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aeria_points_generator_no_surveys_work__73ds

v1.0.0

Published

aeria points generator no surveys |work|

Readme

WORK

LINK > https://byltly.com/2tjn0e



All further support and guidance for deploying Aeria Points Generators are available in the Aeria Points Generator Help Section via the Support menu, or by opening the Help Menu in the Aeria Games Menu (via the Help button on the main menu bar). 


Albertville is a rural community with the majority of the population identifying as white, non-Hispanic. In order to address the need for representative sampling in our study population, the study area was subdivided into segments using a GIS-based population density grid. Forty segments were created through a GIS-based population density grid that was then overlaid with census tract boundaries. A total of 48 segments were then randomly selected and these segments were subdivided using randomly selected starting points. Using a straight line distance and population density grid, the first 100m from the starting point served as the sampling area.


Aerial photographs were overlaid on the segment map and two teams of investigators went through the entire community area to identify and count households in order to identify the appropriate Aeria points. Each segment selected was segmented and a single interviewer conducted face-to-face interviews with all members of each identified household. Interviewers were provided with the segment map and satellite map. Data collection procedures were similar to the study described above and included the use of Geographic Information Systems (GIS) and a population density grid. All data were collected in 2013 and unique identifiers were assigned to each household via the GIS. Mapping and survey data was aggregated, processed and analyzed using Aeria Points Generator . Analysis was limited to visualization of the geographical distribution of Hispanics in the study population. GIS and aerial photography are tools that are commonly used to develop and implement community-based sampling approaches. 84d34552a1