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Harnessing the power of data to support community‐based research

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Volumes of data are generated at every moment as we go through the paces of our daily lives. Many of these data flows are routinely captured through administrative records, social media, and surveys. Historically, agencies at different levels of government have been responsible for curating and reporting statistics about our social, economic, and health conditions associated with these data flows. Recently, the U.S. government has proposed the use of data derived from administrative records at the federal level to support social policy and program evaluation. Why not consider parallel activities at state and local levels? Harnessing local data sources and integrating them with state and federal sources will provide timelier and more geographically specific analyses to support local insights and policy development. Leveraging community‐based participatory research, researchers and civic leaders can work together to identify the questions and execute rigorous, yet flexible, processes for building local sustainable community learning cultures based on data‐driven discovery. In the process of conducting research with local civic leaders, we have observed that issues can be classified into 3 categories: locating and describing a population within a community; estimating a statistic and a measure of variability; and evaluating a program, policy, or procedure. Through a series of case studies, this paper demonstrates the unexpected value in liberating and repurposing local data.

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  • Applications of Computational Statistics > Organizations and Publications
The charts illustrate the distribution of minutes on call (in log base 10 scale) for fire (gold) and medic (green) units for 2012 and 2014. The two horizontal lines mark 10 and 100 min. The variation in time on call is much larger in 2012 than in 2014. There appears to be fewer false alarms and short calls in 2014. Data source: Fire/emergency medical service (EMS) data for Arlington, Virginia (2011–2013). Reprinted with permission from Keller, Lancaster et al. ()
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Fire and emergency medical service ((EMS) unit utilization in Arlington County, Virginia. The top chart shows the unit‐level linked data by time of day for January 25, 2014. The panel below aggregates the units over the hours of the same day, showing the volume of units in‐use and then comparing this to total units available. Data source: Fire/EMS data for Arlington, Virginia (2011–2013). Reprinted with permission from Keller, Lancaster et al. ()
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Comparisons of neighborhood diversity in Arlington, Virginia. The bottom panel shows the Simpson's diversity indices for Arlington, Virginia using local property tax assessments at the census tract (left) and census block group (right) levels. Data source: Arlington County tax assessments, 2013
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House values in Arlington, Virginia. The left map shows median house values based on American Community Survey, and individual local property tax assessment values for the area highlighted by the yellow circle is illustrated on the right map. Data sources: American Community Survey, 2013 and Arlington County tax assessments, 2013
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Correspondence analysis plot of dropout reasons for ninth grade male (left panel) and female (right panel) students in Appalachia (top) versus non‐Appalachia (bottom) regions of Kentucky. Pushout reasons (green) and pullout reasons (blue) are displayed. The correspondence analysis’ principal coordinate points (large gray circles) for the geographic regions are enlarged to aid in interpretation. The further apart the gray circles are on the vertical axis the more varied the dropout profiles are for Appalachia versus non‐Appalachia (i.e., if both gray circles were centered at the intersection of the horizontal and vertical lines the dropout profiles for the two geographic regions would the same). The principal coordinate points for the dropout reasons lie in the neighborhood of the geographic region (gray circles) they are most prevalent in. For example, for both male and female students, there are relatively more dropouts due to boredom in Appalachia than non‐Appalachia; whereas failing classes and teacher conflict are more prevalent in non‐Appalachia. Data sources: Kentucky Statewide Longitudinal Data Systems (school years: 2008–2014), and geographic boundaries of Appalachia, defined by the Appalachian Regional Commission (https://www.arc.gov/appalachian_region/TheAppalachianRegion.asp). Reprinted with permission from Ziemer et al. ()
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Analysis of response times for 10 fire stations in Arlington, Virginia. (a) Top left panel shows the geographic location of the calls that units were dispatched to. The colors identify the responding stations. (b) Top right panel plots the response times in minutes by distance for each station. (c) The bottom panel graphs the 95% credible intervals for response times in seconds controlling for hour of the day, computer‐aided dispatch (CAD) call type, station, year, month, and apparatus type, and limited to incidents within a mile of the station and with response times less than 20 min. Reprinted with permission from Arnsbarger et al. ()
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Identification of nonemergency call locations of 911 fire and EMS callers. Loyal locations are those that calls come from three or more times in 1 month. Loyal locations indicated by larger circles (centrally located) are where vulnerable populations, especially senior populations, seek support. Data sources: Fire/emergency medical service (EMS) data for Arlington, Virginia (2011–2013). Reprinted with permission from Keller, Lancaster et al. ()
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