Detailed Geographic Information Research Articles

Ocurrence dataset of birds in university campuses of Nanjing, Juangsu Province China.

The protection of urban biodiversity plays a critical role in maintaining urban ecological security. As a unique taxon of urban wildlife, birds have been intensively used as an indicator of our surrounding environment. Although the diversity of urban bird species has received increasing attention, present studies are mainly focused on urban parks. As one of the important components of the urban ecosystem, the university campus also plays a critical role in maintaining bird diversity. Due to the lack of observation data, it is a challenge to evaluate the impact of the environmental conditions on campus bird diversity. Given the most concentrated higher education resources in China, Nanjing has a large number of universities, covering a wide area of the urban landscape. The landscape of these universities usually has a high percentage of green space as well as waterbodies, which is conducive to attracting various bird species to inhabit. Here, we conducted a one-year bird survey from January 2019 to December 2019 in 12 universities in Nanjing and provided an occurrence dataset containing detailed species and geographical information, providing a good source to study the ecological and anthropogenic factors on urban bird diversity on a local and larger scale. This dataset represents the first annual record of birds publicly released by 12 universities in Nanjing, Jiangsu Province. It includes classification information, population, distribution and survey details. All data have been published on GBIF.

Clematis brevipes (Ranunculaceae), an interesting and yet imperfectly known species from southern Gansu and northern Sichuan, China

Clematis brevipes (Ranunculaceae), described from southern Gansu province, China, is an interesting and yet imperfectly understood species, with some of its important morphological characters, detailed geographical distribution and habitat information unknown or inconsistently recorded. Based on careful observations of both herbarium specimens (including type material) and living plants in the wild, here we determine the number of leaflets and color of flowers of this species and its geographical distribution. The habitat information is also given for this species for the first time. Furthermore, we discuss aspects of typification of C. brevipes and the affinity of the species with its close allies.

Occurrence dataset of birds in Sihong Hongze Lake Wetlands National Nature Reserve in China.

Hongze Lake is China's fourth largest freshwater lake and is also an important habitat for hundreds of thousands of migratory birds on the East Asian-Australian Flyway (EAAF). Sihong Hongze Lake Wetlands National Nature Reserve is located on the northwest of Hongze Lake, Sihong County, Jiangsu Province. The Reserve is a protected large area of natural lake wetlands, marsh wetlands and riverine wetlands and used as a stopover and wintering habitats for migratory birds. Previous studies have conducted bird diversity and temporal-spatial variation in this Reserve, but only for species of Anseriformes. There is still a lack of a comprehensive dataset on the number of bird species and individuals in this Reserve throughout the year. Our study was conducted from July 2020 to June 2021 to observe bird species composition and individual numbers at Sihong Hongze Lake Wetlands National Nature Reserve and provides an occurrence dataset with detailed species and geographic information. This occurrence dataset is the first public record of birds in Sihong Hongze Lake Wetlands National Nature Reserve for a whole year, which includes the taxonomic information, location information, number, investigation date and endangered level for each species. All data have been published on GBIF.

Linking Individuals to Areas: Protecting Confidentiality While Preserving Research Utility

Modern computational capabilities have brought about concerns about risks associated with the level of information disclosed in public datasets. A tension exists between making data available that protects the confidentiality of individuals while containing sufficiently detailed geographic information to underpin the utility of research. Our aim is to inform data collectors and suppliers about geographic choices for confidentiality protection and to balance this with reassurance to the research community that data will still be fit-for-purpose. We test this using simple logistic regression models, by investigating the interplay between two geographical entities (points for the observations and polygons for area attributes) at a variety of scales, using a synthetic population of 22,000 people. In an England and Wales setting, we do this for individuals located by postcodes and by postal sector and postal district centroids and link these to a variety of census geographies. We also ‘jitter’ postcode coordinates to test the effect of moving people away from their original location. We find a smoothing of relationships up the geographical hierarchy. However, if postal sector centroids are used to locate individuals, linkages to Lower/Medium Super Output Area scales and subsequent results are very similar to the more detailed unit postcodes. Postcode locations jittered by 500–750 m in any direction are likely to allow the same conclusions to be drawn as for the original locations. Within these geographic scenarios, there is likely to be a sufficient level of confidentiality protection while statistical relationships are very similar to those obtained using the most detailed geographic locators.

Wildfire Air Quality Prediction

Wildfires are extremely harmful to the environment. While producing gaseous pollutants and particles that cause smoke, wildfires also release carbon dioxide (CO2), a greenhouse gas that will continue to warm the planet after the wildfire ends. This article delves into the impact of wildfires and air quality on human living conditions. The authors' machine learning models use wildfire data to forecast air quality with detailed indexes and geographic information during a wildfire. The work evaluates the performance of each machine learning model via statistical metrics like mean absolute error (MAE), R-squared (R2), and root mean squared error (RMSE). The experimental results used neural networks to predict a specific value for carbon monoxide (CO), ozone, and PM2.5. These are both promising and accurate, providing meaningful insight into air quality within a region. This work will be useful for cities, governments, citizens, and public safety.

Agent‐based modeling and simulation for the circular economy: Lessons learned and path forward

AbstractCircular economy aims at decoupling human activities from resource use and creating wealth. However, many have questioned the link between increased circularity and sustainability, resulting in several methodological approaches being developed to answer that question. This article analyzes and discusses the insights gained from applying agent‐based modeling and simulation to study the techno‐economic and social conditions promoting circularity and sustainability. This article analyzes the benefits and limitations of this technology and discusses future methodology developments within the circular economy context. Moreover, six limits of the circular economy concept are used to interpret insights from the literature: thermodynamic limits, system boundary limits, limits posed by the physical scale of the economy, limits posed by path dependencies and lock‐in, limits of governance and management, and limits of social and cultural definitions. Promising research avenues are to use this methodology with machine learning, industrial ecology methods, and detailed geographic information.

Картографічний супровід маршруту «Скам’яніле місто» в місті Ценжковіце (Польща)

The article is devoted to the analysis of cartographic support of tourist routes, in particular, the "Petrified City" route in Cenzkowicz (Poland). In the context of consideration of various mapping technologies, in particular the platforms Google Maps, MapMyHike, AllTrails and others, and their possibilities for creating and planning routes. The author draws attention to the importance of these tools for creating tourist maps and navigation materials. The focus of the article is aimed at revealing the geological features and history of the "Petrified City" route through the prism of natural monuments. An overview of the tourist potential of the city of Cenzhkovice and its main attractions is given. The AllTrails application was used to visually represent the route and provide information about the elevation profile and route stopping points. Special attention is paid to the importance of cartographic support for ensuring safety, comfort and enriching the experience of tourists during their travels. This consists in providing detailed geographical information, which contributes to better orientation during the route and improves the quality of the tourist experience.

Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease.

The rapid detection of outbreaks is a key step in the effective control and containment of infectious diseases. In particular, the identification of cases which might be epidemiologically linked is crucial in directing outbreak-containment efforts and shaping the intervention of public health authorities. Often this requires the detection of clusters of cases whose numbers exceed those expected by a background of sporadic cases. Quantifying exceedances rapidly is particularly challenging when only few cases are typically reported in a precise location and time. To address such important public health concerns, we present a general method which can detect spatio-temporal deviations from a Poisson point process and estimate the odds of an isolate being part of a cluster. This method can be applied to diseases where detailed geographical information is available. In addition, we propose an approach to explicitly take account of delays in microbial typing. As a case study, we considered invasive group A Streptococcus infection events as recorded and typed by Public Health England from 2015 to 2020.

Occurrence dataset of waterbirds in the Tiaozini Wetland, a World Nature Heritage, China.

Tiaozini, the core area of the Yellow (Bohai) Sea Migratory Bird Habitat in Dongtai, Jiangsu Province and a World Heritage Site, has provided an ideal habitat for migratory birds. As an important hub on the East Asian-Australasian Flyway (EAAF), Tiaozini Wetland provides pivotal stopover and wintering sites for tens of thousands of migratory waterbirds, including some global critically endangered species, such as Spoon-billed Sandpiper (Calidrispygmaea) and Spotted Greenshank (Tringaguttifer). Although many researchers have conducted a lot of studies on waterbirds in Tiaozini Wetland, there is still a lack of a dataset on waterbird species composition and individual quantity in Tiaozini Wetland throughout the year. Here, we conducted a one-year waterbird survey in the Tiaozini Wetland during 2020-2021 and provided an occurrence dataset with detailed species and geographic information. This occurrence dataset is the first public record of species and number of waterbirds in Tiaozini Wetland for a whole year, which includes the taxonomic information, location information, number, investigation date and endangered level for each species. All data have been published on GBIF.

Turizm Coğrafyası Perspektifinden Mindanao Adasının Güney Turizm Bölgeleri

Mindanao, Filipinler’in ikinci en büyük ve kalabalık nüfuslu adasıdır. Görkemli şelaleleriyle, ürkütücü volkanlarıyla, ıssız tropikal plajlarıyla, yoğun yağmur ormanlarıyla ve benzersiz doğal koruma alanlarıyla dikkat çeken Mindanao, Türkiye’den gelen turistlerin en az ziyaret ettikleri ve en az bilgi sahibi oldukları Filipin adasıdır. Dünyanın en büyük ve en yoğun nüfuslu adalarından biri olan Mindanao, dört denize kıyısı olan Filipinlerin en güney büyük adasıdır. Dünyanın en kalabalık nüfuslu yedinci adası olan Mindanao, aynı zamanda da en çok Müslüman kökenli nüfus barındıran Filipinler adasıdır. Turistik rehber özelliklerini taşıyan bu bölgesel coğrafya çalışmasının öncelikli amacı, bu adayı ziyaret eden yatırımcılara, öğrencilere, bilim uzmanlarına, iş adamlarına ve turistlere detaylı coğrafi ve turizm bilgiler sunması ve her bir bölgenin bir yandan coğrafi ve turizm potansiyelini, diğer yandan da kültürel turistik mekânlarını ve doğal turizm çekiciliklerini tanıtmasıdır. Çalışmada Davao, Zamboanga, General Santos, Cotabato, Mati City ve Pagadian yani toplam 6 bölgenin coğrafi özellikleri, turizm merkezleri ve turizm potansiyelleri irdelenmiştir. Mindanao adasının güney kesimlerinde yer alan bu turizm bölgelerinin irdelenmesiyle, çalışmada adanın sadece yarısının incelendiği anlaşılmaktadır. Türkiye’de yapılan bilimsel araştırmalar arasında, büyük bir tropikal adanın çok boyutlu turizm portresi farklı coğrafi bölgeleri kapsayacak şekilde detaylı irdelenmediği için Güney Mindanao olarak ele alınan bu çalışmanın, ülkemizde ilk kez kaleme alınan araştırma örneği olduğu söylenilebilir. Mindanao adasının güney bölgelerinin coğrafi ve turizm potansiyeli ile ilgili Türkiye’de detaylı bir araştırma olmadığına göre, bu çalışma ile önemli bir bilgi boşluğunun doldurulması beklenmektedir.

Remote Sensing Image Classification: A Comprehensive Review and Applications

Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

Neighbors' Effects on University Enrollment

This paper combines detailed geographic information and individual educational records in Chile to provide causal evidence that close neighbors significantly influence enrollment in university. I exploit the quasi-random variation generated by student loans eligibility rules and find that potential applicants are significantly more likely to attend and complete university when their closest neighbor—defined as the closest individual applying to university one year before—becomes eligible for a student loan and enrolls in university. These results highlight the importance of social influences on university enrollment and suggest that financial aid and university access policies may have important spillover effects. (JEL I22, I23, I28, O15, R23)

Economic gradients in loneliness, social isolation and social support: Evidence from the UK Biobank

Despite the substantial literature on how loneliness is associated with poor health and premature mortality, there is little detailed research on the extent of its economic gradients. We provide this evidence using a sample of around 400,000 respondents aged 40–70 years from the UK Biobank, who were assessed between 2006 and 2010. We focus on differences in loneliness, as well as social isolation and a lack of social support, across educational attainment, household income, local area deprivation, and recent experience of financial stress. We employ two statistical approaches, the first exploiting the large sample size and detailed geographical information about where respondents live, so we compare individuals who differ in their economic status but reside within the same postcode district. The second approach exploits the fact that for around 36,000 respondents we observe their social health and economic circumstances at two points in time (second wave of assessment conducted between 2014 and 2020), so we conduct a panel analysis that accounts for intercorrelations between the social health measures, and controls for incomplete follow-up of panel members. Across both approaches, we find a substantially higher probability of reporting loneliness, social isolation and a lack of social support, for men and women with lower economic status. Together with the existing health-loneliness literature, these findings establish a ‘loneliness pathway’ contributing to health inequalities, and consequently a need for effective interventions that might address loneliness and social isolation as part of a broad policy initiative on health inequalities.

Detailed geographic information, conflict exposure, and health impacts

We expand existing research by estimating the impact of exposure to conflict on children’s health outcomes using geographic information on households’ distance from conflict sites—a more accurate measure of shock exposure than the traditional approach using regional-level information—and compare the impact of exposure in utero versus after birth. The identification strategy relies on exogenous variation in the conflict’s geographic extent and timing. Using multiple waves of survey data from Ethiopia and Eritrea, we find that conflict-exposed children have significantly lower height-for-age. With GPS information that enables accounting for households’ distance from the conflict sites, negative impacts of conflict exposure are two to three times larger than if exposure is measured at the imprecise regional level. Results are robust to addressing potential exposure misclassification due to migration happening between the war and the survey collection date.

Local knowledge spillovers and innovation persistence of firms

ABSTRACT Innovation activities of firms tend to be highly persistent. Yet, little is still known about potential spatial contingencies affecting the degree of persistence. This paper analyses the influence of local knowledge spillovers on firms’ persistence in innovation activities. Using a representative panel data set of firms in Germany from 2002 to 2016, complemented by detailed geographic information of patent activity over discrete distances to proxy local knowledge spillovers, we find that the local patenting activity positively moderates persistency in innovation activities. Estimations with different distance bands show that the strength of knowledge spillovers that contribute to innovation persistence attenuates with increasing distance and vanishes beyond 30 kilometres in manufacturing and beyond 20 kilometres in services.

Cave-dwelling gastropods (Mollusca: Gastropoda) of Brazil: state of the art and conservation

An up-to-date list of exclusively cave-dwelling gastropod species recorded in Brazil is presented including updated taxonomy, detailed geographic information, and illustration of types. The list includes 18 cave-exclusive (troglobitic) gastropods encompassing 15 land and three freshwater species, with the status of further species pending additional studies. Their unusual morphology and diversity are discussed, as well as their conservation status and prospects in the current Brazilian environmental and political scenario.

Distribution of tungiasis in latin America: Identification of areas for potential disease transmission using an ecological niche model

Tungiasis is a neglected tropical disease (NTD) found in Sub-Saharan Africa and Latin America. Despite the high frequency in marginalized populations, little information is available on the geography and estimates of the population at risk in endemic regions. Here we used a geostatistical model to map the potential geographic distribution of areas suitable for tungiasis transmission in Latin America and estimated the at-risk population. We developed an ecological niche model (ENM) using tungiasis occurrence records and remotely sensed environmental and socioeconomic data. The potential geographic distribution was then compared to the current population distribution of the region to derive the total population living in urban and rural areas. We identified a total of 138 records of occurrences of tungiasis in Latin America, ranging from Mexico to Argentina; 27 reports were not included in the modeling, due to missing detailed geographic information. The occurrences with detailed geographic information (n=112) included 17 countries in Latin America and the Caribbean. The locations were in environments that primarily consisted of forests (29%), croplands (16•5%), and shrublands (10•9%). We predicted environmentally suitable areas for tungiasis transmission in 45 countries. The estimated human population living in these areas is 450,546,547 with urban centers accounting for 347,007,103 and rural areas 103,539,444. Countries with significant ecological suitability and documented occurrences include Brazil, Colombia, Mexico, Argentina, Bolivia, Ecuador, French Guyana, Guatemala, Haiti, Paraguay, Peru, Trinidad and Tobago, and Venezuela. This is the first study mapping the potential distribution of tungiasis in Latin America, evidencing the need for population-based studies and elaboration of integrated control measures. This project was supported in part by an appointment to the Research Participation Program at the Centers for Disease Control and Prevention administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the Centers for Disease Control and Prevention.

DNA barcodes and morphology reveal two new species of Monodiamesa Kieffer (Diptera: Chironomidae: Prodiamesinae) in Tibetan Plateau.

Two Monodiamesa species collected in all stages and both sexes from the Tibetan Plateau are erected as M. secunditibetica Han and Tang, sp. n. and M. bonalpicola Han and Tang, sp. n. Molecular analysis of barcodes (COI-5P) for some related known species confirms the species validity and allows inference of internal relationships. Detailed description, habitat ecology and geographical information of two new species are provided. Additionally, brief comments on some known eastern Palaearctic species of Monodiamesa are made to give insights on further study.

Exploring the interactive coupled relationship between urban construction and resource environment in Wuhan, China

Megacities are facing internal and external constraints and challenges with the accelerated urbanization. Urbanization can provide convenient and various infrastructures for people, but on the other hand, natural resource and environmental condition will be destroyed due to plenty of land resources used for city development. Nowadays, it is important to keep the balance between urban construction and resource and environment, In order to measure the relationship and how these two factors impact each other, this study took the largest megacity of central China–Wuhan as an example to explore the status of urbanization through a comprehensive analysis framework, and quantify the coupling mechanism and spatial autocorrelation on street level. Through the indicator system, bivariate autocorrelation model, coupling coordination model and spatial lag model (SLM), the main conclusions are listed in the following part. With the increasing distance to the city center, the secondary indicators showed a significant increase in natural resources. Relationship between social economy and regional construction had an obvious overall positive correlation with high aggregation and strong spatial heterogeneity. Streets with lag effects on urban development ability were obviously larger than streets with lag effects on resource and environment level, which means most streets still have potential in terms of resource and environmental supply when compared with the level of urbanization. The results from SLM confirmed that the effects of spatial aggregation can improve both urban development ability and resource and environment level. This study can provide detailed geographical information for policymakers in the development of megacities and surrounding areas based on their urbanization status.

Deep Localization of Static Scans in Mobile Mapping Point Clouds

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).