Contacts

TFC Co-chairs

Dennis Enslinger
City of Prairie Village

Heping Zhan
City of Lee's Summit

MARC Staff

Frank Lenk
Director of Research Services
816-701-8237
flenk@marc.org

Andrea Repinsky
GIS Planner
816-701-8385
arepinsky@marc.org

Links

Creating Sustainable Places

Transportation Outlook 2040 Long-Range Transportation Plan

Imagine KC

MARC Research Services Department

MARC GIS

Detailed forecast report
(3.3MB pdf)

2040 Forecast

The 2040 Forecast provides the estimated number and distribution of population, households, and employment in the seven-county Kansas City region for the year 2040. The forecast is produced by MARC's Research Services staff and the Technical Forecast Committee — which includes planners from local governments and other organizations in the Kansas City region. The forecast is used to inform the long-range transportation planning process in order to incorporate future demand into transportation infrastructure planning.

The 2040 Forecast is an estimate of the land-use change most likely to occur in the Greater Kansas City area by 2040 given past trends, known demographic and economic shifts, and expected changes in federal, state and local government policy. The forecast presents a hybrid of the baseline and adaptive scenarios, where some but not all sustainability goals will be met by 2040. As a result, the final forecast includes about 18 percent of the region’s growth in existing activity centers along corridors that have the potential to be served by transit.

Decline in the region's center is diminished but not eliminated in the 2040 forecast. About 102,000 acres of vacant land are consumed, which is 58 percent of that consumed in the baseline scenario. This results in a savings of about $2 billion in local infrastructure costs compared to the baseline scenario.

2040 Forecast MapForecast data and documents

Forecast maps (.jpg format)

Interactive map

The forecast data can be viewed on an interactive map.

This map viewer documents not only the forecast, but also the data that went into creating it. This includes the data inputs for model calibration, the resulting estimated development probabilities, the land use input data, activity centers, and the baseline and adaptive scenarios.

By switching layers on and off, it is possible to see more clearly how all of this data was combined to yield the final forecast.