Areas Of Population Density Research Articles

Domestic water quality and the discriminatory influence of socio-economic stratification on accessibility to safe water in a part of Osun State, Nigeria

The status of the water supplied to households in an urban local government area in southwest Nigeria was examined. Specific objectives were to characterize household water sources, assess the per capita usage of potable water sources and examine quality of the household water in the area. The purposively selected study area, Olorunda local government area in Osun State, Nigeria (being an urban local government area) was stratified into ‘urban core’ (central business district, CBD or high population density area), ‘transition’ (medium population density area but with less social infrastructure) and ‘sub-urban’ (typically government reserved area and low population density area) based on household density and associative socio-economic status. The methods were a mix of questionnaire administration to heads of 120 households (40 per stratum) and collection of the drinking water samples from the supply sources at four randomly selected households in each stratum. The water samples were analysed for the concentrations of nitrite (NO2-), nitrate (NO3-), fluoride (F-), lead (Pb) and arsenic (As) as well as E-coli bacteria contents based on their importance in determining water quality in urban areas. The results showed that domestic water sources are mainly hand dug well (55.8%), borehole (37.5%) and pipe borne (6.7%). Residents averagely consumed at most 4L of water per day. Furthermore, the investigated chemical parameters varied as NO2 (0.37-0.94 mg/l), NO3 (0.29-0.70 mg/l), and F- (0.003-0.97 mg/l). As and Pb occurred in trace quantities, and there were no pollution threats to consumers. The amount of E coli and total coliform contents were however outside the safe limits for residents, suggesting contamination of household water by the bacteria sources. The study concluded communities with low socio-economic status which do not have access to state-provided tap water source are more likely to consume polluted water than those with high socio-economic status or that are supplied with state-provided tap water. The study advocates improve urban water management for the urban fringe dwellers.

Оценка воздействия лесозаготовительной деятельности на сохранение биоразнообразия северного региона

In the context of sustainable forestry, biodiversity conservation assessment is a necessary element in asset and service accounting. Logging is an important driver of economic growth in the northern regions of Russia. The past twenty years have seen changes in timber harvesting technologies and forest management principles, which has led to an increase in research interest in identifying ecosystem responses in terms of biodiversity conservation. This study aims to assess changes in biodiversity within the active forest management territory of the Komi Republic, encompassing ten municipal districts and 24 forestry areas. The objectives of the study include evaluating the impact of factors such as population density, road network, and intact area on biodiversity preservation. Statistical analysis methods were utilized to assess the biodiversity status, focusing on the number of rare species threatened by logging. The analysis studied changes in species abundance, territorial distribution across forestry areas, and changes in their conservation status. Using statistical and factor analysis methods, the study identified the impact of forestry and anthropogenic factors on the conservation of species. The novelty of the research consists in identifying the stability degree of ecosystems by analyzing the state of large areas that have experienced long-term anthropogenic impacts on the Komi Republic territory. Most forests within the active forest management zone demonstrated favorable ecosystem conditions, with logging activities causing lichen disappearance and reduced fish numbers only in individual small areas. The anthropogenic impact had minimal influence on the number of rare species due to low population and road network density. Large areas of undisturbed forests contribute to the natural preservation of biodiversity, but their significant reduction in the southern area causes a threat to the existence of rare species. The greatest impact on the change in biodiversity on the territories affected by active forest management is caused by factors such as the level of territorial exploration, the preservation of undisturbed forests, and selective logging. The practical significance of this study lies in the potential application of the proposed algorithm by large Russian forest businesses when conducting their operations.

POTENTIAL OF BLACK SOLDIER FLY (BSF) IN REDUCING MUNICIPAL FOOD LOSS AND WASTE (FLW) AT TAMAN SARI DISTRICT, WEST JAKARTA

Black Soldier Fly (BSF) larvae are known to have the ability to process food loss and waste and reduce the weight of food loss and waste, by producing decomposition products that have beneficial values. Food loss and waste needs to be reduced to reduce the burden of waste entering the Final Disposal (TPA). Aim: The purpose of this study was to determine the ability of BSF larvae and to determine the quality of the decomposition results in processing food loss and waste in the Taman Sari District on a laboratory scale. Methodology and Results: Taman Sari Distrisct is a high dense populated area Total days of bioconversion activity is 12 days, giving back 1,000 grams of municipal waste organic, containing food loss and food waste, according to variation is given repeatedly on the 1st, 5th, and 8th days for each 12 sample code. The amount of 5 days old (5-DOL) maggot used for each sample code is 30 grams. Conclusion, significance, and impact study: The percentage reduction in processing from 3 kg of food loss and waste can reach 86% with the average residue produced in the form of 415.67 grams of solid compost and 221.7 grams of prepupa. Bred prepupa will evolve into several stages: pupa, fly, egg, baby larvae, and 5-DOL for 57 days on its life cycle.

Auditing state-owned enterprise through predictive analytics and function transformation

PurposeOne of the best ways to assist China is through infrastructure investment. China might become more resilient to natural calamities by pouring more money into its transport network. Analyzing the relationship between China's degree of planned expansion and the country's current network of transport hubs can help with city development estimates. A wide range of factors were taken into consideration while evaluating China's dominance and the caliber of its transportation infrastructure. Using a geographical autocorrelation model and a coupling coordination model, the dynamic link between China's adaptability and the caliber of its transportation infrastructure is examined.Design/methodology/approachChina's northwest is underdeveloped in comparison to the southeast, which has a high level of resilience and development of its transportation infrastructure. The relationship between the levels of resilience upheld by China's transport infrastructure is suggested to be coordinated.FindingsThe authors find a positive geographical autocorrelation between the degree of coupling coordination and the degree of agglomeration, despite the fact that the distance between cities increases with time. They now believe that there is a connection between an area's population density and the degree of interspousal cooperation within. The consequence is an improvement in both national security and economic prosperity. The facilities for disaster management and transportation in China have received several proposals for improvement.Practical implicationsThe authors' Practical Implications suggests that scale inefficiency is a major contributor to the relatively poor efficiency of China's primary inland river ports. Different types of inland river ports may have vastly different water system efficiencies. Input and output congestion at China's important interior river ports has reached 51%, making it very clear that massive amounts of valuable port resources are being wasted.Originality/valueMany variables, such as climate and human error, affect the total amount of goods that can be moved via inner river ports. Ports situated either higher up or lower down the same canal may perform better or worse, respectively, depending on the circumstances.

Analysis of cycling network evolution in OpenStreetMap through a data quality prism

Abstract. Cycling practice has been constantly increasing for several years and the COVID crisis has just accelerated the process. Indeed, more and more municipalities have developed new cycle paths to facilitate cycling. Considering this increasing interest for cycling, it makes sense to study how this recent evolution is reflected in the underlying representation of the cycling network in the geographic databases. Main studies analysing the evolution of the road network focus on the motor vehicle network in the major cities of the world. These studies do not seem applicable to cycling network specially to some low population density areas or even to smaller cities. This paper analyses the changes in the cycling network through OSM data from a data freshness perspective. These changes can be either updates from changes in the real-world network or upgrades to the network. To these end, we propose a method using a Monte Carlo simulation (MCS) to analyse the frequency of changes in cycling routes in several areas with different population density, all in the Loire Valley region in France. We also define the cycling network, which is a very complex concept and we explain how it is represented in OSM data and suffers from different data quality issues. Results show that the number of changes across time are similar in areas having a similar population density, while being lower in low population density areas. These phenomena is higher in the cycling network compared to other networks.

Demographic analysis of diagnostic patterns in head and neck fibromyxosarcoma.

e23546 Background: Fibromyxosarcoma (MFS) is a rare form of cancer that originates in connective tissue, starting in the arms and legs, but spreads into healthy tissues elsewhere in the body. 42% of MFS patients succumb to the disease within 5 years of diagnosis, with most cases being fatal in less than 2 years post-diagnosis. Due to the scarcity of literature regarding MFS, with no prior otolaryngological study on MFS, the diagnostic trend of the disease can assist in understanding its epidemiology. To determine diagnostic patterns, demographic factors of head and neck MFS patients in the National Cancer Database (NCDB) were analyzed. Methods: Using the National Cancer Database (NCDB) data from 2004 – 2020, a retrospective analysis was performed that included patients with a histologically-confirmed MFS diagnosis in the head and neck (N = 67). Descriptive statistics were used to analyze demographic factors: age, sex, race, social status, number of surgical procedures, facility type, insurance status, Charleson-Deyo score, and type of palliative care received. Regression analysis was used to interpret incidence trends. Results: 67 total patients were identified in NCDB with head and neck MFS between 2004 – 2020 with a steady amount of diagnoses per year (R2 = 0.093). The top primary site was the maxillary sinuses (22.4%). The average age of diagnosis was 54.1 years (SD = 18.0, range = 10 – 90 years). MFS was more present in males (59.7%) than females (40.3%). The majority of the patients were non-Hispanic White (82.1%). More patients lived in counties of metropolitan areas of a population of 1 million or more (55.2%) than in other urban/rural areas (13.5%). 82.1% of patients underwent surgical procedures at the primary site, with 28.4% being through a primary incision in the nose. More patients were treated in an academic/research program (52.2%) than in a non-academic program (28.4%). Most of the patients were privately insured (59.7%). More patients had a median income in the top quartile (44.8%) compared to patients in the second, third, or fourth quartiles. Most individuals had a Charlson-Deyo score of 0 (82.1%). The majority of the patients did not receive palliative care (97.0%). Conclusions: This is the first analysis of otolaryngological MFS using the NCDB, which expands our knowledge on the sociodemographic factors associated with its diagnosis. From all the years analyzed, MFS remains a rare disease. The majority of patients diagnosed with MFS tend to be non-Hispanic, White males with the primary site in the maxillary sinus. MFS patients are mostly treated in academic/research programs and live in densely-populated areas. This analysis provides an initial overview of the impact of demographic factors in patients with MFS, but examination into the impact of these variables on the prognosis and outcome of MFS patients would be valuable in understanding the social inequity surrounding this malignancy.

Home health care routing and scheduling in densely populated communities considering complex human behaviours

This study focuses on the home health care routing problem (HHCRP) in the scenario of high population density areas where many elders live closely together. This study considers two main objectives. The first is to reduce travel and wait times for nurses or elders. The second concerns socially related objectives in scheduling problems, such as ‘quality of life’ and empowerment, by considering assumptions related to the acquaintanceship and mutual preferences of nurses and elders. This study models the effects of mutual preferences and acquaintanceship on service time in HHCRP. We use the Markov decision process and chance-constrained programming (CCP) to model the system to conserve the sequential service provision parameters and better represent the influence of stochastic service times. Because traditional deterministic algorithms cannot solve such a model, we apply a model-free reinforcement learning algorithm, Q-learning (QL), as well as the ant colony optimisation (ACO) algorithm. Thus, we tackle this problem by developing a model and algorithm to solve complex, large-scale systems. This study’s theoretical and practical contributions are verified by feedback from researchers and practitioners.

Prediction of risk factors for scrub typhus from 2006 to 2019 based on random forest model in Guangzhou, China.

Scrub typhus is an increasingly serious public health problem, which is becoming the most common vector-borne disease in Guangzhou. This study aimed to analyse the correlation between scrub typhus incidence and potential factors and rank the importance of influential factors. We collected monthly scrub typhus cases, meteorological variables, rodent density (RD), Normalised Difference Vegetation Index (NDVI), and land use type in Guangzhou from 2006 to 2019. Correlation analysis and a random forest model were used to identify the risk factors for scrub typhus and predict the importance rank of influencing factors related to scrub typhus incidence. The epidemiological results of the scrub typhus cases in Guangzhou between 2006 and 2019 showed that the incidence rate was on the rise. The results of correlation analysis revealed that a positive relationship between scrub typhus incidence and meteorological factors of mean temperature (Tmean ), accumulative rainfall (RF), relative humidity (RH), sunshine hours (SH), and NDVI, RD, population density, and green land coverage area (all p < 0.001). Additionally, we tested the relationship between the incidence of scrub typhus and the lagging meteorological factors through cross-correlation function, and found that incidence was positively correlated with 1-month lag Tmean , 2-month lag RF, 2-month lag RH, and 6-month lag SH (all p < 0.001). Based on the random forest model, we found that the Tmean was the most important predictor among the influential factors, followed by NDVI. Meteorological factors, NDVI, RD, and land use type jointly affect the incidence of scrub typhus in Guangzhou. Our results provide a better understanding of the influential factors correlated with scrub typus, which can improve our capacity for biological monitoring and help public health authorities to formulate disease control strategies.

Spatial Statistics and Severity of Highway Accidents in Nakhon Pathom, Thailand

The aim of this study was to use spatial statistics and geographic information systems to identify high-risk areas for highway accidents in Nakhon Pathom, Thailand. Secondary data from the Ministry of Transport on the locations of accidents in the road network between 2021-2022 was analyzed using Equivalent Property Damage Only (EPDO), Spatial Autocorrelation, Kernel Density Estimation, and hotspot analysis. The study focused on Nakhon Pathom, a province in Central Thailand, and found that high-risk areas were concentrated along major routes with heavy traffic and high population density, including both urban and community areas. The study also identified specific risk spots, with Kamphaeng Saen District and Highways NO. 321(Kamphaeng Saen-Thung Khok Road), NO. 3231(Den Makham-Bang Len Road), and NO. 3232(Nong Phong Nok - Pai Chedi Road) being particularly affected, as well as Sam Phran District and Highway NO. 375(Ban Bo-Phra Prathon Road). These findings provide important insights into the clustering of accidents and their risk spots, which can be used to improve traffic safety in Nakhon Pathom.

Anticipating Current and Coming Technologies that Affect U.S. Travel Choices

Understanding the preferences for new and future transportation technologies is important to ensure an efficient and equitable future transportation system. A survey was conducted of Americans’ preferences for several such technologies. Americans are concerned about vehicle range and charging station availability for electric vehicles (EVs) and hesitant about autonomous vehicle (AV) safety. Opinions about many transportation technologies, such as vertical takeoff and landing (i.e., air taxis), shared parking, and air-drone delivery are mixed. These less familiar technologies require continued tracking of preferences. A 55% increase is estimated in the probability of an individual choosing a battery electric vehicle (BEV) pickup truck if its fuel economy increases by about 9%. This result supports a market for BEV pickup trucks currently under development by many automakers. The preference for vehicle autonomation appears to depend on the use case. Driving task automation is preferred by residents of low-density, car-dependent areas where long commutes are common. In contrast, automated parking technologies are favored by those living in denser communities. Intermittent bus lanes are favored by those living in high population density areas, but not among those in areas with high shares of zero-vehicle households. These results provide indications of where to direct future research in the field.

The rise and fall of acute rheumatic fever and rheumatic heart disease: a mini review.

The incidences of acute rheumatic fever (ARF) and rheumatic heart disease (RHD), which were leading causes of death in children in the 1920s, have decreased substantially. Considering the recent resurgence of scarlet fever and increased incidence of streptococcal pharyngitis in children, an investigation of the current status of ARF and RHD may be worthwhile. To summarize the prevalence trends, pathogenic factors, and prevention strategies for ARF and RHD in children. A selective search of literature published between January 1920 and February 2023 was done in PubMed, using the terms "acute rheumatic fever", "rheumatic heart disease", "group A Streptococcus", "pharyngitis", "pharyngeal tonsillitis", "scarlet fever", "impetigo", "obstructive sleep apnea syndrome" and "child". Overcrowded homes and inadequate sanitation led to recurrent group A streptococcal infection, and the causal relationship between group A streptococcal infection and ARF/RHD was well established. Streptococcal infectious diseases, such as group A streptococcal pharyngeal tonsillitis, SF, impetigo, and obstructive sleep apnea syndrome, were associated with the occurrence of ARF and RHD. ARF and RHD were still prevalent in young people of developing countries and economically poor populations of high-income countries. Universal disease registration systems were critical to locating disease outbreaks, tracking disease transmission, and identifying high-risk populations. Four-level prevention strategies were effective in reducing the incidence and mortality of ARF and RHD. Registry and preventive measures for ARF and RHD should be strengthened in areas of dense population; poor sanitation; resurgence of SF; and high incidence of streptococcal pharyngitis, impetigo, and obstructive sleep apnea syndrome.

Examining Rail Transportation Route of Crude Oil in the United States Using Crowdsourced Social Media Data

Safety issues associated with transporting crude oil by rail have been a concern since the boom of the U.S. domestic shale oil production in 2012. During the last decade, over 300 crude-oil-by-rail incidents have occurred in the United States. Some of them have caused adverse consequences including fire and hazardous materials leakage. However, only limited information on crude-on-rail routes and their associated risks is available to the public. To this end, this study proposed an unconventional way to reconstruct crude-on-rail routes using geotagged photos harvested from the Flickr website. The proposed method linked the geotagged photos of crude oil trains posted online with national railway networks to identify potential railway segments that those crude oil trains were traveling on. A shortest path-based method was applied to infer the complete crude-on-rail routes, by utilizing the confirmed railway segments as well as their directional information. Validation of the inferred routes was performed using a public map and official crude oil incident data. The results suggested that the inferred routes based on geotagged photos had high coverage, with approximately 96% of the documented crude oil incidents aligned with the reconstructed crude-on-rail network. The inferred crude oil train routes were found to pass through several metropolitan areas of high population density, who were exposed to potential risk. These findings could improve situational awareness for policy makers and transportation planners. In addition, with the inferred routes, this study has established a good foundation for future crude oil train risk-analyses along the rail route.

Assessing spatial variation in water supply of a city using dasymetric mapping

Abstract Wastewater has the potential to act as a supplement when freshwater resources are limited. To utilize this, city administrators need to identify the type and quantity of wastewater generated and its various functional uses. In countries like India, where detailed data about wastewater is not available, any potential reuse proposal is difficult to design and execute. To cover this gap, this research proposes a technique using dasymetric mapping and Voronoi diagrams to estimate the quantity and spatial distribution of wastewater generated in a city, based on population data and water supply information, by mapping and identifying the variation in the supply. The developed research framework is tested in Bhopal city in India. The results suggest that there is higher water consumption in areas of high population density, whereas wastewater generation displays a greater variation based on land use. Among 217 Voronoi polygons created throughout the city, eight have wastewater generation at more than 10 litres/sq. m of area. The total wastewater generated while considering only municipal water supply was 148.32 MLD. On intersecting the Voronoi polygons with ward boundaries, it is found that six wards generate wastewater more than 50 litres/sq. m, whereas seven wards generate less than 1 litre/sq. m of area.

Demotorization and Space: The Influence of Spatial Factors on Car-Dependency Reduction in France

Although car ownership continues to rise worldwide, temporary or more lasting phases of demotorization (reduction in the number of vehicles owned) are taking place at the household level. Existing studies show that the probability of demotorization increases at certain stages of the life cycle, for example, associated with a reduction in household size or income, or a move to a neighborhood with better transit provision. However, the rationale and temporalities of the decision-making processes involved remain obscure. This knowledge could be useful in informing public action on the measures needed in different categories of territories and populations to encourage a steady and sustainable fall in car ownership. As its contribution to these questions, this article focuses on the influence of spatial factors on household demotorization. The methodology draws on 51 interviews conducted in 2018 with demotorized households in four French urban areas (Paris, Lyon, Bordeaux, and Dijon). The findings highlight the role of the characteristics of the current place of residence, changes in the place of residence or place of work, and the spatial dimensions of travel socialization. If, as things stand, permanent and voluntary relinquishment of the car is only possible in very dense urban areas, our results show firstly that there is a strong case for working on mobility representations and practices from a very early age and, secondly, the importance of implementing planning policies and alternatives to the private car that are credible in areas of lower population density.

Human and natural impacts on the U.S. freshwater salinization and alkalinization: A machine learning approach

Ongoing salinization and alkalinization in U.S. rivers have been attributed to inputs of road salt and effects of human-accelerated weathering in previous studies. Salinization poses a severe threat to human and ecosystem health, while human derived alkalinization implies increasing uncertainty in the dynamics of terrestrial sequestration of atmospheric carbon dioxide. A mechanistic understanding of whether and how human activities accelerate weathering and contribute to the geochemical changes in U.S. rivers is lacking. To address this uncertainty, we compiled dissolved sodium (salinity proxy) and alkalinity values along with 32 watershed properties ranging from hydrology, climate, geomorphology, geology, soil chemistry, land use, and land cover for 226 river monitoring sites across the coterminous U.S. Using these data, we built two machine-learning models to predict monthly-aggregated sodium and alkalinity fluxes at these sites. The sodium-prediction model detected human activities (represented by population density and impervious surface area) as major contributors to the salinity of U.S. rivers. In contrast, the alkalinity-prediction model identified natural processes as predominantly contributing to variation in riverine alkalinity flux, including runoff, carbonate sediment or siliciclastic sediment, soil pH and soil moisture. Unlike prior studies, our analysis suggests that the alkalinization in U.S. rivers is largely governed by local climatic and hydrogeological conditions.

Occurrence Characteristics of Microplastics and Metal Elements in the Surface Water of Huangpu River and Their Associations with Metal Resistance Genes

Urban rivers have been regarded as the "hotspots" for microplastic (MPs) and metal contamination as they play important roles in pollution migration. However, as important sinks and sources of resistance genes, there has been little to no research investigating the associations between MPs, metal contaminations, and metal resistance genes (MRGs). Ten water samples were collected from the Huangpu River in situ; along with metal elements, MPs characteristics analyzed. Metal resistance genes and mobile genetic elements (MGEs) in waters and MPs were detected using metagenomic technology. As a result, the highest metal concentration was that of Sb in surface water (3.16±0.419) μg·L-1. The average abundance of MPs was (1.78±0.84) n·L-1, and the peak levels located in industrial and densely populated areas, which was significantly higher than those in agricultural and low population density areas. Fibrous, small-size (<0.5 mm), and transparent polyethylene terephthalate (PET) were the largest contributors of MPs. Eighteen MRGs were detected in all the samples. The relative abundance of MRGs in water was 1.68±0.21. The most dominant MRGs subtypes were merR and ruvB, which are subtypes resistant to mercury and Multi_metals. Correlation analysis showed that chromium and nickel in waters were significantly positively associated with MRG-Cr, MRG-Ni, and Multi_metals resistance genes. For MPs particles, the relative abundance of MRGs was 1.63±0.53. The most dominant MRGs subtypes were merT-P and copB, which also belong to mercury-resistant and Multi_metals. The Multi_metals resistance gene, ctpC, cueA, czrA, kmtR, etc., had significant positive associations with Ni, Cr, and Sb in waters. Compared with water samples, MPs selectively enriched merT-P, copB, ziaA, sodA, and dmeF. Additionally, the co-occurrence patterns of MRGs and MGEs were explored based on network analysis. In water samples, the transposases (tnpA_1 and tnpA_2), integrase (qacEdelta), and insertion sequence (IS91) were the major contributors of the horizontal gene transfer (HGT) of specific MRGs. Multiple subtypes resistant to copper and Multi_metals resistance genes on MPs were positively associated with IncFIC(FII), Rep7, rep7, and rep13, which were subtypes of plasmids. The presence of MPs exerted a significant impact on HGT of specific MRGs mediated by plasmids.

Impacts and predictions of urban expansion on habitat quality in the densely populated areas: A case study of the Yellow River Basin, China

The conflict between the human-environment is more prominent in densely populated areas (DPA) where urban expansion is increasingly disturbing the ecological environment, resulting in significant damage to habitat quality (HQ). However, a few studies have examined the impacts of urban expansion on HQ in the DPA under different scenarios, especailly in the Yellow River Basin. Therefore, we quantitatively analyzed the impacts of urban expansion on HQ from 1990 to 2018 by coupling the FLUS-InVEST model and predicted the changes of urban expansion and HQ by 2030. The results showed that: (1) the urban expansion rate of DPA was 46.4%. Among them, Hohhot-Baotou-Ordos-Yulin (HBOY) urban agglomeration expanded at the highest rate, with an expansion intensity of 0.16%. The urban expansion pattern was mainly edge expansion. (2) The overall HQ decreased by 1.6%. The HQ patch level was affected by urban expansion, and the higher-level habitat patches were easily shifted to lower-level habitat patches. Low HQ patches increased by 38.6% while high HQ gradually decreased. (3) Under the natural development scenario, the rate of urban expansion was 28.9% and HQ decreased by 2.9%. However, HQ only descesaed by 0.8% under HQ constraint scenario, indicating that urban expansion in a constrained way is of great benefit to improve HQ. These results provide a reference for ecological conservation and sustainable development in DPA.

Comparing the Travel Behavior of Older Users Between Station-Based and Dockless Bike Sharing Systems

As the population worldwide continues to age, providing improved mobility services for older people becomes increasingly crucial. In recent years, the maturation of bike sharing has led older people to progressively incorporate it into their everyday lives. In this study, station-based bike sharing (SBBS) and dockless bike sharing (DBS) are investigated in relation to older population mobility. This research relies on the fusion of multiple data sources, including SBBS and DBS trip records, point of interest (POI), and meteorological conditions in Nanjing, China. A zero-inflated negative binomial regression model is used to compare the travel behaviors among various age groups with the exposure variable. SBBS outperforms DBS in relation to age equity, while diverse travel behavior patterns are exhibited between the age groups. Although younger users are the majority of users, older users travel more frequently than younger users in bike sharing services. Also, older users are more likely to use shared bikes near the religious and health POIs, as well as in higher population density areas. This study provides beneficial insights into the transport equity and service improvement of bike sharing systems.

Spatial heterogeneity of driving factors-induced impacts for global long-term surface urban heat island

ABSTRACT A series of empirical analytical tools have been adopted to investigate the driving mechanisms of surface urban heat islands (SUHI) on a global scale, among which spatial heterogeneity is yet to be fully elucidated. In this study, we investigated the spatial non-stationarity of the driving factors concerning surface properties, climate conditions, and urbanization processes for global long-term SUHI. First, the potential impact on SUHI was explored using global ordinary least squares regression. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) from local perspectives were employed for comparison. The results show that the MGWR has the highest goodness of fit at 0.87, 0.73, 0.90, 0.74, 0.85, and 0.76 for annual day/night (AD/AN), summer day/night (SD/SN), and winter day/night (WD/WN) scales, respectively. Although both global and local schemes exhibit similar influencing magnitudes and signs on the SUHI, the MGWR is better at capturing spatial non-stationarity. Globally, for AD, AN, SD, SN, WD, and WN, the coefficients of the urban-rural vegetation index difference (ΔEVI) and surface albedo difference (ΔWSA), urban mean precipitation (MAP), wind speed (WS), population density (PD), and urban area (UA) are −0.50, +0.30, +0.16, +1.31, −0.03, and +0.03, respectively, at daytime, and −0.38, −0.33, −0.39, −0.10, +0.18, and +0.08, respectively, at night-time. Given the spatial heterogeneity of multiple factors, ΔEVI exhibits a strong mitigation effect on the SD SUHI especially in arid zones. The negative influence of ΔWSA on night-time SUHI demonstrates a strong latitudinal disparity and greater sensitivity in the equatorial zone. The positive correlations between MAP and AD/SD SUHIs have evident latitudinal and longitudinal variations. The mitigation effect of WS displayed distinct coastal amplification, especially in WD. In contrast, the PD and UA presented prominent positive impacts on night-time SUHI with less seasonal contrast.

Dispersal inference from population genetic variation using a convolutional neural network.

The geographic nature of biological dispersal shapes patterns of genetic variation over landscapes, making it possible to infer properties of dispersal from genetic variation data. Here, we present an inference tool that uses geographically distributed genotype data in combination with a convolutional neural network to estimate a critical population parameter: the mean per-generation dispersal distance. Using extensive simulation, we show that our deep learning approach is competitive with or outperforms state-of-the-art methods, particularly at small sample sizes. In addition, we evaluate varying nuisance parameters during training-including population density, demographic history, habitat size, and sampling area-and show that this strategy is effective for estimating dispersal distance when other model parameters are unknown. Whereas competing methods depend on information about local population density or accurate inference of identity-by-descent tracts, our method uses only single-nucleotide-polymorphism data and the spatial scale of sampling as input. Strikingly, and unlike other methods, our method does not use the geographic coordinates of the genotyped individuals. These features make our method, which we call "disperseNN," a potentially valuable new tool for estimating dispersal distance in nonmodel systems with whole genome data or reduced representation data. We apply disperseNN to 12 different species with publicly available data, yielding reasonable estimates for most species. Importantly, our method estimated consistently larger dispersal distances than mark-recapture calculations in the same species, which may be due to the limited geographic sampling area covered by some mark-recapture studies. Thus genetic tools like ours complement direct methods for improving our understanding of dispersal.