National, state, and local institutions are actively collecting data about broadband internet availability. Maps displaying broadband availability, adoption, and speed are useful to policymakers, broadband providers, and grantmaking organizations because they help identify areas of need, inform priorities, and direct broadband deployment funds. However, current broadband availability data collection methodologies possess several drawbacks that result in inaccurate and out of date maps. Several state-level efforts provide avenues for policymakers to improve the quality of broadband maps by partnering with communities, consumers, providers, and researchers to collect high-quality data.
- Several national broadband availability maps currently exist but they have well-known drawbacks, including overstating broadband coverage. While federal legislation has been passed to address issues with the current broadband maps, several states have undertaken efforts to create their own state-level maps.
- Georgia and Massachusetts have organized comprehensive, household-level broadband data collection projects in collaboration with internet service providers. Other states, including Wisconsin and Iowa, have combined public and private data sources to create maps with several types of information at the census block level. Finally, several other states, including West Virginia, maintain broadband availability maps through the collection of crowdsourced internet speed test data.
- In Missouri, one pilot project demonstrated the feasibility of creating maps of every broadband serviceable structure in three counties. Additionally, the Missouri Broadband Resource Rail currently combines several data sources to generate a statewide broadband availability map.
- State-level broadband mapping efforts are not standardized, so there is no single template for creating and maintaining accurate maps.
- Affordability is typically not reflected in broadband availability maps.
- Maps created using self-reported speed test data suffer from data bias issues such as participation rates and home network conditions (e.g., number of devices currently using the internet) at the time of the test.