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For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.
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Destiny 2 player activity dataset from MMO Populations, combining monthly enhanced players and 30-day daily estimates generated from public signals.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Socio-demographic variables and saying positive about destiny.
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TwitterDemographics, smoking, vaping and quitting status of the 2018 International Tobacco Control Four Country Smoking and Vaping Survey study sample.
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TwitterComprehensive YouTube channel statistics for Destiny, featuring 850,000 subscribers and 715,161,152 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in US. Track 3,529 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.
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TwitterDuring the mid-1800s the American population followed the country’s Manifest Destiny; as land was acquired, westward migration towards the Pacific occurred for various reasons.THE U.S. HISTORY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for Earth Science education are now freely available. The U.S. History GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading high school U.S. History textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device agnostic. The activities harmonize with the C3 curriculum standards for social studies education. Activity topics include:· The Great Exchange· The 13 Colonies - 1700s· The War Before Independence (The American Revolution)· The War of 1812· Westward, ho! (Trails west)· The Underground Railroad· From Compromise to Conflict· A nation divided: The Civil War· Native American Lands· Steel and the birth of a city (natural resources)· World War I· Dust Bowl· A day that lived in infamy (Pearl Harbor)· Operation Overlord - D-Day· Hot spots in the Cold WarTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries.
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Urban planning is a lengthy and settled process, the results of which usually emerge after several years or even decades. That is why it is necessary for a proper urban design of cities to use parameters that are able to predict and gauge the potential long-term behaviour of urban development.In the tourist towns of the Mediterranean coast, the long-term design is often at odds with the generation of business profits in the short term. This paper presents the results of this phenomenon for an interesting case of a Spanish Mediterranean coastal city created from scratch in the 1960s and turned into a tourist destination today hypertrophied.La Manga del Mar Menor in the Murcia region every year reaches a population of more than 250,000 people during the summer, which is reduced to just a few dozen in winter. This crowded environment with an asymmetric behaviour submits annual progressive impoverishment in its economic return. This questionable profitability is the result of a misguided urban development; its results are analyzed through the evolution of the land market and the resulting urbanization in the last fifty years, with a GIS methodology. (C) 2014 Elsevier Ltd. All rights reserved.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.