Home‐range estimators are commonly tested with simulated animal locational data in the laboratory before the estimators are used in practice. Although kernel density estimation (KDE) has performed well as a home‐range estimator for simulated data, several recent studies have reported its poor performance when used with data collected in the field. This difference may be because KDE and other home‐range estimators are generally tested with simulated point locations that follow known statistical distributions, such as bivariate normal mixtures, which may not represent well the space‐use patterns of all wildlife species. We used simulated animal locational data of 5 point pattern shapes that represent a range of wildlife utilization distributions to test 4 methods of home‐range estimation: 1) KDE with reference bandwidths, 2) KDE with least‐squares cross‐validation, 3) KDE with plug‐in bandwidths, and 4) minimum convex polygon (MCP). For the point patterns we simulated, MCP tended to produce more accurate area estimates than KDE methods. However, MCP estimates were markedly unstable, with bias varying widely with both sample size and point pattern shape. The KDE methods performed best for concave distributions, which are similar to bivariate normal mixtures, but still overestimated home ranges by about 40–50% even in the best cases. For convex, linear, perforated, and disjoint point patterns, KDE methods overestimated home‐range sizes by 50–300%, depending on sample size and method of bandwidth selection. These results indicate that KDE does not produce home‐range estimates that are as accurate as the literature suggests, and we recommend exploring other techniques of home‐range estimation.

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Massive and rapid testing is crucial for containing the spread of COVID-19. Health and policy planners must ensure that access to and uptake of SARS-CoV-2 testing is adequate and equitable. This study measures the spatial accessibility to testing sites in Florida at the census tract level at the end of May 2020, using the 2-step floating catchment area method that integrates both driving and walking modes. Accessibility scores were found to be heterogeneous across geographic regions and among different groups of people. In particular, many rural areas were in a testing desert. While people in larger cities tended to have better accessibility to testing, many did not have adequate accessibility at that time due to both capacity limitations and spatial factors. In particular, people without access to private vehicles and the elderly faced disadvantages in accessibility to testing sites even in urban areas. However, Black and low-income groups were disproportionally concentrated in neighbourhoods with above-average accessibility due to their closer proximity to testing sites. These results suggest that increased efforts are needed to reach vulnerable populations, including the elderly and those without private vehicles.

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