The New York Time's article, "Where are the Hardest Places to Live in the U.S.?" is an attempt to understand, at a county level, which areas of the country are "doing better" or "doing worse." The article uses six metrics which are averaged at the county level and then each county is ranked. The average of the six metric rankings is then used to give each county an overall score which weighs each of the six metrics equally. The metrics used are: education (percentage of residents with at least a bachelor's degree), median household income, unemployment rate, disability rate, life expectancy and obesity. This means that four of the metrics are essentially proxies for wealth and two are proxies for health.
The result, in my opinion, is a misleading map for three main reasons. The first, is that by making four of the six metrics economic and weighing each metric the same, you wind up with a map that essentially shows where people are rich versus where they are poor. This equates wealth with "doing better" and ignores other metrics like social connectivity or happiness proxies such as quality of life indexes or cost of living verse median income (they did try to include social mobility but lacked data for the whole country). Certainly, marking an area as the "hardest to live," as the title of the article suggests, while only considering income but not cost of living is misleading at best.
The outcome, is a heavy bias toward urban and suburban counties which tend to be where wealth agglomerates and results in what is essentially a population density map. The simple recreation of a population density map is a common phenomenon when using various kinds of spatial data. There are some exceptions, such as Wyoming (and other parts of the midWest and West), which shows up almost entirely as "doing better" despite having the lowest population and second lowest population density of any state. An interesting anomaly, but one that is given no analysis within the article. My guess is that these areas are propped up by recent booms in the oil and gas industry which has an outsized impact due to the otherwise low population rate in these areas.
The second way that this map is misleading is that it is aggregated to the county level. This misleads a reader into assuming than any given county is homogenous when in fact it may have large percentages of its population that are at both extremes for the metrics used. A dense city, for example, may show up as "doing better" despite having far more citizens on the low end of the spectrum than in many (less populated) counties marked as "doing worse." As a spatial tool it may be helpful in identifying parts of the country "doing better" or "doing worse," such as the Southeast which shows up largely as "doing worse." But as a tool to identify where larger amounts of the actual population are "doing better" or "doing worse" it fails.
The final reason the map is misleading has to do with some of the metrics used. For example, education (percentage of residents with at least a bachelor's degree) fails to consider whether or not a bachelor's degree is a pre-requisite for employment in the area. If employment is largely service or industrial work in a given area, then a high school or trade school diploma may be fully sufficient. It mistakenly assumes that all citizens need a bachelor's degree to "do better."
Another example is disability rate, the article uses it as an additional proxy for unemployment. However, high rates of disability do not necessarily equate to a problem with economic opportunity in the area, but rather could be the result of agglomeration of disabled persons due to the presence of specific infrastructure or services available to them. This phenomenon would surely show up more predominately on a per capita basis in less populated rural areas and be masked in high density urban ones.
Similarly, life expectancy may be skewed due to a high rate of infant mortality, a serious problem, but one which is often attributed to lack of geographical proximity to healthcare services, which tends to be rural areas. However serious infant mortality may be, it might be the case that those who do not die during infancy live very long lives with a high quality of life years. Quality life years may be a better proxy for health than simply life expectancy, as it is possible to live longer, yet spend many of those years in very poor health.
In general, I found the attempt to visualize areas of the country that are "doing better" or "doing worse" to be relatively well done. The author clearly made an attempt to be comprehensive by using six metrics (and attempting to use more), and in terms of understanding large geographical swaths, like the Southeast, that may be "doing worse", I think it may achieve its goal.
However, I find any attempt to gain meaning at a smaller granularity, such as county or sub-county level to be misleading at best and potentially heavily biased toward rich urbanized areas as the only indicator of "doing better" at the expense of those who may choose a more rural lifestyle. I found the map element to be quite interesting, but the analysis within the text of the piece is not very substantive or meaningful considering the amount of work that went into the creation of the map. Simply listing statistics for various counties at the extremes of the scale is not a great guide to help readers unpack the information.