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Background: The creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV)., Methods: We trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math., Results: The initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time., Conclusions: The results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements., (© 2024. The Author(s).)

Purpose: To explore the inter-regional health index at the city level to contribute to the reduction of health inequalities., Methods: Employed the health determinant model to select indicators for the urban health index of Shenzhen City. Utilized principal component analysis, the weights of these indicators are determined to construct the said health index. Subsequently, the global Moran's index and local Moran's index are utilized to investigate the geographical spatial distribution of the urban health index across various administrative districts within Shenzhen., Results: The level of urban health index in Shenzhen exhibits spatial clustering and demonstrates a positive spatial correlation (2017, Moran's I  = 0.237; 2019, Moran's I  = 0.226; 2021, Moran's I  = 0.217). However, it is noted that this clustering displays a relatively low probability (90% confidence interval). Over the period from 2017 to 2019, this spatial clustering gradually diminishes, suggesting a narrowing of health inequality within economically developed urban areas., Conclusion: Our study reveals the urban health index in a relatively high-income (Shenzhen) in a developing country. Certain spatially correlated areas in Shenzhen present opportunities for the government to address health disparities through regional connectivity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yongheng, Shan, Fei, Jinglin, Liyue, Xiaoying, Tingxiao and Hongrui.)

We estimate a measure of segregation, experienced isolation, that captures individuals' exposure to diverse others in the places they visit over the course of their days. Using Global Positioning System (GPS) data collected from smartphones, we measure experienced isolation by race. We find that the isolation individuals experience is substantially lower than standard residential isolation measures would suggest but that experienced isolation and residential isolation are highly correlated across cities. Experienced isolation is lower relative to residential isolation in denser, wealthier, more educated cities with high levels of public transit use and is also negatively correlated with income mobility., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Background: Cutaneous leishmaniasis (CL) has been reported in recent years in South Khorasan Province, a desert region of eastern Iran, where the main species is Leishmania tropica. Little is known of the influence of geography and climate on its distribution, and so this study was conducted to determine geo-climatic factors by using geographic information system., Methods: The home addresses of patients with CL patients who were diagnosed and notified from 2009 to 2017 were retrieved from the provincial health center and registered on the village/town/city point layer. The effects of mean annual rainfall (MAR) and mean annual humidity (MAH), mean annual temperature (MAT), maximum annual temperature (MaxMAT), minimum annual temperature (MinMAT), mean annual number of high-velocity wind days (MAWD), mean annual frosty days (MAFD) and snowy days (MASD), elevation, soil type and land cover on CL distribution were examined. The geographical analysis was done using ArcMap software, and univariate and multivariate binary logistic regression were applied to determine the factors associated with CL., Results: A total of 332 CL patients were identified: 197 (59.3%) male and 135 (40.7%) female. Their mean age was 29.3 ± 2.1 years, with age ranging from 10 months to 98 years. CL patients came from a total of 86 villages/towns/cities. By multivariate analysis, the independent factors associated with increased CL were urban setting (OR = 52.102), agricultural land cover (OR = 3.048), and MAWD (OR = 1.004). Elevation was a protective factor only in the univariate analysis (OR = 0.999). Soil type, MAH, MAT, MinMAT, MaxMAT, and MAFD did not influence CL distribution in eastern Iran., Conclusions: The major risk zones for CL in eastern Iran were urban and agricultural areas with a higher number of windy days at lower altitudes. Control strategies to reduce human vector contact should be focused in these settings., (© 2021. The Author(s).)

Human travel is one of the primary drivers of infectious disease spread. Models of travel are often used that assume the amount of travel to a specific destination decreases as cost of travel increases with higher travel volumes to more populated destinations. Trip duration, the length of time spent in a destination, can also impact travel patterns. We investigated the spatial patterns of travel conditioned on trip duration and find distinct differences between short and long duration trips. In short-trip duration travel networks, trips are skewed towards urban destinations, compared with long-trip duration networks where travel is more evenly spread among locations. Using gravity models to inform connectivity patterns in simulations of disease transmission, we show that pathogens with shorter generation times exhibit initial patterns of spatial propagation that are more predictable among urban locations. Further, pathogens with a longer generation time have more diffusive patterns of spatial spread reflecting more unpredictable disease dynamics., Competing Interests: The authors have declared that no competing interests exist.

Geographical characteristics have been proven to be effective in improving the quality of point-of-interest (POI) recommendation. However, existing works on POI recommendation focus on cost (time or money) of travel for a user. An important geographical aspect that has not been studied adequately is the neighborhood effect, which captures a user's POI visiting behavior based on the user's preference not only to a POI, but also to the POI's neighborhood. To provide an interpretable framework to fully study the neighborhood effect, first, we develop different sets of insightful features, representing different aspects of neighborhood effect. We employ a Yelp data set to evaluate how different aspects of the neighborhood effect affect a user's POI visiting behavior. Second, we propose a deep learning-based recommendation framework that exploits the neighborhood effect. Experimental results show that our approach is more effective than two state-of-the-art matrix factorization-based POI recommendation techniques., Competing Interests: The first author (Guangchao Yuan) is employed by Microsoft. Microsoft provided support in the form of salaries for Guangchao Yuan, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of the author is articulated in the ‘author contributions’ section. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

This paper classifies the karst landscapes of the Petén Plateau and defines the Mirador-Calakmul Karst Basin by illustrating the distribution of its karst hydrologic features. Archaeological and spatial research of the Mirador-Calakmul area of Guatemala and Mexico has shown it to be a karst basin with geopolitical implications. Current research characterizes the karst landscapes of the Petén Plateau, maps the distribution of karst hydrologic features, and delineates the basin in geomorphological terms. To further this aim, multiple forms of remote sensing data including orthophotographs, a satellite Digital Elevation Model, satellite multispectral images, and Light Detection and Ranging (LiDAR) data have been integrated to interpret the karst features in the study area. Outcrop study and thin section analysis of the upper Buena Vista Formation document that the dominant lithologies are a shallow water algal boundstone interbedded with terrestrial caliche. Karst landforms have been mapped over the Petén Plateau and we identify five karst landscapes, the largest of which is a fluviokarst landscape dominated by karst valleys. We further map karst hydrologic features including seasonal swamps, dolines, intermittent lakes, intermittent streams, solution-enhanced fractures, and springs all of which are characteristic of drainage basins. Boundaries of the karst basin are mapped from multiple lines of evidence including distribution of the karst valleys, a line of springs along the western boundary of the fluviokarst landscape, and a surface drainage analysis. We capture and classify hydrologic data points and develop a regional groundwater map that indicates subsurface flow from east to west within the basin. A drainage map illustrates the extensive system of karst valleys, boundaries, and inferred groundwater flow paths of the Mirador-Calakmul Karst Basin. It was within this geomorphological setting that the ancient Maya developed an extensive civilization during the Middle and Late Preclassic periods (1000 BCE-150 CE)., Competing Interests: The authors have read the journal’s policy and have the following competing interests: RE is an employee of Terra Geo Solutions. However, the company was founded after this study concluded. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.

The concept of urban vibrancy has become increasingly important in the study of cities. A vibrant urban environment is an area of a city with high levels of human activity and interactions. Traditionally, studying our cities and what makes them vibrant has been very difficult, due to challenges in data collection on urban environments and people's location and interactions. Here, we rely on novel sources of data to investigate how different features of our cities may relate to urban vibrancy. In particular, we explore whether there are any differences in which urban features make an environment vibrant for different age groups. We perform this quantitative analysis by extracting urban features from OpenStreetMap and the Italian census, and using them in spatial models to describe urban vibrancy. Our analysis shows a strong relationship between urban features and urban vibrancy, and particularly highlights the importance of third places, which are urban places offering opportunities for social interactions. Our findings provide evidence that a combination of mobile phone data with crowdsourced urban features can be used to better understand urban vibrancy., Competing Interests: The authors have declared that no competing interests exist.

The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to the formulation of preventive interventions, particularly since the effects of physical distancing measures and upcoming vaccines on reducing susceptible social contacts and eventually halting transmission remain unclear. Here, using anonymized mobile geolocation data in China, we devise a mobility-associated social contact index to quantify the impact of both physical distancing and vaccination measures in a unified way. Building on this index, our epidemiological model reveals that vaccination combined with physical distancing can contain resurgences without relying on stay-at-home restrictions, whereas a gradual vaccination process alone cannot achieve this. Further, for cities with medium population density, vaccination can reduce the duration of physical distancing by 36% to 78%, whereas for cities with high population density, infection numbers can be well-controlled through moderate physical distancing. These findings improve our understanding of the joint effects of vaccination and physical distancing with respect to a city's population density and social contact patterns.

We examined the feasibility of estimating the spatial distribution of urban populations based on first-aid calls based on one high-density place, the Shanghai urban area and one low-density place, the Nanhai District of Foshan City in Guangdong Province. We aggregated the population and the total number of first-aid calls on digital maps divided by grids based on a Geographic Information System (GIS). Geographically weighted regression was applied to test the correlation between the population distribution simulated by first-aid call data and the actual residency. The impact of different population densities, different grid cell sizes and different types of first-aid calls on simulation correlation were tested. We found that the use of first-aid call data could explain 60-95% of the actual population distribution in Shanghai using a grid with 1000*1000 m cell size, while the Nanhai experience was that first-aid calls could only explain 4-76% of the actual population distribution using a grid with 2000*2000 m cell size. Thus, the higher the population density, the better the simulation effect. For a high-population density area, the overall accuracy of simulation can reach as high as 0.878 at the 1-km2 resolution. However, there are different kinds of first-aid calls and for the best estimation of the population distribution in densely populated areas, we suggest using first-aid calls from people requiring acute medical care rather than all first-aid call data.

Introduction: Analyzing key factors of motorcycle accidents is an effective method to reduce fatalities and improve road safety. Association Rule Mining (ARM) is an efficient data mining method to identify critical factors associated with injury severity. However, the existing studies have some limitations in applying ARM: (a) Most studies determined parameter thresholds of ARM subjectively, which lacks objectiveness and efficiency; (b) Most studies only listed rules with high parameter thresholds, while lacking in-depth analysis of multiple-item rules. Besides, the existing studies seldom conducted a spatial analysis of motorcycle accidents, which can provide intuitive suggestions for policymakers., Method: To address these limitations, this study proposes an ARM-based framework to identify critical factors related to motorcycle injury severity. A method for parameter optimization is proposed to objectively determine parameter thresholds in ARM. A method of factor extraction is proposed to identify individual key factors from 2-item rules and boosting factors from multiple-item rules. Geographic information system (GIS) is adopted to explore the spatial relationship between key factors and motorcycle injury severity., Results and Conclusions: The framework is applied to a case study of motorcycle accidents in Victoria, Australia. Fifteen attributes are selected after data preprocessing. 0.03 and 0.7 are determined as the best thresholds of support and confidence in ARM. Five individual key factors and four boosting factors are identified to be related to fatal injury. Spatial analysis is conducted by GIS to present hot spots of motorcycle accidents. The proposed framework has been validated to have better performance on parameter optimization and rule analysis in ARM. Practical applications: The hot spots of motorcycle accidents related to fatal factors are presented in GIS maps. Policymakers can refer to those maps straightforwardly when decision making. This framework can be applied to various kinds of traffic accidents to improve the performance of severity analysis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier Ltd.)

Importance: It is unknown how well cell phone location data portray social distancing strategies or if they are associated with the incidence of coronavirus disease 2019 (COVID-19) cases in a particular geographical area., Objective: To determine if cell phone location data are associated with the rate of change in new COVID-19 cases by county across the US., Design, Setting, and Participants: This cohort study incorporated publicly available county-level daily COVID-19 case data from January 22, 2020, to May 11, 2020, and county-level daily cell phone location data made publicly available by Google. It examined the daily cases of COVID-19 per capita and daily estimates of cell phone activity compared with the baseline (where baseline was defined as the median value for that day of the week from a 5-week period between January 3 and February 6, 2020). All days and counties with available data after the initiation of stay-at-home orders for each state were included., Exposures: The primary exposure was cell phone activity compared with baseline for each day and each county in different categories of place., Main Outcomes and Measures: The primary outcome was the percentage change in COVID-19 cases 5 days from the exposure date., Results: Between 949 and 2740 US counties and between 22 124 and 83 745 daily observations were studied depending on the availability of cell phone data for that county and day. Marked changes in cell phone activity occurred around the time stay-at-home orders were issued by various states. Counties with higher per-capita cases (per 100 000 population) showed greater reductions in cell phone activity at the workplace (β, -0.002; 95% CI, -0.003 to -0.001; P < 0.001), areas classified as retail (β, -0.008; 95% CI, -0.011 to -0.005; P < 0.001) and grocery stores (β, -0.006; 95% CI, -0.007 to -0.004; P < 0.001), and transit stations (β, -0.003, 95% CI, -0.005 to -0.002; P < 0.001), and greater increase in activity at the place of residence (β, 0.002; 95% CI, 0.001-0.002; P < 0.001). Adjusting for county-level and state-level characteristics, counties with the greatest decline in workplace activity, transit stations, and retail activity and the greatest increases in time spent at residential places had lower percentage growth in cases at 5, 10, and 15 days. For example, counties in the lowest quartile of retail activity had a 45.5% lower growth in cases at 15 days compared with the highest quartile (SD, 37.4%-53.5%; P < .001)., Conclusions and Relevance: Our findings support the hypothesis that greater reductions in cell phone activity in the workplace and retail locations, and greater increases in activity at the residence, are associated with lesser growth in COVID-19 cases. These data provide support for the value of monitoring cell phone location data to anticipate future trends of the pandemic.

Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein., Competing Interests: The authors have declared that no competing interests exist.

Background and Aims: Diabetic nephropathy are one of the most important causes of renal failure and dysfunction in diabetic patients and imposes high costs for the healthcare system. Studies conducted in Iran show different prevalence. This study aims to determine the overall prevalence of nephropathy in patients with type 2 diabetes in Iran with a systematic review and meta-analysis., Methods: The present study was carried out by meta-analysis from January 2000 to May 2020. Related articles were obtained through searching in Scopus, Cochrane, Library, ScienceDirect, Embase, SID, Magiran, Barakatkns, Medline (PubMed), and Google Scholar. Heterogeneity of studies was studied using I 2 index and data were analyzed using Comprehensive Meta-Analysis and Arc map (ArcGIS 10.6) software., Results: In a review of 18 articles and 6190 subjects aged 20-83 years, the overall prevalence of nephropathy in patients with type 2 diabetes in Iran was 30.6% (95%CI: 24.3-37.7) based on a meta-analysis. The highest prevalence of nephropathy in patients with type 2 diabetes was obtained in Tehran province with 57.3% (95%CI: 51.5-62.9) in 2008 and the lowest prevalence of nephropathy in patients with type 2 diabetes was obtained in Hamedan province with 14.4% (95%CI: 11.3-18.8) in 2016. An increase in sample size and research year reduces the prevalence of nephropathy in patients with type 2 diabetes in Iran, and differences were statistically significant (P < 0.05)., Conclusion: Considering the high prevalence of nephropathy in patients with type 2 diabetes in Iran, it is essential that health policymakers take effective measures to reduce this disease in diabetic patients., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.)

Built environments have been cited as important facilitators of activity and research using geographic information systems (GIS) has emerged as a novel approach in exploring environmental determinants. The Active Children Through Individual Vouchers Evaluation Project used GIS to conduct a cross-sectional analysis of how teenager's (aged 13-14) environments impacted on their amount of activity and influences fitness. The ACTIVE Project recruited 270 participants aged 13-14 (year 9) from 7 secondary schools in south Wales, UK. Demographic data and objective measures of accelerometery and fitness were collected from each participant between September and December 2016. Objective data was mapped in a GIS alongside datasets relating to activity provision, active travel routes, public transport stops, main roads and natural resources. This study shows that fitness and physical activity are not correlated. Teenagers who had higher levels of activity also had higher levels of sedentary time/inactivity. Teenagers showed higher amounts of moderate-to-vigorous physical activity if their homes were closer to public transport. However, they were also more active if their schools were further away from public transport and natural resources. Teenagers were fitter if schools were closer to natural resources. Sedentary behaviour, fitness and activity do not cluster in the same teenagers. Policymakers/planning committees need to consider this when designing teenage friendly environments. Access to public transport, active travel, green space and activities that teenagers want, and need could make a significant difference to teenage health., Competing Interests: All authors declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.

Coastlines mapping techniques or the coastline automated analyses have been sought after. In practice, various sorts of seacoasts, for example, biological, silty, arenaceous, artificial, and bedrock coasts, have their own attributes, which force various degrees of intricacy on coastline mapping. As an extraordinary kind of complex artificial coast, aquaculture coast is shaped by the farming of aquatic organisms on silt tidal flats. With the rapid growth of coastal aquaculture in recent years, aquaculture coasts have increased in some developing countries. It has been estimated that aquaculture coasts constitute about 30% of all coastlines in mainland China. In order to identify, monitor, model, and manage the vast expanse of coastal aquaculture, effective methods of extracting aquaculture coastlines from remotely sensed imagery are desired. Secondly, with the rapid economic development in coastal areas, the development of coastal zone resources is also increasing day by day, which benefits the development of island coastal zone. Using oneself has become an important link in the development of marine economy. Due to the limited coastal resources and low environmental carrying capacity, the overexploitation and utilization of coastal resources will lead to a series of problems, such as coastal erosion, coastal migration and accumulation, island area reduction, etc., Both man-made activities and natural factors will lead to coastline changes, which will lead to corresponding changes in coastal ecological environment, thus affecting the coordinated development of coastal economy and the survival of coastal residents. Therefore, efficient, accurate and timely acquisition of coastline information and research on the spatial-temporal changes of coastline are of great significance to the protection of the living environment of coastal residents, the effective development of island and coastal resources, the coordination of sustainable economic development in coastal areas and the mitigation of marine disasters. This paper presents a review of those papers reporting coastline extraction and land use and land cover (LULC) change analysis using remote sensing (RS) and geographic information system (GIS) technology., (© 2020 Walter de Gruyter GmbH, Berlin/Boston.)

Contemporary theories on players' intensity distribution in team sports suggest that they regulate their outputs using pacing strategies. There is currently limited information on how movement patterns and pacing strategies of rugby union players in different position groups (forwards and backs) vary when exposed to different bout types (whole game, starter or finisher). Global positioning system (GPS) and accelerometer data were collected from 100 professional match participations to determine temporal effects on movement patterns. For forwards, finishers (players who entered the game as substitutes) demonstrated significantly greater high-speed running distance (% difference, ± 90%CI; magnitude-based inference and effect size) (↑ 55, ±17%; very likely large) and acceleration frequency (↑ 78, ±59%; very likely large) than whole game players. For backs, starters (players who started the game and were later substituted) displayed greater high-speed running distance than whole game players (↑ 27, ±21%; ES =  likely medium) but this difference did not achieve statistical significance ( p  = .07). Forwards displayed "slow-positive" pacing strategies regardless of bout type, while backs displayed "flat" pacing strategies. Forwards and backs adopt different pacing strategies regardless of bout type, with forwards demonstrating progressively greater performance decrements over the course of the match. These findings reflect differing physical demands, notably contact and running loads, of players in different positions.

Over the past 20 years, GPS collars have emerged as powerful tools for the study of nonhuman primate (hereafter, "primate") movement ecology. As the size and cost of GPS collars have decreased and performance has improved, it is timely to review the use and success of GPS collar deployments on primates to date. Here we compile data on deployments and performance of GPS collars by brand and examine how these relate to characteristics of the primate species and field contexts in which they were deployed. The compiled results of 179 GPS collar deployments across 17 species by 16 research teams show these technologies can provide advantages, particularly in adding to the quality, quantity, and temporal span of data collection. However, aspects of this technology still require substantial improvement in order to make deployment on many primate species pragmatic economically. In particular, current limitations regarding battery lifespan relative to collar weight, the efficacy of remote drop-off mechanisms, and the ability to remotely retrieve data need to be addressed before the technology is likely to be widely adopted. Moreover, despite the increasing utility of GPS collars in the field, they remain substantially more expensive than VHF collars and tracking via handheld GPS units, and cost considerations of GPS collars may limit sample sizes and thereby the strength of inferences. Still, the overall high quality and quantity of data obtained, combined with the reduced need for on-the-ground tracking by field personnel, may help defray the high equipment cost. We argue that primatologists armed with the information in this review have much to gain from the recent, substantial improvements in GPS collar technology.

Soil erosion is a global environmental threat, and Land Use Land Cover Changes (LUCC) have significant impacts on it. Nepal, being a mountainous country, has significant soil erosion issues. To examine the effects of LUCC on water erosion, we studied the LUCC in Sarada, Rapti and Thuli Bheri river basins of Nepal during the 1995-2015 period using the Remote Sensing. We calculated the average annual soil loss using the Revised Universal Soil Loss Equation and Geographical Information System. Our results suggest that an increase in the agricultural lands at the expense of bare lands and forests escalated the soil erosion through the years; rates being 5.35, 5.47 and 6.03 t/ha/year in 1995, 2007 and 2015, respectively. Of the different land uses, agricultural land experienced the most erosion, whereas the forests experienced the least erosion. Agricultural lands, particularly those on the steeper slopes, were severely degraded and needed urgent soil and water conservation measures. Our study confirms that the long term LUCC has considerable impacts on soil loss, and these results can be implemented in similar river basins in other parts of the country., Competing Interests: The authors have declared that no competing interests exist.

Context: Rugby union is a field-based collision sport with high injury rates. Associations between injury characteristics and global positioning system-derived movement demands in rugby union athletes are yet to be investigated., Objective: To investigate associations between match injuries and movement demands, anthropometrics, and physical performance in under-20-years university-level rugby union players., Design: Descriptive epidemiology study., Setting: Competition season., Patients or Other Participants: Rugby union players (n = 34, age = 19.3 ± 0.6 years) from a university club were recruited., Main Outcome Measure(s): Acute medical attention non-time-loss (NTL), medical-attention time-loss (TL), and total medical-attention (MA) injuries sustained were recorded. Principal component (PC) analysis was performed on player-movement demand variables to identify independent-movement demand components. Pearson correlation and bivariate linear regression were used to test associations between match injuries and PCs. Anthropometric and physical performance measures were tested as predictors of match injuries using a forward stepwise multiple regression analysis., Results: Backs had lower anthropometric and performance measures than forwards ( P < .05), whereas forwards performed fewer weekly movement demands than backs ( P < .05). Increases in body mass and skinfold thickness were associated with more injuries ( P < .05). Principal component analysis revealed 3 PCs representing overall performance, high-intensity running (HIR) performance, and impacts. Increases in HIR were associated with decreases in NTL upper limb and trunk ( r = -0.32, P = .03), NTL musculoskeletal ( r = -0.36, P = .05), NTL total ( r = -0.46, P < .01), TL musculoskeletal ( r = -0.30, P = .05), MA musculoskeletal ( r = -0.41, P < .01), and MA total ( r = -0.48, P < .01) injuries. Increases in impacts were associated with increased TL ( r = 0.32, P = .03) and MA ( r = 0.33, P = .03) head or neck injuries., Conclusions: Backs experienced greater weekly movement demands than forwards. Increases in HIR demands were associated with decreased acute injuries in university rugby players. Increases in impacts were associated with more acute head or neck injuries. Positional differences in movement demands, anthropometrics, and physical performance highlight the need for position-specific training.

Objective: To determine the association among number of factors influenced by asthma using geographic information system., Methods: The cross-sectional study was conducted in Landhi and Korangi towns of Karachi from 2011 to 2013, and comprised ecological mapping and multi-criteria evaluation techniques to discover the relationship of local environmental settings with asthma. Additionally, exacerbating environment and the root causes within the local settings were assessed. Data was gathered using an extended version of the questionnaire developed by the International Union against Tuberculosis and Lung Disease. Data was analysed by using ArcGIS 10., Results: The findings are very alarming as almost 40% (468,930 estimated pop 1998 census) of the study population lived in high asthma-prone environment, having a very high risk of respiratory disorders, including asthma., Conclusions: The integrated environmental effect in the form of respiratory disorders was appraised, focusing on asthma by using multi-criteria analysis.

Background: Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals' geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method., Methods: This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios., Results: The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual's home and all the other daily activity locations., Conclusions: This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing.

Background Congenital hypothyroidism (CH) is one of the main causes of mental retardation in neonates. The disease is associated with genetic, climatic and environmental factors. Accordingly, the present study was conducted to determine the incidence rate and correlation of geographic factors with CH in Isfahan province in order to explain the role of climatic factors in the incidence of disease in this province. Methods This cross-sectional study was conducted to determine the incidence rate and geographical distribution of CH in neonates born in the Isfahan province from the beginning of 2002 to the end of 2015. To estimate the geographic distribution, the ArcGIS software (version 10.3) was used and to investigate the relationship between geographical factors and CH, the Poisson regression test and dispersion diagrams were used. Data analysis was done using the SPSS16 software. Results According to the results of the screening of 1,006,404 neonates born in Isfahan, the mean incidence rate of CH in all infants was 2.3, and that in male and female infants was 2.39 and 2.2 per 1000 live births, respectively. There was a statistically significant correlation between the city of residence (p = 0.01), climatic conditions (p = 0.044), annual precipitation (p = 0.027), sunlight (p = 0.01) and CH. However, there was no significant relationship between the mean altitude above sea level (p = 0.6), land use (p = 0.4) and relative humidity (p = 0.24). Conclusions Based on the findings of this study, the incidence rate of CH in Isfahan province was higher than the average of the country. Moreover, among the geographical factors, climatic conditions, annual precipitation and sunlight were associated with CH.

Objective: To compare spatial patterns of congenital syphilis (CS) with those of socioeconomic and medical variables in Paraíba Valley, São Paulo, between 2012 and 2016., Methods: Ecological and exploratory study developed using spatial analysis tools, with information on CS cases obtained from official data reports. Rates were found for CS cases per 1,000 live births, number of family health teams and pediatricians available in the health system per 100,000 inhabitants, and social vulnerability index values. Thematic maps were constructed with these variables and compared using TerraView 4.2.2 software. Estimated global Moran (IM) indexes were calculated. In order to detect areas with priority attention regarding the incidence of CS, BoxMaps were developed. The Spearman correlation was estimated for the variable values and compared using the Kruskal-Wallis test. P <0.05 was significant., Results: 144,613 births and 870 CS cases (6.04/1000 live births) occurred during the study period. The average value of CS rates per municipality was 4.0±4.1, (0.0-17.6/1000 live births). Higher CS rates occurred in municipalities of the Upper Vale do Paraíba, contrary to the proportions of pediatricians who were in the far east of the region. The thematic maps of the variables presented a mosaic aspect, which characterized the random distribution of the variables. The IM were not significant. No significant correlation was found between the variables. The BoxMap identified eight municipalities with high CS rates., Conclusions: Even though it was not possible to identify a spatial pattern of CS rates, it was shown that eight municipalities deserve the attention of city managers.

The study aimed to examine associations of neighborhood built environments and proximity of food outlets (BE measures) with body weight status using pooled data from an international study (IPEN Adult). Objective BE measures were calculated using geographic information systems for 10,008 participants (4463 male, 45%) aged 16-66 years in 14 cities. Participants self-reported proximity to three types of food outlets. Outcomes were body mass index (BMI) and overweight/obesity status. Male and female weight status associations with BE measures were estimated by generalized additive mixed models. Proportion (95% CI) of overweight (BMI 25 to <30) ranged from 16.6% (13.1, 19.8) to 41.1% (37.3, 44.7), and obesity (BMI ≥ 30) from 2.9% (1.3, 4.4) to 31.3% (27.7, 34.7), with Hong Kong being the lowest and Cuernavaca, Mexico highest for both proportions. Results differed by sex. Greater street intersection density, public transport density and perceived proximity to restaurants (males) were associated with lower odds of overweight/obesity (BMI ≥ 25). Proximity to public transport stops (females) was associated with higher odds of overweight/obesity. Composite BE measures were more strongly related to BMI and overweight/obesity status than single variables among men but not women. One standard deviation improvement in the composite measures of BE was associated with small reductions of 0.1-0.5% in BMI but meaningful reductions of 2.5-5.3% in the odds of overweight/obesity. Effects were linear and generalizable across cities. Neighborhoods designed to support public transport, with food outlets within walking distance, may contribute to global obesity control., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: This study aimed to assess the pattern of use of EDs, factors contributing to the visits, geographical distribution and outcomes in people aged 65 years or older to a large hospital in Dublin., Methods: A retrospective analysis of 2 years of data from an urban university teaching hospital ED in the southern part of Dublin was reviewed for the period 2014-2015 (n=103 022) to capture the records of attenders. All ED presentations by individuals 65 years and older were extracted for analysis. Address-matched records were analysed using QGIS, a geographic information systems (GIS) analysis and visualisation tool to determine straight-line distances travelled to the ED by age., Results: Of the 49 538 non-duplicate presentations in the main database, 49.9% of the total are women and 49.1% are men. A subset comprised of 40 801 had address-matched records. When mapped, the data showed a distinct clustering of addresses around the hospital site but this clustering shows different patterns based on age cohort. Average distances travelled to ED are shorter for people 65 and older compared with younger patients. Average distances travelled for those aged 65-74 was 21 km (n=4177 presentations); for the age group 75-84, 18 km (n=2518 presentations) and 13 km for those aged 85 and older (n=2104 presentations). This is validated by statistical tests on the clustered data. Self-referral rates of about 60% were recorded for each age group, although this varied slightly, not significantly, with age., Conclusions: Health planning at a regional level should account for the significant number of older patients attending EDs. The use of GIS for health planning in particular can assist hospitals to improve their understanding of the origin of the cohort of older ED patients., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Spatial weight matrices play a key role in econometrics to capture spatial effects. However, these constructs are prone to clustering and can be challenging to analyse in common statistical packages such as STATA. Multiple observations of survey participants in the same location (or cluster) have traditionally not been dealt with appropriately by statistical packages. It is common that participants are assigned Geographic Information System (GIS) data at a regional or district level rather than at a small area level. For example, the Demographic Health Survey (DHS) generates GIS data at a cluster level, such as a regional or district level, rather than providing coordinates for each participant. Moreover, current statistical packages are not suitable for estimating large matrices such as 20,000 × 20,000 (reflective of data within large health surveys) since the statistical package limits the N to a smaller number. In addition, in many cases, GIS information is offered at an aggregated level of geographical areas. To alleviate this problem, this paper proposes a bootstrap approach that generates an inverse distance spatial weight matrix for application in econometric analyses of health survey data. The new approach is illustrated using DHS data on uptake of HIV testing in low and middle income countries.

Background: Decision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS)., Methods: Makes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map., Results: An example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: "non-priority", "non-priority tendency", "priority tendency" and "priority", for the fight against diseases., Conclusion: The new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities.

Background: Getting a random household sample during a survey can be expensive and very difficult especially in urban area and non-specialist. This study aimed to test an alternative method using freely available aerial imagery., Methods: A gridded map and random selection method was used to select households for interviews. A hundred numbered of points were put along the edges of an updated map of Maroua. Then two numbers were randomly draw at a time and a line was drawn between those two numbers. A lot of different kinds of shapes of different sizes obtained were numbered. Ten shapes were randomly draw and the one selected were considered as 'neighbourhoods'. A grid of 30 m × 30 m was drawn over each and then numbered. 202 grids considered here as households were randomly selected from the ten neighbourhoods for interviews., Results: Out of 202 households visited, only 4 were found to be something other than a house. In addition, 30 sampled households (14.85%) were abandoned or the occupants had relocated elsewhere. This method resulted in an accuracy level of 72%, its advantage is the ability to generate efficient random sample at relatively low cost as well the time required., Conclusions: The method proposed in this study was efficient and cost-effective when compared to the infield generation of a household inventory or Global Positioning System (GPS) tracking of households. It can then be used by researchers in low-incomes countries where funding for research is a challenge. However, this method needs to train the investigators on how to use the GPS.

Sustainable sources like wind, solar, and geothermal power are defined as a clean source of renewable energy which has a less harmful impact on the environment than other energy sources such as coal, natural gas and oil. Turkey is one of the energy-importing countries where air pollution has been become an inevitable environmental concern. Thus, investments on sustainable sources have been developed rapidly in recent years in Turkey. This paves the way for studying a site selection problem considering both solar and wind energy in Igdir Province located in the east part of Turkey. In the literature, there are many studies on solar-wind energy to select a desirable site for both energy sources, and many solution techniques have been proposed dealing with this problem. In this study, one of multi-criteria decision-making methods named analytical hierarchy process (AHP) and geographical information systems (GIS) are used to determine suitable site selection for solar-wind energy investigating four counties of Igdir: Tuzluca, Igdir Central, Karakoyunlu and Aralik. The aim of this work is first to investigate possible locations for solar-wind power plant installation using a mapping method, GIS, and then, AHP is applied to the problem to obtain optimum areas for both solar-wind energy. Also, more accurate results are provided comparing results of two methods, GIS and AHP. The results reveal that 524.5 km 2 for solar power plant and 147.2 km 2 for wind turbine are suitable while only 49.1 km 2 is suitable for solar-wind power plan installation.

Introduction: Globally, injury incidence and injury-fatality rates are higher in regional and remote areas. Recovery following serious injury is complex and requires a multi-disciplinary approach to management and community re-integration to optimise outcomes. A significant knowledge gap exists in understanding the regional variations in hospital and post-discharge outcomes following serious injury. The aim of this study was to review the evidence exploring the association between the geographic location, including both location of the event and place of residence, and outcomes following injury., Materials and Methods: A scoping review was used to investigate this topic and provide insight into geographic variation in outcomes following traumatic injury. Seven electronic databases and reference lists of relevant articles were searched from inception to October 2018. Studies were included if they measured injury-related mortality, outcomes associated with hospital admission, post-injury physical or psychological function and analysed these outcomes in relation to geographic location., Results: Of the 2,213 studies identified, 47 studies were included revealing three key groups of outcomes: mortality (n = 35), other in-hospital outcomes (n = 8); and recovery-focused outcomes (n = 12). A variety of measures were used to classify rurality across studies with inconsistent definitions of rurality/remoteness. Of the studies reporting injury-related mortality, findings suggest that there is a greater risk of fatality in rural areas overall and in the pre-hospital phase. For those patients that survived to hospital, the majority of studies included identified no difference in mortality between rural and urban patient groups. In the small number of studies that reported other in-hospital and recovery outcomes no consistent trends were identified., Conclusion: Rural patients had a higher overall and pre-hospital mortality following injury. However, once admitted to hospital, there was no significant difference in mortality. Inconsistencies were noted across measures of rurality measures highlighting the need for more specific and consistent international classification methods. Given the paucity of data on the impact of geography on non-mortality outcomes, there is a clear need to develop a larger evidence base on regional variation in recovery following injury to inform the optimisation of post-discharge care services., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

A retrospective study was conducted using 250 clinical records of brown trout (Salmo trutta L.) with saprolegniosis by Saprolegnia parasitica, which had been collected from 8 rivers and 1 fish farm in the province of León (Spain). A geographic information system (GIS) was used to obtain skin lesion distribution patterns in males and females. Lesions in wild brown trout affected 15.31 ± 13.33% of the body surface, with a mean of 12.76 ± 6.56 lesions per fish. In addition, 51.23% of wild trout presented lesions with necrosis of the skin or fins. The pattern obtained when not distinguishing between sexes indicated that saprolegniosis lesions are mainly located above the lateral line and most frequently affect the dorsal cephalic region, the adipose fin, the peduncle and the caudal fin. However, differences were observed between males and females. Farmed trout presented a lower percentage of affected body surface (2.06 ± 4.36) and a lower number of lesions with and without necrosis because they received preventive treatment for saprolegniosis., (© 2019 John Wiley & Sons Ltd.)

Background: It is well known that the burden caused by cancer can vary geographically, which may relate to differences in health, economics or lifestyle. However, to date, there was no comprehensive picture of how the cancer burden, measured by cancer incidence and survival, varied by small geographical area across Australia., Methods: The Atlas consists of 2148 Statistical Areas level 2 across Australia defined by the Australian Statistical Geography Standard which provide the best compromise between small population and small area. Cancer burden was estimated for males, females, and persons separately, with 50 unique sex-specific (males, females, all persons) cancer types analysed. Incidence and relative survival were modelled with Bayesian spatial models using the Leroux prior which was carefully selected to provide adequate spatial smoothing while reflecting genuine geographic variation. Markov Chain Monte Carlo estimation was used because it facilitates quantifying the uncertainty of the posterior estimates numerically and visually., Results: The results of the statistical model and visualisation development were published through the release of the Australian Cancer Atlas ( https://atlas.cancer.org.au ) in September, 2018. The Australian Cancer Atlas provides the first freely available, digital, interactive picture of cancer incidence and survival at the small geographical level across Australia with a focus on incorporating uncertainty, while also providing the tools necessary for accurate estimation and appropriate interpretation and decision making., Conclusions: The success of the Atlas will be measured by how widely it is used by key stakeholders to guide research and inform decision making. It is hoped that the Atlas and the methodology behind it motivates new research opportunities that lead to improvements in our understanding of the geographical patterns of cancer burden, possible causes or risk factors, and the reasons for differences in variation between cancer types, both within Australia and globally. Future versions of the Atlas are planned to include new data sources to include indicators such as cancer screening and treatment, and extensions to the statistical methods to incorporate changes in geographical patterns over time.

Purpose: Due to geographical disparities, many people with profound or severe disabilities experience considerable delays in rehabilitation treatment, resulting in threats to quality of life. This pilot study aims to identify areas in Greater Brisbane, Australia, with a higher concentration of people with profound or severe disabilities and to evaluate access to rehabilitation services in these areas. Methods: Data came from the 2016 Australian Census of Population and Housing and the National Health Services Directory. Four frequently used rehabilitation services by individuals with profound or severe disabilities (i.e., occupational therapy, physiotherapy, speech pathology and psychology) were the focus of the analysis. The data were analyzed using geospatial analysis methods (e.g., spatial scan statistic and network analysis). Results: A higher concentration of rehabilitation services was found in the regions with lower disability prevalence and lower potential demand for rehabilitation services. In contrast, the regions with higher disability prevalence and higher potential demand for rehabilitation services experienced poorer access to rehabilitation services. Conclusion: The findings are expected to inform policy decisions about the prioritization of rehabilitation resources and derive evidence for planning more responsive service delivery. Implications for rehabilitation The current study has demonstrated the utilization of geographic information system methods to facilitate rehabilitation service planning. Identification of disability concentration may inform locally responsive rehabilitation service delivery. Spatial assessment of mismatch between supply and potential demand may assist policy makers and service providers in the prioritization of rehabilitation resources. The current study contributes to the World Health Organization's call for action to ensure adequate access to rehabilitation services by people with profound or severe disabilities.

Ensuring access to healthcare in emergency health situations is a persistent concern for health system planners. Emergency services, including critical care units for severe burns and coronary events, are amongst those for which travel time is the most crucial, potentially making a difference between life and death. Although it is generally assumed that access to healthcare is not an issue in densely populated urban areas due to short distances, we prove otherwise by applying improved methods of assessing accessibility to emergency services by the urban poor that take traffic variability into account. Combining unique data on emergency health service locations, traffic flow variability and informal settlements boundaries, we generated time-cost based service areas to assess the extent to which emergency health services are reachable by urban slum dwellers when realistic traffic conditions and their variability in time are considered. Variability in traffic congestion is found to have significant impact on the measurement of timely access to, and availability of, healthcare services for slum populations. While under moderate traffic conditions all slums in Dhaka City are within 60-minutes travel time from an emergency service, in congested traffic conditions only 63% of the city's slum population is within 60-minutes reach of most emergency services, and only 32% are within 60-minutes reach of a Burn Unit. Moreover, under congested traffic conditions only 12% of slums in Dhaka City Corporation comply with Bangladesh's policy guidelines that call for access to 1 health service per 50,000 population for most emergency service types, and not a single slum achieved this target for Burn Units. Emergency Obstetric Care (EmOC) and First Aid & Casualty services provide the best coverage, with nearly 100% of the slum population having timely access within 60-minutes in any traffic condition. Ignoring variability in traffic conditions results in a 3-fold overestimation of geographic coverage and masks intra-urban inequities in accessibility to emergency care, by overestimating geographic accessibility in peripheral areas and underestimating the same for central city areas. The evidence provided can help policy makers and urban planners improve health service delivery for the urban poor. We recommend that taking traffic conditions be taken into account in future GIS-based analysis and planning for healthcare service accessibility in urban areas., Competing Interests: The authors have declared that no competing interests exist.

Background: Mental health is a neglected area within the healthcare systems in developing nations, including Sri Lanka. However, the rapid population aging and the resulting rise in psychological issues among the elderly are becoming major concerns within the country. Therefore, a proper assessment of distribution of mental healthcare facilities within the country is much needed in reducing the unmet need among people with psychological symptoms. In this study, the distribution of mental health services within the district of Colombo, which is the capital of the country, was assessed., Methods: With this view, mapping of mental healthcare services within the district of Colombo was carried out using the Geographic Information System to assess service and human resource availability and physical accessibility to the elderly within the district., Results: Certain deficiencies in availability and accessibility of the services were identified which were more significant toward eastern parts of the district., Conclusions: Establishment of new mental healthcare facilities as well as upgrading of already available centres while ensuring availability of professional human resources to these centres is a timely need., (© 2019 Japanese Psychogeriatric Society.)

We employ data from the Adolescent Health and Development in Context Study-a representative sample of urban youth ages 11-17 in and around the Columbus, OH area-to investigate the feasibility and validity of smartphone-based geographically explicit ecological momentary assessment (GEMA). Age, race, household income, familiarity with smartphones, and self-control were associated with missing global positioning systems (GPS) coverage, whereas school day was associated with discordance between percent of time at home based on GPS-only versus recall-aided space-time budget data. Fatigue from protocol compliance increases missing GPS across the week, which results in more discordance. Although some systematic differences were observed, these findings offer evidence that smartphone-based GEMA is a viable method for the collection of activity space data on urban youth., (© 2018 Society for Research on Adolescence.)

This paper proposes a two-step integrated method for identifying traffic accident (TA) hotspots on a roadway network. The first step includes a spatial analysis method called network kernel density estimation (KDE). The second step is a network screening method using the critical crash rate, which it described in the Highway Safety Manual (HSM). The method was examined by using three years of TAs (2011-2013) in Sherbrooke, Canada. The network KDE uses TAs to graphically display sites with a high crash density. Two different crash patterns were used for identifying these locations: (1) a crash pattern that includes three-year aggregated crash data, and (2) a crash pattern that involves three-year merged crash data. The results of the two crash patterns were evaluated based on a prediction accuracy index (PAI). It was found that the results obtained from the merged crash data outperformed the other. On the other hand, crash clustering in a site does not imply a site is hotspot and it is better to tested by other factors. High crash density locations were then tested by the critical crash rate, which helps to create an accurate comparison of sites. The importance of the critical crash rate is that it takes several factors into account such as the amount of exposure, the type of intersection, variance in crash data, etc. We realized that the hotspots determined using the two methods reflect very problematic locations and filter out the locations that do not have a problem. This approach could help transportation authorities and safety specialists to identify and prioritize sites that require more safety attention., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Diarrhea remains one of the top five causes of disease and death among young children in developing nations. Fortunately, scientists are making progress developing vaccines against enterotoxigenic E. coli (ETEC) and Shigella, two of the leading diarrhea pathogens. As vaccine developers start to consider field efficacy trials of these vaccines, they should be aware of the importance of evaluating not only vaccine direct effects on the immunized, but also the herd effects that vaccination can afford to the unimmunized in a community. In a workshop held at the conference titled "Vaccines against Shigella and ETEC (VASE)", we described to participants what herd effects are and we presented on methods used in cholera and rotavirus studies that could be useful for future ETEC and Shigella vaccine trials conducted in low and middle-income nations. We also presented evidence on the effects of vaccine herd effects for estimates of vaccine cost-effectiveness., (Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Activity spaces (AS), captured using GPS tracking devices, are measures of dynamic exposure to the built environment (BE)., Methods: Seven days of Global Positioning Systems (GPS) tracking data were obtained for 433 adult participants in the Seattle Obesity Study (SOS II). Heights and weights were measured. Dietary intakes from a food frequency questionnaire were used to calculate Healthy Eating Index (HEI 2010) scores. Linear regression analyses examined associations between AS measures: daily route length, convex hull, and radius of gyration, and diet quality and health outcomes, adjusting for covariates., Results: AS measures did not vary by age, gender, race/ethnicity, or socioeconomic status. AS measures were not associated with diet quality or with self-reported obesity or diabetes. One AS measure, route length (in miles), was associated with being employed, living in the suburbs, and with distance and time commuting to work., Conclusion: Spatial mobility studies based on GPS tracking of environmental exposure need to demonstrate a link to relevant health outcomes., (Copyright © 2019. Published by Elsevier Ltd.)

As activity space measures are increasingly used to estimate exposure to environmental determinants of health, little is known about the stability of these measures over time. To test the stability of GPS-derived measures of activity-space footprint, size, and environmental features over time, we compared 14-day measures at baseline and six months later for 35 adults in a large city. Activity-space measures were based on convex hulls and 500 m route buffers, and included the geographic footprint (i.e. location of the activity space), size (i.e., area in square miles; (Cummins, 2007)), and environmental features including supermarket, fast-food restaurant, and parkland density. The proportion of the participants' smaller geographic footprint covered by the larger was, on average, 0.64 (SD 0.17) for the 500 m route buffer and 0.84 (SD 0.18) for the convex hull. Mean percent change in activity space size ranged from 36.3% (mean daily 500 m route buffer) to 221.3% (cumulative convex hull). Mean percent change in the density of environmental features ranged from 28.8 to 66.5%. Forty-one percent to 92.4% of the variance at one timepoint was predicted by environmental features measured within approximately six months. Activity-space size and environmental features were moderately to highly stable over six months, although there was considerable variation in stability between measures. Strategies for addressing measurement error in studies of activity space-health associations are discussed., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Purpose: The objective of our study was to incorporate stricter probable nonfatal opioid overdose case criteria, and advanced epidemiologic approaches to more reliably detect local clustering in nonfatal opioid overdose activity in EMS runs data., Methods: Data were obtained using emsCharts for our study area in southwestern Pennsylvania from 2007 to 2018. Cases were identified as emergency medical service (EMS) responses where naloxone was administered, and improvement was noted in patient records between initial and final Glasgow Coma Score. A subsample of all-cause EMS responses sites were used as controls and exact matched to cases on sex and 10-year-age category. Clustering was assessed using difference in Ripley's K function for cases and controls and Kulldorff scan statistics., Results: Difference in K functions indicated no significant difference in probable nonfatal overdose EMS runs across the study area compared to all-cause EMS runs. However, scan statistics did identify significant local clustering of probable nonfatal overdose EMS runs (maximum likelihood = 16.40, P = 0.0003)., Conclusions: Results highlight relevance of EMS data to detect community-level overdose activity and promote reliable use through stricter case definition criteria and advanced methodological approaches. Techniques examined have the potential to improve targeted delivery of neighborhood-level public health response activities using a near real-time data source., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Predicting breed-specific environmental suitability has been problematic in livestock production. Native breeds have low productivity but are thought to be more robust to perform under local conditions than exotic breeds. Attempts to introduce genetically improved exotic breeds are generally unsuccessful, mainly due to the antagonistic environmental conditions. Knowledge of the environmental conditions that are shaping the breed would be needed to determine its suitability to different locations. Here, we present a methodology to predict the suitability of breeds for different agro-ecological zones using Geographic Information Systems tools and predictive habitat distribution models. This methodology was tested on the current distribution of two introduced chicken breeds in Ethiopia: the Koekoek, originally from South Africa, and the Fayoumi, originally from Egypt. Cross-validation results show this methodology to be effective in predicting breed suitability for specific environmental conditions. Furthermore, the model predicts suitable areas of the country where the breeds could be introduced. The specific climatic parameters that explained the potential distribution of each of the breeds were similar to the environment from which the breeds originated. This novel methodology finds application in livestock programs, allowing for a more informed decision when designing breeding programs and introduction programs, and increases our understanding of the role of the environment in livestock productivity.

Patterns of spatial behavior dictate how we use our infrastructure, encounter other people, or are exposed to services and opportunities. Understanding these patterns through the analysis of data commonly available through commodity smartphones has become an important arena for innovation in both academia and industry. The resulting datasets can quickly become massive, indicating the need for concise understanding of the scope of the data collected. Some data is obviously correlated (for example GPS location and which WiFi routers are seen). Codifying the extent of these correlations could identify potential new models, provide guidance on the amount of data to collect, and even provide actionable features. However, identifying correlations, or even the extent of correlation, is difficult because the form of the correlation must be specified. Fractal-based intrinsic dimensionality directly calculates the minimum number of dimensions required to represent a dataset. We provide an intrinsic dimensionality analysis of four smartphone datasets over seven input dimensions, and empirically demonstrate an intrinsic dimension of approximately two., Competing Interests: The authors have declared that no competing interests exist. Data was collected using Ethica Data Systems software. KGS and NDO hold shares in Ethica Systems as the company spun out of their labs.

Background: Mobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others., Objective: The objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices., Methods: A mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week., Results: The evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones., Conclusions: This study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant., (©Julia Rosemary Thorpe, Birgitte Hysse Forchhammer, Anja M Maier. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 30.05.2019.)

After an earthquake, the important task of emergency rescue work is to minimize casualties, but due to the suddenness of earthquake disasters, it is difficult to obtain enough disaster information immediately, especially personnel distribution and movement information. The traditional methods of obtaining disaster data are through reports from the disaster area or field investigations by the emergency rescue team; this work lags, and its efficiency is low. This paper analyzes the feasibility of using mobile phone location signal data in earthquake emergency rescue work in several respects, such as quantity, location, change rate, and epicentral distance. The results show that mobile phone location signal data can quickly obtain the situation of personnel distribution and quantity after an earthquake, and we find the change rate, distance, etc., can determine the approximate range of the earthquake impact field. Through the data distribution in different time periods, the movement of personnel after the earthquake can be obtained. Based on several situations, we can determine the basic situation of the disaster-stricken areas in times after the earthquake, especially the personnel relevant to the situation, and these data can provide a scientific basis for emergency rescue decision making., Competing Interests: The authors have declared that no competing interests exist.