Estimating experienced racial segregation in US cities using large-scale GPS data.

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.

The effect of geo-climatic determinants on the distribution of cutaneous leishmaniasis in a recently emerging focus in eastern Iran.

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.

An interpretable framework for investigating the neighborhood effect in POI recommendation.

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.

Geomorphology of the Mirador-Calakmul Karst Basin: A GIS-based approach to hydrogeologic mapping.

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).

Trip duration drives shift in travel network structure with implications for the predictability of spatial disease spread.

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.

Modelling urban vibrancy with mobile phone and OpenStreetMap data.

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.

Integrated vaccination and physical distancing interventions to prevent future COVID-19 waves in Chinese cities.

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.

Variabilidad regional en incidencia, características generales y resultados finales de la parada cardiaca extrahospitalaria en España: Registro OHSCAR / Regional variation in the incidence, general characteristics, and outcomes of prehospital cardiac arrest in Spain: the Out-of-Hospital Spanish Cardiac Arrest Registry

OBJETIVOS: Existe gran variabilidad internacional en la incidencia y los resultados en la atención a la parada cardiaca extrahospitalaria (PCRE). El objetivo es conocer si existe variabilidad en la incidencia, características y resultados en supervivencia en la atención a la PCRE por los servicios extrahospitalarios de emergencias (SEM) de España. MÉTODO: Análisis descriptivo, retrospectivo de los datos del registro OHSCAR correspondientes al periodo octubre2013-octubre 2014, que incluye pacientes atendidos por 19 SEM de España con intento de reanimación. Se recogieron los casos atendidos y variables clave sobre la asistencia a una PCRE: incidencia, características del paciente, del evento, de la actuación previa a los equipos de emergencias (EE), de la reanimación realizada, y de los principales tratamientos hospitalarios. Se comparó la situación neurológica al alta hospitalaria de los casos con ingreso hospitalario. RESULTADOS: La incidencia de casos con intento de reanimación y todas las características generales, salvo la distribución por sexo, presentaron diferencias estadísticamente significativas entre los SEM participantes (p < 0,001). Hubo diferencias significativas en los tratamientos hospitalarios recibidos y en los resultados finales, tanto en la proporción de pacientes que llegaron con pulso espontáneo al hospital, 30,5%, rango entre 21,3% y 56,1% (p < 0,001), como en el porcentaje de altas hospitalaria con categoría 1 o 2 de la clasificación Cerebral Perfomance Categories (CPC), sobre el total de ingresados, 31,8%, rango entre 17,2% y 58,3% (p < 0,001). CONCLUSIONES: Existe una importante variabilidad entre los SEM españoles en la incidencia de casos con intento de reanimación, en todas las variables clave y en la supervivencia al alta hospitalaria de la atención a la PCRE

BACKGROUND AND OBJECTIVE: The incidence and outcomes of care for out-of-hospital cardiac arrest (OHCA) vary greatly from country to country. We aimed to study variation in the incidence, characteristics, and outcomes of care for OHCAs given by Spanish prehospital emergency services. METHODS: Descriptive retrospective analysis of data from the Out-of-Hospital Spanish Cardiac Arrest Registry (OHSCAR) from October 2013 to October 2014. Attempts by 19 Spanish emergency services to resuscitate patients were studied. All OHCA cases were reviewed to obtain the following data: incidence, patient and event characteristics, prior emergencies, resuscitation attempts, and the main treatments provided in the hospital. If a patient was admitted, we compared the neurologic status on hospital discharge. RESULTS: Statistically significant differences were detected between emergency services (P< .0001) in the incidence of attempted resuscitation and all general characteristics except sex. Hospital treatments and outcomes also differed significantly: pulse had been restored on arrival of 30.5% of patients (range 21.3% to 56.1%, P< .001), and 31.8% of admitted patients were discharged in cerebral performance categories 1 or 2 (range 17.2% to58.3%,P< .001). CONCLUSION: Differences in the incidence of resuscitation attempts, key variables, and survival at discharge from the hospital are present in OHCA cases attended by prehospital emergency services in different regions of Spain

Analysis of motorcycle accidents using association rule mining-based framework with parameter optimization and GIS technology.

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.

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops.

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.

Prevalence of nephropathy in patients with type 2 diabetes in Iran: A systematic review and meta-analysis based on geographic information system (GIS).

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 I2 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.

Cell Phone Activity in Categories of Places and Associations With Growth in Cases of COVID-19 in the US.

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 (ß, -0.002; 95% CI, -0.003 to -0.001; P < 0.001), areas classified as retail (ß, -0.008; 95% CI, -0.011 to -0.005; P < 0.001) and grocery stores (ß, -0.006; 95% CI, -0.007 to -0.004; P < 0.001), and transit stations (ß, -0.003, 95% CI, -0.005 to -0.002; P < 0.001), and greater increase in activity at the place of residence (ß, 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.

Coastline extraction and land use change analysis using remote sensing (RS) and geographic information system (GIS) technology - A review of the literature.

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.

How does the built environment affect teenagers (aged 13-14) physical activity and fitness? A cross-sectional analysis of the ACTIVE Project.

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.

CONGENITAL SYPHILIS IN THE PARAÍBA VALLEY USING A SPATIAL APPROACH.

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.

Special model of susceptible district of dengue fever in Ngemplak Subdistrict, Boyolali

Background of study: As an effort of prevention of Dengue Fever Occurence, it must be applied an information system which is able to give fast, exact, complete, and easy understood information. Because of that, it needs to be developed a Geographical Information System {Sistem Informasi Geografis (SIG)} in society health service, that is related to the distribution and risking factors of Dengue Fever Occurence. The Geographical Information System model can show the description of spacial distribution of Dengue Fever cases in a district, the tendency of illness spreading, and whether there is clustering of the illness or not, so it can identify unsafe/susceptible level in a district to Dengue Fever Occurrence. METHOD: The design of this research is observational analytic with Cross Sectional approach, using Geographical Information System in spacial analysis. The samples of the research are all dengue fever victims recorded at public health centre in Ngemplak Subdistrict, Boyolali Regency from 2016 to 2017, which are 93 respondents. The data analysis is done by univariate, bivariate with Contingency Coefficient test and spacial analysis in Geographical Information System approach. THE RESULT OF THE STUDY: The result of the recearch shows risking factors that is proved significantly related to dengue fever occurence. Those are population density percentage (p = 0.003) and the number of free mosquito larva percentage (p = 0.001). Spacialy, districts in Ngemplak Subdistrict, Boyolali, that have highest susceptible level to dengue fever are Sindon and Gagak Sipat village

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Assessment of asthma-prone environment in Karachi, Pakistan using GIS modeling.

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.