Optimized Hotspot Analysis Research Articles

Time series analysis of COVID-19 stringency measures on the spatio- temporal dynamics of air pollution

Introduction: Execution of COVID-19 lockdown measures caused variations in air pollution worldwide. This paper investigates the impact of COVID-19 stringency measures on the spatio-temporal dynamics of air pollution in Mumbai, India, using a comprehensive two-and-a-half-year pandemic period dataset. Materials and methods: We classified the pandemic period into 7 phases and 21 sub-phases based on the severity of the Oxford COVID-19 Government Response Tracker (OxCGRT) Stringency Index (SI). Optimized Hotspot analysis (OHS) and Ordinary Least Square Regression models explored the spatio-temporal fluctuations and the effect of stringency measures on air quality. Results: The R2 value varied; with the best model R2 of 0.61 for Particulate Matters (PM10) and Nitrogen dioxide (NO2) and lowest of 0.23 for Sulfur dioxide (SO2). A 10-point increase in SI caused a 3-7% reduction in air pollutants. Substantial reduction in average PM10, PM2.5, NO2, and Carbon monoxide (CO) was observed throughout the COVID-19 phases. Meteorology and SI collectively caused maximum reduction of 82.6%, 72.7%, 53.8%, 52.2%, 49.1%, 28.4% for NO2, PM2.5, PM10, NH3, CO, and SO2 respectively, during complete or extreme lockdown phases. Except SO2, seasonality significantly influenced the pollutant concentrations. Winter was the worst period while monsoon was the best. OHS identified central Mumbai wards as hotspots and areas close to the national park as coldspots. Conclusion: PM10, NO2 and CO were more affected by SI measures than NH3 and SO2. For a rapid emergency response to high PM10, implementation of SI, very high (≥ 80 score) and above is advised. Findings of this study have significant public health policy implications, especially among global south nations.

Resurgence of Clinical Malaria in Ethiopia in the Era of Anopheles stephensi Invasion.

The invasion of Anopheles stephensi into Africa poses a potential threat to malaria control and elimination on the continent. However, it is not clear if the recent malaria resurgence in Ethiopia has linked to the expansion of An. stephensi. We aimed to summarize the major achievements and lesson learnt in malaria control in Ethiopia from 2001 to 2022, to assess the new challenges and prospects for the control of An. stephensi. We obtained the clinical malaria case reports, antimalarial drug treatment records, insecticide-treated and long-lasting insecticidal net (ITN/LLIN) distribution and utilization records, and indoor residual spraying (IRS) coverage data from the Ethiopian Ministry of Health (MoH) for the period 2001-2022. We analyzed clinical malaria hotspots using spatially optimized hotspot analysis. We investigated malaria outbreaks in 2022 and examined the potential role of An. stephensi in the outbreaks.Clinical malaria cases in Ethiopia decreased by 80%, from 5.2 million cases (11% confirmed) in 2004 to 1.0 million cases (92% confirmed) in 2018; however, cases increased steadily to 2.6 million confirmed cases (98% confirmed) in 2022. Plasmodium vivax cases and proportion have increased significantly in the past 5 years. Clinical malaria hotspots are concentrated along the western Ethiopian border areas and have grown significantly from 2017 to 2022. Major malaria outbreaks in 2022/23 were detected in multiple sites across Ethiopia, and An. stephensi was the predominant vector in some of these sites, however, it was absence from many of the outbreak sites. The malaria burden has been significantly reduced in Ethiopia in the past two decades, but in recent years it has increased substantially, and the cause of such increase is a subject of further investigation. Major gaps exist in An. stephensi research, including vector ecology, surveillance, and control tools, especially for adult mosquito control.

Identifying impervious surface hot spots in Ho Chi Minh City using object-based classification and spatial statistics

Impervious surface is often a primary indicator for urbanization. The rapid expansion of impervious surface area (ISA) can lead to a variety of environmental issues such as the change in local climate, urban heat islands, urban flooding, biodiversity loss, and the depletion of air quality. In this paper, satellite data from Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 were employed to extract impervious areas in Ho Chi Minh City (HCMC) from 2009 to 2022 by integrating the object-based image analysis and Kernel density estimation method. Regarding spatial analysis, optimized hot spot analysis (OHSA) and emerging hot spot analysis (EHSA) were used to detect specific spatial distribution and spatio-temporal changes of ISA hot spots over the 12 years. Study results show the hot spots of ISA with 99% confidence interval increased considerably from 28,625 ha in 2009 to 44,400 ha in 2022. Through emerging hot spot analysis, many kinds of ISA hot spots including persistent, new, intensifying, sporadic, and potential hot spots were traced over time. New hot spots had a total area of 14,975 ha, located mainly in Cu Chi, Thu Duc city, whereas potential hot spots, which are likely to become new hot spots in the future, were over 28,000 ha, making up 19% of the study area. Thus, the results from this study can provide a preliminary prediction of ISA distribution for HCMC in the coming years.

Analysis of road traffic accidents and casualties associated with electric bikes and bicycles in Guangzhou, China: A retrospective descriptive analysis

IntroductionElectric bicycles (e-bikes) and bicycles in large Chinese cities have recently witnessed substantial growth in ridership. According to related accident trends, this study analyzed characteristics and spatial distribution in the period when e-bike-related accidents rapidly increased to propose priority measures to reduce accident casualties. MethodsFor e-bike- and bicycle-related accident data from the Guangzhou Public Security Traffic Management Integrated System, linear regression was used to examine the trends in the number of accidents and age-adjusted road traffic casualties from 2011 to 2021. Then, for the period when e-bike-related accidents rapidly increased, descriptive statistics were computed regarding rider characteristics, illegal behaviors, road types, collision objects and their accident liability. One-way analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test. P < 0.05 was considered statistically significant. Finally, the density distribution of accidents was presented, and Moran's I (MI) was used for assessing spatial autocorrelation. Hotspots were identified based on an optimized hotspot analysis tool. ResultsBetween 2011 and 2021, the number of accidents and casualty rate (per 100,000 population) increased for e-bikes but decreased for bicycles. After 2018, e-bike-related accidents increased rapidly, and bicycle-related accidents plateaued. Accident hotspots were concentrated in central city areas and suburban areas close to the former. Three-quarters of accidents occurred in motorized vehicle lanes. Most occurred on roads without physically segregated nonmotorized vehicle lanes. More than three-fifths of the accidents involved motor vehicles with at least four wheels. The prevalence (per 100 people) of casualties among e-bike rider victims and cyclist victims accounted for 92.0 % and 96.5 %, respectively. A total of 71.6 % of e-bike-related accidents involved migrant workers. Riding in motorized vehicle lanes was the most common illegal behavior. ConclusionsAlthough e-bike-related and bicycle-related accidents presented similar characteristics, the sharp increase in e-bike-related accidents requires attention. To improve e-bike safety, governments should develop appropriate countermeasures to prevent riders from riding on motorways, such as improving road infrastructure, adjusting the driver's license system and addressing priority control areas.

Growing crisis of urban rat infestation in Karaj city, Iran: Insights from hotspot mapping and social network analysis

To control the increasing growth of rat populations in metropolitan areas, several factors need to be considered to mitigate the risks posed by rats. Identifying rat density hotspots with more efficient methods and assessing people's experiences and attitudes toward them can be useful in prioritizing rat control efforts. We focused on the two most common species of rats (Rattus rattus and Rattus norvegicus) in the metropolis of Karaj, the fourth most populous and the largest immigrant-friendly city in Iran. The urban rat infestation is one of the problems caused by the high population growth in Karaj and the insufficient development of urban infrastructure and waste collection. It has led to discontent and public concern about rat infestations and there is a need to address the associated threats. We applied the Kernel Density Function and Optimized Hotspot Analysis to the presence points of the aforementioned types of rats and found that while Optimized Hotspot projects a macro view of hotspots at the city level, the Kernel Density Function prioritizes neighborhoods for initial actions. In addition, social network analysis with complementary regression models was conducted to determine people's experiences. The results show that fear of rats and concern about their presence form the basis of people's attitudes toward rats and lead to feelings of hatred. Moreover, economic harm reinforces people's motivation to control or eliminate this species. Despite their unpleasant feelings toward rats, people have a great influence on the growth of rat infestations by creating food sources through improper waste disposal. They prefer to use traps and poison instead of disposing of household waste and following the principles of cleanliness as preventive measures and believe that the lack of proper garbage collection by the municipality is the most important reason for the growth of rat populations. They are not sufficiently aware of their position in addressing this crisis. Our findings argue for the formation of a stronger network between the relevant authorities and residents, especially those living in the hotspots, to educate them about their role in rat control and to gain their cooperation in improving the city's waste management system.

Machine Learning Approach to Enhance Highway Railroad Grade Crossing Safety by Analyzing Crash Data and Identifying Hotspot Crash Locations

Safe railway operation is vital for public safety, the environment, and property. Concurrent with climbing amounts of rail traffic on the Canadian rail network are increases in the last decade in the annual crash counts for derailment, collision, and highway railroad grade crossings (HRGCs). HRGCs are important spatial areas of the rail network, and the development of community areas near railway tracks increases the risk of HRGC crashes between highway vehicles and moving trains, resulting in consequences varying from property damage to injuries and fatalities. This research aims to identify major factors that cause HRGC crashes and affect the severity of associated casualties. Using these causal factors and ensemble algorithms, machine learning models were developed to analyze HRGC crashes and the severity of associated casualties between 2001 and 2022 in Canada. Furthermore, spatial autocorrelation and optimized hotspot analysis tools from ArcGIS software were used to identify hotspot locations of HRGC crashes. The optimized hotspot analysis shows the clustering of HRGC crashes around major Canadian cities. The analysis of cluster characteristics supports the results obtained for causal factors of HRGC crashes. These research outcomes help one to better understand the major causal factors and hotspot locations of HRGC crashes and assist authorities in implementing countermeasures to improve the safety of HRGCs across the rail network.

Assessing heat risk in a sub-saharan African humid city, Lagos, Nigeria, using numerical modelling and open-source geospatial socio-demographic datasets

In Sub-Saharan Africa, many cities are facing an increased risk of heat due to climate change and rapid urbanization. This poses a particular threat in areas with limited adaptive capacity. However, there is a lack of comprehensive heat risk assessment in the region, possibly due to the absence of high-resolution weather data. This study aims to address this gap by proposing and demonstrating a methodology for mapping high-risk areas in a tropical humid city, specifically focusing on Lagos, Nigeria. The approach utilises advanced numerical modelling techniques and open-source geospatial data.The urbanised Weather Research and Forecasting (WRF) model is employed to simulate Humidex-based heat stress during a specific heatwave event in March 2020. Open-source high resolution geospatial datasets were used to assess heat exposure and vulnerability. The urban areas were classified based on the Local Climate Zone (LCZ) scheme. Spatial analysis techniques, including Moran’s I test and Optimized Hot Spot Analysis (OHSA), were used to identify spatial clustering patterns and hot spots of heat risk areas.Moreover, using Gi* statistics in OHSA, the risk layer was categorised into hot, cold, and non-significant spots at various levels of significance (90 %, 95 %, and 99 %). Mapping the hot spots at the highest confidence level of 99 % identified Critical Heat Risk Zones (CHRZ), covering an area of approximately 423 km2. The results showed significant heat risk in highly urbanised LCZs. Further investigation indicated that the largest proportion of high-risk zones corresponded to densely populated and highly urbanised LCZs- LCZ3 (59 %), LCZ 6(21 %), and LCZ 7(17 %). Notably, these areas coincide with two well-known slums in Lagos, emphasizing the need for targeted interventions and planning measures in these areas.The findings highlight the magnitude and extent of heat risk within the city and emphasize the urgent need for targeted climate change adaptation and mitigation strategies in the identified high-risk zones.

Spatial Patterns of Solar Photovoltaic System Diffusion Kisumu County, Kenya

Purpose: This research aligns with Sustainable Development Goal 7, contributing to the progress outlined in the 2030 Agenda for Sustainable Development and the commitments of the Paris Climate Agreement. Specifically, this study focuses on the spatial analysis of solar photovoltaic (PV) systems, offering valuable insights for academic exploration and informing public policy decisions related to the widespread adoption of this increasingly vital renewable energy technology. The outcomes of this project transcend academic significance, extending to practical applications for energy practitioners, policymakers, academics, and future researchers. The meticulous tracking of solar PV system spatial patterns in Kisumu County yields data that not only benefits its residents but also serves as a valuable resource for the entire nation. This information will be instrumental for current energy practitioners, policymakers, academicians, and prospective researchers seeking to advance the collective knowledge in this field.&#x0D; Methodology: The study adopted a Quasi-Experimental research design to explore various social phenomena, aiming to identify key facts. Utilizing statistical evidence, we conducted numerical comparisons and statistical inferences to validate or refute the research questions. Locational information on households utilizing small home systems was extracted from a secondary Solar Database. This data underwent georeferencing, enhancing our comprehension of the actual geographical distribution of households and facilitating the achievement of our research objectives. In the process of data analysis, we employed inferential statistics, specifically regression analysis, conducted using ArcGIS PRO powered by ESRI. The utilization of ArcGIS Pro extended to the creation of an empirical model. This model was designed to probe into the factors influencing the observed spatial diffusion patterns, providing a robust analytical framework for our investigation.&#x0D; Findings: In the initial objective, cluster and outlier analysis unveiled a distinct low-high cluster pattern for solar home systems (SHS). The optimized hotspot analysis consistently identified SHS hotspots and cold spots within the region, particularly aligning with urban areas, notably Kisumu. The second objective exposed factors influencing diffusion, revealing negative correlations with population density, household density, and poverty rate, indicating diminished adoption in densely populated and impoverished areas. Conversely, positive correlations with income, education, and electrification rates signaled heightened adoption in wealthier, educated communities. Despite consistent diffusion trends, an empirical model underscored the substantial impact of income and electricity on SHS diffusion. The third objective disclosed that between 2016-2021, SHS diffusion contributed to the mitigation of 268,581.6 metric tons of carbon emissions.&#x0D; Unique Contribution to Theory, Practice and Policy: This research makes a distinctive contribution to theory by delving into the impact of solar home systems (SHS) in Kenya, particularly within the context of the country's commitment to reduce greenhouse gas (GHG) emissions. The theoretical foundation lies in addressing the existing gap in understanding the spatial distribution and diffusion patterns of SHS and their role in GHG reduction, aligning with Kenya's focus on renewable energy adoption.

Configuring the groundwater potential zone spatially using optimized hotspot analysis

Configuring the groundwater potential zone spatially using optimized hotspot analysis

Wildfires Risk Assessment Using Hotspot Analysis and Results Application to Wildfires Strategic Response in the Region of Tangier-Tetouan-Al Hoceima, Morocco

In recent years, changes in climate, land cover, and sociodemographic dynamics have created new challenges in wildfire management. As a result, advanced and integrated approaches in wildfire science have emerged. The objective of our study is to use geospatial analysis to identify strategic responses to wildfires in the Tangier-Tetouan-Al Hoceima (TTA) region, widely reputed to exhibit the most significant incidences of wildfires in Morocco. We adopted a combined approach, using burned area products (Fire_CCI51: 2002–2020) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and active fires from the Fire Information for Resource Management System (FIRMS: 2001–2022) and processing them with spatiotemporal statistical methods: optimized hotspot analysis (OHA) and emerging hotspot analysis (EHA). The main findings indicate that the TTA region recorded an average of 39.78 km2/year of burned areas, mostly located in forests (74%), mainly cork oak and matorral stands (50%). The OHA detected hotspots covering 2081 km2, with 63% concentrated in the provinces of Chefchaouen and Larache. Meanwhile, clusters of EHA extended over 740 km2 and were composed of the oscillating coldspot (OCS) and oscillating hotspot (OHS) patterns at 50% and 30%, respectively. Additionally, an average of 149 fires/year occurred, located mostly in forests (75%), mainly cork oak and matorral stands (61%). The OHA detected active fire hotspots covering 3904 km2, with 60% located in the provinces of Chefchaouen and Larache. Clusters of EHA over 941 km2 were composed of the oscillating hotspot (OHS) and new hotspot (NHS) patterns at 57% and 25%, respectively. The prevalence of the oscillating and new models mirrors, respectively, the substantial fluctuations in wildfires within the region alternating between periods of high and low wildfire activities and the marked increase in fires in recent times, which has occasioned the emergence of novel hotspots. Additionally, we identified six homogeneous wildfire zones to which we assigned three strategic responses: “maintain” (73% of the territory), “monitor and raise awareness” (14% of the territory), and “reinforce” (13% of the territory). These strategies address current wildfire management measures, which include prevention, risk analysis, preparation, intervention, and rehabilitation. To better allocate firefighting resources, strategic responses were classified into four priorities (very high, high, medium, and low). Last, the wildfire zoning and strategic responses were validated using burned areas from 2021 to 2023, and a global scheme was suggested to assess the effectiveness of future wildfire measures.

A Spatio-Temporal Mapping and Bayesian Modelling of Fever, Cough and short Rapid Breaths, as Symptoms of Pneumonia in Under-Five Children in Nigeria

Background Pneumonia remains a public health challenge in most parts of the world, with Nigeria having the highest number of pneumonia-related deaths. Understanding the geographical distribution, trends and risk factors associated with some symptoms of pneumonia can aid an appropriate preventive intervention towards abating its burden in Nigeria. Method This cross-sectional study used data from the 2008, 2013, and 2018 Nigeria Demographic Health Surveys. The outcome variables include fever, cough, and short, rapid breaths as selected symptoms of pneumonia. Optimized hotspot analysis was used to identify areas with a significantly high prevalence of fever, cough, and short rapid breaths, and a MCMC random-intercept logistic regression model was fitted to each symptom. Results The prevalence of cough was 12.1%, 10.1% and 16.9% in 2008, 2013 and 2018 respectively, 16.2%, 13.3%, and 25.7% for fever; and 41.7%, 42.5% and 6.5% for short rapid breaths respectively with variations across the states. Children aged 6-11 months had the highest higher odds (adjusted odds ratio (aOR) =1.95, 95% Credible Interval (CrI): 1.77, 2.18) of having cough, compared to children of other age groups. Similar pattern was noticed for fever and short rapid breaths. Other significantly associated factors with symptoms include the mother’s education, mother’s age, residence type, housing quality, wealth index and region. Conclusion The northern states were more characterized by short, rapid breaths while the southern states were more characterized by fever but both regions have a fair share of cough among under-five children. For cough and fever symptoms, the increased prevalence between 2013 and 2018 indicates more attention is needed. There are also higher odds of finding the symptoms amongst children aged 6-11 months compared to children of other age groups, especially in the North-eastern part of the country. Hence, government needs to strengthen immunization against diseases such as Hib, pneumococcus, measles and whooping cough (pertussis) which has been shown to be the most effective way to prevent pneumonia. Adequate nutrition is also key to improving children's natural defenses. Educated mothers needs to be adequately sensitized to understand that exclusive breastfeeding for the first 6 months of life of a child is paramount.

GIS based hotspot analysis and health risk assessment of groundwater arsenic from an unconfined deep aquifer of Lahore, Pakistan.

Use of groundwater for drinking purpose poses serious hazards of arsenic contamination particularly in plains of western Himalayan region. Therefore, current study was designed to investigate the level of Arsenic (As) in the water obtained from tubewells in a metropolitan city of Lahore, Pakistan and assess the human health risk. So, a total of 73 tubewells were sampled randomly in the manner that the whole study region was covered without any clustering. The water samples were analyzed for As using atomic absorption spectrophotometer. These samples were also tested for total dissolved solids, chlorides, pH, alkalinity, turbidity, hardness and calcium. GIS based hotspots analysis technique was used to investigate the spatial distribution patterns. Our results revealed that only one sample out of total 73 had arsenic level below the WHO guideline of 10μg/L. The spatial distribution map of arsenic revealed that the higher concentrations of arsenic are present in the north-western region of Lahore. The cluster and outlier analysis map using Anselin Local Moran's I statistic indicated the presence of an arsenic cluster in the west of River Ravi. Furthermore, the optimized hotspot analysis based on Getis-Ord Gi* statistics confirmed the statistical significance (P < 0.05) and (P < 0.01) of these samples from the vicinity of River Ravi. Regression analysis showed that variables such as turbidity, alkalinity, hardness, chlorides, calcium and total dissolved solids were significantly (all P < 0.05) associated with level of Arsenic in tubewells. Whereas, PH and electrical conductivity and other variables like town, year of installation, depth and diameter of the wells were not significantly associated with Arsenic concentrations in tubewells. Principal component analysis (PCA) exhibited that the random distribution of tubewell samples showed no distinct clustering with towns studied. Health risk assessment based on hazard and Cancer risk index revealed serious risk of developing carcinogenic and non-carcinogenic diseases particularly in children. The health risk due to prevalence of high As concentration in tubewells' water need to be mitigated immediately to avoid worst consequences in future.

Identification of Emerging Roadkill Hotspots on Korean Expressways Using Space-Time Cubes.

Collisions with wild animals on high-speed expressways not only lead to roadkill but can also cause accidents that incur considerable human and economic costs. Based on roadkill data from 2004-2019 for four common wildlife species involved in collisions with vehicles on expressways in Korea (water deer, common raccoon dog, Korean hare, and wild boar), the present study conducted optimized hotspot analysis and identified spatiotemporal patterns using a space-time cube (STC) approach. Temporal and spatial differences in the roadkill data were observed between species. Water deer were the most common roadkill species of the four studied, with hotspots in the southern region of the capital area, in the Chungnam region, and in the western Chungbuk and Gangwon-do regions. However, the instances of water deer roadkill over time differed between each region. In addition, it was found that the number of cases of wild boar roadkill has increased recently. In particular, a number of new hotspot areas were observed centered on the metropolitan area Gyeonggi-do, which contains a high population and significant infrastructure. Overall, the emerging hotspot analysis based on STCs was able to determine cold spot and hotspot trends over time, allowing for a more intuitive understanding of spatiotemporal clustering patterns and associated changes than cumulative density-oriented hotspot analysis. As a result, it becomes easier to analyze the causes of roadkill and to establish reduction measures according to priority.

Spatial Analysis in Areas with High Occurrence of Accidents Caused by Tityus serrulatus and Tityus bahiensis (Scorpiones: Buthidae) in Brazil.

In Brazil, scorpion stings are recorded in high numbers with an increasing epidemiological situation in most municipalities. In the present study, data between 1998 and 2018 in Americana, São Paulo, were analyzed. In total, 4122 records on scorpion stings were georeferenced using a Garmin eTREX 30X global positioning system device, with WGS84 datum projection and Universal Transverse Mercator zone 23S. Multiple Poisson regression was used to explore the relationship between the incidence rates of stings and urban planning areas (UPAs). Eight quantitative variables were used to establish the environmental and anthropic characterization of UPAs associated or not associated with scorpionism. A spatial analysis was performed for geoprocessing maps of Americana using spatial statistics tools (optimized hotspot analysis and kernel density function) from cartographic clusters in the ArcMap software, version 10.5. The optimized hotspot analysis tool identified spatial clusters with high values of the incidence of scorpion stings in the surroundings of all UPAs in the municipality. The estimation of the kernel function of event intensity showed a wide distribution of stings across the area of the entire municipal territory, with UPA-02 and UPA-06 exhibiting the highest occurrence of scorpion stings. Six significant hotspots were established as highest-event-density areas (with occurrences of 160-270) and were contiguous to 4 environmental protection areas, located in more peripheral regions, and to 2 municipal cemeteries, which were located in urban central areas. This analysis showed that the risk of scorpion stings in different Americana's UPAs has increased occurrence and incidence intensity.

Detecting abnormal seismic activity areas of Anatolian plate and deformation directions using Python Geospatial libraries

Various inaccurate traditional models have resulted in major ambiguities and gaps in the interpretation of Anatolian plate deformation directions. To address this issue, a GIS-based spatial statistical analysis method was used for the first time to detect the directional distribution of deformation along the Anatolian Plate in Turkey. Two strategies were used in this study: firstly, identifying the abnormally active seismic areas by detecting significant hotspot and cold spot clusters and confirming this detection using optimized hotspot analysis for earthquake events that occurred from 1900 to the end of 2019. Secondly, detecting the directional distribution of deformation using a Standard Deviational Ellipse (SDE) by calculating the standard deviation of the x and y coordinates from the mean center for each set of earthquake events in the Anaconda Python Platform and ArcGIS 10.8 software. Our improved geostatistical analysis results confirmed the existence of abnormal seismic hazard zones within the study area and three deformation directions: the east-west trend, the southeast-northwest trend, and the south-north trend.

Investigating the Spatial Relationship between Social Vulnerability and Healthcare Facility Distribution in Nassau County, New York

Health is a fundamental human right, yet healthcare facilities are not distributed equitably across all communities. This study aims to investigate the distribution of healthcare facilities in Nassau County, New York, and examine whether the distribution is equitable across different social vulnerability levels. An optimized hotspot analysis was conducted on a dataset of 1695 healthcare facilities-dental, dialysis, ophthalmic, and urgent care-in Nassau County, and social vulnerability was measured using the FPIS codes. The study found that healthcare facilities were disproportionately distributed in the county, with a higher concentration in areas of low social vulnerability compared to areas of high social vulnerability. The majority of healthcare facilities were found to be clustered in two ZIP codes-11020 and 11030-that rank among the top ten wealthiest in the county. The results of this study suggest that socially vulnerable residents in Nassau County are at a disadvantage when it comes to attaining equitable access to healthcare facilities. The distribution pattern highlights the need for interventions to improve access to care for marginalized communities and to address the underlying determinants of healthcare facility segregation in the county.

Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation

Identifying and prioritizing hazardous road traffic crash locations is an efficient way to mitigate road traffic crashes, treat point locations, and introduce regulations for area-wide changes. A sound method to identify blackspots (BS) and area-wide hotspots (HS) would help increase the precision of intervention, reduce future crash incidents, and introduce proper measures. In this study, we implemented the operational definitions criterion in the Hungarian design guideline for road planning, reducing the huge number of crashes that occurred over three years for the accuracy and simplicity of the analysis. K-means and hierarchical clustering algorithms were compared for the segmentation process. K-means performed better, and it is selected after comparing the two algorithms with three indexes: Silhouette, Davies–Bouldin, and Calinski–Harabasz. The Empirical Bayes (EB) method was employed for the final process of the BS identification. Three BS were identified in Budapest, based on a three-year crash data set from 2016 to 2018. The optimized hotspot analysis (Getis-Ord Gi*) using the Geographic Information System (GIS) technique was conducted. The spatial autocorrelation analysis separates the hotspots, cold spots, and insignificant areas with 95% and 90% confidence levels.

Earthquake hotspot and coldspot: Where, why and how?

• Global earthquake hotspot and coldspot were identified. • Statistical methods of hotspot analysis and optimized hotspot analysis. • Benioff/subduction zones are the most potential hotspot zone. • Optimized hotspot analysis emerges as the most probabilistic method. Global tectonic activities are playing an important role in the occurrences of devastating earthquakes and related long-term changes in the earth's system surface. However, the plate tectonics processes and their interaction with the earth's crust are very much complex, and it is a subject of unending debate. Therefore, tectonism-induced landslide, tsunami, liquefaction, and fire are significant earthquake-related hazards, which have a larger potential and overwhelming impact on life and infrastructural properties throughout the world. In this study, we have emphasized the identification of earthquake hotspot and coldspot zones considering historical earthquake data across the plate boundary of the world. Here, a total of 7773 historical earthquake points were collected as input parameters with three-moment magnitude (M w ) classes (<4.5, 4.5-6.0, and >6.0). Two statistical methods namely hotspot analysis (Getis-Ord GI*) and optimized hotspot analysis were used in the detection of global earthquake hotspot and coldspot zones using the geographic information system (GIS) platform. Hotspot and coldspot zone are identified under 99%, 95%, and 90% confidence levels. Alongside, here we have also discussed the paradigm, evidence of tectonic, and historical earthquakes, and how and why they are formed with the help of the existing theoretical constraints. The result indicates that the Pacific ring of fire, Peru-Chile Trench, and the mid-Atlantic oceanic ridge is fall in the hotspot zones of 99%, 95%, and 90% confidence levels.

HOTSPOT ANALYSIS AND COMPARISON BETWEEN SATELLITE-DERIVED AEROSOL OPTICAL DEPTH AND GROUND-BASED PARTICULATE MATTER MEASUREMENTS IN METRO MANILA

Abstract. Highly urbanized regions such as the Metro Manila area in the Philippines contribute to the deterioration of air quality through overpopulation, excessive vehicle emissions, and industrialization. However, the limited number of ground monitoring stations hinders the detailed estimation of the region’s overall air quality. Satellite-derived air pollutant concentrations have been used in several research studies as a substitute or supplementary to ground-based data due to their extensive spatial and temporal coverage. Using the aerosol optical depth (AOD) from the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and ground measurements of coarse particulate matter (PM10), this study explores the comparison between satellite-derived and ground-based air pollutant concentrations measured from 2017 to 2020 through trend analysis of monthly average values per city. With 16 stations located in different cities, the monthly average values of AOD vs PM10 showed inconsistent results due to significant gaps in the ground data. Through optimized hotspot analysis, it was found that 7.24% of the Metro Manila region are considered hotspots using the MAIAC AOD values from 2017 to 2019 (pre-pandemic). From 2018 to 2020 (pandemic), 23.86% of Metro Manila are counted as hotspots. The AOD derived from satellite imagery and hotspot analysis can be used for future studies that focus on the development of models to predict ground pollutant values and the designation of non-attainment areas.

Applying OHSA to Detect Road Accident Blackspots

With increasing numbers of crashes and injuries, understanding traffic accident spatial patterns and identifying blackspots is critical to improve overall road safety. This study aims at detecting blackspots using optimized hot spot analysis (OHSA). Traffic accidents were classified by their participants and severity to explore the relationship between blackspots and different types of accidents. Based on the outputs of incremental spatial autocorrelation, OHSA was then implemented on different types of accidents. Finally, the performance of OHSA in evaluating the road safety level of the proposed RBT index are examined using a binary correlation analysis (i.e., R2 = 0.89). The results show that: (1) The optimal scale distance varies from 0.6 km to 2.8 km and is influenced by the distance of the travel mode. (2) Central cities, with 54.6% of the total accidents, experiences more rigorous challenges regarding traffic safety than satellite cities. (3) There are many types of black spots in vulnerable communities, but in some specific areas, there are only black spots of non-motor vehicle accidents. Considering the practical significance of the above results, policy makers and traffic engineers are expected to give higher attention to central cities and vulnerable communities or prioritize the implementation of relevant optimization measures.