Heat Severity in LA 2023

<i>***Data pulled from <a href='https://tpl.maps.arcgis.com/home/item.html?id=db5bdb0f0c8c4b85b8270ec67448a0b6' target='_blank' rel='nofollow ugc noopener noreferrer'>2023 Trust for Public Lands' image service.</a>***</i><div><br /></div><div><div style='font-family:inherit; font-size:16px;'><b>Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.</b></div><div style='font-family:inherit; font-size:16px;'><b><br /></b></div><div style='font-family:inherit; font-size:16px;'><b>The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the <a href='https://en.wikipedia.org/wiki/Jenks_natural_breaks_optimization' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='_blank' rel='nofollow ugc noopener noreferrer'>Jenks Natural Breaks</a> classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.</b></div><div style='font-family:inherit; font-size:16px;'><b><br /></b></div><div style='font-family:inherit; font-size:16px;'><b>This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. </b></div></div>