Now that the population change estimates are out, here is a quick peek at Ohio’s estimates. First up – the extent of gains and losses in our counties. No surprises here; Most of the Appalachian counties have seen population losses. Specifically, Appalachia lost 48,211 persons while the rest of the state gained 200,896 persons. The largest gains were in Franklin (146,768), Delaware (30,654), Warren (19,353), Hamilton (14,312) and Butler (14,243).
The Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) Program and the Small Area Health Insurance Estimates (SAHIE) Program are two of my favorite ‘go-to’ sources for small-area estimates. Both are easily grabbed via tidycensus so the first thing I’d like to do is explore county-level trends going as far back as is possible – 2008-2016 for SAHIE, 2006-2017 for all SAIPE estimates except for school-age (5 to 17 in families) poverty rate estimates that allow for longer comparisons (2005-2017).
The founding report of the Appalachian Regional Commission does a wonderful job of highlighting the state of affairs in Appalachia circa 1950 and 1960. The prose is not too shabby even if it careens every now and then into the romantic: “Graphs and tables can hardly relate the acutely personal story of a child in a remote valley, his horizon of opportunity limited to the enclosing hills; nor the despair of his father, who, idled by forces beyond his control and seeing no prospect of future employment, must live month in and month out with the vision of that child repeating his own history.
The opioid crisis is an issue in most parts of the country and Ohio is no exception, with some of the highest numbers of Fentanyl encounters reported by law enforcement. Although one could, I suppose, try to identify county-level deaths due to drug overdoses via CDC Wonder, this is a quick look at the data provided by the Ohio Hospital Association’s Overdose Data Sharing Program.
A client needed state-level estimates of the percent of opportunity youth (defined either as 16-19 or 16-24 year-old persons who are neither in school nor employed) in each state plus Washington DC. The end result would be three years of estimates that matched the numbers put out by Measure of America’s yearly reports on the subject.. I had used Anthony Damico’s fantastic repository Analyze Survey Data for Free for BRFSS, DHS, and other data but never with the Census Bureau’s Public Use Microdata Samples (PUMS) data.
It is yet another rainy day but not really since the Hocking River that meanders through our town is under flood-watch. Things must be bad enough else why would the University cancel all classes for Monday. To whit: “A forecast for heavy rains and high winds have resulted in a flood warning for Athens County beginning this weekend into Monday. Based on current forecasts, specific areas on campus may be impacted by flooding Sunday evening into Monday morning.
I’ve been itching to get back into the himal database and Elizabeth Hawley’s passing jolted me back into action. I wanted to start by looking at the peaks themselves, hoping this would give me a better understanding of the fields before I delve into the expedition data. We have data on 457 peaks, but only 377 are open and only 309 of these have been climbed. Information about the date of the summiting is missing for 3 peaks (we have the year and the month but not the day) so that drops the dataset down to 306 peaks.