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Developing High-Resolution Descriptions of Urban Heat Islands: A Public Health Imperative


Two maps of Portland, Oregon, located at the confluence of the Columbia and Willamette rivers, are shown. One shows the range of temperatures at 6 AM; the other shows temperatures at 7 PM. At 6 AM, temperatures range from 13.0°C (55.5°F) to 18.2°C (64.8°F). At 7 PM, temperatures ranged from 21.7°C (71.1°F) to 33.2°C (91.7°F).

Empirically derived 1-m–resolution descriptions of (A) morning and (B) evening urban heat islands in Portland, Oregon, on August 25, 2014. Temperatures were recorded in 1-hour periods at 6 AM and 7 PM. In the morning, low-lying vegetation cover had the strongest effect on temperature; in the evening, temperatures were most strongly affected by variation in building heights. High-resolution data sets such as those used here can inform preparation for extreme heat events and public health interventions.

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Top-down view (component A) of a small portion of the Portland, Oregon, canopy; canopy (trees) areas are indicated by green pixels, whereas not-canopy areas are indicated by white pixels. The image is overlaid with a series of 8 increasingly larger red concentric rings (or buffers), depicting ground distances in the calculation of canopy within a specific buffer distance. A yellow star in the center indicates a point of interest for the buffering process. Component B has a similar scene to component A; however the concentric rings have been removed and slightly more area of the raw canopy data are visible. The next components (C, D, E, and F), illustrate the output of a standard focal, or moving window, analysis: the data set from component B begins to blur, as if going out of focus, as each pixel in the scene converts from canopy and not canopy values to a number representing the percentage of ground cover classified as canopy within each buffer distance (component , 50 meters; component D, 100 meters; component E, 200 meters, and component C, 600 meters.

Figure. Example of the process of creating buffers on a raster. The input data (A) is converted from vector to a raster that contains pixel values representing the results for specified distances. In this example, the raw data has the values 1 (green, representing canopy) and 0 (white, representing not-canopy). The output describes the percentage of land cover classified as canopy.

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Page last reviewed: September 15, 2016