Three Methods for Synthesizing Base-Year Built Form for Integrated Land Use–Transport Models

Abstract

Newer integrated land use–transport models require as a base-year input a representation of existing built form (building stock by type, age, density, and other variables). Available data on floor space inventory have been found to be both incomplete and inconsistent with employment and population data in three land use–transport modeling exercises in the United States: in Sacramento, California; in the state of Oregon; and in Oahu, Hawaii. This paper reports on three methods used to synthesize a built-form input at a detailed microlevel for these three models. Each method uses existing land cover data and reconciles them with population data, employment data, and data on the consumption of floor space by activity. All three approaches assign floor space quantities by type to a detailed microlevel (grid cell or parcel) inventory of land. The approaches differ in their algorithmic approach, motivations, and data requirements.

Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations

“…Other approaches are the agent-based land use models, for example, UrbanSim (14), ILUTE (15), or the models by Hunt et al (16). These models face difficulties similar to the experiences described here, particularly the need to assemble a consistent agent-based view of the world from diverse data sources (17).…”

A Multi‐objective Optimization Approach for Disaggregating Employment Data

“…These methods make use of algorithms and examine disaggregation methods that could be more applicable to the South African context. Abraham et al (2005) identified a problem in the accuracy of data on floor space inventory in three land use transportation modeling projects. The floor space data was incomplete, and it showed inconsistencies with both population and employment data.…”

“…The last algorithm was the Oahu approach, which resulted in jobs assigned to certain grid cells as well as synthesized floor space in grid cells. This approach by Abraham et al (2005) shows the advantage in using floor space when disaggregating data. Patterson et al (2010) considered two methods for using UrbanSim when there is only aggregated data available for the study area.…”

“…The last algorithm was the Oahu approach, which resulted in jobs assigned to certain grid cells as well as synthesized floor space in grid cells. This approach by Abraham et al (2005) shows the advantage in using floor space when disaggregating data.…”

A parametric model of residential built form for forecasting the viability of sustainable technologies

“…Some studies have aimed to do this by representing the neighbourhood layout and buildings using pre-designed land parcels or cells. For example, UrbanSim used 2.25 ha grid cells (Waddell et al, 2003), but this results in difficulties matching and reconciling data and outputs of different types and scales (Abraham et al, 2005). Modelling at this parcel scale rather than at building scale also means that the uptake and yield of building-scale supply, such as rainwater harvesting, needs to be input based on assumptions about average values per parcel (Willuweit & O'Sullivan, 2013).…”