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Estimating crime scene temperatures from nearby meteorological station data
تخمین دمای صحنه جرم از داده های ایستگاه هواشناسی مجاور-2020 The importance of temperature data in minimum postmortem interval (minPMI) estimations in criminal
investigations is well known. To maximise the accuracy of minPMI estimations, it is imperative to
investigate the different components involved in temperature modelling, such as the duration of
temperature data logger placement at the crime scene and choice of nearest weather station to compare
the crime scene data to. Currently, there is no standardised practice on how long to leave the temperature
data logger at the crime scene and the effects of varying logger duration are little known. The choice of the
nearest weather station is usually made based on availability and accessibility of data from weather
stations in the crime scene vicinity. However, there are no guidelines on what to look for to maximise the
comparability of weather station and crime scene temperatures.
Linear regression analysis of scene data with data from weather stations with varying time intervals,
distances, altitudes and microclimates showed the greatest goodness of
fit (R2), i.e. the highest compatibility between datasets, after 4–10 days. However, there was no significant improvement in
estimation of crime scene temperatures beyond a 5-day regression period. The smaller the distance
between scene and weather station and the higher the similarity in environment, such as altitude and
geographical area, resulted in greater compatibility between datasets.
Overall, the study demonstrated the complexity of choosing the most comparable weather station to
the crime scene, especially because of a high variation in seasonal temperature and numerous influencing
factors such as geographical location, urban ‘heat island effect’ and microclimates. Despite subtle
differences, for both urban and rural areas an optimal data fit was generally reached after about
five consecutive days within a radius of up to 30 km of the ‘crime scene’. With increasing distance and
differing altitudes, a lower overall data fit was observed, and a diminishing increase in R2 values was
reached after 4–10 consecutive days. These results demonstrate the need for caution regarding distances
and climate differences when using weather station data for retrospective regression analyses for
estimating temperatures at crime scenes. However, the estimates of scene temperatures from regression
analysis were better than simply using the temperatures from the nearest weather station. This study
provides recommendations for data logging duration of operation, and a baseline for further research into
producing standard guidelines for increasing the accuracy of minPMI estimations and, ultimately, greater
robustness of forensic entomology evidence in court. Keywords: Temperature modelling | Minimum post-mortem interval | Micro-climate | Forensic ecology | Temperature datalogger |
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