Mapping pollen allergenicity from urban trees in Valencia: A tool for green infrastructure planning

Urban trees provide many benefits to citizens but also have associated disservices such as pollen allergenicity. Pollen allergies affect 40% of the European population, a problem that will be exacerbated with climate change by lengthening the pollen season. The allergenic characteristics of the urban trees and urban parks of the city of Valencia (Spain) have been studied. The Value of Potential Allergenicity (VPA) was calculated for all species. The most abundant allergenic trees with a very high VPA were the cypresses, followed by Platanus x hispanica and species of genera Morus, Acer and Fraxinus, with a high VPA. On the contrary, Citrus x aurantium, Melia azedarach, Washingtonia spp., Brachychiton spp. and Jacaranda mimosifolia were among the most abundant low allergenic trees. VPA was mapped for the city and a hot spot analysis was applied to identify areas of clustering of high and low VPA values. This geostatistical analysis provides a comprehensive representation of the VPA patterns which is very useful for urban green infrastructure planning. The Index of Urban Green Zone Allergenicity (IUGZA) was calculated for the main parks of the city. The subtropical and tropical flora component included many entomophilous species and the lowest share of high and very high allergenic trees in comparison with the Mediterranean and Temperate components. Overall, a diversification of tree species avoiding clusters of high VPA trees, and the prioritization of species with low VPA are good strategies to minimize allergy-related impacts of urban trees on human health.

A novel approach to identify priority areas for optimal nutrient management in mixed land-use watersheds through nutrient budget assessment

Excessive nutrient supply in agricultural regions has led to various environmental issues, thereby requiring concentrated management owing to its persistent upward trend. Nutrient budgets (NBs), a vital agricultural environmental indicator, are employed for nutrient management in agricultural areas, using data surveyed by administrative agencies. However, the spatial extent of nutrient data for nutrient budgeting is limited by administrative boundaries according to the surveying organization, posing challenges in interpreting spatial patterns at the watershed level. In this study, a novel approach was developed to identify priority nutrient management areas by applying hot spot spatial analysis to watershed-level NBs, considering hydrological characteristics. This method was applied to approximately 850 subwatersheds across the Republic of Korea, where land cover characteristics are complex. Reassessing nutrient budgets at the watershed scale, accounting for overlapping administrative boundary areas and crop cultivation ratios, indicated similar levels between the two methods. Hot spot analysis revealed that watersheds with elevated NBs mirrored the spatial patterns of livestock excreta and cropland. The spatial distribution characteristics of watersheds with high nutrient levels in rivers corresponded with the concentration characteristics of industrial and commercial areas. Therefore, applying watershed-level NBs based on land cover ratios that consider nutrient input characteristics in agricultural regions is deemed appropriate for selecting priority nutrient management areas. Collectively, this study presents a method for selecting nutrient management priority areas by simultaneously considering the spatial characteristics of various environmental factors, such as land cover, livestock excreta, river water quality, and land area-based watershed-specific NBs. The proposed approach, considering mixed land cover characteristics, is anticipated to be valuable for selecting priority management areas in watersheds with diverse pollution sources. Future research is needed to explore nutrient budgets within watersheds, the influence of land use on pollution sources, and their correlation with water quality.

Assessing the impact of booster vaccination on diphtheria transmission: Mathematical modeling and risk zone mapping

The COVID-19 pandemic caused significant disruptions in the healthcare system, affecting vaccinations and the management of diphtheria cases. As a consequence of these disruptions, numerous countries have experienced a resurgence or an increase in diphtheria cases. West Java province in Indonesia is identified as one of the high-risk areas for diphtheria, experiencing an upward trend in cases from 2021 to 2023. To analyze the situation, we developed an SIR model, which integrated DPT and booster vaccinations to determine the basic reproduction number, an essential parameter for infectious diseases. Through spatial analysis of geo-referenced data, we identified hotspots and explained diffusion in diphtheria case clusters. The calculation of R0 resulted in an R0 = 1.17, indicating the potential for a diphtheria outbreak in West Java. To control the increasing cases, one possible approach is to raise the booster vaccination coverage from the current 64.84% to 75.15%, as suggested by simulation results. Furthermore, the spatial analysis revealed that hot spot clusters were present in the western, central, and southern regions, posing a high risk not only in densely populated areas but also in rural regions. The diffusion pattern of diphtheria clusters displayed an expansion-contagious pattern. Understanding the rising trend of diphtheria cases and their geographic distribution can offer crucial insights for government and health authorities to manage the number of diphtheria cases and make informed decisions regarding the best prevention and intervention strategies.

Spatial Cluster Change of Schistosoma japonicum Transmission Foci in Indonesia During the Schistosomiasis Elimination Program

PURPOSE

The Government of Indonesia committed to eliminating schistosomiasis by 2025. Collaboratively snail control became one of the crucial strategies to ensure that the prevalence of Schistosoma japonicum in Oncomelania hupensis lindoensis reaches zero by the end of the program. This research investigated the spatial cluster change of S. japonicum transmission foci in Indonesia between 2017 and 2021.

METHODS

We mapped the snail foci, collected the snails, and calculated the snail density. We also conducted laboratory tests to detect the existence of cercariae in the snails. Identified infected snails were used to calculate the infection rate (IR) or snails' prevalence of schistosome cercariae among freshwater snails. We then analysed the spatial cluster using the Getis-Ord Gi* statistic to identify the hot and cold spots.

RESULTS

The 5-year schistosomiasis elimination program successfully declined 18.84% of the snail foci and reduced 40.37% of the infected snail foci. Local spatial autocorrelation of snail density and infection rate identified that in 2017 and 2021, the number of cold spots decreased by 53.91% and 0%, while hot spots increased by 2.63% and 56.1%. The presence of more hot spots suggests a rise in the number of foci with high snail density and infection rates. The implementation of snail control was not optimal, and the parasite transmission through domestic animals still existed, causing the spatial cluster of hot spots to change during this period. Most hotspots have been observed near settlements, primarily in cocoa plantations, developed and deserted rice fields, grassland, and bush wetlands.

CONCLUSION

During the schistosomiasis elimination program, the number of hot spots increased while cold spots decreased, and there were notable changes in the geographical distribution of hot spots, indicating a shift in the clustering pattern of schistosomiasis cases. The findings become essential for policymakers, particularly in selecting priority areas for intervention. In the Discussion section, we demonstrated the selection process based on the existence of hot and cold spots. Furthermore, we proposed that enhancing cross-sector integration is crucial, particularly in connection with the management of S. japonicum transmission through domestic animals.

The spatial distribution mechanism of PM2.5 and NO2 on the eastern coast of China

The spatial distribution characteristics of multi-air pollutants and their impacts are difficult to quantify effectively. As PM2.5 and NO2 are the main air pollutants, it is of great significance to explore the spatial causes of their pollution and their interaction mechanism. This study used machine learning (LightGBM) and hot spot analysis to map the spatial distribution of PM2.5 and NO2 in Southwest Fujian (SWFJ) in 2018 and their key pollution areas. Then, the factors and interactive detection of geographical detectors were used to conduct a detailed analysis of the quantitative impact of potential factors such as human activities, terrain, air pollutants, and meteorology on PM2.5 and NO2 pollution. From this we can learn that 1. LightGBM has good stability for drawing the spatial distribution of PM2.5 and NO2. 2. The spatial mechanism of PM2.5 and NO2 can be effectively interpreted from a massive data and macro perspective. 3. A large amount of evidence shows that potential factors such as human activities, topography, air pollutants and meteorology have direct or indirect effects on PM2.5 and NO2 pollution in the SWFJ area. This includes the direct impact of local road traffic emissions on the distribution of PM2.5 and NO2 pollution, the digestion of both by vegetation, the mutual transformation of atmospheric pollutants themselves, and the impact of meteorological conditions. This study not only confirms the effectiveness of machine learning combined with geographical detectors to promote the study of regional air pollution mechanisms, but also confirms the feasibility of exploring the spatial distribution mechanisms of various air pollutants. Therefore, this study is of great significance for explaining the spatial distribution of PM2.5 and NO2, and can also provide reference for policy formulation to reduce regional PM2.5 and NO2 concentrations.

Cadmium accumulation in paddy soils affected by geological weathering and mining: Spatial distribution patterns, bioaccumulation prediction, and safe land usage

The abnormal enrichment of cadmium (Cd) in soil caused by rock weathering and mining activities is an issue in southern China. Although the soil Cd content in these regions is extremely high, the bioavailability of Cd in the soils differs significantly. The carbonate area (CBA) and tin-mining area (TIA) in Hezhou City were investigated to determine the primary features of soil Cd mobility in these regions and improve environmental management. Lateral and vertical spatial distributions revealed different accumulation and migration mechanisms of soil Cd in the CBA and TIA. Further analyses revealed that mining activities and geological weathering resulted in different soil geochemical parameters, thus yielding significantly lower levels of Cd in rice grains in the CBA than in the TIA. The random forest (RF) model predicted the bioaccumulation factor (BAF) (R2 = 0.69) better than the support vector machine (SVM) model (R2 = 0.68). Subsequently, a novel land management scheme was proposed based on soil Cd and the prediction of Cd in rice to optimize the spatial resources of agricultural land and ensure the safety of rice for consumption. This study provides a novel approach for land management in Cd-contaminated areas.

COVID-19 case rates, spatial mobility, and neighbourhood socioeconomic characteristics in Toronto: a spatial-temporal analysis

OBJECTIVES

This study has two primary research objectives: (1) to investigate the spatial clustering pattern of mobility reductions and COVID-19 cases in Toronto and their relationships with marginalized populations, and (2) to identify the most relevant socioeconomic characteristics that relate to human mobility and COVID-19 case rates in Toronto's neighbourhoods during five distinct time periods of the pandemic.

METHODS

Using a spatial-quantitative approach, we combined hot spot analyses, Pearson correlation analyses, and Wilcoxon two-sample tests to analyze datasets including COVID-19 cases, a mobile device-derived indicator measuring neighbourhood-level time away from home (i.e., mobility), and socioeconomic data from 2016 census and Ontario Marginalization Index. Temporal variations among pandemic phases were examined as well.

RESULTS

The paper identified important spatial clustering patterns of mobility reductions and COVID-19 cases in Toronto, as well as their relationships with marginalized populations. COVID-19 hot spots were in more materially deprived neighbourhood clusters that had more essential workers and people who spent more time away from home. While the spatial pattern of clusters of COVID-19 cases and mobility shifted slightly over time, the group socioeconomic characteristics that clusters shared remained similar in all but the first time period. A series of maps and visualizations were created to highlight the dynamic spatiotemporal patterns.

CONCLUSION

Toronto's neighbourhoods have experienced the COVID-19 pandemic in significantly different ways, with hot spots of COVID-19 cases occurring in more materially and racially marginalized communities that are less likely to reduce their mobility. The study provides solid evidence in a Canadian context to enhance policy making and provide a deeper understanding of the social determinants of health in Toronto during the COVID-19 pandemic.

Spatio-temporal evaluation of respiratory disease based on the information provided by patients admitted to a medical college hospital in Bangladesh using geographic information system

In Bangladesh respiratory illnesses are one of the leading risk factors for death and disability. Limited access to healthcare services, indoor and outdoor air pollution, large-scale use of smoking materials, allergens, and lack of awareness are among the known leading factors contributing to respiratory illness in Bangladesh. Key initiatives taken by the government to handle respiratory illnesses include, changing of respiratory health policy, building awareness, enhancing healthcare facility, and promoting prevention measures. Despite all these efforts, the number of individuals suffering from respiratory diseases has increased steadily during the recent years. This study aims at examining the distribution pattern of respiratory diseases over space and time using Geographic Information System, which is expected to contribute to the better understand of the factors contributing to respiratory illness development. To achieve the aims of the study two interviews were conducted among patients with respiratory sickness in the medicine and respiratory medicine units of Rajshahi Medical College Hospital between January and April of 2019 and 2020 following the guidelines provided by the Ethics Committee, Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh (ethical approval reference number: 2018/08). Principal component extraction and spatial statistical analyses were performed to identify the key respiratory illnesses and their geographical distribution pattern respectively. The results indicate, during January-February the number of patients was a lot higher compared to March-April. The patients were hospitalized mainly due to four respiratory diseases (chronic obstructive pulmonary disease, asthma, pneumonia, and pulmonary hypertension). Geographical distribution pattern of respiratory disease cases also varied considerably between the years as well as months of the years. This information seems reasonable to elucidate the spatio-temporal distribution of respiratory disease and thus improve the existing prevention, control, and cure practices of respiratory illness of the study area. Approach used in this study to elicit spatio-temporal distribution of repertory disease can easily be implemented in other areas with similar geographical settings and patients' illness information from hospital.

Identification of soil parent materials in naturally high background areas based on machine learning

Recently, farmlands with high geological background of Cd derived from carbonate rock (CA) and black shale areas (BA) have received wide attention. However, although both CA and BA belong to high geological background areas, the mobility of soil Cd differs significantly between them. In addition to the difficulty in reaching the parent material in deep soil, it is challenging to perform land use planning in high geological background areas. This study attempts to determine the key soil geochemical parameters related to the spatial patterns of lithology and the main factors influencing the geochemical behavior of soil Cd, and ultimately uses them and machine-learning methods to identify CA and BA. In total, 10,814 and 4323 surface soil samples were collected from CA and BA, respectively. Hot spot analysis revealed that soil properties and soil Cd were significantly correlated with the underlying bedrock, except for TOC and S. Further research confirmed that the concentration and mobility of Cd in high geological background areas were mainly affected by pH and Mn. The soil parent materials were then predicted using artificial neural network (ANN), random forest (RF) and support vector machine (SVM) models. The ANN and RF models showed higher Kappa coefficients and overall accuracies than those of the SVM model, suggesting that ANNs and RF have the potential to predict soil parent materials from soil data, which might help in ensuring safe land use and coordinating activities in high geological background areas.

Biogas potential from agricultural waste and its CO2 emission reduction: a case study of Hubei Province, China

Biogas produced from agricultural waste can have potential benefits, such as offer clean renewable energy, protect the ecological environment, and reduce CO₂ emission. However, few studies have been conducted on the biogas potential from agricultural waste and its CO₂ emission reduction at the county level. Herein, the biogas potential from agricultural waste was calculated, and its spatial distribution in Hubei Province in 2017 was determined using a geographic information system. Then, an evaluation model for the competitive advantage of the biogas potential from agricultural waste was established using entropy weight and linear weighting methods. Moreover, the space partition of the biogas potential from agricultural waste was obtained through hot spot analysis. Lastly, the standard coal equivalent of biogas, the equivalent of coal consumption of biogas, and the CO₂ emission reduction based on the space partition result were estimated. Results showed that the total and average biogas potentials from agricultural waste in Hubei Province were 18,498,317,558.54 and 222,871,295.89 m³, respectively. Qianjiang City, Jianli County, Xiantao City, and Zaoyang City had a high competitive advantage in the biogas potential from agricultural waste. The CO₂ emission reduction of the biogas potential from agricultural waste was mainly in classes I and II.

Identification of the spatial patterns and controlling factors of Se in soil and rice in Guangxi through hot spot analysis

Selenium (Se) is essential to human health, anti-cancer, possessing antioxidant, and antiviral properties. In this study, the spatial patterns of rice Se and their varying relationship with soil Se on a regional scale were studied using hot spot analysis for the agricultural soils in Guangxi. According to the hot and cold spot maps, rice Se correlates positively with soil Se in Guangxi agricultural soils. High rice Se accompanies high soil Se in the central part of Guangxi (e.g., Liuzhou, Laibin), and low rice Se is in line with low soil Se in the western part (e.g., Baise). However, the hot spot analysis maps indicate that southwestern Guangxi exhibits a special characteristic of low rice Se with high soil Se (e.g., Chongzuo). This special pattern is strongly associated with the high concentrations of Fe₂O₃ (ferromanganese nodules) in the carbonate rock area. The hot spot analysis proves useful in revealing the spatial patterns of rice Se in Guangxi and identifying the hidden patterns.

Spatial distribution differences of cholinesterase in healthy Chinese under the influence of geographical environmental factors

The main targets of this were to screen the factors that may influence the distribution of cholinesterase (CHE) reference value in healthy people, and further explored the geographical distribution differences of CHE reference value in China. In this study, we collected the CHE data of 17,601 healthy people from 173 cities in China to analyse the correlation between CHE and 22 geography secondary indexes through spearman regression analysis. Six indexes with significant correlation were extracted, and a ridge regression model was built, and the country's urban CHE reference value of healthy Chinese was predicted. By using the disjunctive kriging method, we obtained the geographical distribution of CHE reference values for healthy people in China. The reference value of CHE for healthy Chinese was significantly correlated with the 6 secondary indexes, namely, latitude (°), altitude (m), annual average temperature (°C), annual average relative humidity (%) and annual precipitation (mm), and topsoil sand gravel percentage (% wt). The geographical distribution of CHE values of healthy Chinese showed a trend of being higher in southeast China and lower in northwest. This study lays a foundation for further research on the mechanism of different influencing factors on the reference value of CHE index. A ridge regression model composed of significant influencing factors has been established to provide the basis for formulating reference criteria for the treatment factors of the liver damage diseases and liver cancer using CHE reference values in different regions.

A new drought index and its application based on geographically weighted regression (GWR) model and multi-source remote sensing data

Drought is the most widespread natural disaster in the world. How to monitor regional drought scientifically and accurately has become a hot topic for many scholars. In this paper, Geographically Integrated Dryness Index (GIDI) was integrated from an assortment source including Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI) (as the dependent variable) based on geographically weighted regression method. Besides, the comprehensive drought situation and changing trends in China from 2001 to 2019 were also examined. The results showed that (1) GIDI has excellent performance in monitoring medium- and long-term droughts and the monitoring results shows 2003, 2016, and 2019 were relatively wet years, while 2007, 2009, and 2011 were major drought years, and spring and March were the most frequent droughts season and month, respectively, and (2) except for the middle and upper reaches of the Yellow River and Northeastern China, which have a tendency to become wet, other places have a tendency to fluctuating dry. This study took advantage of simple and efficient methods to integrate existing indices to obtain a new index for monitoring a wider range of droughts, taking into account the physical mechanism of drought formation and the time scale of drought development, so it can scientifically evaluate the spatial and temporal distribution characteristics of drought and changes.

Habitat quality dynamics in China's first group of national parks in recent four decades: Evidence from land use and land cover changes

As the most biodiversity-rich part of the protected areas system, habitats within the pilot national parks have long been threatened by drastic human-induced land use and land cover changes. The growing concern about habitat loss has spurred China's national park project to shift from pilot to construction phase with the official establishment of China's first group of national parks (CFGNPs) in October 2021. But far too little attention has been paid to the synergistic work concerning the habitat quality (HQ) dynamics of all five national parks. Here, the InVEST model, combined with a satellite-derived land use and land cover product and a hot spot analysis (HSA) method, was used to investigate the HQ dynamics at the park- and pixel-scale within the CFGNPs. Our results demonstrate that the past ecological conservation practices within national parks have been unpromising, especially in Giant Panda National Park, Northeast China Tiger and Leopard National Park (NCTL), and Wuyi Mountain National Park (WYM), where HQ as a whole showed a significant decline. Furthermore, more than half of Hainan Tropical Rainforest National Park (87.2%), WYM (77.4%), and NCTL (52.9%) showed significant HQ degradation from 1980 to 2019. Besides, increasing trends in the area shares of HQ degraded pixels were observed in all five national parks from 1980-1999 to 2000-2019. The HSA implied that the hot spots of high HQ degradation rates tend to occur in areas closer to urban settlements or on the edge of national parks, where human activities are intensive. Despite these disappointing findings, we highlighted from the observed local successes and the HQ plateau that the construction of CFGNPs is expected to reverse the deteriorating HQ trends. Thus, we concluded our paper by proposing an HSA-based regulatory zoning scheme that includes five subzones to guide the future construction of China's national park system.

An overview of plutonium isotopes in soils, China: Distribution, spatial patterns, and sources

Plutonium (Pu) is an anthropogenic radionuclide which has drawn significant attentions due to its radiotoxicity, and the sources of plutonium linked with nuclear accidents and contaminations. The ²⁴⁰Pu/²³⁹Pu atom ratio is source dependent and can be used as a fingerprint to determine the sources of radioactive contaminant. However, the distribution and sources of plutonium in soils of China have not yet been systematically studied at a national scale up to date. The distribution, spatial patterns, and sources of plutonium in soils of China were discussed in this work. The concentrations of ^239,240Pu are in the range of 0.002-4.824 mBq/g with a large variation, and the ^239,240Pu concentrations in surface soils increase with the increasing latitude, which affects by multi-factors such as organic matter and particle size, etc. The inventories of ^239,240Pu are in the range of 7.31-554 Bq/m². The weighted average of ²⁴⁰Pu/²³⁹Pu atom ratios (0.180 ± 0.004) in all surface samples is good agreement with the ratio of global fallout (0.180 ± 0.014) of the nuclear weapons tests, this indicate that the major source of plutonium in China is global fallout. However, among some sites, distinctly lower ²⁴⁰Pu/²³⁹Pu atom ratio compared to the global fallout values were observed in the northwest China, indicating a significant contribution from other source besides the global fallout. Furthermore, the spatial clustering patterns of hot spots (high values) and cold spots (low values) for plutonium showing the clear associations with nuclear tests, especially the Chinese Lop Nor nuclear weapons tests (CNTs) and the Semipalatinsk nuclear weapons tests (STS). Radioactive material including plutonium from the STS or CNTs was transported by the prevailing westerlies to the northwest China. This review about the fingerprints and distribution of plutonium in soils of China will help researchers to establish a reference database for future radiation risk assessment and environmental radioactive management.

Investigation of land cover (LC)/land use (LU) change affecting forest and seminatural ecosystems in Istanbul (Turkey) metropolitan area between 1990 and 2018

This study was conducted to examine the land cover (LC)/land use (LU) change affecting forest and seminatural ecosystems and the spatio-temporal development of urban expansion between 1990 and 2018 in the city of Istanbul, where urbanization is the most intense in Turkey. For this purpose, using Corine Land Cover (1990, 2000, 2006, 2012, and 2018) dataset, the land cover of the area was determined in 5 different classes (artificial surface, agriculture, forest, water bodies, water), maps were produced, and tabular data were created. The changes in LC/LU between 1990 and 2018 were determined according to the Puyravaud land cover change rate and hot spot analysis methods. According to our findings, we determined that urbanization in Istanbul expanded the most in the east-west direction, and the agricultural and forest areas gradually decreased by 3.02% and 6.66% respectively; urban areas increased at the same rate of 9.69%. It is predicted that this change will continue increasing until 2030 when the forecasting method is applied in the field. It has been determined that the most important reasons for this situation are local government policies, population growth, and economic development initiatives applied in the area. As a result, it has emerged that measures should be taken based on sustainability and naturalness approaches to design urban development plans and to protect natural areas on a large scale, in order to limit possible LC/LU conversion from natural structure to urbanization in the area.

Redeploy manure resources to enhance the agro-pastoral cycle

Returning manure to the land is a critical link in the internal cycle of agricultural systems, but excess manure leads to water eutrophication. The traditional manure re-use method brings pathogenic microorganisms, heavy metals, antibiotic resistance genes (ARGs), insect eggs, and other contaminants into the soil, posing a great threat to the ecological environment and human health. Clarifying the spatial distribution patterns of manure nutrient supply and farmland nutrient demand can help guide a more efficient and harmless way to return manure to farmland. This work counted data on cultivation and breeding in 356 cities on the Chinese mainland from 2015 to 2019 and calculated the livestock breeding volume (LB), total environmental capacity (C), and remaining environmental capacity (RC) accordingly. The Spatial Autocorrelation Model (SAC) was used to analyze the distribution patterns of the three. Data results show that China currently has the potential to double LB, but most cities in the west have excess manure due to the mismatched distribution of LB and C. The hot spot analysis results demonstrate the priority/general areas of manure management and the export/import areas of manure resources. The results of the outlier analysis show that some cities located at the boundary of RC Cold/Hot spot areas (e.g., Chengdu City) can perform resource replacement nearby to relieve local environmental pressure. This study analyzes the potential and realistic resistance to utilizing manure as an organic nutrient resource and provides a reference for developing manure management links.

Identification of Areas Highly Vulnerable to Land Conversion: A Case Study From Southern Thailand

Land conversion is having major impacts on wildlife globally, and thus understanding and predicting patterns of land conversion is an important component of conservation planning. Southeast Asia is undergoing rapid habitat conversion; however, most countries in the region have very limited human resources devoted to planning, and typically land-cover trend assessments are often challenging. Here we demonstrate a rapid method for land-cover change quantification for areas of terrestrial, mangrove and peat swamp forests at high risk from land conversion that can be quickly and simply predicted using southern Thailand as an example. Land-cover maps from two time periods (1995/1996 and 2015/2016) were produced and compared to determine changes between the two time periods. Five land-cover categories (terrestrial forest, mangrove forest, peat swamp forest, human settlement, agriculture) were estimated along with land-cover changes. Hot spots of high percentage change for human settlement and agriculture were identified, and vulnerable habitats were mapped including terrestrial forest, mangrove forest and peat swamp forest. Between 1996 and 2016, 22.1% of terrestrial forests, 26.2% of mangrove forests and 55% of peat swamp forests were lost. The losses of these natural habitats were clearly associated with agricultural expansion. Approximately 10.6%, 14.3% and 33% of terrestrial, mangrove and peat swamp forest remaining were identified as highly vulnerable, of which the majority were at the boundaries between natural and human-dominated areas. The technique offers promise for rapidly identifying high priority areas for more detailed analysis and potential conservation interventions.

Spatial statistics techniques for SPEI and NDVI drought indices: a case study of Khuzestan Province

Drought is a major water resources management issue in Iran. Khuzestan Province is in a drought state due to water shortage. Therefore, identifying areas at high risk of drought and when drought occurs is essential for drought management. For this purpose, this study used precipitation and temperature data of 12 selected stations and MODIS sensor images from the United States Geological Survey database in 2000-2017. The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Normalized Difference Vegetation Index (NDVI) were calculated using the Hargreaves-Samani method and ENVI software. Moreover, different spatial statistics techniques were used in the ArcGIS environment to analyze the results. Also, time series diagrams were drawn, and the trend was evaluated using the Mann-Kendall test. Finally, the distribution of NDVI values was investigated using EasyFit software, and the amount of drought damage was determined using NDVI. The investigation of the cluster maps of the Anselin Local Moran's Index along with hot and cold spots formed for both SPEI and NDVI showed that drought severity was higher at the southern stations than at the semi-northern and northwestern ones in the province. Moreover, the survey results using the EasyFit software showed that the southern stations, including the Ahvaz, Mahshahr, and Omidiyeh-Aghajari stations, were more at risk of drought than the other stations due to the drought threshold. Furthermore, the total damage caused by drought for the Ahvaz and Abadan stations showed a damage rate of 50%.

GIS-based risk mapping of cutaneous leishmaniasis: a survey in an endemic area of Central Iran

Cutaneous leishmaniasis (CL) is a vector-borne infectious disease that is affected by various environmental agents. The main objective of this study was to determine the spatial distribution of CL incidence by using Geographical Information System (GIS). This is a cross-sectional study that was conducted during 5 years from 2014 to 2018 in Isfahan, Iran. We used the required data on each leishmaniasis patient that were recorded from 44 counties of Isfahan in the databases and archive of the Provincial Health Centre. We used GIS for determining the incidence of CL in the high-risk foci. Moran index was used to identify high risk points (clustering in similar values) compared to the values of neighborhood points. Hot spot analysis was conducted by Getis-Ord-Gi. The highest incidence of the disease occurred in the age group of 18-64 years and 61.6% of patients were male. According to seasonal distribution, autumn (58.6%) had the highest frequency. Time trend of incidence showed that it had both decreasing and increasing, and there was a sudden upward trend of disease in 2018 except only two counties. The hot spots were involved the central areas of the Isfahan province slightly toward to the north and southeast of the province. Moran index showed that the differences for all points were not significant (p-value>0.05). Varzaneh (placed in southeast of Isfahan) was the hottest spot and had the worst position for leishmaniasis compared to all years and all cities.

Spatial and temporal analysis of myocardial infarction incidence in Zanjan province, Iran

Background

Myocardial Infarction (MI) is a major important public health concern and has huge burden on health system across the world. This study aimed to explore the spatial and temporal analysis of the incidence of MI to identify potential clusters of the incidence of MI patterns across rural areas in Zanjan province, Iran.

Materials & methods

This was a retrospective and geospatial analysis study of the incidence of MI data from nine hospitals during 2014–2018. Three different spatial analysis methods (Spatial autocorrelation, hot spot analysis and cluster and outlier analysis) were used to identify potential clusters and high-risk areas of the incidence of MI at the study area.

Results

Three thousand eight hundred twenty patients were registered at Zanjan hospitals due to MI during 2014–2018. The overall age-adjusted incidence rate of MI was 343 cases per 100,000 person which was raised from 88 cases in 2014 to 114 cases in 2018 per 100,000 person-year (a 30% increase, P < 0.001). Golabar region had the highest age-adjusted incidence rate of MI (515 cases per 100,000 person). Five hot spots and one high-high cluster were detected using spatial analysis methods.

Conclusion

This study showed that there is a great deal of spatial variations in the pattern of the incidence of MI in Zanjan province. The high incidence rate of MI in the study area compared to the national average, is a warning to local health authorities to determine the possible causes of disease incidence and potential drivers of high-risk areas. The spatial cluster analysis provides new evidence for policy-makers to design tailored interventions to reduce the incidence of MI and allocate health resource to unmet need areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11695-8.

Land use change simulation and spatial analysis of ecosystem service value in Shijiazhuang under multi-scenarios

Simulating changes in the value of ecosystem services caused by land use changes in large cities under multiple scenarios is of great significance for cities to formulate land use policies and improve ecosystem services. Take Shijiazhuang, which is in the process of rapid urbanization, as an example. Based on the remote sensing image data and statistical yearbook of 1988, 1998, 2008, and 2018 as the basic data to analyze and estimate the 30 years of land use and ecosystem service value changes in Shijiazhuang. According to this, the CA-Markov model was used to simulate the land use change in Shijiazhuang under three scenarios in 2030 and estimate the value of ecosystem services under each scenario, using grid tools to visually express the spatial distribution of ecosystem service values and the degree of agglomeration under three scenarios. The results indicate that the most obvious feature of land use change in Shijiazhuang from 1988 to 2018 was that the farmland area decreased year by year, the built-up expanded rapidly, the farmland area decreased by 86,874.75 hm2 in 30 years, and the built-up increased by 154,711.90 hm2. In 1988, 1998, 2008, and 2018, the ecosystem service value of Shijiazhuang was 32.578 billion yuan, 32.799 billion yuan, 29.944 billion yuan, and 31.251 billion yuan respectively. In 2030, under three scenarios of natural development, farmland protection, and ecological protection, the value of ecosystem services is 331.111 billion yuan, 33.670 billion yuan, and 33.891 billion yuan in order. The hot spots are mainly concentrated in the northwest and southwest of Shijiazhuang, and cold spots are concentrated in the eastern cities, counties, and districts. Based on changes in land use brought about by urban expansion, simulating the value of ecosystem services under multiple scenarios in the future, providing scientific guidance for building urban ecological networks, and realizing sustainable urban ecological development.

Discovering hidden spatial patterns and their associations with controlling factors for potentially toxic elements in topsoil using hot spot analysis and K-means clustering analysis

The understanding of sources and controlling factors of potentially toxic elements (PTEs) in soils plays an important role in the improvement of environmental management. With the rapid growth of data volume, effective methods are required for data analytics for the large geochemical data sets. In recent years, spatial machine learning technologies have been proven to have the potential to reveal hidden spatial patterns in order to extract geochemical information. In this study, two spatial clustering techniques of Getis-Ord G_i* statistic and K-means clustering analysis were performed on 15 PTEs in 6,862 topsoil samples from the Tellus datasets of Northern Ireland to investigate the hidden spatial patterns and association with their controlling factors. The spatial clustering patterns of hot spots (high values) and cold spots (low values) for the 15 PTEs were revealed, showing clear association with geological features, especially peat and basalt. Peat was associated with high concentrations of Bi, Pb, Sb and Sn, while basalt was associated with high concentrations of Co, Cr, Cu, Mn, Ni, V and Zn. The high concentrations of As, Ba, Mo and U were associated with mixture of various lithologies, indicating the complicated influences on them. In addition, three hidden patterns in the 6,862 soil samples were revealed by K-means clustering analysis. The soil samples in the first and second clusters were overlaid on the peatland and basalt formation, respectively, while the samples in the third cluster were overlaid on the mixture of the other lithologies. These hidden patterns of soil samples were consistent with the spatial clustering patterns for PTEs, highlighting the dominant control of peat and basalt in the topsoil of Northern Ireland. This study demonstrates the power of spatial machine learning techniques in identifying hidden spatial patterns, providing evidences to extract geochemical knowledge in environmental studies.

Nearshore Sedimentary Mercury Concentrations Reflect Legacy Point Sources and Variable Sedimentation Patterns Under a Natural Recovery Strategy

The St. Lawrence River at Cornwall, Ontario, Canada, received substantial inputs of mercury from local, shoreline-based industries through much of the 20th century. Although emission controls were implemented in the late 20th century to reduce the influx of mercury and other metals entering the river, legacy contamination of riverine sediments continues to be a concern. Monitored natural recovery was prescribed in 2005 to remediate contaminated sediments; however, few surveys have been undertaken to examine its effectiveness on shallow, nearshore sediments in contaminated areas. Surface sediments were collected at shallow, nearshore sites in contaminated zones and upstream reference areas to evaluate the current state of sedimentary contamination of mercury and other metals. A Getis-Ord Gi* "hot spot" analysis was employed to assess the spatial distribution of contaminants. In addition, 3 sediment cores were collected from contaminated zones and dated using radioisotopes (²¹⁰ Pb) to assess sedimentation patterns over time. Results indicated that surface sediments from contaminated zones remained elevated in mercury relative to reference sites but spatial distribution of contaminants was highly heterogeneous. Dated sediment cores suggested that sedimentation was not occurring consistently across all areas; variable sedimentation and resuspension patterns over small spatial scales were likely factors driving heterogeneous sedimentary contamination. Such patterns complicate remediation strategies because unburied sediments may serve as continuing sources of contaminants to the ecosystem. Environ Toxicol Chem 2021;40:1788-1799. © 2021 SETAC.

Spatial analysis of water quality parameters in Hilo Bay, Hawai'i, using a combination of interpolated surfaces and hot spot analysis

Hilo Bay estuary, located on the northeastern side of Hawai'i Island, experiences variability in water quality parameters due to its numerous water inputs. This estuary experiences influxes of water from three sources: groundwater to the east, marine water from the north, and surface water from the Wailuku River to the west. High rainfall and river flow impacts Hilo Bay's water quality including salinity, turbidity, and chlorophyll a concentration. Here, maps of Hilo Bay water quality were examined to assess spatial patterns of these important parameters. Exploring the patterns of these water quality parameters by creating inverse distance weighted (IDW) interpolation surfaces of survey points and clusters based on hot spot analyses during low- and high-flow conditions showed statistically significant differences in spatial water quality in Hilo Bay. Water quality maps after a storm show (1) an overall decrease in salinity, (2) a river plume from the Wailuku River associated with a turbidity hot spot, and (3) a chlorophyll a hot spot offset from the river plume in the center of the bay. Using spatial analysis to analyze water quality throughout the entirety of Hilo Bay before and after storm events can lead to a better understanding of how this ecosystem is affected during these types of events, and furthermore, adopting this method of sampling and analysis allows for a greater representation of water quality all over the bay and can improve the monitoring of water quality in this important ecosystem.

Liaison between exposure to sub-micrometric particulate matter and allergic response in children from a petrochemical industry city

The study examines the association between exposure to sub-micrometric Particulate Matter (PM₁) and allergic response in a group of sensitive young children (age: 2-10 years) from Ploiesti city, Romania. The city is the only urban agglomeration in Europe surrounded by four oil refineries. A panel study was conducted by collecting medical information from children with respiratory illnesses and atopy (n = 135). Hot Spot Analysis revealed the areas of the city that are susceptible to high levels of PM₁. We found a close interaction between exposure to PM₁ outdoor concentrations and various physiological changes and clinical symptoms in children including triggering of allergic reactions, rhinitis, alteration of lung function, upper and lower respiratory tract symptoms, and bronchial asthma. During the 2-year study period, the incidence of hospitalizations was 40.7%. Strong correlations (p < 0.001) were observed between the PM₁ exposure and hospitalizations, and exposure and Immunoglobulin E (IgE). PM₁ exposure was also correlated with eosinophils (p < 0.05). Another positive correlation was observed between hospitalizations and IgE levels (p < 0.05). The mean results of tested indicators were as follows: wheezing (5.3, 95% CI (1.4-1.8); Coeff. of var. (CV) = 30%), IgE (382, 95% CI (349-445); CV = 102%), and EO% (5.3, 95% CI (3.3-4.2); CV = 69.5%). We can conclude that exposure to PM₁ influenced the frequency of wheezing episodes, increased hospitalizations, and the levels of allergic blood indicators in children, especially in infants and pre-schoolers. CAPSULE: Exposure to sub-micrometric particles (PM₁) influences the frequency of wheezing episodes, hospitalizations, and the levels of allergic blood indicators in children, especially in infants and pre-schoolers.

Vascular plant species diversity of Mt. Etna (Sicily): endemicity, insularity and spatial patterns along the altitudinal gradient of the highest active volcano in Europe

Background

Altitudinal variation in vascular plant richness and endemism is crucial for the conservation of biodiversity. Territories featured by a high species richness may have a low number of endemic species, but not necessarily in a coherent pattern. The main aim of our research is to perform an in-depth survey on the distribution patterns of vascular plant species richness and endemism along the elevation gradient of Mt. Etna, the highest active volcano in Europe.

Methods

We used all the available data (literature, herbarium and seed collections), plus hundreds of original (G Giusso, P Minissale, S Sciandrello, pers. obs., 2010–2020) on the occurrence of the Etna plant species. Mt. Etna (highest peak at 3,328 mt a.s.l.) was divided into 33 belts 100 m wide and the species richness of each altitudinal range was calculated as the total number of species per interval. In order to identify areas with high plant conservation priority, 29 narrow endemic species (EE) were investigated through hot spot analysis using the “Optimized Hot Spot Analysis” tool available in the ESRI ArcGIS software package.

Results

Overall against a floristic richness of about 1,055 taxa, 92 taxa are endemic, of which 29 taxa are exclusive (EE) of Mt. Etna, 27 endemic of Sicily (ES) and 35 taxa endemic of Italy (EI). Plant species richness slowly grows up to 1,000 m, then decreases with increasing altitude, while endemic richness shows an increasing percentage incidence along the altitudinal gradient (attributed to the increased isolation of higher elevation). The highest endemic richness is recorded from 2,000 up to 2,800 m a.s.l., while the highest narrow endemic richness (EE) ranges from 2,500 up to 2,800 m a.s.l. Life-form patterns clearly change along altitudinal gradient. In regard to the life-form of the endemics, the most represented are the hemicryptophytes, annual plants (therophytes) are prevailing at lower altitudes and show a decreasing trend with increasing elevation, while chamaephytes are featured by an increasing trend up to 3,100 m of altitude. Furthermore, the results of the hotspot analysis emphasize the high plant conservation priority areas localized in oro-mediterranean (1,800–2,400 m s.l.m.) and cryo-mediterranean (2,400–2,800 m) bioclimatic belts, in correspondence of the oldest substrates of the volcano.

Conclusions

High plant speciation rate caused by increasing isolation with elevation is the most plausible explanation for the largest active volcano in Europe. The high degree of endemic species on Mt. Etna is linked to its geographical, geological and climatic isolation, all important drivers of speciation acting on the population gene flows. The hot spot map obtained represents a useful support for help environmental decision makers to identify priority areas for plant conservation.

Geospatial analysis of Mediterranean diet adherence in the United States

OBJECTIVE

The current study aims to describe the Mediterranean diet (MD) adherence across the US regions, and explore the predictive factors of MD adherence among US adults.

DESIGN

Cross-sectional secondary data analysis. MD adherence score (0-9) was calculated using the Block 98 FFQ. Hot spot analysis was conducted to describe the geospatial distribution of MD adherence across the US regions. Logistic regression explored predictors of MD adherence.

SETTING

Nationwide community-dwelling residency in the USA.

PARTICIPANTS

Adults aged ≥45 years (n 20 897) who participated in the REasons for Geographic and Racial Differences in Stroke study and completed baseline assessment during January 2003 and October 2007.

RESULTS

The mean of MD adherence score was 4·36 (sd 1·70), and 46·5 % of the sample had high MD adherence (score 5-9). Higher MD adherence clusters were primarily located in the western and northeastern coastal areas of the USA, whereas lower MD adherence clusters were majorly observed in south and east-north-central regions. Being older, black, not a current smoker, having a college degree or above, an annual household income ≥ $US 75K, exercising ≥4 times/week and watching TV/video <4 h/d were each associated with higher odds of high MD adherence.

CONCLUSIONS

There were significant geospatial and population disparities in MD adherence across the US regions. Future studies are needed to explore the causes of MD adherence disparities and develop effective interventions for MD promotion in the USA.

Google-truthing to assess hot spots of food retail change: A repeat cross-sectional Street View of food environments in the Bronx, New York

Google Street View (GSV) images can be used to "ground-truth" current and historical food retail data from approximately 2007 - when GSV was launched in a few US cities - to the present, facilitating analyses of food environments over time. A review of GSV images of all food retailers listed in a government database of licensed establishments in the Bronx, New York enabled records to be verified, businesses classified, and retail change quantified. The data revealed several trends likely to affect food access and health: increasing overall numbers of food retailers; the growth of dollar stores; and numerous openings, closings, and ownership changes across all food retail segments. Hot spot analysis identified statistically significant clusters of new dollar stores and bodegas, purveyors of less healthy processed foods, in lower-income neighborhoods in the South Bronx that face elevated rates of diet-related diseases. This article demonstrates the benefits and limitations of using GSV to conduct "virtual" food environment research.

Spatial analysis of individual- and village-level sociodemographic characteristics associated with age at marriage among married adolescents in rural Niger

Background

Niger has the highest prevalence of child marriage in the world. While child marriage in Niger is clearly normative in the sense that it is commonly practiced, the social and contextual factors that contribute to it are still unclear.

Methods

Here, we tested the importance of village-level factors as predictors of young age at marriage for a group of married adolescent girls (N = 1031) in the Dosso district of rural Niger, using multi-level and geographic analyses. We aggregated significant individual level factors to determine whether, independent of a girl’s own sociodemographic characteristics, the impact of each factor is associated at the village level. Finally, we tested for spatial dependence and heterogeneity in examining whether the village-level associations we find with age at marriage differ geographically.

Results

The mean age of marriage for girls in our study was 14.20 years (SD 1.8). Our statistical results are consistent with other literature suggesting that education is associated with delayed marriage, even among adolescent girls. Younger ages at marriage are also associated with a greater age difference between spouses and with a greater likelihood of women being engaged in agricultural work. Consistent with results at the individual level, at the village level we found that the proportion of girls who do agricultural work and the mean age difference between spouses were both predictive of a lower age at marriage for individual girls. Finally, mapping age at marriage at the village level revealed that there is geographical variation in age at marriage, with a cluster of hot spots in the Hausa-dominated eastern area where age at marriage is particularly low and a cluster of cold spots in the Zarma-dominated western areas where age at marriage is relatively high.

Conclusions

Our findings suggest that large-scale approaches to eliminating child marriage in these communities may be less successful if they do not take into consideration geographically and socially determined contextual factors at the village level.

Identification of the co-existence of low total organic carbon contents and low pH values in agricultural soil in north-central Europe using hot spot analysis based on GEMAS project data

Total organic carbon (TOC) contents in agricultural soil are presently receiving increased attention, not only because of their relationship to soil fertility, but also due to the sequestration of organic carbon in soil to reduce carbon dioxide emissions. In this research, the spatial patterns of TOC and its relationship with pH at the European scale were studied using hot spot analysis based on the agricultural soil results of the Geochemical Mapping of Agricultural Soil (GEMAS) project. The hot and cold spot maps revealed the overall spatial patterns showing a negative correlation between TOC contents and pH values in European agricultural soil. High TOC contents accompanying low pH values in the north-eastern part of Europe (e.g., Fennoscandia), and low TOC with high pH values in the southern part (e.g., Spain, Italy, Balkan countries). A special feature of co-existence of comparatively low TOC contents and low pH values in north-central Europe was also identified on hot and cold spot analysis maps. It has been found that these patterns are strongly related to the high concentration of SiO₂ (quartz) in the coarse-textured glacial sediments in north-central Europe. The hot spot analysis was effective, therefore, in highlighting the spatial patterns of TOC in European agricultural soil and helpful to identify hidden patterns.

Using crowd sourcing to locate and characterize conflicts for vulnerable modes

Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data to assess transportation safety; however, in the context of public health where perceptions of safety may influence the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-motor vehicle and other similar conflicts types may define a better performance measure for safety assessment. In the field of transportation safety, an absolute conflict occurs when two parties' paths cross and one of the parties must undertake an evasive maneuver (e.g. change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver is required may also impact public perceptions of safety especially for vulnerable modes. Most of the existing literature focuses on vehicle conflicts. While in the past several years, more research has investigated bicycle and pedestrian conflicts, most of this has focused on the intersection environment. A comprehensive analysis of conflicts appears critical. The major objective of this study is two fold: 1) Development of an innovative and cost effective conflict data collection technique to better understand the conflicts (and their severity) involving vulnerable road users (e.g. bicycle/pedestrian, bicycle/motor vehicle, and pedestrian/motor vehicle) and their severity. 2) Test the effectiveness and practicality of the approach taken and its associated crowd sourced data collection. In an endeavor to undertake these objectives, the researchers developed an android-based crowd-sourced data collection app. The crowd-source data collected using the app is compared with traditional fatality data for hot spot analysis. At the end, the app users provide feedback about the overall competency of the app interface and the performance of its features to the app developers. If widely adopted, the app will enable communities to create their own data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data source at low-cost to help inform their decision making related to bicycle and pedestrian education, encouragement, enforcement, programs, policies, and infrastructure design and planning.

Establishment of trauma registry at Queen Elizabeth Central Hospital (QECH), Blantyre, Malawi and mapping of high risk geographic areas for trauma

BACKGROUND

Less attention is directed toward gaining a better understanding of the burden and prevention of injuries, in low and middle income countries (LMICs). We report the establishment of a trauma registry at the Adult Emergency and Trauma Centre (AETC) at Queen Elizabeth Central Hospital (QECH) in Blantyre, Malawi and identify high risk geographic areas.

METHODS

We devised a paper based two-page trauma registry form. Ten data clerks and all AETC clinicians were trained to complete demographic and clinical details respectively. Descriptive data, regression and hotspot analyses were done using STATA 15 statistical package and ArcGIS (16) software respectively.

RESULTS

There were 3,747 patients from May 2013 to May 2015. The most common mechanisms of injury were assault (38.2%), and road traffic injuries (31.6%). The majority had soft tissue injury (53.1%), while 23.8% had no diagnosis indicated. Fractures (OR 19.94 [15.34-25.93]), head injury and internal organ injury (OR 29.5 [16.29-53.4]), and use of ambulance (OR 1.57 [1.06-2.33]) were found to be predictive of increased odds of being admitted to hospital while assault (OR 0.69 [0.52-0.91]) was found to be associated with less odds of being admitted to hospital. Hot spot analysis showed that at 99% confidence interval, Ndirande, Mbayani and Limbe were the top hot spots for injury occurrence.

CONCLUSION

We have described the process of establishing an integrated and potentially sustainable trauma registry. Significant data were captured to provide details on the epidemiology of trauma and insight on how care could be improved at AETC and surrounding health facilities. This approach may be relevant in similar poor resource settings.

Exploration of spatial patterns of congenital anomalies in Los Angeles County using the vital statistics birth master file

Research has shown linkages between environmental exposures and population health metrics such as low birth weight and incidence of congenital anomalies. While the exact causal relationship between specific environmental teratogens and suspected corresponding congenital anomalies has largely not been established, spatial analysis of anomaly incidence can identify potential locations of increased risk. This study uses the Vital Statistics Birth Master File to map and analyze the rates of congenital anomalies of births from non-smoking mothers 15-35 years old within Los Angeles County. Hot spot analysis shows that the distribution of congenital anomalies is not randomly distributed throughout the county and identified the Antelope Valley and San Gabriel Foothills as two areas with elevated incidence rates. These results are not explained by potential confounders such as maternal age, race, smoking status, or socioeconomic status and seem to correlate well with the concentration of atmospheric ozone. This approach demonstrates the value of using spatial techniques to inform future research efforts and the need to establish and maintain a comprehensive reproductive health surveillance system.

Dynamic programming-based hot spot identification approach for pedestrian crashes

Network screening techniques are widely used by state agencies to identify locations with high collision concentration, also referred to as hot spots. However, most of the research in this regard has focused on identifying highway segments that are of concern to automobile collisions. In comparison, pedestrian hot spot detection has typically focused on analyzing pedestrian crashes in specific locations, such as at/near intersections, mid-blocks, and/or other crossings, as opposed to long stretches of roadway. In this context, the efficiency of the some of the widely used network screening methods has not been tested. Hence, in order to address this issue, a dynamic programming-based hot spot identification approach is proposed which provides efficient hot spot definitions for pedestrian crashes. The proposed approach is compared with the sliding window method and an intersection buffer-based approach. The results reveal that the dynamic programming method generates more hot spots with a higher number of crashes, while providing small hot spot segment lengths. In comparison, the sliding window method is shown to suffer from shortcomings due to a first-come-first-serve approach vis-à-vis hot spot identification and a fixed hot spot window length assumption.

Temporal Variations in the Distribution of West Nile Virus Within the United States; 2000-2008

West Nile Virus (WNV) is a serious illness that has affected thousands of people in the United States. Over 1,000 disease related deaths have occurred since its introduction to American soil in 1999. Spatial statistics are used to analyze distributional trends of human WNV cases from 2000 to 2008 through four analyses: Weighted Mean Center, Standard Deviational Ellipses, Global Moran's I, and Getis-Ord-Gi* statistic (hot spot analysis). We conclude that the directional trend in cases has been from East to West with the area affected increasing with time. Hot spot analysis reveals that recurring counties with a high number of human cases have been in the metro areas of large cities. However, normalized results indicate that the rate of humans showing symptoms of WNV is greatest in rural areas, particularly the Great Plains. These results provide a foundation for future research in analyzing the most persistent hot spots in more detail. Furthermore, these findings may aid decision makers in identifying areas to target for mitigation strategies such as spraying, larval control, and public awareness.