Areas Of High Population Density Research Articles

Assessment of Solid Waste Management Practices Following Private Partnership Operations in Zaria Urban Area of Kaduna, Nigeria

The safe disposal of municipal waste is very important in promoting a healthy environment. High-quality public services and promoting competition in the waste management sector leads to the formation of private sector participation (PSP) to handle solid waste management in Kaduna. A multi-stage sampling technique was adopted which involves dividing the local government into localities. Stage two involves the purposive sampling technique which was adopted in selecting locality with high population and low population density areas, a total of 400 households from both strata were interviewed. Descriptive statistics was used to analyse the information from the questionnaires and results were presented using frequency tables and charts. The findings revealed that quality of service among the PSP operators recorded high success in the high-income areas and much less in the low and medium-income areas. On average, industry productivity was 5.63 tonnes per day per vehicle. 12 out of which in the study area were above this average and in meeting increased productivity, year of experience in operations, number of trips made, number of times trucks were serviced, and adhering to regulatory agency requirements were among the factors influencing company’s productivity in the state. It also shows less effectiveness of the system in waste management in the area. The study, therefore, recommends that regulatory agency should be more aggressive in playing their statutory roles of managing the PSP operators.

Geospatial Distribution of Prenatally and Postnatally Diagnosed Congenital Heart Disease: Implications for Equitable Care from a Fetal Heart Society Research Collaborative Study

To characterize patterns in the geospatial distribution of pre- and post-natally diagnosed congenital heart disease (CHD) across six surgical centers. A retrospective , multicenter case series from the Fetal Heart Society identified patients at six centers from 2012 through 2016 with prenatally (PrND) or postnatally (PoND) diagnosed hypoplastic left heart syndrome (HLHS) or d-transposition of the great arteries (TGA). Geospatial analysis for clustering was done by the average nearest neighbor (ANN) tool or optimized hot spot tool, depending on spatial unit and data type. Both point location and county case rate per 10,000 live births were assessed for geographic clustering or dispersion. Of the 453 CHD cases, 26% were PoND (n =117), and 74% were PrND (n=336). PrND cases, in all but one center, displayed significant geographic clustering by ANN. Conversely, PoND cases tended towards geographic dispersion. Dispersion of PoND HLHS occurred in two centers (ANN =1.59, p <0.001; and 1.47, p = 0.016), and PoND TGA occurred in two centers (ANN = 1.22, p < 0.05; and ANN =1.73, p<0.001). Hot spot analysis of all CHD cases (TGA and HLHS combined) revealed clustering near areas of high population density and the tertiary surgical center. Hot spot analysis of county-level case rate, accounting for population density, found variable clustering patterns. Geographic dispersion among postnatally detected CHD highlights the need for a wider reach of prenatal cardiac diagnosis tailored to the specific needs of a community. Geospatial analysis can support centers in improving the equitable delivery of prenatal care.

Earthquake Detection and Alerting system using ANN and IOT

Earthquakes are one of the deadliest natural disasters. The damage caused by earthquakes is very destructive and the number of deaths are at a very large scale. The National Earthquake Information Centre now detects about 20,000 earthquakes around the globe each year, or approximately 55 per day. Many of the Earth’s earthquake zones coincide with areas of high population density. When a large earthquake occurs in such areas, the results can be catastrophic. The basic parameter to determine an earthquake before it was going to occur is the p wave or primary shock wave which is not destructive to infrastructure because it travels parallel to the propagation of seismic wave. A series of destructive waves that follows p waves are s waves or secondary waves which has a speed of about 60% that of p waves. The movement of the s wave is perpendicular to the direction of propagation of the seismic wave and hence it is extremely risky to infrastructures and cause a lot of damage. This research aims to build an earthquake detection and alerting system using a low-cost accelerometer, ESP32 Wi-Fi module, and ANN algorithm. The input earthquake data is taken by the accelerometer and with the help of the ESP32 Wi-Fi module the data is sent to a system that processes the ANN algorithm with Multi layered perceptron trained with stochastic gradient descent learning algorithm and detects whether an earthquake is going to occur or not and alerts users with ESP32 Wi-Fi module. Key Words: Artificial Neural Networks, ESP32, Accelerometer, Earthquake, Multi-layer perceptron, Sigmoid function, Stochastic Gradient Descent.

Adaptive Time-Location Sampling for COMPASS: A SARS-CoV-2 Prevalence Study in Fifteen Diverse Communities in the United States.

COVID-19 has placed a disproportionate burden on underserved racial and ethnic groups, community members working in essential industries, those living in areas of high population density, and those reliant on in-person services such as transportation. The goal of this study was to estimate the cross-sectional prevalence of SARS-CoV-2 (active SARS-CoV-2 or prior SARS-CoV-2 infection) in children and adults attending public venues in 15 sociodemographically diverse communities in the United States and to develop a statistical design that could be rigorously implemented amidst unpredictable stay-at-home COVID-19 guidelines. We used time-location sampling with complex sampling involving stratification, clustering of units, and unequal probabilities of selection to recruit individuals from selected communities. We safely conducted informed consent, specimen collection, and face-to-face interviews outside of public venues immediately following recruitment. We developed an innovative sampling design that adapted to constraints such as closure of venues, changing infection hotspots, and uncertain policies. We updated both the sampling frame and the selection probabilities over time using information acquired from prior weeks. We created site-specific survey weights that adjusted sampling probabilities for nonresponse and calibrated to county-level margins on age and sex at birth. Although the study itself was specific to COVID-19, the strategies presented in this article could serve as a case study that can be adapted for performing population-level inferences in similar settings and could help inform rapid and effective responses to future global public health challenges.

Planning location of parcel lockers using group Analytic Hierarchy Process in Spherical Fuzzy environment

Parcel lockers have emerged as an effective solution to address inefficiencies in last-mile delivery. However, the selection of an appropriate location plays a crucial role in ensuring optimal service and enhanced logistic performance. To address this challenge, this work aims to define the best location for parcel lockers in the capital of Ireland, Dublin by conducting a novel decision-making model. The Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) has been adopted to deal with the uncertainty in the decision maker preferences, while the Euclidean Distance-Based Aggregation Method (EDBAM) has been conducted to aggregate the decision makers preferences. The proposed integrated model is an applicable approach to elude AHP deterministic disadvantages, moreover, it is the first application of SF-AHP and EDBAM to select parcel lockers’ location. The findings, including the identification of the top three optimal locations, namely “Post office,” “On commercial area,” and “High population density area,” along with their respective weight scores, provide valuable insights for policymakers making strategic decisions regarding parcel locker placements in Dublin city.

Identifying Factors for Supporting Early Warning Flood using Clustering Approach And Geo-Spatial Analysis

Floods in Jakarta are very frequent and caused by many factors, from very high rainfall to flash floods. This natural disaster has greatly impact the society, from economy to health problems. In this research, we used ArcGIS Pro with unsupervised clustering method HDBSCAN to create a prototype that would make it easier for people to find out the area that is prone to floods. We received a lot of positive input from many respondents who had seen our prototype design and how it can assist people as the early warning to floods. We analyzed the factors lead the flood early warning system, those factors are high density population distance from river to the high-density population area, drainage system, rainfall intensity, and flood history. We will group flood point with the unsupervised clustering method.

The spatial dynamics of Ukraine air quality impacted by the war and pandemic

ABSTRACT In recent years, our world has experienced significant disruptions due to the COVID-19 pandemic, and Russia's 2022 invasion of Ukraine, impacting human activities and the global environment. This paper explored air quality changes in Ukraine due to COVID-19, and Russia's invasion of Ukraine using on-demand with a what-you-see-is-what-you-get approach. During the COVID-19 pandemic, strict quarantine policies in Ukraine led to a 2% reduction in tropospheric NO2 concentration before the lockdown and 4% during the lockdown period. Cities like Kyiv, Donetsk, and Dnipro exhibited reductions of 5%, 11%, and 16%, respectively. Total SO2 column concentration decreased by 6% before the lockdown and 2.5% during the lockdown period, except in high population density areas. Kyiv showed the highest reduction of 17% in SO2 concentration, while Donetsk and Dnipro exhibited an 11% reduction. However, during the Russian invasion, there was a significant increase in tropospheric NO2 concentration in heavily destroyed Kharkiv while most eastern regions experienced a reduction. The total SO2 column was 48% higher before the war but reduced throughout the country after the war, except for in Kyiv and a few central regions. These findings can contribute to analyzing air pollution and building digital twin simulations for future reconstruction scenarios.

The effect of population density on outcomes of major trauma patients in Ireland

BackgroundTime-sensitive emergencies in areas of low population density have statistically poorer outcomes. This includes incidents of major trauma. This study assesses the effect that population density at a receiving hospital of a major trauma patient has on survival. MethodsPatients meeting Trauma Audit Research Network criteria for major trauma from 2016 to 2020 in Ireland were included in this retrospective observational study. Incident data were retrieved from the Major Trauma Audit, while data on population density were calculated from Irish state sources. The primary outcome measure of survival to discharge was compared to population density using logistic regression, adjusted for demographic and incident variables. Records were divided into population density tertiles to assess for between-group differences in potential predictor variables. ResultsPopulation density at a receiving hospital had no impact on mortality in Irish major trauma patients from our logistic regression model (OR = 1.01, 95% CI 0.98–1.05, p = 0.53). Factors that did have an impact were age, Charlson Comorbidity Index, Injury Severity Score, and the presence of an Orthopaedic Surgery service at the receiving hospital (all p < 0.001). Age and Charlson Comorbidity Index differed slightly by population density tertile; both were higher in areas of high population density (all p < 0.001). ConclusionsSurvival to discharge in Irish major trauma patients does not differ substantially based on population density. This is an important finding as Ireland moves to a new trauma system, with features based on population distribution. An Orthopaedic Surgery service is an important feature of a major trauma receiving hospital and its presence improves outcomes.

The distribution and characterisation of microplastics in air, surface water and sediment within a major river system

Rivers are key pathways for the transfer of microplastics (MP) to marine environments. However, there are considerable uncertainties about the amount of microplastics transported by rivers to the ocean; this results in inaccuracies in our understanding of microplastic quantity and transport by freshwater systems. Additionally, it has been suggested that rivers may represent long-term sinks, with microplastics accumulating in sediment due to their high density or other biological, chemical, and physical factors. The atmosphere is also an important pathway by which airborne microplastics may enter aquatic habitats. Here, we compare for first time microplastics type and concentration in these key environmental mediums (air, water and sediment) along a major river (Ganges), from sea to source to understand 1) the abundance, 2) the spatial distribution, and 3) characteristics. Mean microplastic abundance settling from the atmosphere was 41.12 MP m2 day−1; while concentrations in sediment were 57.00 MP kg−1 and in water were 0.05 MP L−1. Across all sites and environmental mediums, rayon (synthetically altered cellulose) was the dominant polymer (54–82 %), followed by acrylic (6–23 %) and polyester (9–17 %). Fibres were the dominant shape (95–99 %) and blue was the most common colour (48–79 %). Across water and sediment environmental mediums, the number of microplastics per sample increased from the source of the Ganges to the sea. Additionally, higher population densities correlated with increased microplastic abundance for air and water samples. We suggest that clothing is likely to be the prominent source of microplastics to the river system, influenced by atmospheric deposition, wastewater and direct input (e.g. handwashing of clothes in the Ganges), especially in high density population areas. However, we suggest that subsequent microplastic release to the marine environment is strongly influenced by polymer type and shape, with a large proportion of denser microplastics settling in sediment prior to the river discharging to the ocean.

Electric grid resilience: The effects of conductor coverings, enhanced tree trimming, and line characteristics on tree-related power outages

Tree damage is a major cause of storm power outages in forested regions of the world which results in substantial economic losses each year. Utilities must understand the effectiveness of resilience programs to make the best use of limited resources. Our study evaluated the effectiveness of covered conductors and enhanced tree trimming (ETT) in reducing tree-related outages during storms for an electric overhead distribution system. Our study area was the Eversource Energy service area in Connecticut which has heavy forest cover coinciding with areas of high population density. We controlled for the effects of confounding factors including weather, power line phase, and tree canopy cover. Covered conductors and ETT were similarly effective in reducing power outages with median rate reductions of 0.19 - 0.29 outages/km/year. When used in combination, covered conductors with ETT had a significant additive effect with median reductions of 0.29 – 0.33 outages/km/year compared to bare conductors with no ETT. Multiphase power lines had median outage rates that were 0.17 – 0.37 outages/km/year lower than outage rates for single-phase power lines. Our findings will help utilities select and justify their choices of resilience strategies and more accurately predict outage locations in advance of approaching storms.

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.

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.

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.

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.

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.

Cryptosporidiosis threat under climate change in China: prediction and validation of habitat suitability and outbreak risk for human-derived Cryptosporidium based on ecological niche models

BackgroundCryptosporidiosis is a zoonotic intestinal infectious disease caused by Cryptosporidium spp., and its transmission is highly influenced by climate factors. In the present study, the potential spatial distribution of Cryptosporidium in China was predicted based on ecological niche models for cryptosporidiosis epidemic risk warning and prevention and control.MethodsThe applicability of existing Cryptosporidium presence points in ENM analysis was investigated based on data from monitoring sites in 2011–2019. Cryptosporidium occurrence data for China and neighboring countries were extracted and used to construct the ENMs, namely Maxent, Bioclim, Domain, and Garp. Models were evaluated based on Receiver Operating Characteristic curve, Kappa, and True Skill Statistic coefficients. The best model was constructed using Cryptosporidium data and climate variables during 1986‒2010, and used to analyze the effects of climate factors on Cryptosporidium distribution. The climate variables for the period 2011‒2100 were projected to the simulation results to predict the ecological adaptability and potential distribution of Cryptosporidium in future in China.ResultsThe Maxent model (AUC = 0.95, maximum Kappa = 0.91, maximum TSS = 1.00) fit better than the other three models and was thus considered the best ENM for predicting Cryptosporidium habitat suitability. The major suitable habitats for human-derived Cryptosporidium in China were located in some high-population density areas, especially in the middle and lower reaches of the Yangtze River, the lower reaches of the Yellow River, and the Huai and the Pearl River Basins (cloglog value of habitat suitability > 0.9). Under future climate change, non-suitable habitats for Cryptosporidium will shrink, while highly suitable habitats will expand significantly (χ2 = 76.641, P < 0.01; χ2 = 86.836, P < 0.01), and the main changes will likely be concentrated in the northeastern, southwestern, and northwestern regions.ConclusionsThe Maxent model is applicable in prediction of Cryptosporidium habitat suitability and can achieve excellent simulation results. These results suggest a current high risk of transmission and significant pressure for cryptosporidiosis prevention and control in China. Against a future climate change background, Cryptosporidium may gain more suitable habitats within China. Constructing a national surveillance network could facilitate further elucidation of the epidemiological trends and transmission patterns of cryptosporidiosis, and mitigate the associated epidemic and outbreak risks.Graphical

Association of long-term exposure to air pollution with chronic sleep deprivation in South Korea: A community-level longitudinal study, 2008–2018

Background and objectiveAlthough there are many findings about the effects of fine particulate matter (PM2.5) and sleep deprivation on health respectively, the association between PM2.5 and chronic sleep deprivation has rarely been investigated. Thus, we aimed to investigate this association using a nationwide survey in South Korea. MethodWe examined the association between long-term exposure to PM2.5 and chronic sleep deprivation using a national cross-sectional health survey covering the entire 226 districts in inland South Korea from 2008 to 2018, with a machine learning-based national air pollution prediction model with 1 km2 spatial resolution. ResultsChronic sleep deprivation was positively associated with PM2.5 in the total population (odds ratio (OR): 1.09, 95% confidence interval (CI): 1.05–1.13) and sub-population (low, middle, high population density areas with OR: 1.127, 1.09, and 1.059, respectively). The association was consistently observed in both sexes (males with OR: 1.09, females with OR: 1.09)) and was more pronounced in the elderly population (OR: 1.12) than in the middle-aged (OR: 1.07) and young (OR: 1.09) populations. ConclusionsOur results are consistent with the hypothesis regarding the relationship between long-term PM2.5 exposure and chronic sleep deprivation, and the study provides quantitative evidence for public health interventions to improve air quality that can affect chronic sleep conditions.

Are the epidemic prevention facilities effective? How cities should choose epidemic prevention facilities: Taking Wuhan as an example.

The COVID-19 pandemic highlighted the limitations of urban public health emergency response capabilities. Taking Wuhan as an example, this study used breakpoint regression, kernel density analysis, overlay analysis, and accessibility analysis from Stata and ArcGIS, and divided epidemic prevention facilities into the basic epidemic prevention facilities (hospitals), and the emergency epidemic prevention facilities (mobile cabin hospitals) for further analysis. The results showed that over 70% of the basic epidemic prevention facilities in Wuhan were located in high density population areas. On the contrary, most of the emergency epidemic prevention facilities were located in low density population areas. The local treatment effect of the implementation of the emergency epidemic prevention facility policy is about 1, indicating that there was a significant impact of emergency epidemic prevention facilities on outbreak control, which passed the bandwidth test. What's more, the analysis of the accessibility of residential points revealed that more than 67.3% of people from the residential points could arrive at the epidemic prevention facilities within 15 min, and only 0.1% of them took more than 20 min to arrive. Therefore, the epidemic prevention facilities can effectively curb the spread of the epidemic, and people from residential areas can quickly get there. This study summarized the spatial characteristics of epidemic prevention facilities in Wuhan and analyzed the importance of them, thus providing a new perspective for future research on upgrading the city's comprehensive disaster prevention system.