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Demographic and Land Use Model

SANDAG uses four integrated models in its demographic, economic, and land use forecasts.

Demographic, Economic, and Land Use Modeling

SANDAG uses four integrated models in its demographic, economic, and land use forecasts: (1) the Demographic and Economic Forecasting Model (DEFM), (2) the Interregional Commute Model (IRCM), (3) the Urban Development Model (UDM) and (4) the Population Age, Sex, and Ethnicity Forecast (PASEF), in conjunction with the Transportation Model.

A noteworthy feature of the forecasting process is the feedback of information from one model to another. (See Figure 1.) For example, regionwide projections of jobs and housing from DEFM are used in the IRCM and then the output from the IRCM is used to adjust the output from DEFM. DEFM then provides the regionwide projections that serve as the basis for UDM and PASEF. Similarly, data from UDM and PASEF are major inputs to the transportation model, and then transportation model data are used in subsequent UDM calculations. A key feature of the modeling system is the central role that land use and transportation policies play in determining future travel patterns and the associated location of people, houses, and jobs.

Figure 1: Modeling Process

These interrelated models satisfy the federal requirements specified in the Clean Air Act Amendments of 1990 and the Safe, Accountable, Flexible, Efficient, Transportation Equity Act: A Legacy for Users (SAFETEA-LU). These legislative acts mandate that transportation plans consider the long-range effects of the interaction between land uses and the transportation system.

Demographic and Economic Forecasting Model (DEFM)

The Demographic and Economic Forecasting Model (DEFM) is comprised of an econometric model and a demographic model and is currently used for SANDAG’s regionwide projections. DEFM produces an annual forecast of the size and structure of the region’s economy as well as a corresponding demographic forecast. For the economic forecast, DEFM relates historical changes in the region’s economy to historical changes in the national economy using a series of econometric equations that are interrelated (also known as a simultaneous econometric model). The demographic module uses a cohort-survival model to forecast population by age, gender, and ethnicity. DEFM produces a wealth of data about the region’s future economic and demographic characteristics. Among the more important elements are the size and composition of the population, employment by industrial sector, household and personal income, housing units by structure type, vacancy status and persons per household, labor force, and school enrollment.

The initial concept of DEFM in the late 1970s was a result of a cooperative modeling effort between SANDAG and the County of San Diego that combined various forecasting tools. With some improvements and modifications, the first version of the model was successfully used for 18 years. Since then, DEFM has been expanded and revised to improve performance.

Model Structure:

DEFM is designed to forecast population and economic variables for the region. To forecast demographic variables, DEFM considers factors such as birth rates, survival rates, and the age, sex, and ethnic distributions of the resident population. Economic variables including employment, income, and housing supply are forecast based on assumptions about national, state, and local growth patterns and inter-industry relationships.

Interregional Commute Model (IRCM)

The Interregional Commute Model (IRCM) accounts for individuals who work in the region but live outside its boundaries. The IRCM predicts the future residential location of the workers holding new jobs created in the San Diego region. The residential location can be either inside the San Diego region, in Orange County, southwest Riverside County, Imperial County, or in Tijuana/Northern Baja California. The main result from the IRCM is the number of new housing units containing workers who are employed in the San Diego region that will be built in the region and the number that will be built in one of the four surrounding regions.

Model Structure

The IRCM assigns the residential location of workers based upon the accessibility of potential residential sites to job locations, the availability of residential land for development, and the relative price of homes. There are three basic tenets of the IRCM. These three basic tenets also underlie the gravity model used in the Urban Development Model (UDM).

Urban Development Model (UDM)

The Urban Development Model (UDM) allocates employment, population, housing and income from the regional forecast produced by DEFM to neighborhoods and jurisdictions within the region. The model is designed to forecast the location of residential and non-residential activity within the region for 5 year periods. Major model inputs include the current spatial distribution of jobs, housing units, income, and population. Land use data collected from local jurisdictions including general plans, policies, and current and future transportation infrastructure are also critical to the model.

UDM also satisfies the federal requirements specified in the Clean Air Act Amendments of 1990 and the Safe, Accountable, Flexible, Efficient, Transportation Equity Act: A Legacy for Users (SAFETEA-LU). These legislative acts mandate that transportation plans consider the long-range effects of the interaction between land uses and the transportation system.

Model Structure:

UDM has three major components. The first component allocates regional employment. The second component determines the location of residential activity, based on the spatial distribution of employment from the first component. The final component of UDM provides a forecast of other demographic and economic characteristics including occupied units, population, household income, and employment by industrial classification.

Population by Age, Sex, and Ethnicity Forecast (PASEF)

Model Overview

The program for forecasting detailed demographic characteristics (age, sex, and ethnicity - PASEF) is a demographic model designed to forecast detailed demographic characteristics at a neighborhood level. The detailed demographic forecast comes directly from DEFM, but requires aggregating the single year of age detail into the five-year age groups used in PASEF, and an adjustment for special populations. The model projects population for 18 five-year age groups (0-4, 5-9…,80-84, and 85+) broken down by gender and ethnicity for the region and smaller geographies.

Special Populations:

The forecast technique accounts separately for special populations which include military and college population. Special populations are treated differently from non-special populations because their characteristics remain relatively stable over time. Therefore, while PASEF incorporates changes in the overall size of the special population, it assumes that their age, sex, and ethnicity profile remain unchanged over time. PASEF forecasts special populations using a bottom-up method whereby census tracts are forecast first, then aggregated to sub-regional areas (SRAs) and the region. The regional and SRA special population estimates are then used in the calculation of non-special population estimates.

Non-Special Populations:

The non-special population forecast uses a top-down method – first for the region, then for SRAs, next for census tracts, and finally for MGRAs, with the larger geographic areas serving as controls . PASEF derives the non-special demographic characteristics population for the region by subtracting the special population estimates. For purposes of controlling, PASEF also creates a regional non-special population estimate by sex and ethnic group.

A two step method provides the non-special population forecasts for SRAs. The first step computes the sex and ethnic composition, and the second step computes the age composition within each sex and ethnic group.

The sex and ethnic composition is based on the change in the sex and ethnic group shares for each SRA and is an exogenous input based on historical trends. These trends are derived from Census information and SANDAG’s latest detailed demographic characteristic estimates. PASEF next computes the non-special population by age within each sex and ethnic group.

During the second step, PASEF controls the age forecast within each sex and ethnic group to the non-special demographic characteristic forecast for the SRA.

For census tracts classified as non-special, PASEF starts with the initial demographic characteristics estimates previously developed, and for controlling purposes, creates a non-special population estimate by sex and ethnic group. Next, PASEF uses the same 2-step controlling method used for SRAs to derive the sex and ethnicity estimates.

The final stage in PASEF distributes the demographic characteristics estimates from the census tracts to the MGRAs. The model assumes that each MGRA has the same demographic characteristic distribution as the census tract in which it lies.

Validation and Calibration

The demographic, economic, and land use forecasts are developed in a collaborative process. SANDAG staff works closely with a wide range of professionals outside the agency when preparing forecasts. For the regional forecast (DEFM), SANDAG convenes a Regionwide Forecast Technical Advisory Working Group, which is composed of experts in demography, housing, economics, and other disciplines from state and local agencies, local universities, and the private sector. This committee is responsible for reviewing the regional model structure, data inputs, and assumptions. Feedback from the committee is incorporated into the model. The committee also evaluates the forecast results. With the DEFM forecasts, SANDAG has a track record of less than 0.5 percent error, on average, per forecast year.

SANDAG also relies on the Regional Planning Technical Working Group for advice on the forecast, which provides information for jurisdictions, communities and other areas within the region. This working group comprises the local jurisdictions’ planning directors or their designees and representatives from other agencies within the region that use the forecast data for facility and infrastructure planning. This working group assists with local land use assumptions that are among the most important inputs to the forecasting process.

Modeling Software

The DEFM model relies upon proprietary software, MetrixND, licensed from ITRON. IRCM is a spreadsheet-based model. Software that implements UDM and PASEF has been developed in C#. Tables with model data were created and stored in Microsoft SQL Server databases.

Data Sources

These models require a wealth of data from a variety of sources. These sources are outlined in the table below:

Data Source(s) Model(s)
Housing U.S. Census Bureau, San Diego County Assessor, local jurisdictions DEFM, UDM
Jobs (by industry) U.S. Bureau of Labor Statistics, California Employment Development Department, U.S. Department of Defense, local jurisdictions DEFM, UDM
Labor market (employment, unemployment, labor force participation) U.S. Bureau of Labor Statistics DEFM
Population and demographic characteristics U.S. Census Bureau, California Department of Finance DEFM, UDM, PASEF
Price levels and inflation U.S. Bureau of Labor Statistics, National Association of Realtors, DataQuick Information Systems DEFM, IRCM
Public finance California Department of Finance DEFM
Travel times SANDAG transportation model IRCM, UDM
United States projections U.S. Census Bureau, and economic projections purchased from private-sector vendor (varies depending on series) DEFM
Vital records (births, deaths) California Department of Health DEFM