) polyvalent forms occurred nearly three decades ago. Attempts to conceive and disseminate a clearer explanation of it employ metaphors seeking to better relate SA to a student’s or spatial scientist’s personal knowledge databank. However, not one of these uses the jigsaw puzzle metaphor appearing in this paper, which exploits an analogy between concrete visual content organization and abstract map patterns of attributes. It not only makes SA easier to understand, which furnishes a useful pedagogic tool for teaching novices and others about it, but also discloses that many georeferenced data should contain a positive–negative SA mixture. Empirical examples corroborate this mixture’s existence, as well as the tendency for marked positive SA to characterize remotely sensed and moderate (net) positive SA to characterize socio-economic/demographic, georeferenced data. Full article

There has been a growth in the number of composite indicator tools used to assess community risk, vulnerability, and resilience, to assist study and policy planning. However, existing research shows that these composite indicators vary extensively in method, selected variables, aggregation methods, and sample size. The result is a plethora of qualitative and quantitative composite indices to choose from. Despite each providing valuable location-based information about specific communities and their qualities, the results of studies, each using disparate methods, cannot easily be integrated for use in decision making, given the different index attributes and study locations. Like many regions in the world, the Arctic is experiencing increased variability in temperatures as a direct consequence of a changing planetary climate. Cascading effects of changes in permafrost are poorly characterized, thus limiting response at multiple scales. We offer that by considering the spatial interaction between the effects of permafrost, infrastructure, and diverse patterns of community characteristics, existing research using different composite indices and frameworks can be augmented. We used a system-science and place-based knowledge approach that accounts for sub-system and cascade impacts through a proximity model of spatial interaction. An estimated ‘permafrost vulnerability surface’ was calculated across Alaska using two existing indices: relevant infrastructure and permafrost extent. The value of this surface in 186 communities and 30 military facilities was extracted and ordered to match the numerical rankings of the Denali Commission in their assessment of permafrost threat, allowing accurate comparison between the permafrost threat ranks and the PVI rankings. The methods behind the PVI provide a tool that can incorporate multiple risk, resilience, and vulnerability indices to aid adaptation planning, especially where large-scale studies with good geographic sample distribution using the same criteria and methods do not exist. Full article

Reference evapotranspiration (ETo) estimation is essential for water resources management. The present research compares four different ETo estimators based on reanalysis data (ERA5-Land) and in situ observations from three different cultivation sites in Greece. ETo based on FAO56-Penman–Monteith (FAO-PM) is compared to ETo calculated from the empirical methods of Copais, Valiantzas and Hargreaves-Samani using both reanalysis and in situ data. The daily and monthly biases of each method are calculated against the FAO56-PM method. ERA5-Land data are also compared to ground-truth observations. Additionally, a sensitivity analysis is conducted on each site for different cultivation periods. The present research finds that the use of ERA5-Land data underestimates ground-truth-based ETo by 35%, approximately, when using the FAO56-PM method. Additionally, the use of other methodologies also shows underestimation of ETo when calculated with ERA5-Land data. On the contrary, the use of the Valiantzas and Copais methodologies with in situ observations shows overestimation of ETo when compared to FAO56-PM, in the ranges of 32–62% and 24–56%, respectively. The sensitivity analysis concludes that solar radiation and relative humidity are the most sensitive variables of the Copais and Valiantzas methodologies. Overall, the Hargreaves-Samani methodology was found to be the most efficient tool for ETo estimation. Finally, the evaluation of the ERA5-Land data showed that only air temperature inputs can be utilized with high levels of confidence. Full article

Climate change is altering the conditions to which communities have adapted. The Köppen–Geiger classification system can provide a compact metric to identify regions with notable changes in climatic conditions. Shifting Köppen–Geiger climate zones will be especially impactful in regions with large populations. This study uses high-resolution datasets on Köppen–Geiger climate zones and populations to quantify the number of people affected by shifting climate zones (i.e., population exposure to shifting climate zones). By the end of this century, 9–15% of the Earth’s land surface is projected to shift its climate zone. These shifts could affect 1.3–1.6 billion people (14–21% of the global population). Many of the affected people live in areas that were classified as temperate in the historical period. These areas are projected to be classified as tropical or arid in the future. This study presents a new metric for exposure to climate change: the number of people living in areas whose climate zone classification is projected to shift. It also identifies populations that may face climatic conditions in the future that deviate from those to which they have adapted. Full article

Colonia communities, which host forgotten Americans, lack essential services such as portable water, adequate wastewater and solid waste disposal, adequate drainage, and adequate paved roads. The aim of this study is to investigate five key aspects of the colonias in the Rio Grande Valley (RGV), which include the total count of colonias in the valley, their susceptibility to public health hazards, flooding occurrences, the transformations that have occurred over the past two decades, and community resilience. This research utilizes two datasets, namely the Colonia Database from the Texas Secretary of State and the community resiliency estimates from the Census Bureau. Geographical information systems (GIS) methods are employed to analyze the spatial and temporal distribution of colonia communities. The principal results reveal that colonia communities host 14% of the RGV’s total 1.37 million population. About half of the total colonia population resides in Hidalgo County, followed by Starr, Cameron, and Willacy counties. About 87% of the total colonia communities exist in census tracts characterized by low or very low community resiliency. Furthermore, 26% of the total colonia communities experiencing flooding after rainfall are in tracts with low or very low community resiliency. This study provides the major conclusion that while there have been slight improvements in the colonias’ susceptibility to public health risks within the past two decades, there still remains significant developmental work. Without tackling these challenges, achieving meaningful progress in community resilience becomes a daunting task. Applying an environmental justice lens to the issues faced by colonia communities helps shed light on the systemic inequalities and injustices they experience. Full article

This paper analyzes the performance of the open-source OpenDroneMap image processing software (ODM) across multiple platforms. We tested desktop and laptop computers as well as high-performance cloud computing and supercomputers. Multiple machine configurations (CPU cores and memory) were used. We used eBee S.O.D.A. drone image datasets from Namibia and northern Finland. For testing, we used the OpenDroneMap command line tool with default settings and the fast orthophoto option, which produced a good quality orthomosaic. We also used the “rerun-all option” to ensure that all jobs started from the same point. Our results show that ODM processing time is dependent upon the number of images, a high number of which can lead to high memory demands, with low memory leading to an excessively long processing time. Adding additional CPU cores is beneficial to ODM up to a certain limit. A 20-core machine seems optimal for a dataset of about 1000 images, although 10 cores will result only in slightly longer processing times. We did not find any indication of improvement when processing larger datasets using 40-core machines. For 1000 images, 64 GB memory seems to be sufficient, but for larger datasets of about 8000 images, higher memory of up to 256 GB is required for efficient processing. ODM can use GPU acceleration, at least in some processing stages, reducing processing time. In comparison to commercial software, ODM seems to be slower, but the created orthomosaics are of equal quality. Full article