Well hello, summer. Many local governments, nonprofits, utility companies, and other organizations find themselves asking the same questions during heat waves.

• Where are we able to set up cooling centers that are needed?
• Where are seniors who are particularly vulnerable to heat stroke?
• Where should we target incentives and programs to assist households in getting a heat pump or mini split cooling system?
• Where do people need window air conditioning (AC) units?
• When should we activate free admission to the city pools? Extended hours at the splash pads?

Up until now, we’ve only had part of the data needed to answer these questions (we have maps of seniors, for example). With the newest data product from the U.S. Census Bureau, we have a big piece of the puzzle that was lacking before: Local Air Conditioning Estimates (LACE). This is an experimental data product produced by cross-survey modeling using both the American Housing Survey and the American Community Survey.

Available in ArcGIS Living Atlas

The Living Atlas Policy Maps team downloaded the data directly from census.gov, processed it by giving it the Living Atlas treatment, and published it to ArcGIS Online for all analysts to use.

A layer with data for the nation, states, counties, and census tracts is now available for your mapping, charting, and analysis needs. This ready-to-use layer can be used within ArcGIS Pro and ArcGIS Online. Since this layer is in ArcGIS Living Atlas, it has field-level metadata, a full Item Details page, a defined extent, and many more Living Atlas quality standards. The data can also be exported (upon sign-in) for offline workflows.

Additionally, a web map is available for users who would rather start with a pre-configured web map for use in Instant Apps, Dashboards, StoryMaps, custom apps, and mobile apps.

The layer’s symbology is a high-contrast high-to-low color ramp depicting the percentage of households without air conditioning. The map’s symbology takes this one step further by using both color and size to depict both the percentage and the count of households. The layer and web map are multi-scale, meaning they show state data when zoomed out far enough, then county and tract data as you zoom in.

Potential maps to be made

• What are the utility costs for households with AC vs. those without?
• Where are areas with low AC access and with high wet bulb temperatures and humidity, and with low elevation? (most at-risk)
• What are the characteristics of the housing stock that don’t have AC? Are they older structures? Smaller structures? Do they have tile floors?
• How does access to AC at home vary across people in different occupations? (People who work and go to school indoors, vs. those who work outdoors or in hot kitchens or warehouses)

You can use Arcade to pull additional information into the pop-up, or use the Join Features analysis tool to create a layer with additional attributes to set you up for relationship mapping.

Situational Irony

In closing, I must share a little situational irony with you all. The air conditioning has been out in my building while working on this project. While setting up my fan and refilling my water, I got a news notification that Maryland will pass a state-wide air conditioning requirement for apartment buildings. My state doesn’t have that just yet, so time to refill my ice pop molds and take my child to the splash pad to beat the heat!